{"id":20,"date":"2021-12-29T14:21:09","date_gmt":"2021-12-29T14:21:09","guid":{"rendered":"http:\/\/mlv.cs.ihu.gr\/?page_id=20"},"modified":"2026-04-23T11:17:20","modified_gmt":"2026-04-23T11:17:20","slug":"publications","status":"publish","type":"page","link":"https:\/\/mlv.cs.duth.gr\/index.php\/publications\/","title":{"rendered":"Publications"},"content":{"rendered":"\t\t<div data-elementor-type=\"wp-page\" data-elementor-id=\"20\" class=\"elementor elementor-20\" data-elementor-settings=\"[]\">\n\t\t\t\t\t\t\t<div class=\"elementor-section-wrap\">\n\t\t\t\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-d1418d5 elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"d1418d5\" data-element_type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-60653f9\" data-id=\"60653f9\" data-element_type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t\t\t<div class=\"elementor-element elementor-element-f8cbc5d elementor-widget elementor-widget-heading\" data-id=\"f8cbc5d\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<style>\/*! elementor - v3.5.3 - 28-12-2021 *\/\n.elementor-heading-title{padding:0;margin:0;line-height:1}.elementor-widget-heading .elementor-heading-title[class*=elementor-size-]>a{color:inherit;font-size:inherit;line-height:inherit}.elementor-widget-heading .elementor-heading-title.elementor-size-small{font-size:15px}.elementor-widget-heading .elementor-heading-title.elementor-size-medium{font-size:19px}.elementor-widget-heading .elementor-heading-title.elementor-size-large{font-size:29px}.elementor-widget-heading .elementor-heading-title.elementor-size-xl{font-size:39px}.elementor-widget-heading .elementor-heading-title.elementor-size-xxl{font-size:59px}<\/style><h3 class=\"elementor-heading-title elementor-size-default\">2026<\/h3>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-7e0fdc7 elementor-widget elementor-widget-heading\" data-id=\"7e0fdc7\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<h3 class=\"elementor-heading-title elementor-size-default\">Total: 17 (#Journals: 10, #Conferences: 5, #Book Chapters: 2, #Books: 0)<\/h3>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-4e4dfeb elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"4e4dfeb\" data-element_type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-d6f3eec\" data-id=\"d6f3eec\" data-element_type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t\t\t<div class=\"elementor-element elementor-element-35d24c3 elementor-position-left elementor-vertical-align-top elementor-widget elementor-widget-image-box\" data-id=\"35d24c3\" data-element_type=\"widget\" data-widget_type=\"image-box.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<style>\/*! elementor - v3.5.3 - 28-12-2021 *\/\n.elementor-widget-image-box .elementor-image-box-content{width:100%}@media (min-width:768px){.elementor-widget-image-box.elementor-position-left .elementor-image-box-wrapper,.elementor-widget-image-box.elementor-position-right .elementor-image-box-wrapper{display:-webkit-box;display:-ms-flexbox;display:flex}.elementor-widget-image-box.elementor-position-right .elementor-image-box-wrapper{text-align:right;-webkit-box-orient:horizontal;-webkit-box-direction:reverse;-ms-flex-direction:row-reverse;flex-direction:row-reverse}.elementor-widget-image-box.elementor-position-left .elementor-image-box-wrapper{text-align:left;-webkit-box-orient:horizontal;-webkit-box-direction:normal;-ms-flex-direction:row;flex-direction:row}.elementor-widget-image-box.elementor-position-top .elementor-image-box-img{margin:auto}.elementor-widget-image-box.elementor-vertical-align-top .elementor-image-box-wrapper{-webkit-box-align:start;-ms-flex-align:start;align-items:flex-start}.elementor-widget-image-box.elementor-vertical-align-middle .elementor-image-box-wrapper{-webkit-box-align:center;-ms-flex-align:center;align-items:center}.elementor-widget-image-box.elementor-vertical-align-bottom .elementor-image-box-wrapper{-webkit-box-align:end;-ms-flex-align:end;align-items:flex-end}}@media (max-width:767px){.elementor-widget-image-box .elementor-image-box-img{margin-left:auto!important;margin-right:auto!important;margin-bottom:15px}}.elementor-widget-image-box .elementor-image-box-img{display:inline-block}.elementor-widget-image-box .elementor-image-box-title a{color:inherit}.elementor-widget-image-box .elementor-image-box-wrapper{text-align:center}.elementor-widget-image-box .elementor-image-box-description{margin:0}<\/style><div class=\"elementor-image-box-wrapper\"><figure class=\"elementor-image-box-img\"><a href=\"https:\/\/doi.org\/10.1080\/10447318.2026.2659296\" target=\"_blank\"><img width=\"406\" height=\"296\" src=\"https:\/\/mlv.cs.duth.gr\/wp-content\/uploads\/2026\/04\/Fuzzy-Cognitive-Maps-for-Interpretable-AI-Based-Modeling-of-Student-Engagement-and-Academic-Grit.png\" class=\"elementor-animation-grow attachment-full size-full\" alt=\"\" loading=\"lazy\" \/><\/a><\/figure><div class=\"elementor-image-box-content\"><h5 class=\"elementor-image-box-title\"><a href=\"https:\/\/doi.org\/10.1080\/10447318.2026.2659296\" target=\"_blank\">Fuzzy Cognitive Maps for Interpretable AI-Based Modeling of Student Engagement and Academic Grit<\/a><\/h5><p class=\"elementor-image-box-description\">E. Kytidou, E. Vrochidou, and G.A. Papakostas<br>\nEngineering Applications of Artificial International Journal of Human\u2013Computer Interaction, p. 1-20.<\/p><\/div><\/div>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-6b33241 elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"6b33241\" data-element_type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-192cfe3\" data-id=\"192cfe3\" data-element_type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t\t\t<div class=\"elementor-element elementor-element-8002fe1 elementor-position-left elementor-vertical-align-top elementor-widget elementor-widget-image-box\" data-id=\"8002fe1\" data-element_type=\"widget\" data-widget_type=\"image-box.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<div class=\"elementor-image-box-wrapper\"><figure class=\"elementor-image-box-img\"><a href=\"https:\/\/doi.org\/10.1016\/j.engappai.2026.114818\" target=\"_blank\"><img width=\"1536\" height=\"1024\" src=\"https:\/\/mlv.cs.duth.gr\/wp-content\/uploads\/2026\/04\/A-Large-Language-Model-Based-Multi-Agent-Framework-for-Sustainable-Industrial-Design.png\" class=\"elementor-animation-grow attachment-full size-full\" alt=\"\" loading=\"lazy\" \/><\/a><\/figure><div class=\"elementor-image-box-content\"><h5 class=\"elementor-image-box-title\"><a href=\"https:\/\/doi.org\/10.1016\/j.engappai.2026.114818\" target=\"_blank\">A Large Language Model-Based Multi-Agent Framework for Sustainable Industrial Design<\/a><\/h5><p class=\"elementor-image-box-description\">V. Alevizos, N. Gerolimos, D. Georgantzelis, and G.A. Papakostas<br>\nEngineering Applications of Artificial Intelligence, vol. 176, no, 2, p. 114818.<\/p><\/div><\/div>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-3ad3c9e elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"3ad3c9e\" data-element_type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-d4df426\" data-id=\"d4df426\" data-element_type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t\t\t<div class=\"elementor-element elementor-element-8698918 elementor-position-left elementor-vertical-align-top elementor-widget elementor-widget-image-box\" data-id=\"8698918\" data-element_type=\"widget\" data-widget_type=\"image-box.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<div class=\"elementor-image-box-wrapper\"><figure class=\"elementor-image-box-img\"><a href=\"https:\/\/doi.org\/10.1109\/SmartAgriSuSY68475.2025.11466978\" target=\"_blank\"><img width=\"546\" height=\"283\" src=\"https:\/\/mlv.cs.duth.gr\/wp-content\/uploads\/2026\/04\/KiwiAssist_-A-Question-Answering-Assistant-for-Kiwifruit-Disease-Management.png\" class=\"elementor-animation-grow attachment-full size-full\" alt=\"\" loading=\"lazy\" \/><\/a><\/figure><div class=\"elementor-image-box-content\"><h5 class=\"elementor-image-box-title\"><a href=\"https:\/\/doi.org\/10.1109\/SmartAgriSuSY68475.2025.11466978\" target=\"_blank\">KiwiAssist: A Question-Answering Assistant for Kiwifruit Disease Management<\/a><\/h5><p class=\"elementor-image-box-description\">G. Dalakouras, T. Kalampokas, E. Vrochidou, and G.A. Papakostas<br>\nProceedings of the 2025 International Congress on Smart Agriculture and Sustainable Systems (SmartAgri&SuSY), Marrakech, Morocco, p. 1-6.<\/p><\/div><\/div>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-cb11384 elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"cb11384\" data-element_type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-91d95b5\" data-id=\"91d95b5\" data-element_type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t\t\t<div class=\"elementor-element elementor-element-fd5482d elementor-position-left elementor-vertical-align-top elementor-widget elementor-widget-image-box\" data-id=\"fd5482d\" data-element_type=\"widget\" data-widget_type=\"image-box.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<div class=\"elementor-image-box-wrapper\"><figure class=\"elementor-image-box-img\"><a href=\"https:\/\/doi.org\/10.3390\/biomimetics11040238\" target=\"_blank\"><img width=\"1536\" height=\"1024\" src=\"https:\/\/mlv.cs.duth.gr\/wp-content\/uploads\/2026\/04\/Integrating-Biomimetic-Reasoning-Into-Early-Stage-Design-Thinking-for-Sustainable-Textile-Development.png\" class=\"elementor-animation-grow attachment-full size-full\" alt=\"\" loading=\"lazy\" \/><\/a><\/figure><div class=\"elementor-image-box-content\"><h5 class=\"elementor-image-box-title\"><a href=\"https:\/\/doi.org\/10.3390\/biomimetics11040238\" target=\"_blank\">Integrating Biomimetic Reasoning Into Early-Stage Design Thinking for Sustainable Textile Development<\/a><\/h5><p class=\"elementor-image-box-description\">N. Gerolimos, K. Kiskira, E. Sfyroera, J. Tsoumas, V. Alevizos, S. Plakantonaki, M. Foka, and G. Priniotakis<br>\nBiomimetics, vol. 11, no.4, p. 238.<\/p><\/div><\/div>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-0944e70 elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"0944e70\" data-element_type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-4f31dec\" data-id=\"4f31dec\" data-element_type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t\t\t<div class=\"elementor-element elementor-element-ea35c0a elementor-position-left elementor-vertical-align-top elementor-widget elementor-widget-image-box\" data-id=\"ea35c0a\" data-element_type=\"widget\" data-widget_type=\"image-box.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<div class=\"elementor-image-box-wrapper\"><figure class=\"elementor-image-box-img\"><a href=\"https:\/\/doi.org\/10.3390\/info17040340\" target=\"_blank\"><img width=\"552\" height=\"194\" src=\"https:\/\/mlv.cs.duth.gr\/wp-content\/uploads\/2026\/04\/Computer-Vision-in-Spiritual-Seeing_-Recognition-of-Christian-Saints-in-Orthodox-Iconography.png\" class=\"elementor-animation-grow attachment-full size-full\" alt=\"\" loading=\"lazy\" \/><\/a><\/figure><div class=\"elementor-image-box-content\"><h5 class=\"elementor-image-box-title\"><a href=\"https:\/\/doi.org\/10.3390\/info17040340\" target=\"_blank\">Computer Vision in Spiritual Seeing: Recognition of Christian Saints in Orthodox Iconography<\/a><\/h5><p class=\"elementor-image-box-description\">I.I. Sidiropoulos, K.D. Apostolidis, E. Vrochidou, and G.A. Papakostas<br>\nInformation, vol. 17, no.4, p. 340.<\/p><\/div><\/div>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-ff44ec1 elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"ff44ec1\" data-element_type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-717e100\" data-id=\"717e100\" data-element_type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t\t\t<div class=\"elementor-element elementor-element-854f013 elementor-position-left elementor-vertical-align-top elementor-widget elementor-widget-image-box\" data-id=\"854f013\" data-element_type=\"widget\" data-widget_type=\"image-box.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<div class=\"elementor-image-box-wrapper\"><figure class=\"elementor-image-box-img\"><a href=\"https:\/\/doi.org\/10.1109\/3ict68299.2025.11442232\" target=\"_blank\"><img width=\"1536\" height=\"1024\" src=\"https:\/\/mlv.cs.duth.gr\/wp-content\/uploads\/2026\/03\/AI-Copilots-for-Machine-Learning-Pipelines_-A-Comparative-Study-on-Code-Quality-and-Model-Performance.png\" class=\"elementor-animation-grow attachment-full size-full\" alt=\"\" loading=\"lazy\" \/><\/a><\/figure><div class=\"elementor-image-box-content\"><h5 class=\"elementor-image-box-title\"><a href=\"https:\/\/doi.org\/10.1109\/3ict68299.2025.11442232\" target=\"_blank\">AI Copilots for Machine Learning Pipelines: A Comparative Study on Code Quality and Model Performance<\/a><\/h5><p class=\"elementor-image-box-description\">N. Flamourtzoglou, E. Vrochidou, and G.A. Papakostas<br>\nProceedings of the 2025 International Conference on Innovation and Intelligence for Informatics, Computing, and Technologies (3ICT), Sakhir, Bahrain, p. 1-6.<\/p><\/div><\/div>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-c3857d5 elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"c3857d5\" data-element_type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-3be90f0\" data-id=\"3be90f0\" data-element_type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t\t\t<div class=\"elementor-element elementor-element-58a1e79 elementor-position-left elementor-vertical-align-top elementor-widget elementor-widget-image-box\" data-id=\"58a1e79\" data-element_type=\"widget\" data-widget_type=\"image-box.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<div class=\"elementor-image-box-wrapper\"><figure class=\"elementor-image-box-img\"><a href=\"https:\/\/doi.org\/10.1109\/3ict68299.2025.11442066\" target=\"_blank\"><img width=\"1024\" height=\"1024\" src=\"https:\/\/mlv.cs.duth.gr\/wp-content\/uploads\/2026\/03\/Reinforcement-Learning-for-Robust-Explosive-Demolition_-Overcoming-Collapse-Failures-through-Adaptive-Decision-Making.png\" class=\"elementor-animation-grow attachment-full size-full\" alt=\"\" loading=\"lazy\" \/><\/a><\/figure><div class=\"elementor-image-box-content\"><h5 class=\"elementor-image-box-title\"><a href=\"https:\/\/doi.org\/10.1109\/3ict68299.2025.11442066\" target=\"_blank\">Reinforcement Learning for Robust Explosive Demolition: Overcoming Collapse Failures through Adaptive Decision Making<\/a><\/h5><p class=\"elementor-image-box-description\">M. Tsaousidis, T. Kalampokas, A. Moutafidou, E. Vrochidou, and G.A. Papakostas<br>\nProceedings of the 2025 International Conference on Innovation and Intelligence for Informatics, Computing, and Technologies (3ICT), Sakhir, Bahrain, p. 1-5.<\/p><\/div><\/div>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-b16c337 elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"b16c337\" data-element_type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-8a5987e\" data-id=\"8a5987e\" data-element_type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t\t\t<div class=\"elementor-element elementor-element-6d21c17 elementor-position-left elementor-vertical-align-top elementor-widget elementor-widget-image-box\" data-id=\"6d21c17\" data-element_type=\"widget\" data-widget_type=\"image-box.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<div class=\"elementor-image-box-wrapper\"><figure class=\"elementor-image-box-img\"><a href=\"https:\/\/doi.org\/10.1016\/j.procs.2026.01.006\" target=\"_blank\"><img width=\"1536\" height=\"1024\" src=\"https:\/\/mlv.cs.duth.gr\/wp-content\/uploads\/2026\/03\/Gender-Debiasing-Word-Embeddings-for-Low-Resource-Languages_-A-Case-Study-in-Greek.png\" class=\"elementor-animation-grow attachment-full size-full\" alt=\"\" loading=\"lazy\" \/><\/a><\/figure><div class=\"elementor-image-box-content\"><h5 class=\"elementor-image-box-title\"><a href=\"https:\/\/doi.org\/10.1016\/j.procs.2026.01.006\" target=\"_blank\">Gender-Debiasing Word Embeddings for Low-Resource Languages: A Case Study in Greek<\/a><\/h5><p class=\"elementor-image-box-description\">S. Tsimenidis, T. Kalampokas, M. Charmpi, E. Vrochidou, L. Moussiades, S. Xanthopoulos, and G.A. Papakostas<br>\nProceedings of the 7th International Conference on AI in Computational Linguistics, Dubai, \nUnited Arab Emirates, p. 38-46.<\/p><\/div><\/div>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-f769f40 elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"f769f40\" data-element_type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-22fc3ea\" data-id=\"22fc3ea\" data-element_type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t\t\t<div class=\"elementor-element elementor-element-11139cd elementor-position-left elementor-vertical-align-top elementor-widget elementor-widget-image-box\" data-id=\"11139cd\" data-element_type=\"widget\" data-widget_type=\"image-box.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<div class=\"elementor-image-box-wrapper\"><figure class=\"elementor-image-box-img\"><a href=\"https:\/\/doi.org\/10.3390\/ai7030108\" target=\"_blank\"><img width=\"424\" height=\"288\" src=\"https:\/\/mlv.cs.duth.gr\/wp-content\/uploads\/2026\/03\/AI-Enabled-Digital-Twins-in-Agriculture.png\" class=\"elementor-animation-grow attachment-full size-full\" alt=\"\" loading=\"lazy\" \/><\/a><\/figure><div class=\"elementor-image-box-content\"><h5 class=\"elementor-image-box-title\"><a href=\"https:\/\/doi.org\/10.3390\/ai7030108\" target=\"_blank\">AI-Enabled Digital Twins in Agriculture<\/a><\/h5><p class=\"elementor-image-box-description\">M. Tsaousidis, T. Kalampokas, E. Vrochidou, G.\u0391. Papakostas<br>\nAI, vol. 7, no.3, p. 8108.<\/p><\/div><\/div>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-1f50172 elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"1f50172\" data-element_type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-1f5e179\" data-id=\"1f5e179\" data-element_type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t\t\t<div class=\"elementor-element elementor-element-602a559 elementor-position-left elementor-vertical-align-top elementor-widget elementor-widget-image-box\" data-id=\"602a559\" data-element_type=\"widget\" data-widget_type=\"image-box.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<div class=\"elementor-image-box-wrapper\"><figure class=\"elementor-image-box-img\"><a href=\"https:\/\/doi.org\/10.1117\/12.3096410\" target=\"_blank\"><img width=\"505\" height=\"225\" src=\"https:\/\/mlv.cs.duth.gr\/wp-content\/uploads\/2026\/03\/Quantum-Assisted-Visual-Quality-Assessment-of-Pomegranates-using-Hybrid-Transfer-Learning.png\" class=\"elementor-animation-grow attachment-full size-full\" alt=\"\" loading=\"lazy\" \/><\/a><\/figure><div class=\"elementor-image-box-content\"><h5 class=\"elementor-image-box-title\"><a href=\"https:\/\/doi.org\/10.1117\/12.3096410\" target=\"_blank\">Quantum-Assisted Visual Quality Assessment of Pomegranates using Hybrid Transfer Learning<\/a><\/h5><p class=\"elementor-image-box-description\">T. Kalampokas, E. Vrochidou, G.\u0391. Papakostas<br>\nProceedings of the Eighteenth International Conference on Machine Vision (ICMV 2025), Paris, France, p. 1411416.<\/p><\/div><\/div>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-97ed669 elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"97ed669\" data-element_type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-428da92\" data-id=\"428da92\" data-element_type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t\t\t<div class=\"elementor-element elementor-element-5983264 elementor-position-left elementor-vertical-align-top elementor-widget elementor-widget-image-box\" data-id=\"5983264\" data-element_type=\"widget\" data-widget_type=\"image-box.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<div class=\"elementor-image-box-wrapper\"><figure class=\"elementor-image-box-img\"><a href=\"https:\/\/doi.org\/10.3390\/electronics15040889\" target=\"_blank\"><img width=\"1536\" height=\"1024\" src=\"https:\/\/mlv.cs.duth.gr\/wp-content\/uploads\/2026\/03\/On-the-Dynamics-of-Ergonomic-Load-in-Biomimetic-Self-Organizing-Systems.png\" class=\"elementor-animation-grow attachment-full size-full\" alt=\"\" loading=\"lazy\" \/><\/a><\/figure><div class=\"elementor-image-box-content\"><h5 class=\"elementor-image-box-title\"><a href=\"https:\/\/doi.org\/10.3390\/electronics15040889\" target=\"_blank\">On the Dynamics of Ergonomic Load in Biomimetic Self-Organizing Systems<\/a><\/h5><p class=\"elementor-image-box-description\">N. Gerolimos, V. Alevizos, and G. Priniotakis<br>\nElectronics, vol. 15, no.4, p. 889.<\/p><\/div><\/div>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-ffb64d6 elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"ffb64d6\" data-element_type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-733bbd0\" data-id=\"733bbd0\" data-element_type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t\t\t<div class=\"elementor-element elementor-element-c6ff699 elementor-position-left elementor-vertical-align-top elementor-widget elementor-widget-image-box\" data-id=\"c6ff699\" data-element_type=\"widget\" data-widget_type=\"image-box.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<div class=\"elementor-image-box-wrapper\"><figure class=\"elementor-image-box-img\"><a href=\"https:\/\/doi.org\/10.3390\/bdcc10030074\" target=\"_blank\"><img width=\"1536\" height=\"1024\" src=\"https:\/\/mlv.cs.duth.gr\/wp-content\/uploads\/2026\/03\/Data-Driven-Ergonomic-Load-Dynamics-for-Human\u2013Autonomy-Teams.png\" class=\"elementor-animation-grow attachment-full size-full\" alt=\"\" loading=\"lazy\" \/><\/a><\/figure><div class=\"elementor-image-box-content\"><h5 class=\"elementor-image-box-title\"><a href=\"https:\/\/doi.org\/10.3390\/bdcc10030074\" target=\"_blank\">Data-Driven Ergonomic Load Dynamics for Human\u2013Autonomy Teams<\/a><\/h5><p class=\"elementor-image-box-description\">N. Gerolimos, V. Alevizos, and G. Priniotakis<br>\nBig Data and Cognitive Computing, vol. 10, no.3, p. 74.<\/p><\/div><\/div>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-fdfb342 elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"fdfb342\" data-element_type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-f7617e6\" data-id=\"f7617e6\" data-element_type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t\t\t<div class=\"elementor-element elementor-element-f1ab069 elementor-position-left elementor-vertical-align-top elementor-widget elementor-widget-image-box\" data-id=\"f1ab069\" data-element_type=\"widget\" data-widget_type=\"image-box.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<div class=\"elementor-image-box-wrapper\"><figure class=\"elementor-image-box-img\"><a href=\"https:\/\/doi.org\/10.3390\/systems14020215\" target=\"_blank\"><img width=\"1024\" height=\"1536\" src=\"https:\/\/mlv.cs.duth.gr\/wp-content\/uploads\/2026\/03\/A-Methodological-Framework-for-Chaos-Aware-Evaluation-of-Self-Organization-in-Swarm-Based-Engineering-Systems.png\" class=\"elementor-animation-grow attachment-full size-full\" alt=\"\" loading=\"lazy\" \/><\/a><\/figure><div class=\"elementor-image-box-content\"><h5 class=\"elementor-image-box-title\"><a href=\"https:\/\/doi.org\/10.3390\/systems14020215\" target=\"_blank\">A Methodological Framework for Chaos-Aware Evaluation of Self-Organization in Swarm-Based Engineering Systems<\/a><\/h5><p class=\"elementor-image-box-description\">N. Gerolimos, V. Alevizos, and G. Priniotakis<br>\nSystems, vol. 14, no.2, p. 215.<\/p><\/div><\/div>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-be0760f elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"be0760f\" data-element_type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-6534fad\" data-id=\"6534fad\" data-element_type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t\t\t<div class=\"elementor-element elementor-element-0e73c39 elementor-position-left elementor-vertical-align-top elementor-widget elementor-widget-image-box\" data-id=\"0e73c39\" data-element_type=\"widget\" data-widget_type=\"image-box.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<div class=\"elementor-image-box-wrapper\"><figure class=\"elementor-image-box-img\"><a href=\"https:\/\/doi.org\/10.3390\/designs10010019\" target=\"_blank\"><img width=\"476\" height=\"360\" src=\"https:\/\/mlv.cs.duth.gr\/wp-content\/uploads\/2026\/02\/Biomimetic-Compliance-in-Ergonomic-Product-Design_-A-Comprehensive-Synthesis-and-Research-Roadmap.png\" class=\"elementor-animation-grow attachment-full size-full\" alt=\"\" loading=\"lazy\" \/><\/a><\/figure><div class=\"elementor-image-box-content\"><h5 class=\"elementor-image-box-title\"><a href=\"https:\/\/doi.org\/10.3390\/designs10010019\" target=\"_blank\">Biomimetic Compliance in Ergonomic Product Design: A Comprehensive Synthesis and Research Roadmap<\/a><\/h5><p class=\"elementor-image-box-description\">N. Gerolimos, V. Alevizos, E. Sfyroera, J. Tsoumas, G. Priniotakis, and G.A. Papakostas<br>\nDesigns, vol. 10, no.1, p. 19.<\/p><\/div><\/div>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-3417e3b elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"3417e3b\" data-element_type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-58f9b6f\" data-id=\"58f9b6f\" data-element_type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t\t\t<div class=\"elementor-element elementor-element-e638fd2 elementor-position-left elementor-vertical-align-top elementor-widget elementor-widget-image-box\" data-id=\"e638fd2\" data-element_type=\"widget\" data-widget_type=\"image-box.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<div class=\"elementor-image-box-wrapper\"><figure class=\"elementor-image-box-img\"><a href=\"https:\/\/doi.org\/10.1007\/978-3-031-98022-0_2\" target=\"_blank\"><img width=\"1536\" height=\"1024\" src=\"https:\/\/mlv.cs.duth.gr\/wp-content\/uploads\/2026\/01\/Data-Mining-and-Integration-Approaches-in-AI-Driven-Drug-Discovery.png\" class=\"elementor-animation-grow attachment-full size-full\" alt=\"\" loading=\"lazy\" \/><\/a><\/figure><div class=\"elementor-image-box-content\"><h5 class=\"elementor-image-box-title\"><a href=\"https:\/\/doi.org\/10.1007\/978-3-031-98022-0_2\" target=\"_blank\">Data Mining and Integration Approaches in AI-Driven Drug Discovery<\/a><\/h5><p class=\"elementor-image-box-description\">S. Tsimenidis, E. Vrochidou, and G.A. Papakostas<br>\nEditor(s): Lavecchia, A. In Applied Artificial Intelligence for Drug Discovery. Springer, Cham.<\/p><\/div><\/div>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-4736c22 elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"4736c22\" data-element_type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-72a3118\" data-id=\"72a3118\" data-element_type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t\t\t<div class=\"elementor-element elementor-element-7d441bf elementor-position-left elementor-vertical-align-top elementor-widget elementor-widget-image-box\" data-id=\"7d441bf\" data-element_type=\"widget\" data-widget_type=\"image-box.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<div class=\"elementor-image-box-wrapper\"><figure class=\"elementor-image-box-img\"><a href=\"https:\/\/doi.org\/10.3390\/su18010249\" target=\"_blank\"><img width=\"1024\" height=\"1536\" src=\"https:\/\/mlv.cs.duth.gr\/wp-content\/uploads\/2026\/01\/AI-Precision-Agriculture-and-Tourism-for-Sustainable-Regional-Development_-The-Case-of-the-Aegean-Islands-and-Crete-Greece.png\" class=\"elementor-animation-grow attachment-full size-full\" alt=\"\" loading=\"lazy\" \/><\/a><\/figure><div class=\"elementor-image-box-content\"><h5 class=\"elementor-image-box-title\"><a href=\"https:\/\/doi.org\/10.3390\/su18010249\" target=\"_blank\">AI, Precision Agriculture and Tourism for Sustainable Regional Development: The Case of the Aegean Islands and Crete, Greece<\/a><\/h5><p class=\"elementor-image-box-description\">S. Lotsis, I. Georgousis, and G.A. Papakostas<br>\nSustainability, vol. 18,  no. 1,  p. 249.<\/p><\/div><\/div>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-aa31b9b elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"aa31b9b\" data-element_type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-1a42790\" data-id=\"1a42790\" data-element_type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t\t\t<div class=\"elementor-element elementor-element-51118ef elementor-position-left elementor-vertical-align-top elementor-widget elementor-widget-image-box\" data-id=\"51118ef\" data-element_type=\"widget\" data-widget_type=\"image-box.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<div class=\"elementor-image-box-wrapper\"><figure class=\"elementor-image-box-img\"><a href=\"https:\/\/doi.org\/10.1016\/B978-0-443-41624-8.00023-1\" target=\"_blank\"><img width=\"964\" height=\"645\" src=\"https:\/\/mlv.cs.duth.gr\/wp-content\/uploads\/2026\/01\/Recent-Developments-in-Electroencephalography-with-Deep-Learning-Models.png\" class=\"elementor-animation-grow attachment-full size-full\" alt=\"\" loading=\"lazy\" \/><\/a><\/figure><div class=\"elementor-image-box-content\"><h5 class=\"elementor-image-box-title\"><a href=\"https:\/\/doi.org\/10.1016\/B978-0-443-41624-8.00023-1\" target=\"_blank\">Recent Developments in Electroencephalography with Deep Learning Models<\/a><\/h5><p class=\"elementor-image-box-description\">M. Marinis, E. Vrochidou, and G.A. Papakostas<br>\nEditor(s): Tuan Anh Nguyen, T.A. In Advances in Bioelectromagnetism, pp. 177-193, Academic Press.<\/p><\/div><\/div>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-414c9e1 elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"414c9e1\" data-element_type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-8ec20bd\" data-id=\"8ec20bd\" data-element_type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t\t\t<div class=\"elementor-element elementor-element-029d175 elementor-widget elementor-widget-spacer\" data-id=\"029d175\" data-element_type=\"widget\" data-widget_type=\"spacer.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<div class=\"elementor-spacer\">\n\t\t\t<div class=\"elementor-spacer-inner\"><\/div>\n\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-c11265d elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"c11265d\" data-element_type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-787c6f4\" data-id=\"787c6f4\" data-element_type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t\t\t<div class=\"elementor-element elementor-element-f342612 elementor-widget elementor-widget-heading\" data-id=\"f342612\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<h3 class=\"elementor-heading-title elementor-size-default\">2025<\/h3>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-ba65ee6 elementor-widget elementor-widget-heading\" data-id=\"ba65ee6\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<h3 class=\"elementor-heading-title elementor-size-default\">Total: 47 (#Journals: 33, #Conferences: 10, #Book Chapters: 4, #Books: 0)<\/h3>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-860c6cc elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"860c6cc\" data-element_type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-4a6c7ea\" data-id=\"4a6c7ea\" data-element_type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t\t\t<div class=\"elementor-element elementor-element-726e2a1 elementor-position-left elementor-vertical-align-top elementor-widget elementor-widget-image-box\" data-id=\"726e2a1\" data-element_type=\"widget\" data-widget_type=\"image-box.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<div class=\"elementor-image-box-wrapper\"><figure class=\"elementor-image-box-img\"><a href=\"https:\/\/doi.org\/10.3390\/app152413024\" target=\"_blank\"><img width=\"1536\" height=\"1024\" src=\"https:\/\/mlv.cs.duth.gr\/wp-content\/uploads\/2025\/12\/Deep-Radiomic-Fusion-for-Early-Detection-of-Pancreatic-Ductal-Adenocarcinoma.png\" class=\"elementor-animation-grow attachment-full size-full\" alt=\"\" loading=\"lazy\" \/><\/a><\/figure><div class=\"elementor-image-box-content\"><h5 class=\"elementor-image-box-title\"><a href=\"https:\/\/doi.org\/10.3390\/app152413024\" target=\"_blank\">Deep-Radiomic Fusion for Early Detection of Pancreatic Ductal Adenocarcinoma<\/a><\/h5><p class=\"elementor-image-box-description\">G. Lekkas, E. Vrochidou, and G.A. Papakostas<br>\nApplied Science, vol. 15, p. 13024.<\/p><\/div><\/div>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-2209d3a elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"2209d3a\" data-element_type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-f9aabc9\" data-id=\"f9aabc9\" data-element_type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t\t\t<div class=\"elementor-element elementor-element-113c38f elementor-position-left elementor-vertical-align-top elementor-widget elementor-widget-image-box\" data-id=\"113c38f\" data-element_type=\"widget\" data-widget_type=\"image-box.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<div class=\"elementor-image-box-wrapper\"><figure class=\"elementor-image-box-img\"><a href=\"https:\/\/doi.org\/10.1145\/3747897.3747901\" target=\"_blank\"><img width=\"1024\" height=\"1024\" src=\"https:\/\/mlv.cs.duth.gr\/wp-content\/uploads\/2025\/11\/BioSwarmEV-Swarm-Intelligence-Based-Electric-Vehicle-Energy-Consumption-Model-with-Biological-Pattern-Formation.png\" class=\"elementor-animation-grow attachment-full size-full\" alt=\"\" loading=\"lazy\" \/><\/a><\/figure><div class=\"elementor-image-box-content\"><h5 class=\"elementor-image-box-title\"><a href=\"https:\/\/doi.org\/10.1145\/3747897.3747901\" target=\"_blank\">BioSwarmEV Swarm Intelligence-Based Electric Vehicle Energy Consumption Model with Biological Pattern Formation<\/a><\/h5><p class=\"elementor-image-box-description\">V. Alevizos, N. Georlimos, Z. Yue, S. Edralin, C. Xu, G.A. Papakostas, and E. Vrochidou<br>\nProceedings of the Sixth International Conference on Digital Age & Technological Advances for Sustainable Development, p. 22\u201327, 2025.<\/p><\/div><\/div>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-dc1f333 elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"dc1f333\" data-element_type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-92b9cef\" data-id=\"92b9cef\" data-element_type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t\t\t<div class=\"elementor-element elementor-element-6767f3f elementor-position-left elementor-vertical-align-top elementor-widget elementor-widget-image-box\" data-id=\"6767f3f\" data-element_type=\"widget\" data-widget_type=\"image-box.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<div class=\"elementor-image-box-wrapper\"><figure class=\"elementor-image-box-img\"><a href=\"https:\/\/doi.org\/10.1007\/978-981-96-8694-0_9\" target=\"_blank\"><img width=\"1536\" height=\"1024\" src=\"https:\/\/mlv.cs.duth.gr\/wp-content\/uploads\/2025\/11\/Toward-the-Reliable-Deployment-of-Computer-Vision-Pipelines-by-Leveraging-MLOps-and-Explainable-AI.png\" class=\"elementor-animation-grow attachment-full size-full\" alt=\"\" loading=\"lazy\" \/><\/a><\/figure><div class=\"elementor-image-box-content\"><h5 class=\"elementor-image-box-title\"><a href=\"https:\/\/doi.org\/10.1007\/978-981-96-8694-0_9\" target=\"_blank\">Toward the Reliable Deployment of Computer Vision Pipelines by Leveraging MLOps and Explainable AI<\/a><\/h5><p class=\"elementor-image-box-description\">E. Nerantzis, G. Symeonidis, A. Vouta Papageorgiou, and G.A. Papakostas<br>\nProceedings of International Conference on Information Technology and Artificial Intelligence. ITAI 2025. Lecture Notes in Networks and Systems, vol 1506, (2025).<\/p><\/div><\/div>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-ffbdc20 elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"ffbdc20\" data-element_type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-4a003a4\" data-id=\"4a003a4\" data-element_type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t\t\t<div class=\"elementor-element elementor-element-fe5383f elementor-position-left elementor-vertical-align-top elementor-widget elementor-widget-image-box\" data-id=\"fe5383f\" data-element_type=\"widget\" data-widget_type=\"image-box.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<div class=\"elementor-image-box-wrapper\"><figure class=\"elementor-image-box-img\"><a href=\"https:\/\/doi.org\/10.4108\/eetiot.9404%20\" target=\"_blank\"><img width=\"1536\" height=\"1024\" src=\"https:\/\/mlv.cs.duth.gr\/wp-content\/uploads\/2025\/11\/Edge-Computing-for-Computer-Vision-in-IoT_-Feasibility-and-Directions.png\" class=\"elementor-animation-grow attachment-full size-full\" alt=\"\" loading=\"lazy\" \/><\/a><\/figure><div class=\"elementor-image-box-content\"><h5 class=\"elementor-image-box-title\"><a href=\"https:\/\/doi.org\/10.4108\/eetiot.9404%20\" target=\"_blank\">Edge Computing for Computer Vision in IoT: Feasibility and Directions<\/a><\/h5><p class=\"elementor-image-box-description\">P. Savvidis  and G.A. Papakostas<br>\nEAI Endorsed Transactions on Internet of Things, vol. 11, (2025).<\/p><\/div><\/div>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-31aa9ef elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"31aa9ef\" data-element_type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-6200363\" data-id=\"6200363\" data-element_type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t\t\t<div class=\"elementor-element elementor-element-c529b02 elementor-position-left elementor-vertical-align-top elementor-widget elementor-widget-image-box\" data-id=\"c529b02\" data-element_type=\"widget\" data-widget_type=\"image-box.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<div class=\"elementor-image-box-wrapper\"><figure class=\"elementor-image-box-img\"><a href=\"https:\/\/doi.org\/10.1007\/s42044-025-00348-3\" target=\"_blank\"><img width=\"961\" height=\"642\" src=\"https:\/\/mlv.cs.duth.gr\/wp-content\/uploads\/2025\/11\/Ai-Enabled-Multimodal-Analysis-Enhances-Detection-of-Motor-Neuron-Disease-Pathways.png\" class=\"elementor-animation-grow attachment-full size-full\" alt=\"\" loading=\"lazy\" \/><\/a><\/figure><div class=\"elementor-image-box-content\"><h5 class=\"elementor-image-box-title\"><a href=\"https:\/\/doi.org\/10.1007\/s42044-025-00348-3\" target=\"_blank\">Ai-Enabled Multimodal Analysis Enhances Detection of Motor Neuron Disease Pathways<\/a><\/h5><p class=\"elementor-image-box-description\">V. Alevizos and G.A. Papakostas<br>\n Iran Journal of Computer Science, (2025).<\/p><\/div><\/div>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-f27eb56 elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"f27eb56\" data-element_type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-ff21789\" data-id=\"ff21789\" data-element_type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t\t\t<div class=\"elementor-element elementor-element-0d97f0c elementor-position-left elementor-vertical-align-top elementor-widget elementor-widget-image-box\" data-id=\"0d97f0c\" data-element_type=\"widget\" data-widget_type=\"image-box.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<div class=\"elementor-image-box-wrapper\"><figure class=\"elementor-image-box-img\"><a href=\"https:\/\/doi.org\/10.1007\/s10278-025-01684-3\" target=\"_blank\"><img width=\"128\" height=\"128\" src=\"https:\/\/mlv.cs.duth.gr\/wp-content\/uploads\/2025\/10\/Pointmaps-to-Practice_-3D-Multi-view-Ocular-Lesion-Mapping.png\" class=\"elementor-animation-grow attachment-full size-full\" alt=\"\" loading=\"lazy\" \/><\/a><\/figure><div class=\"elementor-image-box-content\"><h5 class=\"elementor-image-box-title\"><a href=\"https:\/\/doi.org\/10.1007\/s10278-025-01684-3\" target=\"_blank\">Pointmaps to Practice: 3D Multi-view Ocular Lesion Mapping<\/a><\/h5><p class=\"elementor-image-box-description\">V. Alevizos and G.A. Papakostas<br>\nJournal of Imaging Informatics in Medicine, (2025).<\/p><\/div><\/div>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-61c2829 elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"61c2829\" data-element_type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-1fcc3a7\" data-id=\"1fcc3a7\" data-element_type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t\t\t<div class=\"elementor-element elementor-element-1d7d767 elementor-position-left elementor-vertical-align-top elementor-widget elementor-widget-image-box\" data-id=\"1d7d767\" data-element_type=\"widget\" data-widget_type=\"image-box.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<div class=\"elementor-image-box-wrapper\"><figure class=\"elementor-image-box-img\"><a href=\"https:\/\/doi.org\/10.23919\/SoftCOM66362.2025.11197352\" target=\"_blank\"><img width=\"1536\" height=\"1024\" src=\"https:\/\/mlv.cs.duth.gr\/wp-content\/uploads\/2025\/10\/Image-Based-Deep-Learning-for-Biomedical-Timeseries_-Enhancing-1D-Signal-Classification-Via-2D-Transformations.png\" class=\"elementor-animation-grow attachment-full size-full\" alt=\"\" loading=\"lazy\" \/><\/a><\/figure><div class=\"elementor-image-box-content\"><h5 class=\"elementor-image-box-title\"><a href=\"https:\/\/doi.org\/10.23919\/SoftCOM66362.2025.11197352\" target=\"_blank\">Image-Based Deep Learning for Biomedical Timeseries: Enhancing 1D Signal Classification Via 2D Transformations<\/a><\/h5><p class=\"elementor-image-box-description\">G. Lekkas, P. Georgiadis, E. V. Gkouvrikos, E. Vrochidou, and G.A. Papakostas<br>\nProceedings of 2025 International Conference on Software, Telecommunications and Computer Networks (SoftCOM), Split, Croatia, p.1-6.<\/p><\/div><\/div>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-9f7b93e elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"9f7b93e\" data-element_type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-3d37993\" data-id=\"3d37993\" data-element_type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t\t\t<div class=\"elementor-element elementor-element-f4f1d19 elementor-position-left elementor-vertical-align-top elementor-widget elementor-widget-image-box\" data-id=\"f4f1d19\" data-element_type=\"widget\" data-widget_type=\"image-box.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<div class=\"elementor-image-box-wrapper\"><figure class=\"elementor-image-box-img\"><a href=\"https:\/\/doi.org\/10.23919\/SoftCOM66362.2025.11197395\" target=\"_blank\"><img width=\"1024\" height=\"1024\" src=\"https:\/\/mlv.cs.duth.gr\/wp-content\/uploads\/2025\/10\/Computer-Vision-in-Extended-Reality.png\" class=\"elementor-animation-grow attachment-full size-full\" alt=\"\" loading=\"lazy\" \/><\/a><\/figure><div class=\"elementor-image-box-content\"><h5 class=\"elementor-image-box-title\"><a href=\"https:\/\/doi.org\/10.23919\/SoftCOM66362.2025.11197395\" target=\"_blank\">Computer Vision in Extended Reality<\/a><\/h5><p class=\"elementor-image-box-description\">E. Siandri, Z. Kasapi, S. Chatzistamatis, G. Lekkas, E. Vrochidou, and G.A. Papakostas<br>\nProceedings of 2025 International Conference on Software, Telecommunications and Computer Networks (SoftCOM), Split, Croatia, p.1-6.<\/p><\/div><\/div>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-4ad3a9c elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"4ad3a9c\" data-element_type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-8a2bed5\" data-id=\"8a2bed5\" data-element_type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t\t\t<div class=\"elementor-element elementor-element-1660491 elementor-position-left elementor-vertical-align-top elementor-widget elementor-widget-image-box\" data-id=\"1660491\" data-element_type=\"widget\" data-widget_type=\"image-box.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<div class=\"elementor-image-box-wrapper\"><figure class=\"elementor-image-box-img\"><a href=\"https:\/\/doi.org\/10.23919\/SoftCOM66362.2025.11197333\" target=\"_blank\"><img width=\"1024\" height=\"1024\" src=\"https:\/\/mlv.cs.duth.gr\/wp-content\/uploads\/2025\/10\/Social-Robot-Selection-in-Primary-Education_-A-Multi-Criteria-Decision-Making-Approach.png\" class=\"elementor-animation-grow attachment-full size-full\" alt=\"\" loading=\"lazy\" \/><\/a><\/figure><div class=\"elementor-image-box-content\"><h5 class=\"elementor-image-box-title\"><a href=\"https:\/\/doi.org\/10.23919\/SoftCOM66362.2025.11197333\" target=\"_blank\">Social Robot Selection in Primary Education: A Multi-Criteria Decision-Making Approach<\/a><\/h5><p class=\"elementor-image-box-description\">M. Tzampazaki, E. Vrochidou, G. Lekkas, T. Kalampokas, and G.A. Papakostas<br>\nProceedings of 2025 International Conference on Software, Telecommunications and Computer Networks (SoftCOM), Split, Croatia, p.1-6.<\/p><\/div><\/div>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-f7745c2 elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"f7745c2\" data-element_type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-f5e5bb2\" data-id=\"f5e5bb2\" data-element_type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t\t\t<div class=\"elementor-element elementor-element-b492896 elementor-position-left elementor-vertical-align-top elementor-widget elementor-widget-image-box\" data-id=\"b492896\" data-element_type=\"widget\" data-widget_type=\"image-box.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<div class=\"elementor-image-box-wrapper\"><figure class=\"elementor-image-box-img\"><a href=\"https:\/\/doi.org\/10.1158\/1538-7445.AM2025-2800\" target=\"_blank\"><img width=\"393\" height=\"393\" src=\"https:\/\/mlv.cs.duth.gr\/wp-content\/uploads\/2025\/04\/Unraveling-Long-non-Coding-RNA-Dynamics-in-Uveal-Melanoma_-Insights-into-Pathogenesis-Biomarkers-and-Targeted-Therapies.png\" class=\"elementor-animation-grow attachment-full size-full\" alt=\"\" loading=\"lazy\" \/><\/a><\/figure><div class=\"elementor-image-box-content\"><h5 class=\"elementor-image-box-title\"><a href=\"https:\/\/doi.org\/10.1158\/1538-7445.AM2025-2800\" target=\"_blank\">Unraveling Long non-Coding RNA Dynamics in Uveal Melanoma: Insights into Pathogenesis, Biomarkers, and Targeted Therapies<\/a><\/h5><p class=\"elementor-image-box-description\">V. Alevizos, N. Gerolimos, S. Edralin, C. Xu, A. Simasiku, G. Priniotakis, G.A. Papakostas, and Z. Yue<br>\nProceedings of the American Association for Cancer Research Annual (AACR) Meeting 2025, Chicago, IL, USA.<\/p><\/div><\/div>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-d9eb358 elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"d9eb358\" data-element_type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-be872ba\" data-id=\"be872ba\" data-element_type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t\t\t<div class=\"elementor-element elementor-element-38d6784 elementor-position-left elementor-vertical-align-top elementor-widget elementor-widget-image-box\" data-id=\"38d6784\" data-element_type=\"widget\" data-widget_type=\"image-box.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<div class=\"elementor-image-box-wrapper\"><figure class=\"elementor-image-box-img\"><a href=\"https:\/\/doi.org\/10.3390\/technologies13100477\" target=\"_blank\"><img width=\"1536\" height=\"1024\" src=\"https:\/\/mlv.cs.duth.gr\/wp-content\/uploads\/2025\/10\/Sustainable-Swarm-Intelligence_-Assessing-Carbon-Aware-Optimization-in-High-Performance-AI-Systems.png\" class=\"elementor-animation-grow attachment-full size-full\" alt=\"\" loading=\"lazy\" \/><\/a><\/figure><div class=\"elementor-image-box-content\"><h5 class=\"elementor-image-box-title\"><a href=\"https:\/\/doi.org\/10.3390\/technologies13100477\" target=\"_blank\">Sustainable Swarm Intelligence: Assessing Carbon-Aware Optimization in High-Performance AI Systems<\/a><\/h5><p class=\"elementor-image-box-description\">V. Alevizos, N. Gerolimos, E.A. Leligkou, G. Hompis, G. Priniotakis, and G.A.; Papakostas,<br>\nTechnologies, vol. 13, no. 10, p. 477.<\/p><\/div><\/div>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-f0aae40 elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"f0aae40\" data-element_type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-e2034bf\" data-id=\"e2034bf\" data-element_type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t\t\t<div class=\"elementor-element elementor-element-4ecd1d9 elementor-position-left elementor-vertical-align-top elementor-widget elementor-widget-image-box\" data-id=\"4ecd1d9\" data-element_type=\"widget\" data-widget_type=\"image-box.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<div class=\"elementor-image-box-wrapper\"><figure class=\"elementor-image-box-img\"><a href=\"https:\/\/doi.org\/10.3390\/app152011126\" target=\"_blank\"><img width=\"1024\" height=\"1024\" src=\"https:\/\/mlv.cs.duth.gr\/wp-content\/uploads\/2025\/10\/A-Correlation-Between-Earthquake-Magnitude-and-Pre-Seismic-Gravity-Field-Variations-over-Its-Epicenter.png\" class=\"elementor-animation-grow attachment-full size-full\" alt=\"\" loading=\"lazy\" \/><\/a><\/figure><div class=\"elementor-image-box-content\"><h5 class=\"elementor-image-box-title\"><a href=\"https:\/\/doi.org\/10.3390\/app152011126\" target=\"_blank\">A Correlation Between Earthquake Magnitude and Pre-Seismic Gravity Field Variations over Its Epicenter<\/a><\/h5><p class=\"elementor-image-box-description\">C. Chariskou E. Vrochidou, and G.A. Papakostas <br>\nApplied Science, vol. 15, no. 20, p. 11126.<\/p><\/div><\/div>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-79c1afc elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"79c1afc\" data-element_type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-15949ba\" data-id=\"15949ba\" data-element_type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t\t\t<div class=\"elementor-element elementor-element-d03fbc1 elementor-position-left elementor-vertical-align-top elementor-widget elementor-widget-image-box\" data-id=\"d03fbc1\" data-element_type=\"widget\" data-widget_type=\"image-box.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<div class=\"elementor-image-box-wrapper\"><figure class=\"elementor-image-box-img\"><a href=\"https:\/\/doi.org\/10.3390\/math13203283\" target=\"_blank\"><img width=\"1024\" height=\"1024\" src=\"https:\/\/mlv.cs.duth.gr\/wp-content\/uploads\/2025\/10\/Truncated-Multicomplex-and-Higher-Order-Topological-Models-in-ALS-Drug-Discovery.png\" class=\"elementor-animation-grow attachment-full size-full\" alt=\"\" loading=\"lazy\" \/><\/a><\/figure><div class=\"elementor-image-box-content\"><h5 class=\"elementor-image-box-title\"><a href=\"https:\/\/doi.org\/10.3390\/math13203283\" target=\"_blank\">Truncated Multicomplex and Higher-Order Topological Models in ALS Drug Discovery <\/a><\/h5><p class=\"elementor-image-box-description\">V. Alevizos and G.A. Papakostas <br>\nMathematics, vol. 13, no. 20, p. 3283.<\/p><\/div><\/div>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-daa0890 elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"daa0890\" data-element_type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-8c45b0f\" data-id=\"8c45b0f\" data-element_type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t\t\t<div class=\"elementor-element elementor-element-666670d elementor-position-left elementor-vertical-align-top elementor-widget elementor-widget-image-box\" data-id=\"666670d\" data-element_type=\"widget\" data-widget_type=\"image-box.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<div class=\"elementor-image-box-wrapper\"><figure class=\"elementor-image-box-img\"><a href=\"https:\/\/doi.org\/10.3390\/fi17100467\" target=\"_blank\"><img width=\"1024\" height=\"1024\" src=\"https:\/\/mlv.cs.duth.gr\/wp-content\/uploads\/2025\/10\/Beyond-Accuracy_-Benchmarking-Machine-Learning-Models-for-Efficient-and-Sustainable-SaaS-Decision-Support-.png\" class=\"elementor-animation-grow attachment-full size-full\" alt=\"\" loading=\"lazy\" \/><\/a><\/figure><div class=\"elementor-image-box-content\"><h5 class=\"elementor-image-box-title\"><a href=\"https:\/\/doi.org\/10.3390\/fi17100467\" target=\"_blank\">Beyond Accuracy: Benchmarking Machine Learning Models for Efficient and Sustainable SaaS Decision Support<\/a><\/h5><p class=\"elementor-image-box-description\">E. Mavridou, E. Vrochidou, M. Selvesakis, and G.A. Papakostas <br>\nFuture Internet, vol. 17, no. 10, p. 467.<\/p><\/div><\/div>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-572d5e3 elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"572d5e3\" data-element_type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-ace0b22\" data-id=\"ace0b22\" data-element_type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t\t\t<div class=\"elementor-element elementor-element-db48cfa elementor-position-left elementor-vertical-align-top elementor-widget elementor-widget-image-box\" data-id=\"db48cfa\" data-element_type=\"widget\" data-widget_type=\"image-box.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<div class=\"elementor-image-box-wrapper\"><figure class=\"elementor-image-box-img\"><a href=\"https:\/\/doi.org\/10.3390\/machines13100909\" target=\"_blank\"><img width=\"1480\" height=\"880\" src=\"https:\/\/mlv.cs.duth.gr\/wp-content\/uploads\/2025\/10\/Bridging-AI-and-Maintenance_-Fault-Diagnosis-in-Industrial-Air-Cooling-Systems-Using-Deep-Learning-and-Sensor-Data.png\" class=\"elementor-animation-grow attachment-full size-full\" alt=\"\" loading=\"lazy\" \/><\/a><\/figure><div class=\"elementor-image-box-content\"><h5 class=\"elementor-image-box-title\"><a href=\"https:\/\/doi.org\/10.3390\/machines13100909\" target=\"_blank\">Bridging AI and Maintenance: Fault Diagnosis in Industrial Air-Cooling Systems Using Deep Learning and Sensor Data<\/a><\/h5><p class=\"elementor-image-box-description\">I. Polymeropoulos, S. Bezyrgiannidis, E. Vrochidou, and G.A. Papakostas<br>\nMachines, vol. 13, no. 10, p. 909.<\/p><\/div><\/div>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-1b1ceba elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"1b1ceba\" data-element_type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-1534e79\" data-id=\"1534e79\" data-element_type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t\t\t<div class=\"elementor-element elementor-element-eb0e0af elementor-position-left elementor-vertical-align-top elementor-widget elementor-widget-image-box\" data-id=\"eb0e0af\" data-element_type=\"widget\" data-widget_type=\"image-box.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<div class=\"elementor-image-box-wrapper\"><figure class=\"elementor-image-box-img\"><a href=\"https:\/\/doi.org\/10.3390\/jimaging11090322\" target=\"_blank\"><img width=\"720\" height=\"635\" src=\"https:\/\/mlv.cs.duth.gr\/wp-content\/uploads\/2025\/09\/Empirical-Evaluation-of-Invariances-in-Deep-Vision-Models-.png\" class=\"elementor-animation-grow attachment-full size-full\" alt=\"\" loading=\"lazy\" \/><\/a><\/figure><div class=\"elementor-image-box-content\"><h5 class=\"elementor-image-box-title\"><a href=\"https:\/\/doi.org\/10.3390\/jimaging11090322\" target=\"_blank\">Empirical Evaluation of Invariances in Deep Vision Models<\/a><\/h5><p class=\"elementor-image-box-description\">K. Keremis, E. Vrochidou, and G.A. Papakostas<br>\nJournal of Imaging, vol. 11, no. 9, p. 322.<\/p><\/div><\/div>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-cf11d3a elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"cf11d3a\" data-element_type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-2a1a820\" data-id=\"2a1a820\" data-element_type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t\t\t<div class=\"elementor-element elementor-element-2bef750 elementor-position-left elementor-vertical-align-top elementor-widget elementor-widget-image-box\" data-id=\"2bef750\" data-element_type=\"widget\" data-widget_type=\"image-box.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<div class=\"elementor-image-box-wrapper\"><figure class=\"elementor-image-box-img\"><a href=\"https:\/\/doi.org\/10.3390\/pr13092920\" target=\"_blank\"><img width=\"1057\" height=\"685\" src=\"https:\/\/mlv.cs.duth.gr\/wp-content\/uploads\/2025\/09\/Exposing-Vulnerabilities_-Physical-Adversarial-Attacks-on-AI-Based-Fault-Diagnosis-Models-in-Industrial-Air-Cooling-Systems.png\" class=\"elementor-animation-grow attachment-full size-full\" alt=\"\" loading=\"lazy\" \/><\/a><\/figure><div class=\"elementor-image-box-content\"><h5 class=\"elementor-image-box-title\"><a href=\"https:\/\/doi.org\/10.3390\/pr13092920\" target=\"_blank\">Exposing Vulnerabilities: Physical Adversarial Attacks on AI-Based Fault Diagnosis Models in Industrial Air-Cooling Systems<\/a><\/h5><p class=\"elementor-image-box-description\">S. Bezyrgiannidis, I. Polymeropoulos, E. Vrochidou, and G.A. Papakostas<br>\nProcesses, vol. 13, no. 9, p. 2920.<\/p><\/div><\/div>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-13e17d9 elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"13e17d9\" data-element_type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-320c3f8\" data-id=\"320c3f8\" data-element_type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t\t\t<div class=\"elementor-element elementor-element-20b6efb elementor-position-left elementor-vertical-align-top elementor-widget elementor-widget-image-box\" data-id=\"20b6efb\" data-element_type=\"widget\" data-widget_type=\"image-box.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<div class=\"elementor-image-box-wrapper\"><figure class=\"elementor-image-box-img\"><a href=\"https:\/\/doi.org\/10.3390\/app15189905\" target=\"_blank\"><img width=\"1021\" height=\"785\" src=\"https:\/\/mlv.cs.duth.gr\/wp-content\/uploads\/2025\/09\/Advanced-Graph\u2013Physics-Hybrid-Framework-AGPHF-for-Holistic-Integration-of-AI-Driven-Graph-and-Physics-Methodologies-to-Promote-Resilient-Wastewater-Management-in-Dynamic-Real-World-Conditions.png\" class=\"elementor-animation-grow attachment-full size-full\" alt=\"\" loading=\"lazy\" \/><\/a><\/figure><div class=\"elementor-image-box-content\"><h5 class=\"elementor-image-box-title\"><a href=\"https:\/\/doi.org\/10.3390\/app15189905\" target=\"_blank\">Advanced Graph\u2013Physics Hybrid Framework (AGPHF) for Holistic Integration of AI-Driven Graph- and Physics- Methodologies to Promote Resilient Wastewater Management in Dynamic Real-World Conditions<\/a><\/h5><p class=\"elementor-image-box-description\">V. Alevizos, N. Gerolimos, Z. Yue, S. Edralin, C. Xu, G.A. Papakostas, E. Vrochidou, G. Marnellos, and M. Mustafa<br>\nApplied Science, vol. 15, no. 18, p. 9905.<\/p><\/div><\/div>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-dc7b282 elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"dc7b282\" data-element_type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-6d1316d\" data-id=\"6d1316d\" data-element_type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t\t\t<div class=\"elementor-element elementor-element-e82851f elementor-position-left elementor-vertical-align-top elementor-widget elementor-widget-image-box\" data-id=\"e82851f\" data-element_type=\"widget\" data-widget_type=\"image-box.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<div class=\"elementor-image-box-wrapper\"><figure class=\"elementor-image-box-img\"><a href=\"https:\/\/doi.org\/10.3390\/w17172653\" target=\"_blank\"><img width=\"1023\" height=\"781\" src=\"https:\/\/mlv.cs.duth.gr\/wp-content\/uploads\/2025\/09\/Multi-Agentic-Water-Health-Surveillance.png\" class=\"elementor-animation-grow attachment-full size-full\" alt=\"\" loading=\"lazy\" \/><\/a><\/figure><div class=\"elementor-image-box-content\"><h5 class=\"elementor-image-box-title\"><a href=\"https:\/\/doi.org\/10.3390\/w17172653\" target=\"_blank\">Multi-Agentic Water Health Surveillance<\/a><\/h5><p class=\"elementor-image-box-description\">V. Alevizos, Z. Yue, S. Edralin, C. Xu, N. Gerolimos, and G.A. Papakostas<br>\nWater, vol. 17, no. 17, p. 2653.<\/p><\/div><\/div>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-fee7b2d elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"fee7b2d\" data-element_type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-37d88f6\" data-id=\"37d88f6\" data-element_type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t\t\t<div class=\"elementor-element elementor-element-07e08fa elementor-position-left elementor-vertical-align-top elementor-widget elementor-widget-image-box\" data-id=\"07e08fa\" data-element_type=\"widget\" data-widget_type=\"image-box.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<div class=\"elementor-image-box-wrapper\"><figure class=\"elementor-image-box-img\"><a href=\"https:\/\/doi.org\/10.3390\/bioengineering12080849\" target=\"_blank\"><img width=\"256\" height=\"256\" src=\"https:\/\/mlv.cs.duth.gr\/wp-content\/uploads\/2025\/08\/Advancements-in-Radiomics-Based-AI-for-Pancreatic-Ductal-Adenocarcinoma.png\" class=\"elementor-animation-grow attachment-full size-full\" alt=\"\" loading=\"lazy\" \/><\/a><\/figure><div class=\"elementor-image-box-content\"><h5 class=\"elementor-image-box-title\"><a href=\"https:\/\/doi.org\/10.3390\/bioengineering12080849\" target=\"_blank\">Advancements in Radiomics-Based AI for Pancreatic Ductal Adenocarcinoma<\/a><\/h5><p class=\"elementor-image-box-description\">G. Lekkas, E. Vrochidou, and G.A. Papakostas<br>\nBioengineering, vol. 12, no. 8, p. 849.<\/p><\/div><\/div>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-43f5b97 elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"43f5b97\" data-element_type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-c7dba6a\" data-id=\"c7dba6a\" data-element_type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t\t\t<div class=\"elementor-element elementor-element-e3d7417 elementor-position-left elementor-vertical-align-top elementor-widget elementor-widget-image-box\" data-id=\"e3d7417\" data-element_type=\"widget\" data-widget_type=\"image-box.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<div class=\"elementor-image-box-wrapper\"><figure class=\"elementor-image-box-img\"><a href=\"https:\/\/doi.org\/10.1016\/j.artmed.2025.103219\" target=\"_blank\"><img width=\"419\" height=\"551\" src=\"https:\/\/mlv.cs.duth.gr\/wp-content\/uploads\/2025\/07\/Mapping-the-Brain_-AI-driven-Radiomic-Approaches-to-Mental-Disorders.png\" class=\"elementor-animation-grow attachment-full size-full\" alt=\"\" loading=\"lazy\" \/><\/a><\/figure><div class=\"elementor-image-box-content\"><h5 class=\"elementor-image-box-title\"><a href=\"https:\/\/doi.org\/10.1016\/j.artmed.2025.103219\" target=\"_blank\">Mapping the Brain: AI-driven Radiomic Approaches to Mental Disorders<\/a><\/h5><p class=\"elementor-image-box-description\">S.S. Moumgiakmas, E. Vrochidou, and G.A. Papakostas<br>\nArtificial Intelligence in Medicine, vol. 168, p. 103219.<\/p><\/div><\/div>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-e4693c7 elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"e4693c7\" data-element_type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-023561d\" data-id=\"023561d\" data-element_type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t\t\t<div class=\"elementor-element elementor-element-1997b15 elementor-position-left elementor-vertical-align-top elementor-widget elementor-widget-image-box\" data-id=\"1997b15\" data-element_type=\"widget\" data-widget_type=\"image-box.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<div class=\"elementor-image-box-wrapper\"><figure class=\"elementor-image-box-img\"><a href=\"https:\/\/doi.org\/10.1109\/MNANO.2025.3585997\" target=\"_blank\"><img width=\"408\" height=\"411\" src=\"https:\/\/mlv.cs.duth.gr\/wp-content\/uploads\/2025\/07\/To-What-Extent-Can-Fuzzy-Cognitive-Maps-Be-Quantum.png\" class=\"elementor-animation-grow attachment-full size-full\" alt=\"\" loading=\"lazy\" \/><\/a><\/figure><div class=\"elementor-image-box-content\"><h5 class=\"elementor-image-box-title\"><a href=\"https:\/\/doi.org\/10.1109\/MNANO.2025.3585997\" target=\"_blank\">To What Extent Can Fuzzy Cognitive Maps Be Quantum?<\/a><\/h5><p class=\"elementor-image-box-description\">T. Kalampokas and G.A. Papakostas<br>\nIEEE Nanotechnology Magazine, vol. , no. , p. .<\/p><\/div><\/div>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-212792b elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"212792b\" data-element_type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-cdf7ae4\" data-id=\"cdf7ae4\" data-element_type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t\t\t<div class=\"elementor-element elementor-element-c75d4e1 elementor-position-left elementor-vertical-align-top elementor-widget elementor-widget-image-box\" data-id=\"c75d4e1\" data-element_type=\"widget\" data-widget_type=\"image-box.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<div class=\"elementor-image-box-wrapper\"><figure class=\"elementor-image-box-img\"><a href=\"https:\/\/doi.org\/10.1016\/j.compag.2025.110735\" target=\"_blank\"><img width=\"256\" height=\"256\" src=\"https:\/\/mlv.cs.duth.gr\/wp-content\/uploads\/2025\/07\/A-Vision-based-Pruning-Algorithm-for-Cherry-Tree-Structure-Elements-Segmentation-and-Exact-Pruning-Points-Determination.png\" class=\"elementor-animation-grow attachment-full size-full\" alt=\"\" loading=\"lazy\" \/><\/a><\/figure><div class=\"elementor-image-box-content\"><h5 class=\"elementor-image-box-title\"><a href=\"https:\/\/doi.org\/10.1016\/j.compag.2025.110735\" target=\"_blank\">A Vision-based Pruning Algorithm for Cherry Tree Structure Elements Segmentation and Exact Pruning Points Determination<\/a><\/h5><p class=\"elementor-image-box-description\">A. Kefalas, T. Kalampokas, E. Vrochidou, and G.A. Papakostas<br>\nComputers and Electronics in Agriculture, vol. 237, no. C, p, 110735.<\/p><\/div><\/div>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-9ee241c elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"9ee241c\" data-element_type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-378004a\" data-id=\"378004a\" data-element_type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t\t\t<div class=\"elementor-element elementor-element-12d6192 elementor-position-left elementor-vertical-align-top elementor-widget elementor-widget-image-box\" data-id=\"12d6192\" data-element_type=\"widget\" data-widget_type=\"image-box.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<div class=\"elementor-image-box-wrapper\"><figure class=\"elementor-image-box-img\"><a href=\"https:\/\/doi.org\/10.1002\/9781394274277.ch13\" target=\"_blank\"><img width=\"863\" height=\"721\" src=\"https:\/\/mlv.cs.duth.gr\/wp-content\/uploads\/2025\/07\/Livestock-Monitoring-and-Welfare.png\" class=\"elementor-animation-grow attachment-full size-full\" alt=\"\" loading=\"lazy\" \/><\/a><\/figure><div class=\"elementor-image-box-content\"><h5 class=\"elementor-image-box-title\"><a href=\"https:\/\/doi.org\/10.1002\/9781394274277.ch13\" target=\"_blank\">Livestock Monitoring and Welfare<\/a><\/h5><p class=\"elementor-image-box-description\">V. Kanakaris, E. Vrochidou, and G.A. Papakostas<br>\nEditor(s): Kumar, A., Verma, J.P., Jain, R. In Emerging Smart Agricultural Practices Using Artificial Intelligence. IEEE<\/p><\/div><\/div>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-a4a6990 elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"a4a6990\" data-element_type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-78901ea\" data-id=\"78901ea\" data-element_type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t\t\t<div class=\"elementor-element elementor-element-d5c7568 elementor-position-left elementor-vertical-align-top elementor-widget elementor-widget-image-box\" data-id=\"d5c7568\" data-element_type=\"widget\" data-widget_type=\"image-box.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<div class=\"elementor-image-box-wrapper\"><figure class=\"elementor-image-box-img\"><a href=\"https:\/\/doi.org\/10.3390\/electronics14132680\" target=\"_blank\"><img width=\"553\" height=\"548\" src=\"https:\/\/mlv.cs.duth.gr\/wp-content\/uploads\/2025\/07\/Interframe-Forgery-Video-Detection_-Datasets-Methods-Challenges-and-Search-Directions.png\" class=\"elementor-animation-grow attachment-full size-full\" alt=\"\" loading=\"lazy\" \/><\/a><\/figure><div class=\"elementor-image-box-content\"><h5 class=\"elementor-image-box-title\"><a href=\"https:\/\/doi.org\/10.3390\/electronics14132680\" target=\"_blank\">Interframe Forgery Video Detection: Datasets, Methods, Challenges, and Search Directions<\/a><\/h5><p class=\"elementor-image-box-description\">M.M. Ali, N.I. Ghali, H.M. Hamza, K.M. Hosny, E. Vrochidou, and G.A. Papakostas<br>\nElectronics, vol. 14, no. 13, p. 2680.<\/p><\/div><\/div>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-024bb79 elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"024bb79\" data-element_type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-0fdc991\" data-id=\"0fdc991\" data-element_type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t\t\t<div class=\"elementor-element elementor-element-58a1b5b elementor-position-left elementor-vertical-align-top elementor-widget elementor-widget-image-box\" data-id=\"58a1b5b\" data-element_type=\"widget\" data-widget_type=\"image-box.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<div class=\"elementor-image-box-wrapper\"><figure class=\"elementor-image-box-img\"><a href=\"https:\/\/doi.org\/10.3390\/technologies13070278%20\" target=\"_blank\"><img width=\"337\" height=\"355\" src=\"https:\/\/mlv.cs.duth.gr\/wp-content\/uploads\/2025\/07\/A-Logarithmic-Compression-Method-for-Magnitude-Rich-Data_-The-LPPIE-Approach.png\" class=\"elementor-animation-grow attachment-full size-full\" alt=\"\" loading=\"lazy\" \/><\/a><\/figure><div class=\"elementor-image-box-content\"><h5 class=\"elementor-image-box-title\"><a href=\"https:\/\/doi.org\/10.3390\/technologies13070278%20\" target=\"_blank\">A Logarithmic Compression Method for Magnitude-Rich Data: The LPPIE Approach<\/a><\/h5><p class=\"elementor-image-box-description\">V. Alevizos, Z. Yue, S. Edralin, C. Xu, N. Gerolimos, and G.A. Papakostas<br>\nTechnologies, vol. 13, no. 7, p. 278.<\/p><\/div><\/div>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-6e2952f elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"6e2952f\" data-element_type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-ced5cac\" data-id=\"ced5cac\" data-element_type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t\t\t<div class=\"elementor-element elementor-element-b0d1abe elementor-position-left elementor-vertical-align-top elementor-widget elementor-widget-image-box\" data-id=\"b0d1abe\" data-element_type=\"widget\" data-widget_type=\"image-box.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<div class=\"elementor-image-box-wrapper\"><figure class=\"elementor-image-box-img\"><a href=\"https:\/\/doi.org\/10.3390\/a18070399\" target=\"_blank\"><img width=\"1023\" height=\"1196\" src=\"https:\/\/mlv.cs.duth.gr\/wp-content\/uploads\/2025\/06\/Inspiring-from-Galaxies-to-Green-AI-in-Earth_-Benchmarking-Energy-Efficient-Models-for-Galaxy-Morphology-Classification.png\" class=\"elementor-animation-grow attachment-full size-full\" alt=\"\" loading=\"lazy\" \/><\/a><\/figure><div class=\"elementor-image-box-content\"><h5 class=\"elementor-image-box-title\"><a href=\"https:\/\/doi.org\/10.3390\/a18070399\" target=\"_blank\">Inspiring from Galaxies to Green AI in Earth: Benchmarking Energy-Efficient Models for Galaxy Morphology Classification<\/a><\/h5><p class=\"elementor-image-box-description\">V. Alevizos, E.V. Gkouvrikos, I. Georgousis, S. Karipidou, and G.A. Papakostas<br>\nAlgorithms, vol. 18, no. 7, p. 399.<\/p><\/div><\/div>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-b9c9c82 elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"b9c9c82\" data-element_type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-891d6d3\" data-id=\"891d6d3\" data-element_type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t\t\t<div class=\"elementor-element elementor-element-c291594 elementor-position-left elementor-vertical-align-top elementor-widget elementor-widget-image-box\" data-id=\"c291594\" data-element_type=\"widget\" data-widget_type=\"image-box.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<div class=\"elementor-image-box-wrapper\"><figure class=\"elementor-image-box-img\"><a href=\"https:\/\/doi.org\/10.1007\/978-3-031-96231-8_2\" target=\"_blank\"><img width=\"855\" height=\"767\" src=\"https:\/\/mlv.cs.duth.gr\/wp-content\/uploads\/2025\/06\/Agile-MLOps_-Bridging-the-Gap-Between-Agility-and-Machine-Learning-Operations.png\" class=\"elementor-animation-grow attachment-full size-full\" alt=\"\" loading=\"lazy\" \/><\/a><\/figure><div class=\"elementor-image-box-content\"><h5 class=\"elementor-image-box-title\"><a href=\"https:\/\/doi.org\/10.1007\/978-3-031-96231-8_2\" target=\"_blank\">Agile MLOps: Bridging the Gap Between Agility and Machine Learning Operations<\/a><\/h5><p class=\"elementor-image-box-description\">A.V. Papageorgiou, G.G. Symeonidis, E. Nerantzis, and G.A. Papakostas<br>\nEditor(s): Maglogiannis, I., Iliadis, L., Andreou, A., Papaleonidas, A. In Artificial Intelligence Applications and Innovations. AIAI 2025. IFIP Advances in Information and Communication Technology, vol. 757. Springer, Cham.<\/p><\/div><\/div>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-a4f09a5 elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"a4f09a5\" data-element_type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-79aff2b\" data-id=\"79aff2b\" data-element_type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t\t\t<div class=\"elementor-element elementor-element-871a2b3 elementor-position-left elementor-vertical-align-top elementor-widget elementor-widget-image-box\" data-id=\"871a2b3\" data-element_type=\"widget\" data-widget_type=\"image-box.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<div class=\"elementor-image-box-wrapper\"><figure class=\"elementor-image-box-img\"><a href=\"https:\/\/doi.org\/10.3390\/app15126508\" target=\"_blank\"><img width=\"362\" height=\"349\" src=\"https:\/\/mlv.cs.duth.gr\/wp-content\/uploads\/2025\/06\/Data-Driven-Decision-Support-in-SaaS-Cloud-Based-Service-Models.png\" class=\"elementor-animation-grow attachment-full size-full\" alt=\"\" loading=\"lazy\" \/><\/a><\/figure><div class=\"elementor-image-box-content\"><h5 class=\"elementor-image-box-title\"><a href=\"https:\/\/doi.org\/10.3390\/app15126508\" target=\"_blank\">Data-Driven Decision Support in SaaS Cloud-Based Service Models<\/a><\/h5><p class=\"elementor-image-box-description\">G. Charizanis, E. Mavridou, E. Vrochidou, T. Kalampokas, and G.A. Papakostas<br>\nApplied Science, vol. 15, no. 12, p. 6508.<\/p><\/div><\/div>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-056e125 elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"056e125\" data-element_type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-e5c384b\" data-id=\"e5c384b\" data-element_type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t\t\t<div class=\"elementor-element elementor-element-4d74b35 elementor-position-left elementor-vertical-align-top elementor-widget elementor-widget-image-box\" data-id=\"4d74b35\" data-element_type=\"widget\" data-widget_type=\"image-box.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<div class=\"elementor-image-box-wrapper\"><figure class=\"elementor-image-box-img\"><a href=\"https:\/\/doi.org\/10.1109\/ICCIAA65327.2025.11013566\" target=\"_blank\"><img width=\"744\" height=\"582\" src=\"https:\/\/mlv.cs.duth.gr\/wp-content\/uploads\/2025\/06\/Machine-Learning-in-Employee-Attrition.png\" class=\"elementor-animation-grow attachment-full size-full\" alt=\"\" loading=\"lazy\" \/><\/a><\/figure><div class=\"elementor-image-box-content\"><h5 class=\"elementor-image-box-title\"><a href=\"https:\/\/doi.org\/10.1109\/ICCIAA65327.2025.11013566\" target=\"_blank\">Machine Learning in Employee Attrition<\/a><\/h5><p class=\"elementor-image-box-description\">E. Siandri, Z. Kasapi, E. Mavridou, E. Vrochidou, and G.A. Papakostas<br>\nProceedings of 1st International Conference on Computational Intelligence Approaches and Applications (ICCIAA), Amman, Jordan, p.1-8.<\/p><\/div><\/div>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-76d4209 elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"76d4209\" data-element_type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-ba1fa5a\" data-id=\"ba1fa5a\" data-element_type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t\t\t<div class=\"elementor-element elementor-element-618817f elementor-position-left elementor-vertical-align-top elementor-widget elementor-widget-image-box\" data-id=\"618817f\" data-element_type=\"widget\" data-widget_type=\"image-box.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<div class=\"elementor-image-box-wrapper\"><figure class=\"elementor-image-box-img\"><a href=\"https:\/\/doi.org\/10.3390\/w17091395%20\" target=\"_blank\"><img width=\"369\" height=\"372\" src=\"https:\/\/mlv.cs.duth.gr\/wp-content\/uploads\/2025\/05\/Biomimicry-Inspired-Automated-Machine-Learning-Fit-for-Purpose-Wastewater-Treatment-for-Sustainable-Water-Reuse.png\" class=\"elementor-animation-grow attachment-full size-full\" alt=\"\" loading=\"lazy\" \/><\/a><\/figure><div class=\"elementor-image-box-content\"><h5 class=\"elementor-image-box-title\"><a href=\"https:\/\/doi.org\/10.3390\/w17091395%20\" target=\"_blank\">Biomimicry-Inspired Automated Machine Learning Fit-for-Purpose Wastewater Treatment for Sustainable Water Reuse<\/a><\/h5><p class=\"elementor-image-box-description\">V. Alevizos, Z. Yue, S. Edralin, C. Xu, N. Georlimos, and G.A. Papakostas<br>\nWater, vol. 17, no. 9, p. 1395.<\/p><\/div><\/div>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-878d76f elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"878d76f\" data-element_type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-958edd4\" data-id=\"958edd4\" data-element_type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t\t\t<div class=\"elementor-element elementor-element-7ffd06d elementor-position-left elementor-vertical-align-top elementor-widget elementor-widget-image-box\" data-id=\"7ffd06d\" data-element_type=\"widget\" data-widget_type=\"image-box.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<div class=\"elementor-image-box-wrapper\"><figure class=\"elementor-image-box-img\"><a href=\"https:\/\/doi.org\/10.4018\/979-8-3693-6452-9.ch004\" target=\"_blank\"><img width=\"572\" height=\"326\" src=\"https:\/\/mlv.cs.duth.gr\/wp-content\/uploads\/2025\/04\/Comparison-of-Photogrammetric-Reconstruction-Methods_-The-Case-of-an-Archaeological-Site-With-Two-Software-and-Geovisualization-Modelling-Techniques.png\" class=\"elementor-animation-grow attachment-full size-full\" alt=\"\" loading=\"lazy\" \/><\/a><\/figure><div class=\"elementor-image-box-content\"><h5 class=\"elementor-image-box-title\"><a href=\"https:\/\/doi.org\/10.4018\/979-8-3693-6452-9.ch004\" target=\"_blank\">Comparison of Photogrammetric Reconstruction Methods: The Case of an Archaeological Site With Two Software and Geovisualization Modelling Techniques<\/a><\/h5><p class=\"elementor-image-box-description\">K.A. Tychola, E. Vrochidou, and G.A. Papakostas<br>\nEditor(s):Batchi M. and Moumane A. In Applying Remote Sensing and GIS for Spatial Analysis and Decision-Making, pp. 73-130, \nIGI Global Scientific Publishing.<\/p><\/div><\/div>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-b1213f3 elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"b1213f3\" data-element_type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-3c667c7\" data-id=\"3c667c7\" data-element_type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t\t\t<div class=\"elementor-element elementor-element-71eb2b3 elementor-position-left elementor-vertical-align-top elementor-widget elementor-widget-image-box\" data-id=\"71eb2b3\" data-element_type=\"widget\" data-widget_type=\"image-box.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<div class=\"elementor-image-box-wrapper\"><figure class=\"elementor-image-box-img\"><a href=\"https:\/\/doi.org\/10.1158\/1538-7445.AM2025-2800\" target=\"_blank\"><img width=\"393\" height=\"393\" src=\"https:\/\/mlv.cs.duth.gr\/wp-content\/uploads\/2025\/04\/Unraveling-Long-non-Coding-RNA-Dynamics-in-Uveal-Melanoma_-Insights-into-Pathogenesis-Biomarkers-and-Targeted-Therapies.png\" class=\"elementor-animation-grow attachment-full size-full\" alt=\"\" loading=\"lazy\" \/><\/a><\/figure><div class=\"elementor-image-box-content\"><h5 class=\"elementor-image-box-title\"><a href=\"https:\/\/doi.org\/10.1158\/1538-7445.AM2025-2800\" target=\"_blank\">Unraveling Long non-Coding RNA Dynamics in Uveal Melanoma: Insights into Pathogenesis, Biomarkers, and Targeted Therapies<\/a><\/h5><p class=\"elementor-image-box-description\">V. Alevizos, N. Gerolimos, S. Edralin, C. Xu, A. Simasiku, G. Priniotakis, G.A. Papakostas, and Z. Yue<br>\nProceedings of the American Association for Cancer Research Annual (AACR) Meeting 2025, Chicago, IL, USA.<\/p><\/div><\/div>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-44aa734 elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"44aa734\" data-element_type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-d148a03\" data-id=\"d148a03\" data-element_type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t\t\t<div class=\"elementor-element elementor-element-cba6e63 elementor-position-left elementor-vertical-align-top elementor-widget elementor-widget-image-box\" data-id=\"cba6e63\" data-element_type=\"widget\" data-widget_type=\"image-box.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<div class=\"elementor-image-box-wrapper\"><figure class=\"elementor-image-box-img\"><a href=\"https:\/\/doi.org\/10.3390\/electronics14091705\" target=\"_blank\"><img width=\"473\" height=\"521\" src=\"https:\/\/mlv.cs.duth.gr\/wp-content\/uploads\/2025\/04\/Empowering-Kiwifruit-Cultivation-with-AI_-Leaf-Disease-Recognition-Using-AgriVision-Kiwi-Open-Dataset.png\" class=\"elementor-animation-grow attachment-full size-full\" alt=\"\" loading=\"lazy\" \/><\/a><\/figure><div class=\"elementor-image-box-content\"><h5 class=\"elementor-image-box-title\"><a href=\"https:\/\/doi.org\/10.3390\/electronics14091705\" target=\"_blank\">Empowering Kiwifruit Cultivation with AI: Leaf Disease Recognition Using AgriVision-Kiwi Open Dataset<\/a><\/h5><p class=\"elementor-image-box-description\">T. Kalampokas, E. Vrochidou, E. Mavridou, L.  Iliadis, D. Voglitsis, M. Michalopoulou, G. Broufas,and G.A. Papakostas<br>\nElectronics, vol. 14, no. 9, p. 1705.<\/p><\/div><\/div>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-f1b7a22 elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"f1b7a22\" data-element_type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-4c5418d\" data-id=\"4c5418d\" data-element_type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t\t\t<div class=\"elementor-element elementor-element-5fc2318 elementor-position-left elementor-vertical-align-top elementor-widget elementor-widget-image-box\" data-id=\"5fc2318\" data-element_type=\"widget\" data-widget_type=\"image-box.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<div class=\"elementor-image-box-wrapper\"><figure class=\"elementor-image-box-img\"><a href=\"https:\/\/doi.org\/10.1007\/s44230-025-00098-2\" target=\"_blank\"><img width=\"550\" height=\"551\" src=\"https:\/\/mlv.cs.duth.gr\/wp-content\/uploads\/2025\/04\/Autonomous-Decision-Making-Enhancing-Natural-Disaster-Management-through-Open-World-Machine-Learning_-A-Systematic-Review.png\" class=\"elementor-animation-grow attachment-full size-full\" alt=\"\" loading=\"lazy\" \/><\/a><\/figure><div class=\"elementor-image-box-content\"><h5 class=\"elementor-image-box-title\"><a href=\"https:\/\/doi.org\/10.1007\/s44230-025-00098-2\" target=\"_blank\">Autonomous Decision-Making Enhancing Natural Disaster Management through Open World Machine Learning: A Systematic Review<\/a><\/h5><p class=\"elementor-image-box-description\">N. Gerolimos, V. Alevizos, S. Edralin, C. Xu, G. Priniotakis, G.A. Papakostas, and Z. Yue<br>\nHuman-Centric Intelligent Systems, vol. , no. , p. .<\/p><\/div><\/div>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-ab11b7a elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"ab11b7a\" data-element_type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-34a313a\" data-id=\"34a313a\" data-element_type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t\t\t<div class=\"elementor-element elementor-element-5073998 elementor-position-left elementor-vertical-align-top elementor-widget elementor-widget-image-box\" data-id=\"5073998\" data-element_type=\"widget\" data-widget_type=\"image-box.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<div class=\"elementor-image-box-wrapper\"><figure class=\"elementor-image-box-img\"><a href=\"https:\/\/doi.org\/10.3390\/w17081210\" target=\"_blank\"><img width=\"393\" height=\"395\" src=\"https:\/\/mlv.cs.duth.gr\/wp-content\/uploads\/2025\/04\/Coral-Reef-Calculus_-Natures-Equation-for-Pollution-Control.png\" class=\"elementor-animation-grow attachment-full size-full\" alt=\"\" loading=\"lazy\" \/><\/a><\/figure><div class=\"elementor-image-box-content\"><h5 class=\"elementor-image-box-title\"><a href=\"https:\/\/doi.org\/10.3390\/w17081210\" target=\"_blank\">Coral Reef Calculus: Nature\u2019s Equation for Pollution Control<\/a><\/h5><p class=\"elementor-image-box-description\">V. Alevizos, Z. Yue, S. Edralin, C. Xu, N. Gerolimos, and G.A. Papakostas<br>\nWater, vol. 17, no. 8, p. 1210.<\/p><\/div><\/div>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-2591dbe elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"2591dbe\" data-element_type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-f7c7c71\" data-id=\"f7c7c71\" data-element_type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t\t\t<div class=\"elementor-element elementor-element-d9b9c90 elementor-position-left elementor-vertical-align-top elementor-widget elementor-widget-image-box\" data-id=\"d9b9c90\" data-element_type=\"widget\" data-widget_type=\"image-box.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<div class=\"elementor-image-box-wrapper\"><figure class=\"elementor-image-box-img\"><a href=\"https:\/\/doi.org\/10.3390\/electronics14081624%20\" target=\"_blank\"><img width=\"566\" height=\"561\" src=\"https:\/\/mlv.cs.duth.gr\/wp-content\/uploads\/2025\/04\/Delving-into-YOLO-Object-Detection-Models_-Insights-into-Adversarial-Robustness.png\" class=\"elementor-animation-grow attachment-full size-full\" alt=\"\" loading=\"lazy\" \/><\/a><\/figure><div class=\"elementor-image-box-content\"><h5 class=\"elementor-image-box-title\"><a href=\"https:\/\/doi.org\/10.3390\/electronics14081624%20\" target=\"_blank\">Delving into YOLO Object Detection Models: Insights into Adversarial Robustness<\/a><\/h5><p class=\"elementor-image-box-description\">K.D. Apostolidis and G.A. Papakostas <br>\nElectronics, vol. 14, no. 8, p. 1624.<\/p><\/div><\/div>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-5428204 elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"5428204\" data-element_type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-f547000\" data-id=\"f547000\" data-element_type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t\t\t<div class=\"elementor-element elementor-element-872482e elementor-position-left elementor-vertical-align-top elementor-widget elementor-widget-image-box\" data-id=\"872482e\" data-element_type=\"widget\" data-widget_type=\"image-box.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<div class=\"elementor-image-box-wrapper\"><figure class=\"elementor-image-box-img\"><a href=\"https:\/\/doi.org\/10.24138\/jcomss-2024-0108\" target=\"_blank\"><img width=\"492\" height=\"494\" src=\"https:\/\/mlv.cs.duth.gr\/wp-content\/uploads\/2025\/03\/Harnessing-AI-in-Precision-Agriculture-for-Sustainable-Kiwifruit-Farming.png\" class=\"elementor-animation-grow attachment-full size-full\" alt=\"\" loading=\"lazy\" \/><\/a><\/figure><div class=\"elementor-image-box-content\"><h5 class=\"elementor-image-box-title\"><a href=\"https:\/\/doi.org\/10.24138\/jcomss-2024-0108\" target=\"_blank\">Harnessing AI in Precision Agriculture for Sustainable Kiwifruit Farming<\/a><\/h5><p class=\"elementor-image-box-description\">T. Kalampokas, E. Vrochidou, M. Tzampazaki, and G.A. Papakostas<br>\nJournal of Communications Software and Systems, vol. 21, no. 1, p. 120-131.<\/p><\/div><\/div>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-1939842 elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"1939842\" data-element_type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-96d8367\" data-id=\"96d8367\" data-element_type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t\t\t<div class=\"elementor-element elementor-element-41cbbbb elementor-position-left elementor-vertical-align-top elementor-widget elementor-widget-image-box\" data-id=\"41cbbbb\" data-element_type=\"widget\" data-widget_type=\"image-box.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<div class=\"elementor-image-box-wrapper\"><figure class=\"elementor-image-box-img\"><a href=\"https:\/\/doi.org\/10.1109\/ICAIIC64266.2025.10920672\" target=\"_blank\"><img width=\"899\" height=\"601\" src=\"https:\/\/mlv.cs.duth.gr\/wp-content\/uploads\/2025\/03\/Systematic-Review-on-Sustainable-Design-Thinking-Through-Biomimetic-Approach.png\" class=\"elementor-animation-grow attachment-full size-full\" alt=\"\" loading=\"lazy\" \/><\/a><\/figure><div class=\"elementor-image-box-content\"><h5 class=\"elementor-image-box-title\"><a href=\"https:\/\/doi.org\/10.1109\/ICAIIC64266.2025.10920672\" target=\"_blank\">Systematic Review on Sustainable Design Thinking Through Biomimetic Approach<\/a><\/h5><p class=\"elementor-image-box-description\">V. Alevizos, N. Gerolimos, S. Edralin, C. Xu, A. Simasiku, G. Priniotakis, G.A. Papakostas, and Z. Yue<br>\nProceedings of 2025 International Conference on Artificial Intelligence in Information and Communication (ICAIIC), Fukuoka, Japan, p.219-223.<\/p><\/div><\/div>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-f64b20a elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"f64b20a\" data-element_type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-79811ea\" data-id=\"79811ea\" data-element_type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t\t\t<div class=\"elementor-element elementor-element-cf43d51 elementor-position-left elementor-vertical-align-top elementor-widget elementor-widget-image-box\" data-id=\"cf43d51\" data-element_type=\"widget\" data-widget_type=\"image-box.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<div class=\"elementor-image-box-wrapper\"><figure class=\"elementor-image-box-img\"><a href=\"https:\/\/doi.org\/10.3390\/computers14040119%20\" target=\"_blank\"><img width=\"1046\" height=\"556\" src=\"https:\/\/mlv.cs.duth.gr\/wp-content\/uploads\/2025\/03\/AI-Powered-Software-Development_-A-Systematic-Review-of-Recommender-Systems-for-Programmers.png\" class=\"elementor-animation-grow attachment-full size-full\" alt=\"\" loading=\"lazy\" \/><\/a><\/figure><div class=\"elementor-image-box-content\"><h5 class=\"elementor-image-box-title\"><a href=\"https:\/\/doi.org\/10.3390\/computers14040119%20\" target=\"_blank\">AI-Powered Software Development: A Systematic Review of Recommender Systems for Programmers<\/a><\/h5><p class=\"elementor-image-box-description\">E. Mavridou, E. Vrochidou, T. Kalampokas, V. Kanakaris, and G.A. Papakostas<br>\nComputers, vol. 14, no. 4, p. 119.<\/p><\/div><\/div>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-6965588 elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"6965588\" data-element_type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-39cd100\" data-id=\"39cd100\" data-element_type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t\t\t<div class=\"elementor-element elementor-element-ced5793 elementor-position-left elementor-vertical-align-top elementor-widget elementor-widget-image-box\" data-id=\"ced5793\" data-element_type=\"widget\" data-widget_type=\"image-box.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<div class=\"elementor-image-box-wrapper\"><figure class=\"elementor-image-box-img\"><a href=\"https:\/\/doi.org\/10.3390\/app15063284\" target=\"_blank\"><img width=\"486\" height=\"481\" src=\"https:\/\/mlv.cs.duth.gr\/wp-content\/uploads\/2025\/03\/Reviewing-6D-Pose-Estimation_-Model-Strengths-Limitations-and-Application-Fields.png\" class=\"elementor-animation-grow attachment-full size-full\" alt=\"\" loading=\"lazy\" \/><\/a><\/figure><div class=\"elementor-image-box-content\"><h5 class=\"elementor-image-box-title\"><a href=\"https:\/\/doi.org\/10.3390\/app15063284\" target=\"_blank\">Reviewing 6D Pose Estimation: Model Strengths, Limitations, and Application Fields<\/a><\/h5><p class=\"elementor-image-box-description\">K. Ordoumpozanis and G.A. Papakostas<br>\nApplied Sciences, vol. 15, no. 6, p. 3284.<\/p><\/div><\/div>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-6a7757a elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"6a7757a\" data-element_type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-2e851bf\" data-id=\"2e851bf\" data-element_type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t\t\t<div class=\"elementor-element elementor-element-6915705 elementor-position-left elementor-vertical-align-top elementor-widget elementor-widget-image-box\" data-id=\"6915705\" data-element_type=\"widget\" data-widget_type=\"image-box.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<div class=\"elementor-image-box-wrapper\"><figure class=\"elementor-image-box-img\"><a href=\"https:\/\/doi.org\/10.1117\/12.3055065\" target=\"_blank\"><img width=\"285\" height=\"283\" src=\"https:\/\/mlv.cs.duth.gr\/wp-content\/uploads\/2025\/02\/Annotation-tools-for-computer-vision-tasks.png\" class=\"elementor-animation-grow attachment-full size-full\" alt=\"\" loading=\"lazy\" \/><\/a><\/figure><div class=\"elementor-image-box-content\"><h5 class=\"elementor-image-box-title\"><a href=\"https:\/\/doi.org\/10.1117\/12.3055065\" target=\"_blank\">Annotation Tools for Computer Vision Tasks<\/a><\/h5><p class=\"elementor-image-box-description\">C. Moschidis, E. Vrochidou, and G.A. Papakostas  <br>\nProceedings of Seventeenth International Conference on Machine Vision (ICMV 2024), Edinburgh, United Kingdom.<\/p><\/div><\/div>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-619d496 elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"619d496\" data-element_type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-00b06fa\" data-id=\"00b06fa\" data-element_type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t\t\t<div class=\"elementor-element elementor-element-6e0e785 elementor-position-left elementor-vertical-align-top elementor-widget elementor-widget-image-box\" data-id=\"6e0e785\" data-element_type=\"widget\" data-widget_type=\"image-box.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<div class=\"elementor-image-box-wrapper\"><figure class=\"elementor-image-box-img\"><a href=\"https:\/\/doi.org\/10.1007\/s13748-025-00363-2\" target=\"_blank\"><img width=\"1772\" height=\"1170\" src=\"https:\/\/mlv.cs.duth.gr\/wp-content\/uploads\/2025\/02\/The-potential-of-Large-Language-Models-for-social-robots-in-special-education.png\" class=\"elementor-animation-grow attachment-full size-full\" alt=\"\" loading=\"lazy\" \/><\/a><\/figure><div class=\"elementor-image-box-content\"><h5 class=\"elementor-image-box-title\"><a href=\"https:\/\/doi.org\/10.1007\/s13748-025-00363-2\" target=\"_blank\">The Potential of Large Language Models for Social Robots in Special Education<\/a><\/h5><p class=\"elementor-image-box-description\">E. Voultsiou, E. Vrochidou, L. Moussiades, and G.A. Papakostas <br>\nProgress in Artificial Intelligence, vol. 15, no. , p. .<\/p><\/div><\/div>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-65f6136 elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"65f6136\" data-element_type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-aae45d1\" data-id=\"aae45d1\" data-element_type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t\t\t<div class=\"elementor-element elementor-element-631b940 elementor-position-left elementor-vertical-align-top elementor-widget elementor-widget-image-box\" data-id=\"631b940\" data-element_type=\"widget\" data-widget_type=\"image-box.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<div class=\"elementor-image-box-wrapper\"><figure class=\"elementor-image-box-img\"><a href=\"https:\/\/doi.org\/10.3390\/diagnostics15030352%20\" target=\"_blank\"><img width=\"290\" height=\"348\" src=\"https:\/\/mlv.cs.duth.gr\/wp-content\/uploads\/2025\/02\/Building-Better-Deep-Learning-Models-Through-Dataset-Fusion_-A-Case-Study-in-Skin-Cancer-Classification-with-Hyperdatasets.png\" class=\"elementor-animation-grow attachment-full size-full\" alt=\"\" loading=\"lazy\" \/><\/a><\/figure><div class=\"elementor-image-box-content\"><h5 class=\"elementor-image-box-title\"><a href=\"https:\/\/doi.org\/10.3390\/diagnostics15030352%20\" target=\"_blank\">Building Better Deep Learning Models Through Dataset Fusion: A Case Study in Skin Cancer Classification with Hyperdatasets<\/a><\/h5><p class=\"elementor-image-box-description\">P. Georgiadis, E.V. Gkouvrikos, E. Vrochidou, T. Kalampokas, and G.A. Papakostas<br>\nDiagnostics, vol. 15, no. 3, p. 352.<\/p><\/div><\/div>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-9b9146c elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"9b9146c\" data-element_type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-b7cbd3b\" data-id=\"b7cbd3b\" data-element_type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t\t\t<div class=\"elementor-element elementor-element-3cbc673 elementor-position-left elementor-vertical-align-top elementor-widget elementor-widget-image-box\" data-id=\"3cbc673\" data-element_type=\"widget\" data-widget_type=\"image-box.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<div class=\"elementor-image-box-wrapper\"><figure class=\"elementor-image-box-img\"><a href=\"https:\/\/doi.org\/10.3390\/biomedinformatics5010007\" target=\"_blank\"><img width=\"576\" height=\"617\" src=\"https:\/\/mlv.cs.duth.gr\/wp-content\/uploads\/2025\/01\/Time\u2013Frequency-Transformations-for-Enhanced-Biomedical-Signal-Classification-with-Convolutional-Neural-Networks.png\" class=\"elementor-animation-grow attachment-full size-full\" alt=\"\" loading=\"lazy\" \/><\/a><\/figure><div class=\"elementor-image-box-content\"><h5 class=\"elementor-image-box-title\"><a href=\"https:\/\/doi.org\/10.3390\/biomedinformatics5010007\" target=\"_blank\">Time\u2013Frequency Transformations for Enhanced Biomedical Signal Classification with Convolutional Neural Networks<\/a><\/h5><p class=\"elementor-image-box-description\">G. Lekkas, E. Vrochidou, and G.A. Papakostas<br>\nBioMedInformatics, vol. 5, no. 1, p. 7.<\/p><\/div><\/div>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-9ac105b elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"9ac105b\" data-element_type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-2f6b205\" data-id=\"2f6b205\" data-element_type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t\t\t<div class=\"elementor-element elementor-element-8ad1e02 elementor-position-left elementor-vertical-align-top elementor-widget elementor-widget-image-box\" data-id=\"8ad1e02\" data-element_type=\"widget\" data-widget_type=\"image-box.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<div class=\"elementor-image-box-wrapper\"><figure class=\"elementor-image-box-img\"><a href=\"https:\/\/doi.org\/10.1088\/2631-8695\/adaca7\" target=\"_blank\"><img width=\"361\" height=\"345\" src=\"https:\/\/mlv.cs.duth.gr\/wp-content\/uploads\/2025\/01\/Utilizing-Generative-AI-for-Crack-Detection-in-the-Marble-Industry.png\" class=\"elementor-animation-grow attachment-full size-full\" alt=\"\" loading=\"lazy\" \/><\/a><\/figure><div class=\"elementor-image-box-content\"><h5 class=\"elementor-image-box-title\"><a href=\"https:\/\/doi.org\/10.1088\/2631-8695\/adaca7\" target=\"_blank\">Utilizing Generative AI for Crack Detection in the Marble Industry<\/a><\/h5><p class=\"elementor-image-box-description\">E. Vrochidou, G.K. Sidiropoulos, A.G. Ouzounis, I. Tsimperidis, V. Kalpakis, A. Stamkos, and G.A. Papakostas<br>\nEngineering Research Express, vol. 7, no. 1, 015236.<\/p><\/div><\/div>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-f2fa69a elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"f2fa69a\" data-element_type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-bad76ee\" data-id=\"bad76ee\" data-element_type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t\t\t<div class=\"elementor-element elementor-element-1f5b844 elementor-position-left elementor-vertical-align-top elementor-widget elementor-widget-image-box\" data-id=\"1f5b844\" data-element_type=\"widget\" data-widget_type=\"image-box.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<div class=\"elementor-image-box-wrapper\"><figure class=\"elementor-image-box-img\"><a href=\"https:\/\/doi.org\/10.1016\/B978-0-323-95439-6.00021-1\" target=\"_blank\"><img width=\"1056\" height=\"593\" src=\"https:\/\/mlv.cs.duth.gr\/wp-content\/uploads\/2024\/11\/Benchmarking-convolutional-neural-networks-on-continuous-EEG-signals_-The-case-of-motor-imagery\u2013based-BCI.jpg\" class=\"elementor-animation-grow attachment-full size-full\" alt=\"\" loading=\"lazy\" \/><\/a><\/figure><div class=\"elementor-image-box-content\"><h5 class=\"elementor-image-box-title\"><a href=\"https:\/\/doi.org\/10.1016\/B978-0-323-95439-6.00021-1\" target=\"_blank\">Benchmarking Convolutional Neural Networks on Continuous EEG Signals: The Case of Motor Imagery\u2013Based BCI<\/a><\/h5><p class=\"elementor-image-box-description\">S.S. Moumgiakmas, D. Sakavalas, and G.A. Papakostas<br>\nEditor(s): El-Baz A.S., Suri J.S. In Advances in Neural Engineering, pp. 187-203, Academic Press.<\/p><\/div><\/div>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-5ba2a6d elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"5ba2a6d\" data-element_type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-1e206c1\" data-id=\"1e206c1\" data-element_type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t\t\t<div class=\"elementor-element elementor-element-48ae5b0 elementor-position-left elementor-vertical-align-top elementor-widget elementor-widget-image-box\" data-id=\"48ae5b0\" data-element_type=\"widget\" data-widget_type=\"image-box.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<div class=\"elementor-image-box-wrapper\"><figure class=\"elementor-image-box-img\"><a href=\"https:\/\/doi.org\/10.4018\/979-8-3693-2663-3.ch004\" target=\"_blank\"><img width=\"1168\" height=\"621\" src=\"https:\/\/mlv.cs.duth.gr\/wp-content\/uploads\/2024\/10\/Emotional-Learning-Analytics-in-Education_-Current-Status-Trends-and-Challenges.jpg\" class=\"elementor-animation-grow attachment-full size-full\" alt=\"\" loading=\"lazy\" \/><\/a><\/figure><div class=\"elementor-image-box-content\"><h5 class=\"elementor-image-box-title\"><a href=\"https:\/\/doi.org\/10.4018\/979-8-3693-2663-3.ch004\" target=\"_blank\">Emotional Learning Analytics in Education: Current Status, Trends, and Challenges<\/a><\/h5><p class=\"elementor-image-box-description\">K.A. Tychola, E. Vrochidou, and G.A. Papakostas<br>\nEditor: Sniderman S. In Utilizing Emotional Experience for Best Learning Design Practices, pp. 71-116, IGI Global.<\/p><\/div><\/div>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-98c3998 elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"98c3998\" data-element_type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-7a96b16\" data-id=\"7a96b16\" data-element_type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t\t\t<div class=\"elementor-element elementor-element-563e11c elementor-widget elementor-widget-spacer\" data-id=\"563e11c\" data-element_type=\"widget\" data-widget_type=\"spacer.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<div class=\"elementor-spacer\">\n\t\t\t<div class=\"elementor-spacer-inner\"><\/div>\n\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-d815bcd elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"d815bcd\" data-element_type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-59fea83\" data-id=\"59fea83\" data-element_type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t\t\t<div class=\"elementor-element elementor-element-7011fe2 elementor-widget elementor-widget-heading\" data-id=\"7011fe2\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<h3 class=\"elementor-heading-title elementor-size-default\">2024<\/h3>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-fe5a4bb elementor-widget elementor-widget-heading\" data-id=\"fe5a4bb\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<h3 class=\"elementor-heading-title elementor-size-default\">Total: 24 (#Journals: 9, #Conferences: 12, #Book Chapters: 1, #Books: 2)<\/h3>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-c9707a7 elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"c9707a7\" data-element_type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-33ca3c1\" data-id=\"33ca3c1\" data-element_type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t\t\t<div class=\"elementor-element elementor-element-a5785f3 elementor-position-left elementor-vertical-align-top elementor-widget elementor-widget-image-box\" data-id=\"a5785f3\" data-element_type=\"widget\" data-widget_type=\"image-box.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<div class=\"elementor-image-box-wrapper\"><figure class=\"elementor-image-box-img\"><a href=\"https:\/\/doi.org\/10.5772\/intechopen.1008722\" target=\"_blank\"><img width=\"586\" height=\"791\" src=\"https:\/\/mlv.cs.duth.gr\/wp-content\/uploads\/2025\/01\/IntechOpen2024.png\" class=\"elementor-animation-grow attachment-full size-full\" alt=\"\" loading=\"lazy\" \/><\/a><\/figure><div class=\"elementor-image-box-content\"><h5 class=\"elementor-image-box-title\"><a href=\"https:\/\/doi.org\/10.5772\/intechopen.1008722\" target=\"_blank\">Artificial Intelligence Annual Volume 2024<\/a><\/h5><p class=\"elementor-image-box-description\">George A. Papakostas, Marco Antonio Aceves-Fern\u00e1ndez and Mehmet Emin Aydin (eds.)<br>IntechOpen Series Artificial Intelligence, Volume 31.<\/p><\/div><\/div>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-cb77aee elementor-position-left elementor-vertical-align-top elementor-widget elementor-widget-image-box\" data-id=\"cb77aee\" data-element_type=\"widget\" data-widget_type=\"image-box.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<div class=\"elementor-image-box-wrapper\"><figure class=\"elementor-image-box-img\"><a href=\"https:\/\/doi.org\/10.1007\/978-981-97-7710-5\" target=\"_blank\"><img width=\"827\" height=\"1254\" src=\"https:\/\/mlv.cs.duth.gr\/wp-content\/uploads\/2024\/12\/ICICCT-2024.png\" class=\"elementor-animation-grow attachment-full size-full\" alt=\"\" loading=\"lazy\" \/><\/a><\/figure><div class=\"elementor-image-box-content\"><h5 class=\"elementor-image-box-title\"><a href=\"https:\/\/doi.org\/10.1007\/978-981-97-7710-5\" target=\"_blank\">Proceedings of the 8th International Conference on Inventive Communication and Computational Technologies (ICICCT 2024)<\/a><\/h5><p class=\"elementor-image-box-description\">G. Ranganathan, George A. Papakostas and Yong Shi (eds.)<br>Proceedings of ICICCT 2024, Springer Singapore\n.<\/p><\/div><\/div>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-b6be58d elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"b6be58d\" data-element_type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-6a50685\" data-id=\"6a50685\" data-element_type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t\t\t<div class=\"elementor-element elementor-element-7b3117c elementor-position-left elementor-vertical-align-top elementor-widget elementor-widget-image-box\" data-id=\"7b3117c\" data-element_type=\"widget\" data-widget_type=\"image-box.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<div class=\"elementor-image-box-wrapper\"><figure class=\"elementor-image-box-img\"><a href=\"https:\/\/doi.org\/10.1109\/CICN63059.2024.10847419\" target=\"_blank\"><img width=\"998\" height=\"685\" src=\"https:\/\/mlv.cs.duth.gr\/wp-content\/uploads\/2025\/02\/2024_conf.png\" class=\"elementor-animation-grow attachment-full size-full\" alt=\"\" loading=\"lazy\" \/><\/a><\/figure><div class=\"elementor-image-box-content\"><h5 class=\"elementor-image-box-title\"><a href=\"https:\/\/doi.org\/10.1109\/CICN63059.2024.10847419\" target=\"_blank\">Systematic Review of Swarm Intelligence for Autonomous Radiotherapy Planning Decision-Making<\/a><\/h5><p class=\"elementor-image-box-description\">N. Gerolimos, V. Alevizos, S. Edralin, C. Xu, A. Simasiku, G.A. Papakostas,  Z. Yue and G. Priniotakis<br>\n2024 IEEE 16th International Conference on Computational Intelligence and Communication Networks (CICN), pp. 1175-1178<\/p><\/div><\/div>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-c60e7d0 elementor-position-left elementor-vertical-align-top elementor-widget elementor-widget-image-box\" data-id=\"c60e7d0\" data-element_type=\"widget\" data-widget_type=\"image-box.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<div class=\"elementor-image-box-wrapper\"><figure class=\"elementor-image-box-img\"><a href=\"https:\/\/doi.org\/10.1109\/3ict64318.2024.10824571\" target=\"_blank\"><img width=\"1024\" height=\"431\" src=\"https:\/\/mlv.cs.duth.gr\/wp-content\/uploads\/2025\/01\/MLV_Conf11.jpg\" class=\"elementor-animation-grow attachment-full size-full\" alt=\"\" loading=\"lazy\" \/><\/a><\/figure><div class=\"elementor-image-box-content\"><h5 class=\"elementor-image-box-title\"><a href=\"https:\/\/doi.org\/10.1109\/3ict64318.2024.10824571\" target=\"_blank\">Green AI: Assessing the Carbon Footprint of Fine-Tuning Pre-Trained Deep Learning Models in Medical Imaging<\/a><\/h5><p class=\"elementor-image-box-description\">K. Ordoumpozanis and G. A. Papakostas<br>\n 2024 International Conference on Innovation and Intelligence for Informatics, Computing, and Technologies (3ICT), pp. 214-220<\/p><\/div><\/div>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-754390a elementor-position-left elementor-vertical-align-top elementor-widget elementor-widget-image-box\" data-id=\"754390a\" data-element_type=\"widget\" data-widget_type=\"image-box.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<div class=\"elementor-image-box-wrapper\"><figure class=\"elementor-image-box-img\"><a href=\"https:\/\/doi.org\/10.1109\/ICECCE63537.2024.10823456\" target=\"_blank\"><img width=\"644\" height=\"453\" src=\"https:\/\/mlv.cs.duth.gr\/wp-content\/uploads\/2025\/01\/MLV2024_Conf10.png\" class=\"elementor-animation-grow attachment-full size-full\" alt=\"\" loading=\"lazy\" \/><\/a><\/figure><div class=\"elementor-image-box-content\"><h5 class=\"elementor-image-box-title\"><a href=\"https:\/\/doi.org\/10.1109\/ICECCE63537.2024.10823456\" target=\"_blank\">Handwriting Anomalies and Learning Disabilities through Recurrent Neural Networks and Geometric Pattern Analysis<\/a><\/h5><p class=\"elementor-image-box-description\">V. Alevizos, S. Edralin, A. Simasiku, D. Malliarou, A. Messinis, G.A. Papakostas, C. Xu and Z. Yue<br>\n 2024 International Conference on Electrical, Communication and Computer Engineering (ICECCE), Kuala Lumpur, Malaysia.<\/p><\/div><\/div>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-efe207e elementor-position-left elementor-vertical-align-top elementor-widget elementor-widget-image-box\" data-id=\"efe207e\" data-element_type=\"widget\" data-widget_type=\"image-box.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<div class=\"elementor-image-box-wrapper\"><figure class=\"elementor-image-box-img\"><a href=\"https:\/\/doi.org\/10.1109\/ICDABI63787.2024.10800301\" target=\"_blank\"><img width=\"1342\" height=\"533\" src=\"https:\/\/mlv.cs.duth.gr\/wp-content\/uploads\/2024\/12\/ICDABI2024.png\" class=\"elementor-animation-grow attachment-full size-full\" alt=\"\" loading=\"lazy\" \/><\/a><\/figure><div class=\"elementor-image-box-content\"><h5 class=\"elementor-image-box-title\"><a href=\"https:\/\/doi.org\/10.1109\/ICDABI63787.2024.10800301\" target=\"_blank\">Integrating Artificial Open Generative Artificial Intelligence into Software Supply Chain Security<\/a><\/h5><p class=\"elementor-image-box-description\">V. Alevizos, G.A. Papakostas, A. Simasiku, D. Malliarou, A. Messinis, S. Edralin, C. Xu and Z. Yue<br>\nProceedings of 5th International Conference on Data Analytics for Business and Industry (ICDABI 2024), Zallaq, Bahrain, pp. 200-206.<\/p><\/div><\/div>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-79db7f7 elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"79db7f7\" data-element_type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-34489f2\" data-id=\"34489f2\" data-element_type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t\t\t<div class=\"elementor-element elementor-element-c2a1db2 elementor-position-left elementor-vertical-align-top elementor-widget elementor-widget-image-box\" data-id=\"c2a1db2\" data-element_type=\"widget\" data-widget_type=\"image-box.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<div class=\"elementor-image-box-wrapper\"><figure class=\"elementor-image-box-img\"><a href=\"https:\/\/doi.org\/10.1007\/978-3-031-76584-1_2\" target=\"_blank\"><img width=\"1295\" height=\"1000\" src=\"https:\/\/mlv.cs.duth.gr\/wp-content\/uploads\/2024\/11\/Reverse-Circular-Logarithmic-LBP-for-Diabetic-Foot-Ulcer-Detection1.png\" class=\"elementor-animation-grow attachment-full size-full\" alt=\"\" loading=\"lazy\" \/><\/a><\/figure><div class=\"elementor-image-box-content\"><h5 class=\"elementor-image-box-title\"><a href=\"https:\/\/doi.org\/10.1007\/978-3-031-76584-1_2\" target=\"_blank\">Reverse Circular Logarithmic LBP for Diabetic Foot Ulcer Detection<\/a><\/h5><p class=\"elementor-image-box-description\">V. Alevizos, N. Arampidis, I. Boja, and G.A. Papakostas<br>\nProceedings of Artificial Intelligence over Infrared Images for Medical Applications (AIIIMA 2024), pp. 11-22.<\/p><\/div><\/div>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-6da7c4e elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"6da7c4e\" data-element_type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-92ff349\" data-id=\"92ff349\" data-element_type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t\t\t<div class=\"elementor-element elementor-element-73250c5 elementor-position-left elementor-vertical-align-top elementor-widget elementor-widget-image-box\" data-id=\"73250c5\" data-element_type=\"widget\" data-widget_type=\"image-box.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<div class=\"elementor-image-box-wrapper\"><figure class=\"elementor-image-box-img\"><a href=\"https:\/\/doi.org\/10.1007\/978-3-031-71388-0_8\" target=\"_blank\"><img width=\"523\" height=\"365\" src=\"https:\/\/mlv.cs.duth.gr\/wp-content\/uploads\/2024\/10\/COVID-19-Imposes-Rethinking-of-Conferencing-\u2013-Environmental-Impact-Assessment-of-Artificial-Intelligence-Conferences.png\" class=\"elementor-animation-grow attachment-full size-full\" alt=\"\" loading=\"lazy\" \/><\/a><\/figure><div class=\"elementor-image-box-content\"><h5 class=\"elementor-image-box-title\"><a href=\"https:\/\/doi.org\/10.1007\/978-3-031-71388-0_8\" target=\"_blank\">COVID-19 Imposes Rethinking of Conferencing \u2013 Environmental Impact Assessment of Artificial Intelligence Conferences<\/a><\/h5><p class=\"elementor-image-box-description\">P. Mitsou, N.V. Tsakalidou, E. Vrochidou, and G.A. Papakostas<br>\nProceedings of International Conference on Intelligent Vision and Computing (ICIVC 2023), pp. 90-111.<\/p><\/div><\/div>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-d263c33 elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"d263c33\" data-element_type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-40bc5f6\" data-id=\"40bc5f6\" data-element_type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t\t\t<div class=\"elementor-element elementor-element-f53cc37 elementor-position-left elementor-vertical-align-top elementor-widget elementor-widget-image-box\" data-id=\"f53cc37\" data-element_type=\"widget\" data-widget_type=\"image-box.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<div class=\"elementor-image-box-wrapper\"><figure class=\"elementor-image-box-img\"><a href=\"https:\/\/doi.org\/10.23919\/SoftCOM62040.2024.10721818\" target=\"_blank\"><img width=\"2560\" height=\"1600\" src=\"https:\/\/mlv.cs.duth.gr\/wp-content\/uploads\/2024\/10\/Unraveling-the-Potential-of-AI-Towards-Digital-and-Green-Transformation-in-Kiwifruit-Farming-scaled.jpg\" class=\"elementor-animation-grow attachment-full size-full\" alt=\"\" loading=\"lazy\" \/><\/a><\/figure><div class=\"elementor-image-box-content\"><h5 class=\"elementor-image-box-title\"><a href=\"https:\/\/doi.org\/10.23919\/SoftCOM62040.2024.10721818\" target=\"_blank\">Unraveling the Potential of AI Towards Digital and Green Transformation in Kiwifruit Farming<\/a><\/h5><p class=\"elementor-image-box-description\">T. Kalampokas, Eleni Vrochidou, M. Tzampazaki, and G.A. Papakostas<br>\nProceedings of 2024 International Conference on Software, Telecommunications and Computer Networks (SoftCOM), pp. 1-6, Split, Croatia.<\/p><\/div><\/div>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-ba90365 elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"ba90365\" data-element_type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-2c46e97\" data-id=\"2c46e97\" data-element_type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t\t\t<div class=\"elementor-element elementor-element-b7e369d elementor-position-left elementor-vertical-align-top elementor-widget elementor-widget-image-box\" data-id=\"b7e369d\" data-element_type=\"widget\" data-widget_type=\"image-box.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<div class=\"elementor-image-box-wrapper\"><figure class=\"elementor-image-box-img\"><a href=\"https:\/\/doi.org\/10.23919\/SoftCOM62040.2024.10721629\" target=\"_blank\"><img width=\"2560\" height=\"1600\" src=\"https:\/\/mlv.cs.duth.gr\/wp-content\/uploads\/2024\/10\/Social-Robots-in-Education_-To-Select-or-Not-to-Select-a-Robot-for-a-Teaching-Subject-at-an-Educational-Level-scaled.jpg\" class=\"elementor-animation-grow attachment-full size-full\" alt=\"\" loading=\"lazy\" \/><\/a><\/figure><div class=\"elementor-image-box-content\"><h5 class=\"elementor-image-box-title\"><a href=\"https:\/\/doi.org\/10.23919\/SoftCOM62040.2024.10721629\" target=\"_blank\">Social Robots in Education: To Select or Not to Select a Robot for a Teaching Subject at an Educational Level?<\/a><\/h5><p class=\"elementor-image-box-description\">M. Tzampazaki, E. Vrochidou, and G.A. Papakostas<br>\nProceedings of 2024 International Conference on Software, Telecommunications and Computer Networks (SoftCOM), pp. 1-6, Split, Croatia.<\/p><\/div><\/div>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-ac52d61 elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"ac52d61\" data-element_type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-74f2a4a\" data-id=\"74f2a4a\" data-element_type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t\t\t<div class=\"elementor-element elementor-element-83fa624 elementor-position-left elementor-vertical-align-top elementor-widget elementor-widget-image-box\" data-id=\"83fa624\" data-element_type=\"widget\" data-widget_type=\"image-box.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<div class=\"elementor-image-box-wrapper\"><figure class=\"elementor-image-box-img\"><a href=\"https:\/\/doi.org\/10.3390\/technologies12100175\" target=\"_blank\"><img width=\"649\" height=\"362\" src=\"https:\/\/mlv.cs.duth.gr\/wp-content\/uploads\/2024\/10\/Enhancing-Solar-Plant-Efficiency_-A-Review-of-Vision-Based-Monitoring-and-Fault-Detection-Techniques-.png\" class=\"elementor-animation-grow attachment-full size-full\" alt=\"\" loading=\"lazy\" \/><\/a><\/figure><div class=\"elementor-image-box-content\"><h5 class=\"elementor-image-box-title\"><a href=\"https:\/\/doi.org\/10.3390\/technologies12100175\" target=\"_blank\">Enhancing Solar Plant Efficiency: A Review of Vision-Based Monitoring and Fault Detection Techniques<\/a><\/h5><p class=\"elementor-image-box-description\">I. Polymeropoulos, S. Bezyrgiannidis, E. Vrochidou, and G.A. Papakostas<br>\nTechnologies , vol. 12, no. 10, p. 175.<\/p><\/div><\/div>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-296db20 elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"296db20\" data-element_type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-a88da97\" data-id=\"a88da97\" data-element_type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t\t\t<div class=\"elementor-element elementor-element-921e944 elementor-position-left elementor-vertical-align-top elementor-widget elementor-widget-image-box\" data-id=\"921e944\" data-element_type=\"widget\" data-widget_type=\"image-box.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<div class=\"elementor-image-box-wrapper\"><figure class=\"elementor-image-box-img\"><a href=\"https:\/\/doi.org\/10.3390\/info15090511\" target=\"_blank\"><img width=\"789\" height=\"359\" src=\"https:\/\/mlv.cs.duth.gr\/wp-content\/uploads\/publications\/pubs_icons\/IKDD_ A Keystroke Dynamics Dataset for User Classification.png\" class=\"elementor-animation-grow attachment-full size-full\" alt=\"\" loading=\"lazy\" \/><\/a><\/figure><div class=\"elementor-image-box-content\"><h5 class=\"elementor-image-box-title\"><a href=\"https:\/\/doi.org\/10.3390\/info15090511\" target=\"_blank\">IKDD: A Keystroke Dynamics Dataset for User Classification<\/a><\/h5><p class=\"elementor-image-box-description\">I. Tsimperidis, O.-D. Asvesta, E. Vrochidou, and G.A. Papakostas<br>\nInformation, vol. 15, no. 9, p. 511.<\/p><\/div><\/div>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-0602e1e elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"0602e1e\" data-element_type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-618ea23\" data-id=\"618ea23\" data-element_type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t\t\t<div class=\"elementor-element elementor-element-4715c6a elementor-position-left elementor-vertical-align-top elementor-widget elementor-widget-image-box\" data-id=\"4715c6a\" data-element_type=\"widget\" data-widget_type=\"image-box.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<div class=\"elementor-image-box-wrapper\"><figure class=\"elementor-image-box-img\"><a href=\"https:\/\/doi.org\/10.1007\/978-3-031-66705-3_21\" target=\"_blank\"><img width=\"447\" height=\"391\" src=\"https:\/\/mlv.cs.duth.gr\/wp-content\/uploads\/publications\/pubs_icons\/Deep Learning for Cattle Face Identification.jpg\" class=\"elementor-animation-grow attachment-full size-full\" alt=\"\" loading=\"lazy\" \/><\/a><\/figure><div class=\"elementor-image-box-content\"><h5 class=\"elementor-image-box-title\"><a href=\"https:\/\/doi.org\/10.1007\/978-3-031-66705-3_21\" target=\"_blank\">Deep Learning for Cattle Face Identification<\/a><\/h5><p class=\"elementor-image-box-description\">S. Dede, E. Vrochidou, V. Kanakaris, and G.A. Papakostas<br>\nProceedings of Deep Learning Theory and Applications. DeLTA 2024, pp. 316-335, Dijon, France.<\/p><\/div><\/div>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-747ec16 elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"747ec16\" data-element_type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-d9e546e\" data-id=\"d9e546e\" data-element_type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t\t\t<div class=\"elementor-element elementor-element-6601d6e elementor-position-left elementor-vertical-align-top elementor-widget elementor-widget-image-box\" data-id=\"6601d6e\" data-element_type=\"widget\" data-widget_type=\"image-box.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<div class=\"elementor-image-box-wrapper\"><figure class=\"elementor-image-box-img\"><a href=\"https:\/\/doi.org\/10.1007\/s41870-024-02101-8\" target=\"_blank\"><img width=\"1501\" height=\"615\" src=\"https:\/\/mlv.cs.duth.gr\/wp-content\/uploads\/publications\/pubs_icons\/Leveraging Ground Truth Data and GIS Technologies for Reliable Crop Analysis and Agricultural Production Optimization.png\" class=\"elementor-animation-grow attachment-full size-full\" alt=\"\" loading=\"lazy\" \/><\/a><\/figure><div class=\"elementor-image-box-content\"><h5 class=\"elementor-image-box-title\"><a href=\"https:\/\/doi.org\/10.1007\/s41870-024-02101-8\" target=\"_blank\">Leveraging Ground Truth Data and GIS Technologies for Reliable Crop Analysis and Agricultural Production Optimization<\/a><\/h5><p class=\"elementor-image-box-description\">J. Shah, S. Kothari, J. Verma, and G.A. Papakostas <br>\nInternational Journal of Information Technology, vol. 16, p. 5247\u20135259.<\/p><\/div><\/div>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-6d29f5c elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"6d29f5c\" data-element_type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-6b8a057\" data-id=\"6b8a057\" data-element_type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t\t\t<div class=\"elementor-element elementor-element-92bb008 elementor-position-left elementor-vertical-align-top elementor-widget elementor-widget-image-box\" data-id=\"92bb008\" data-element_type=\"widget\" data-widget_type=\"image-box.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<div class=\"elementor-image-box-wrapper\"><figure class=\"elementor-image-box-img\"><a href=\"https:\/\/doi.org\/10.1109\/ICCSC62074.2024.10616943\" target=\"_blank\"><img width=\"247\" height=\"255\" src=\"https:\/\/mlv.cs.duth.gr\/wp-content\/uploads\/publications\/pubs_icons\/Evaluating the Reliability of Artificial Intelligence in Healthcare_ The Doctors\u2019 Perspective in Northern Greece.png\" class=\"elementor-animation-grow attachment-full size-full\" alt=\"\" loading=\"lazy\" \/><\/a><\/figure><div class=\"elementor-image-box-content\"><h5 class=\"elementor-image-box-title\"><a href=\"https:\/\/doi.org\/10.1109\/ICCSC62074.2024.10616943\" target=\"_blank\">Evaluating the Reliability of Artificial Intelligence in Healthcare: The Doctors\u2019 Perspective in Northern Greece<\/a><\/h5><p class=\"elementor-image-box-description\">E. Givanoudi, E. Vrochidou, and G.A. Papakostas<br>\nProceedings of 2024 International Conference on Circuit, Systems and Communication (ICCSC), pp. 1-6, Fes, Morocco.<\/p><\/div><\/div>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-c364800 elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"c364800\" data-element_type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-8e4f329\" data-id=\"8e4f329\" data-element_type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t\t\t<div class=\"elementor-element elementor-element-d4d3362 elementor-position-left elementor-vertical-align-top elementor-widget elementor-widget-image-box\" data-id=\"d4d3362\" data-element_type=\"widget\" data-widget_type=\"image-box.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<div class=\"elementor-image-box-wrapper\"><figure class=\"elementor-image-box-img\"><a href=\"https:\/\/doi.org\/10.3390\/engproc2024070025\" target=\"_blank\"><img width=\"613\" height=\"673\" src=\"https:\/\/mlv.cs.duth.gr\/wp-content\/uploads\/publications\/pubs_icons\/Machine Learning for Anomaly Detection in Industrial Environments.png\" class=\"elementor-animation-grow attachment-full size-full\" alt=\"\" loading=\"lazy\" \/><\/a><\/figure><div class=\"elementor-image-box-content\"><h5 class=\"elementor-image-box-title\"><a href=\"https:\/\/doi.org\/10.3390\/engproc2024070025\" target=\"_blank\">Machine Learning for Anomaly Detection in Industrial Environments<\/a><\/h5><p class=\"elementor-image-box-description\">D. Grunova, V. Bakratsi, E. Vrochidou, and G.A. Papakostas<br>\nEngineering Proceedings, vol. 70, no. 1, p. 25.<\/p><\/div><\/div>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-2e96a27 elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"2e96a27\" data-element_type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-6d203a9\" data-id=\"6d203a9\" data-element_type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t\t\t<div class=\"elementor-element elementor-element-823a0aa elementor-position-left elementor-vertical-align-top elementor-widget elementor-widget-image-box\" data-id=\"823a0aa\" data-element_type=\"widget\" data-widget_type=\"image-box.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<div class=\"elementor-image-box-wrapper\"><figure class=\"elementor-image-box-img\"><a href=\"https:\/\/doi.org\/10.4018\/979-8-3693-3045-6.ch016\" target=\"_blank\"><img width=\"415\" height=\"316\" src=\"https:\/\/mlv.cs.duth.gr\/wp-content\/uploads\/publications\/pubs_icons\/Photogrammetry_ The \u201cHidden\u201d Contribution in Education.jpg\" class=\"elementor-animation-grow attachment-full size-full\" alt=\"\" loading=\"lazy\" \/><\/a><\/figure><div class=\"elementor-image-box-content\"><h5 class=\"elementor-image-box-title\"><a href=\"https:\/\/doi.org\/10.4018\/979-8-3693-3045-6.ch016\" target=\"_blank\">Photogrammetry: The \u201cHidden\u201d Contribution in Education<\/a><\/h5><p class=\"elementor-image-box-description\">K.A. Tychola, I. Tsimperidis, and G.A. Papakostas<br>\nEditor(s): Chemsi, G., Elimadi, I., Sadiq, M., Radid, M. In Teaching and Assessment in the Era of Education 5.0, pp. 281-300, IGI Global.<\/p><\/div><\/div>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-1ffed88 elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"1ffed88\" data-element_type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-4a64234\" data-id=\"4a64234\" data-element_type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t\t\t<div class=\"elementor-element elementor-element-6998ca8 elementor-position-left elementor-vertical-align-top elementor-widget elementor-widget-image-box\" data-id=\"6998ca8\" data-element_type=\"widget\" data-widget_type=\"image-box.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<div class=\"elementor-image-box-wrapper\"><figure class=\"elementor-image-box-img\"><a href=\"https:\/\/doi.org\/10.1007\/s44217-024-00150-6\" target=\"_blank\"><img width=\"450\" height=\"250\" src=\"https:\/\/mlv.cs.duth.gr\/wp-content\/uploads\/publications\/pubs_icons\/Integration of Wikis in Education_ A Qualitative Systematic Review.gif\" class=\"elementor-animation-grow attachment-full size-full\" alt=\"\" loading=\"lazy\" \/><\/a><\/figure><div class=\"elementor-image-box-content\"><h5 class=\"elementor-image-box-title\"><a href=\"https:\/\/doi.org\/10.1007\/s44217-024-00150-6\" target=\"_blank\">Integration of Wikis in Education: A Qualitative Systematic Review<\/a><\/h5><p class=\"elementor-image-box-description\">N. Karipidis and I. Tsimperidis<br>\nDiscover Education, vol. 3, p. 61<\/p><\/div><\/div>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-6f6b972 elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"6f6b972\" data-element_type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-41c7571\" data-id=\"41c7571\" data-element_type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t\t\t<div class=\"elementor-element elementor-element-f324603 elementor-position-left elementor-vertical-align-top elementor-widget elementor-widget-image-box\" data-id=\"f324603\" data-element_type=\"widget\" data-widget_type=\"image-box.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<div class=\"elementor-image-box-wrapper\"><figure class=\"elementor-image-box-img\"><a href=\"https:\/\/doi.org\/10.3390\/drones8060219\" target=\"_blank\"><img width=\"1440\" height=\"845\" src=\"https:\/\/mlv.cs.duth.gr\/wp-content\/uploads\/publications\/pubs_icons\/Beyond Flight_ Enhancing the Internet of Drones with Blockchain Technologies.png\" class=\"elementor-animation-grow attachment-full size-full\" alt=\"\" loading=\"lazy\" \/><\/a><\/figure><div class=\"elementor-image-box-content\"><h5 class=\"elementor-image-box-title\"><a href=\"https:\/\/doi.org\/10.3390\/drones8060219\" target=\"_blank\">Beyond Flight: Enhancing the Internet of Drones with Blockchain Technologies<\/a><\/h5><p class=\"elementor-image-box-description\">K.A. Tychola, K. Voulgaridis, and T. Lagkas<br>\nDrones, vol. 2024, no. 8, p. 219<\/p><\/div><\/div>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-58c0f7c elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"58c0f7c\" data-element_type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-830b908\" data-id=\"830b908\" data-element_type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t\t\t<div class=\"elementor-element elementor-element-e563ac0 elementor-position-left elementor-vertical-align-top elementor-widget elementor-widget-image-box\" data-id=\"e563ac0\" data-element_type=\"widget\" data-widget_type=\"image-box.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<div class=\"elementor-image-box-wrapper\"><figure class=\"elementor-image-box-img\"><a href=\"https:\/\/doi.org\/10.1109\/PACET60398.2024.10497022\" target=\"_blank\"><img width=\"358\" height=\"486\" src=\"https:\/\/mlv.cs.duth.gr\/wp-content\/uploads\/publications\/pubs_icons\/EEG Channel Selection for Epileptic Seizure Prediction.png\" class=\"elementor-animation-grow attachment-full size-full\" alt=\"\" loading=\"lazy\" \/><\/a><\/figure><div class=\"elementor-image-box-content\"><h5 class=\"elementor-image-box-title\"><a href=\"https:\/\/doi.org\/10.1109\/PACET60398.2024.10497022\" target=\"_blank\">EEG Channel Selection for Epileptic Seizure Prediction<\/a><\/h5><p class=\"elementor-image-box-description\">M. Marinis, E. Vrochidou, and G.A. Papakostas<br>\nProceedings of 2024 Panhellenic Conference on Electronics &amp; Telecommunications (PACET), pp. 1-4, Thessaloniki, Greece.<\/p><\/div><\/div>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-21897ac elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"21897ac\" data-element_type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-3c61627\" data-id=\"3c61627\" data-element_type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t\t\t<div class=\"elementor-element elementor-element-6fcb252 elementor-position-left elementor-vertical-align-top elementor-widget elementor-widget-image-box\" data-id=\"6fcb252\" data-element_type=\"widget\" data-widget_type=\"image-box.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<div class=\"elementor-image-box-wrapper\"><figure class=\"elementor-image-box-img\"><a href=\"https:\/\/doi.org\/10.12688\/f1000research.144350.1\" target=\"_blank\"><img width=\"2048\" height=\"1442\" src=\"https:\/\/mlv.cs.duth.gr\/wp-content\/uploads\/publications\/pubs_icons\/Big Data as a reform opportunity for public sector and real economy_ The case of Greece.jpeg\" class=\"elementor-animation-grow attachment-full size-full\" alt=\"\" loading=\"lazy\" \/><\/a><\/figure><div class=\"elementor-image-box-content\"><h5 class=\"elementor-image-box-title\"><a href=\"https:\/\/doi.org\/10.12688\/f1000research.144350.1\" target=\"_blank\">Big Data as a Reform Opportunity for Public Sector and Real Economy: The Case of Greece<\/a><\/h5><p class=\"elementor-image-box-description\">S. Lotsis, I. Georgousis, and G.A. Papakostas<br>\nF1000Research, vol. 2024, no. 13, p. 234<br><\/p><\/div><\/div>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-af020cb elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"af020cb\" data-element_type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-78c8d2a\" data-id=\"78c8d2a\" data-element_type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t\t\t<div class=\"elementor-element elementor-element-2f40af1 elementor-position-left elementor-vertical-align-top elementor-widget elementor-widget-image-box\" data-id=\"2f40af1\" data-element_type=\"widget\" data-widget_type=\"image-box.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<div class=\"elementor-image-box-wrapper\"><figure class=\"elementor-image-box-img\"><a href=\"https:\/\/doi.org\/10.3390\/app14041471\" target=\"_blank\"><img width=\"1090\" height=\"644\" src=\"https:\/\/mlv.cs.duth.gr\/wp-content\/uploads\/publications\/pubs_icons\/Machine Vision\u2014Moving from Industry 4.0 to Industry 5.0.png\" class=\"elementor-animation-grow attachment-full size-full\" alt=\"\" loading=\"lazy\" \/><\/a><\/figure><div class=\"elementor-image-box-content\"><h5 class=\"elementor-image-box-title\"><a href=\"https:\/\/doi.org\/10.3390\/app14041471\" target=\"_blank\">Machine Vision\u2014Moving from Industry 4.0 to Industry 5.0<\/a><\/h5><p class=\"elementor-image-box-description\">M. Tzampazaki, C. Zografos, E. Vrochidou, and G.A. Papakostas<br>\nApplied Sciences, vol. 14, no. 4, p. 1471<br><\/p><\/div><\/div>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-a00342d elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"a00342d\" data-element_type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-49d9954\" data-id=\"49d9954\" data-element_type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t\t\t<div class=\"elementor-element elementor-element-72642bc elementor-position-left elementor-vertical-align-top elementor-widget elementor-widget-image-box\" data-id=\"72642bc\" data-element_type=\"widget\" data-widget_type=\"image-box.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<div class=\"elementor-image-box-wrapper\"><figure class=\"elementor-image-box-img\"><a href=\"https:\/\/doi.org\/10.54517\/m.v5i1.2464\" target=\"_blank\"><img width=\"1039\" height=\"775\" src=\"https:\/\/mlv.cs.duth.gr\/wp-content\/uploads\/publications\/pubs_icons\/Computer Vision Meets Metaverse.png\" class=\"elementor-animation-grow attachment-full size-full\" alt=\"\" loading=\"lazy\" \/><\/a><\/figure><div class=\"elementor-image-box-content\"><h5 class=\"elementor-image-box-title\"><a href=\"https:\/\/doi.org\/10.54517\/m.v5i1.2464\" target=\"_blank\">Computer Vision Meets Metaverse<\/a><\/h5><p class=\"elementor-image-box-description\">V. Zakynthinou, V, Kanakaris, E. Vrochidou, and G.A. Papakostas<br>\nMetaverse, vol. 5, no. 1, p. 2464<br><\/p><\/div><\/div>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-208d236 elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"208d236\" data-element_type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-e1d48ca\" data-id=\"e1d48ca\" data-element_type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t\t\t<div class=\"elementor-element elementor-element-f6cb39d elementor-position-left elementor-vertical-align-top elementor-widget elementor-widget-image-box\" data-id=\"f6cb39d\" data-element_type=\"widget\" data-widget_type=\"image-box.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<div class=\"elementor-image-box-wrapper\"><figure class=\"elementor-image-box-img\"><a href=\"https:\/\/doi.org\/10.1007\/s00371-023-03237-7\" target=\"_blank\"><img width=\"1772\" height=\"890\" src=\"https:\/\/mlv.cs.duth.gr\/wp-content\/uploads\/publications\/pubs_icons\/Deep learning based computer vision under the prism of 3D point clouds_ A systematic review.png\" class=\"elementor-animation-grow attachment-full size-full\" alt=\"\" loading=\"lazy\" \/><\/a><\/figure><div class=\"elementor-image-box-content\"><h5 class=\"elementor-image-box-title\"><a href=\"https:\/\/doi.org\/10.1007\/s00371-023-03237-7\" target=\"_blank\">Deep Learning based Computer Vision under the Prism of 3D Point Clouds: A Systematic Review<\/a><\/h5><p class=\"elementor-image-box-description\">K.A. Tychola, E. Vrochidou, and G.A. Papakostas<br>\nThe Visual Computer, vol. 40<br><\/p><\/div><\/div>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-d09afba elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"d09afba\" data-element_type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-57e3ac1\" data-id=\"57e3ac1\" data-element_type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t\t\t<div class=\"elementor-element elementor-element-58450d2 elementor-widget elementor-widget-spacer\" data-id=\"58450d2\" data-element_type=\"widget\" data-widget_type=\"spacer.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<div class=\"elementor-spacer\">\n\t\t\t<div class=\"elementor-spacer-inner\"><\/div>\n\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-bdf00b1 elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"bdf00b1\" data-element_type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-5c678d9\" data-id=\"5c678d9\" data-element_type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t\t\t<div class=\"elementor-element elementor-element-a697b9e elementor-widget elementor-widget-heading\" data-id=\"a697b9e\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<h3 class=\"elementor-heading-title elementor-size-default\">2023<\/h3>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-fb1038c elementor-widget elementor-widget-heading\" data-id=\"fb1038c\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<h3 class=\"elementor-heading-title elementor-size-default\">Total: 32 (#Journals: 15, #Conferences: 10, #Book Chapters: 4, #Books: 3)<\/h3>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-872fba8 elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"872fba8\" data-element_type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-f0de383\" data-id=\"f0de383\" data-element_type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t\t\t<div class=\"elementor-element elementor-element-a327feb elementor-position-left elementor-vertical-align-top elementor-widget elementor-widget-image-box\" data-id=\"a327feb\" data-element_type=\"widget\" data-widget_type=\"image-box.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<div class=\"elementor-image-box-wrapper\"><figure class=\"elementor-image-box-img\"><a href=\"https:\/\/doi.org\/10.1007\/978-981-99-7093-3\" target=\"_blank\"><img width=\"827\" height=\"1254\" src=\"https:\/\/mlv.cs.duth.gr\/wp-content\/uploads\/publications\/pubs_icons\/Fourth International Conference on Image Processing and Capsule Networks.png\" class=\"elementor-animation-grow attachment-full size-full\" alt=\"\" loading=\"lazy\" \/><\/a><\/figure><div class=\"elementor-image-box-content\"><h5 class=\"elementor-image-box-title\"><a href=\"https:\/\/doi.org\/10.1007\/978-981-99-7093-3\" target=\"_blank\">Proceedings of the 4th International Conference on Image Processing and Capsule Networks (ICIPCN2023)<\/a><\/h5><p class=\"elementor-image-box-description\">S. Shakya, J.M.R.S. Tavares, A. Fern\u00e1ndez-Caballero, and G.A. Papakostas (eds.)<br>\nProceedings of ICIPCN 2023, Springer Singapore<br><\/p><\/div><\/div>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-e419788 elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"e419788\" data-element_type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-db2b13d\" data-id=\"db2b13d\" data-element_type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t\t\t<div class=\"elementor-element elementor-element-cad9b74 elementor-position-left elementor-vertical-align-top elementor-widget elementor-widget-image-box\" data-id=\"cad9b74\" data-element_type=\"widget\" data-widget_type=\"image-box.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<div class=\"elementor-image-box-wrapper\"><figure class=\"elementor-image-box-img\"><a href=\"https:\/\/doi.org\/10.5772\/intechopen.114024\" target=\"_blank\"><img width=\"679\" height=\"172\" src=\"https:\/\/mlv.cs.duth.gr\/wp-content\/uploads\/publications\/pubs_icons\/Visual Recognition of Food Ingredients_ A Systematic Review.png\" class=\"elementor-animation-grow attachment-full size-full\" alt=\"\" loading=\"lazy\" \/><\/a><\/figure><div class=\"elementor-image-box-content\"><h5 class=\"elementor-image-box-title\"><a href=\"https:\/\/doi.org\/10.5772\/intechopen.114024\" target=\"_blank\">Visual Recognition of Food Ingredients: A Systematic Review<\/a><\/h5><p class=\"elementor-image-box-description\">M. Marinis, E. Georgakoudis, E. Vrochidou, and G.A. Papakostas<br>\nEditor(s): Papakostas, G.A., In Computer Vision - Annual Volume 2023. IntechOpen.<br><\/p><\/div><\/div>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-5130304 elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"5130304\" data-element_type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-99d0e12\" data-id=\"99d0e12\" data-element_type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t\t\t<div class=\"elementor-element elementor-element-c689f1d elementor-position-left elementor-vertical-align-top elementor-widget elementor-widget-image-box\" data-id=\"c689f1d\" data-element_type=\"widget\" data-widget_type=\"image-box.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<div class=\"elementor-image-box-wrapper\"><figure class=\"elementor-image-box-img\"><a href=\"https:\/\/doi.org\/10.1007\/978-3-031-44127-1_13\" target=\"_blank\"><img width=\"684\" height=\"512\" src=\"https:\/\/mlv.cs.duth.gr\/wp-content\/uploads\/publications\/pubs_icons\/Traffic Sign Recognition Robustness in Autonomous Vehicles Under Physical Adversarial Attacks.png\" class=\"elementor-animation-grow attachment-full size-full\" alt=\"\" loading=\"lazy\" \/><\/a><\/figure><div class=\"elementor-image-box-content\"><h5 class=\"elementor-image-box-title\"><a href=\"https:\/\/doi.org\/10.1007\/978-3-031-44127-1_13\" target=\"_blank\">Traffic Sign Recognition Robustness in Autonomous Vehicles Under Physical Adversarial Attacks<\/a><\/h5><p class=\"elementor-image-box-description\">K.D. Apostolidis, E.V. Gkouvrikos, E. Vrochidou, and G.A. Papakostas<br>\nEditor(s): Daimi, K., Alsadoon, A., Coelho, L., In Cutting Edge Applications of Computational Intelligence Tools and Techniques. Studies in Computational Intelligence, vol 1118. Springer, Cham.<br><\/p><\/div><\/div>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-1785609 elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"1785609\" data-element_type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-28afb28\" data-id=\"28afb28\" data-element_type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t\t\t<div class=\"elementor-element elementor-element-0d4adac elementor-position-left elementor-vertical-align-top elementor-widget elementor-widget-image-box\" data-id=\"0d4adac\" data-element_type=\"widget\" data-widget_type=\"image-box.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<div class=\"elementor-image-box-wrapper\"><figure class=\"elementor-image-box-img\"><a href=\"https:\/\/doi.org\/10.3390\/axioms12121091\" target=\"_blank\"><img width=\"410\" height=\"636\" src=\"https:\/\/mlv.cs.duth.gr\/wp-content\/uploads\/publications\/pubs_icons\/Automatic Facial Palsy Detection\u2014From Mathematical Modeling to Deep Learning.png\" class=\"elementor-animation-grow attachment-full size-full\" alt=\"\" loading=\"lazy\" \/><\/a><\/figure><div class=\"elementor-image-box-content\"><h5 class=\"elementor-image-box-title\"><a href=\"https:\/\/doi.org\/10.3390\/axioms12121091\" target=\"_blank\">Automatic Facial Palsy Detection\u2014From Mathematical Modeling to Deep Learning<\/a><\/h5><p class=\"elementor-image-box-description\">E. Vrochidou, V. Papi\u0107, T. Kalampokas, and G.A. Papakostas<br>\nAxioms, vol. 12, no. 12, p.1091<br><\/p><\/div><\/div>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-a94fb05 elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"a94fb05\" data-element_type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-e3e7def\" data-id=\"e3e7def\" data-element_type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t\t\t<div class=\"elementor-element elementor-element-f7d94c2 elementor-position-left elementor-vertical-align-top elementor-widget elementor-widget-image-box\" data-id=\"f7d94c2\" data-element_type=\"widget\" data-widget_type=\"image-box.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<div class=\"elementor-image-box-wrapper\"><figure class=\"elementor-image-box-img\"><a href=\"https:\/\/doi.org\/10.3390\/eng4040154\" target=\"_blank\"><img width=\"965\" height=\"654\" src=\"https:\/\/mlv.cs.duth.gr\/wp-content\/uploads\/publications\/pubs_icons\/Finding the Age and Education Level of Bulgarian-Speaking Internet Users Using Keystroke Dynamics.jpg\" class=\"elementor-animation-grow attachment-full size-full\" alt=\"\" loading=\"lazy\" \/><\/a><\/figure><div class=\"elementor-image-box-content\"><h5 class=\"elementor-image-box-title\"><a href=\"https:\/\/doi.org\/10.3390\/eng4040154\" target=\"_blank\">Finding the Age and Education Level of Bulgarian-Speaking Internet Users Using Keystroke Dynamics<\/a><\/h5><p class=\"elementor-image-box-description\">D. Grunova and I. 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Tsimperidis, D. Grunova, and G.A. 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Kalampokas, S. Krinidis, V. Chatzis, and G.A. Papakostas<br>\n Machine Vision and Applications, vol. 34, p. 97<br><\/p><\/div><\/div>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-421bfb7 elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"421bfb7\" data-element_type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-e907032\" data-id=\"e907032\" data-element_type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t\t\t<div class=\"elementor-element elementor-element-331ece0 elementor-position-left elementor-vertical-align-top elementor-widget elementor-widget-image-box\" data-id=\"331ece0\" data-element_type=\"widget\" data-widget_type=\"image-box.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<div class=\"elementor-image-box-wrapper\"><figure class=\"elementor-image-box-img\"><a href=\"https:\/\/doi.org\/10.1109\/ACCESS.2023.3303961\" target=\"_blank\"><img width=\"752\" height=\"479\" src=\"https:\/\/mlv.cs.duth.gr\/wp-content\/uploads\/publications\/pubs_icons\/Deep Learning and Optimization-Based Methods for Skin Lesions Segmentation_ A Review.png\" class=\"elementor-animation-grow attachment-full size-full\" alt=\"\" loading=\"lazy\" \/><\/a><\/figure><div class=\"elementor-image-box-content\"><h5 class=\"elementor-image-box-title\"><a href=\"https:\/\/doi.org\/10.1109\/ACCESS.2023.3303961\" target=\"_blank\">Deep Learning and Optimization-Based Methods for Skin Lesions Segmentation: A Review<\/a><\/h5><p class=\"elementor-image-box-description\">K.M. Hosny, D. Elshora, E.R. Mohamed, E. Vrochidou, and G.A. 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Vrochidou and G.A. Papakostas<br>\nEditor(s): Bansal, J.C., Uddin, M.S., In Computer Vision and Machine Learning in Agriculture, Volume 3. Algorithms for Intelligent Systems. Springer, Singapore, pp. 177-213.<br><\/p><\/div><\/div>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-5148362 elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"5148362\" data-element_type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-0073b76\" data-id=\"0073b76\" data-element_type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t\t\t<div class=\"elementor-element elementor-element-1648406 elementor-position-left elementor-vertical-align-top elementor-widget elementor-widget-image-box\" data-id=\"1648406\" data-element_type=\"widget\" data-widget_type=\"image-box.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<div class=\"elementor-image-box-wrapper\"><figure class=\"elementor-image-box-img\"><a href=\"https:\/\/doi.org\/10.1109\/AIIoT58121.2023.10174288\" target=\"_blank\"><img width=\"1051\" height=\"458\" src=\"https:\/\/mlv.cs.duth.gr\/wp-content\/uploads\/publications\/pubs_icons\/Fusion of Thermal and RGB Images for Automated Deep Learning Based Marble Crack Detection.png\" class=\"elementor-animation-grow attachment-full size-full\" alt=\"\" loading=\"lazy\" \/><\/a><\/figure><div class=\"elementor-image-box-content\"><h5 class=\"elementor-image-box-title\"><a href=\"https:\/\/doi.org\/10.1109\/AIIoT58121.2023.10174288\" target=\"_blank\">Fusion of Thermal and RGB Images for Automated Deep Learning Based Marble Crack Detection<\/a><\/h5><p class=\"elementor-image-box-description\">E. Vrochidou, G.K. Sidiropoulos, I. Tsimperidis, A.G. Ouzounis, I.T. Sarafis, V. Kalpakis, A. Stamkos, and G.A. Papakostas<br>\nProceedings of 2023 IEEE World AI IoT Congress (AIIoT), pp. 0243-0249, Seattle, WA, USA<br><\/p><\/div><\/div>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-464dc64 elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"464dc64\" data-element_type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-5de5352\" data-id=\"5de5352\" data-element_type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t\t\t<div class=\"elementor-element elementor-element-5c9fee6 elementor-position-left elementor-vertical-align-top elementor-widget elementor-widget-image-box\" data-id=\"5c9fee6\" data-element_type=\"widget\" data-widget_type=\"image-box.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<div class=\"elementor-image-box-wrapper\"><figure class=\"elementor-image-box-img\"><a href=\"https:\/\/doi.org\/10.1007\/s40815-023-01564-4\" target=\"_blank\"><img width=\"11500\" height=\"6500\" src=\"https:\/\/mlv.cs.duth.gr\/wp-content\/uploads\/publications\/pubs_icons\/Fuzzy Cognitive Networks in Diverse Applications Using Hybrid Representative Structures.jpg\" class=\"elementor-animation-grow attachment-full size-full\" alt=\"\" loading=\"lazy\" \/><\/a><\/figure><div class=\"elementor-image-box-content\"><h5 class=\"elementor-image-box-title\"><a href=\"https:\/\/doi.org\/10.1007\/s40815-023-01564-4\" target=\"_blank\">Fuzzy Cognitive Networks in Diverse Applications Using Hybrid Representative Structures<\/a><\/h5><p class=\"elementor-image-box-description\">G.D. Karatzinis, N.A. Apostolikas, Y.S. Boutalis, and G.A. Papakostas<br>\nInternational Journal of Fuzzy Systems, vol. 2023<br><\/p><\/div><\/div>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-d0b55fd elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"d0b55fd\" data-element_type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-0224e54\" data-id=\"0224e54\" data-element_type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t\t\t<div class=\"elementor-element elementor-element-871b2ec elementor-position-left elementor-vertical-align-top elementor-widget elementor-widget-image-box\" data-id=\"871b2ec\" data-element_type=\"widget\" data-widget_type=\"image-box.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<div class=\"elementor-image-box-wrapper\"><figure class=\"elementor-image-box-img\"><a href=\"https:\/\/doi.org\/10.1109\/ACCESS.2023.3286730\" target=\"_blank\"><img width=\"472\" height=\"454\" src=\"https:\/\/mlv.cs.duth.gr\/wp-content\/uploads\/publications\/pubs_icons\/Multi-Class Classification of Plant Leaf Diseases Using Feature Fusion of Deep Convolutional Neural Network and Local Binary Pattern.png\" class=\"elementor-animation-grow attachment-full size-full\" alt=\"\" loading=\"lazy\" \/><\/a><\/figure><div class=\"elementor-image-box-content\"><h5 class=\"elementor-image-box-title\"><a href=\"https:\/\/doi.org\/10.1109\/ACCESS.2023.3286730\" target=\"_blank\">Multi-Class Classification of Plant Leaf Diseases Using Feature Fusion of Deep Convolutional Neural Network and Local Binary Pattern<\/a><\/h5><p class=\"elementor-image-box-description\">K.M. Hosny, W.M. El-Hady, F.M. Samy, E. Vrochidou, and G.A. Papakostas<br>\nIEEE Access, vol. 11, p. 62307-62317<br><\/p><\/div><\/div>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-1b7ff39 elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"1b7ff39\" data-element_type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-69a3e83\" data-id=\"69a3e83\" data-element_type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t\t\t<div class=\"elementor-element elementor-element-958a080 elementor-position-left elementor-vertical-align-top elementor-widget elementor-widget-image-box\" data-id=\"958a080\" data-element_type=\"widget\" data-widget_type=\"image-box.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<div class=\"elementor-image-box-wrapper\"><figure class=\"elementor-image-box-img\"><a href=\"https:\/\/doi.org\/10.1109\/CCWC57344.2023.10099348\" target=\"_blank\"><img width=\"526\" height=\"272\" src=\"https:\/\/mlv.cs.duth.gr\/wp-content\/uploads\/publications\/pubs_icons\/Machine Learning in Embedded Systems_ Limitations, Solutions and Future Challenges.png\" class=\"elementor-animation-grow attachment-full size-full\" alt=\"\" loading=\"lazy\" \/><\/a><\/figure><div class=\"elementor-image-box-content\"><h5 class=\"elementor-image-box-title\"><a href=\"https:\/\/doi.org\/10.1109\/CCWC57344.2023.10099348\" target=\"_blank\">Machine Learning in Embedded Systems: Limitations, Solutions and Future Challenges<\/a><\/h5><p class=\"elementor-image-box-description\">E. Batzolis, E. Vrochidou, G.A. Papakostas<br>\nProceedings of 13th Annual Computing and Communication Workshop and Conference (CCWC), pp. 0345-0350, Las Vegas, NV, USA<br><\/p><\/div><\/div>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-d497dd5 elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"d497dd5\" data-element_type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-64377d4\" data-id=\"64377d4\" data-element_type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t\t\t<div class=\"elementor-element elementor-element-9bd3325 elementor-position-left elementor-vertical-align-top elementor-widget elementor-widget-image-box\" data-id=\"9bd3325\" data-element_type=\"widget\" data-widget_type=\"image-box.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<div class=\"elementor-image-box-wrapper\"><figure class=\"elementor-image-box-img\"><a href=\"https:\/\/doi.org\/10.1007\/978-981-99-0835-6\" target=\"_blank\"><img width=\"827\" height=\"1254\" src=\"https:\/\/mlv.cs.duth.gr\/wp-content\/uploads\/publications\/pubs_icons\/Mobile Computing and Sustainable Informatics.jpg\" class=\"elementor-animation-grow attachment-full size-full\" alt=\"\" loading=\"lazy\" \/><\/a><\/figure><div class=\"elementor-image-box-content\"><h5 class=\"elementor-image-box-title\"><a href=\"https:\/\/doi.org\/10.1007\/978-981-99-0835-6\" target=\"_blank\">Mobile Computing and Sustainable Informatics<\/a><\/h5><p class=\"elementor-image-box-description\">S. Shakya, G.A. Papakostas, and K.A. Kamel (eds.)<br>\nLecture Notes on Data Engineering and Communications Technologies, vol. 166, Springer Singapore<br><\/p><\/div><\/div>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-15c81ea elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"15c81ea\" data-element_type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-3100dd8\" data-id=\"3100dd8\" data-element_type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t\t\t<div class=\"elementor-element elementor-element-5ec9881 elementor-position-left elementor-vertical-align-top elementor-widget elementor-widget-image-box\" data-id=\"5ec9881\" data-element_type=\"widget\" data-widget_type=\"image-box.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<div class=\"elementor-image-box-wrapper\"><figure class=\"elementor-image-box-img\"><a href=\"https:\/\/doi.org\/10.3390\/electronics12112379\" target=\"_blank\"><img width=\"774\" height=\"584\" src=\"https:\/\/mlv.cs.duth.gr\/wp-content\/uploads\/publications\/pubs_icons\/Quantum Machine Learning - An Overview.png\" class=\"elementor-animation-grow attachment-full size-full\" alt=\"\" loading=\"lazy\" \/><\/a><\/figure><div class=\"elementor-image-box-content\"><h5 class=\"elementor-image-box-title\"><a href=\"https:\/\/doi.org\/10.3390\/electronics12112379\" target=\"_blank\">Quantum Machine Learning - An Overview <\/a><\/h5><p class=\"elementor-image-box-description\">K.A. Tychola, T. Kalampokas, and G.A. Papakostas <br>\nElectronics, vol. 12, no. 11, p. 2379<br><\/p><\/div><\/div>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-f9d5ca2 elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"f9d5ca2\" data-element_type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-af3a8f6\" data-id=\"af3a8f6\" data-element_type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t\t\t<div class=\"elementor-element elementor-element-a850afb elementor-position-left elementor-vertical-align-top elementor-widget elementor-widget-image-box\" data-id=\"a850afb\" data-element_type=\"widget\" data-widget_type=\"image-box.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<div class=\"elementor-image-box-wrapper\"><figure class=\"elementor-image-box-img\"><a href=\"https:\/\/doi.org\/10.1007\/978-981-19-9379-4_36\" target=\"_blank\"><img width=\"739\" height=\"600\" src=\"https:\/\/mlv.cs.duth.gr\/wp-content\/uploads\/publications\/pubs_icons\/Cancer Classification from High-Dimensional Multi-omics Data Using Convolutional Neural Networks, Recurrence Plots, and Wavelet-Based Image Fusion.png\" class=\"elementor-animation-grow attachment-full size-full\" alt=\"\" loading=\"lazy\" \/><\/a><\/figure><div class=\"elementor-image-box-content\"><h5 class=\"elementor-image-box-title\"><a href=\"https:\/\/doi.org\/10.1007\/978-981-19-9379-4_36\" target=\"_blank\">Cancer Classification from High-Dimensional Multi-omics Data Using Convolutional Neural Networks, Recurrence Plots, and Wavelet-Based Image Fusion<\/a><\/h5><p class=\"elementor-image-box-description\">S. Tsimenidis and G.A. Papakostas <br>\nProceedings of Third Congress on Intelligent Systems, pp.  495-509, Bengaluru, India<br><\/p><\/div><\/div>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-0af8e4c elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"0af8e4c\" data-element_type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-faf0612\" data-id=\"faf0612\" data-element_type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t\t\t<div class=\"elementor-element elementor-element-861a385 elementor-position-left elementor-vertical-align-top elementor-widget elementor-widget-image-box\" data-id=\"861a385\" data-element_type=\"widget\" data-widget_type=\"image-box.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<div class=\"elementor-image-box-wrapper\"><figure class=\"elementor-image-box-img\"><a href=\"https:\/\/doi.org\/10.1109\/ACCESS.2023.3274669\" target=\"_blank\"><img width=\"1046\" height=\"541\" src=\"https:\/\/mlv.cs.duth.gr\/wp-content\/uploads\/publications\/pubs_icons\/A Holistic Approach on Airfare Price Prediction Using Machine Learning Techniques.png\" class=\"elementor-animation-grow attachment-full size-full\" alt=\"\" loading=\"lazy\" \/><\/a><\/figure><div class=\"elementor-image-box-content\"><h5 class=\"elementor-image-box-title\"><a href=\"https:\/\/doi.org\/10.1109\/ACCESS.2023.3274669\" target=\"_blank\">A Holistic Approach on Airfare Price Prediction Using Machine Learning Techniques<\/a><\/h5><p class=\"elementor-image-box-description\">T. Kalampokas, K. Tziridis, N. Kalampokas, A. Nikolaou, E. Vrochidou, and G.A. Papakostas <br>\nIEEE Access, vol. 11, pp. 46627-46643<br><\/p><\/div><\/div>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-55f772a elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"55f772a\" data-element_type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-83c7ef5\" data-id=\"83c7ef5\" data-element_type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t\t\t<div class=\"elementor-element elementor-element-f957fa4 elementor-position-left elementor-vertical-align-top elementor-widget elementor-widget-image-box\" data-id=\"f957fa4\" data-element_type=\"widget\" data-widget_type=\"image-box.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<div class=\"elementor-image-box-wrapper\"><figure class=\"elementor-image-box-img\"><a href=\"https:\/\/doi.org\/10.1007\/978-981-19-7892-0_24\" target=\"_blank\"><img width=\"578\" height=\"578\" src=\"https:\/\/mlv.cs.duth.gr\/wp-content\/uploads\/publications\/pubs_icons\/Exploiting Deep Learning for Overlapping Chromosome Segmentation.png\" class=\"elementor-animation-grow attachment-full size-full\" alt=\"\" loading=\"lazy\" \/><\/a><\/figure><div class=\"elementor-image-box-content\"><h5 class=\"elementor-image-box-title\"><a href=\"https:\/\/doi.org\/10.1007\/978-981-19-7892-0_24\" target=\"_blank\">Exploiting Deep Learning for Overlapping Chromosome Segmentation<\/a><\/h5><p class=\"elementor-image-box-description\">A. Nikolaou and G.A. Papakostas <br>\nComputer Vision and Robotics. Algorithms for Intelligent Systems<br><\/p><\/div><\/div>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-e984490 elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"e984490\" data-element_type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-c8a37d7\" data-id=\"c8a37d7\" data-element_type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t\t\t<div class=\"elementor-element elementor-element-c8d142b elementor-position-left elementor-vertical-align-top elementor-widget elementor-widget-image-box\" data-id=\"c8d142b\" data-element_type=\"widget\" data-widget_type=\"image-box.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<div class=\"elementor-image-box-wrapper\"><figure class=\"elementor-image-box-img\"><a href=\"https:\/\/doi.org\/10.3390\/info14040232\" target=\"_blank\"><img width=\"1090\" height=\"534\" src=\"https:\/\/mlv.cs.duth.gr\/wp-content\/uploads\/publications\/pubs_icons\/Structure Learning and Hyperparameter Optimization Using an Automated Machine Learning (AutoML) Pipeline.png\" class=\"elementor-animation-grow attachment-full size-full\" alt=\"\" loading=\"lazy\" \/><\/a><\/figure><div class=\"elementor-image-box-content\"><h5 class=\"elementor-image-box-title\"><a href=\"https:\/\/doi.org\/10.3390\/info14040232\" target=\"_blank\">Structure Learning and Hyperparameter Optimization Using an Automated Machine Learning (AutoML) Pipeline<\/a><\/h5><p class=\"elementor-image-box-description\">K. Filippou, G. Aifantis, G.A. Papakostas, and G.E. Tsekouras <br>\nInformation, vol. 14, no. 4, p. 232<br>\n<\/p><\/div><\/div>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-27c90d4 elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"27c90d4\" data-element_type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-93ae3e0\" data-id=\"93ae3e0\" data-element_type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t\t\t<div class=\"elementor-element elementor-element-2564d1c elementor-position-left elementor-vertical-align-top elementor-widget elementor-widget-image-box\" data-id=\"2564d1c\" data-element_type=\"widget\" data-widget_type=\"image-box.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<div class=\"elementor-image-box-wrapper\"><figure class=\"elementor-image-box-img\"><a href=\"https:\/\/doi.org\/10.1201\/9781003053262\" target=\"_blank\"><img width=\"600\" height=\"600\" src=\"https:\/\/mlv.cs.duth.gr\/wp-content\/uploads\/publications\/pubs_icons\/Quantum Image Analysis \u2013 Status and Perspectives.png\" class=\"elementor-animation-grow attachment-full size-full\" alt=\"\" loading=\"lazy\" \/><\/a><\/figure><div class=\"elementor-image-box-content\"><h5 class=\"elementor-image-box-title\"><a href=\"https:\/\/doi.org\/10.1201\/9781003053262\" target=\"_blank\">Quantum Image Analysis \u2013 Status and Perspectives<\/a><\/h5><p class=\"elementor-image-box-description\">K. Tziridis, T. Kalampokas, and G.A. Papakostas <br>\nChapter 6 - Editor(s): El-Sayed M. El-Alfy, George Bebis, Mengchu Zhou, In Intelligent Image and Video Analytics, Taylor & Francis Group, 2023, 48 Pages, ISBN 9781003053262<br>\n<\/p><\/div><\/div>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-84d22cc elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"84d22cc\" data-element_type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-f04b430\" data-id=\"f04b430\" data-element_type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t\t\t<div class=\"elementor-element elementor-element-efa3ba0 elementor-position-left elementor-vertical-align-top elementor-widget elementor-widget-image-box\" data-id=\"efa3ba0\" data-element_type=\"widget\" data-widget_type=\"image-box.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<div class=\"elementor-image-box-wrapper\"><figure class=\"elementor-image-box-img\"><a href=\"https:\/\/doi.org\/10.1145\/3575879.3575880\" target=\"_blank\"><img width=\"506\" height=\"267\" src=\"https:\/\/mlv.cs.duth.gr\/wp-content\/uploads\/publications\/pubs_icons\/Performance Benchmarking of Visual Human Tracking Algorithms for UAVs.png\" class=\"elementor-animation-grow attachment-full size-full\" alt=\"\" loading=\"lazy\" \/><\/a><\/figure><div class=\"elementor-image-box-content\"><h5 class=\"elementor-image-box-title\"><a href=\"https:\/\/doi.org\/10.1145\/3575879.3575880\" target=\"_blank\">Performance Benchmarking of Visual Human Tracking Algorithms for UAVs<\/a><\/h5><p class=\"elementor-image-box-description\">T. Kalampokas, G.A. Papakostas, V. Chatzis, and S. Krinidis <br>\nProceedings of the 26th Pan-Hellenic Conference on Informatics, pp. 1\u20137, Athens, Greece<br>\n<\/p><\/div><\/div>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-f17f4f1 elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"f17f4f1\" data-element_type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-9b633bd\" data-id=\"9b633bd\" data-element_type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t\t\t<div class=\"elementor-element elementor-element-75dc80c elementor-position-left elementor-vertical-align-top elementor-widget elementor-widget-image-box\" data-id=\"75dc80c\" data-element_type=\"widget\" data-widget_type=\"image-box.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<div class=\"elementor-image-box-wrapper\"><figure class=\"elementor-image-box-img\"><a href=\"https:\/\/doi.org\/10.1007\/s11042-023-15056-y\" target=\"_blank\"><img width=\"947\" height=\"751\" src=\"https:\/\/mlv.cs.duth.gr\/wp-content\/uploads\/publications\/pubs_icons\/Connecting national flags \u2013 a deep learning approach.png\" class=\"elementor-animation-grow attachment-full size-full\" alt=\"\" loading=\"lazy\" \/><\/a><\/figure><div class=\"elementor-image-box-content\"><h5 class=\"elementor-image-box-title\"><a href=\"https:\/\/doi.org\/10.1007\/s11042-023-15056-y\" target=\"_blank\">Connecting National Flags \u2013 A Deep Learning Approach<\/a><\/h5><p class=\"elementor-image-box-description\">T. Kalampokas, D. Mentizis, E. Vrochidou, and G.A. Papakostas <br>\nMultimedia Tools and Applications (2023)<br>\n<\/p><\/div><\/div>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-7cea02f elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"7cea02f\" data-element_type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-38aa081\" data-id=\"38aa081\" data-element_type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t\t\t<div class=\"elementor-element elementor-element-a052810 elementor-position-left elementor-vertical-align-top elementor-widget elementor-widget-image-box\" data-id=\"a052810\" data-element_type=\"widget\" data-widget_type=\"image-box.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<div class=\"elementor-image-box-wrapper\"><figure class=\"elementor-image-box-img\"><a href=\"https:\/\/doi.org\/10.1007\/978-981-19-7753-4_51\" target=\"_blank\"><img width=\"374\" height=\"266\" src=\"https:\/\/mlv.cs.duth.gr\/wp-content\/uploads\/publications\/pubs_icons\/Infant Crying Patterns\u2019 Analysis Using Machine Learning.png\" class=\"elementor-animation-grow attachment-full size-full\" alt=\"\" loading=\"lazy\" \/><\/a><\/figure><div class=\"elementor-image-box-content\"><h5 class=\"elementor-image-box-title\"><a href=\"https:\/\/doi.org\/10.1007\/978-981-19-7753-4_51\" target=\"_blank\">Infant Crying Patterns\u2019 Analysis Using Machine Learning<\/a><\/h5><p class=\"elementor-image-box-description\">V.-N. Tsakalidou, E. Vrochidou, and G.A. Papakostas <br>\nProceedings of Fourth International Conference on Communication, Computing and Electronics Systems, pp. 671\u2013680<br>\n<\/p><\/div><\/div>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-c036084 elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"c036084\" data-element_type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-bff01a8\" data-id=\"bff01a8\" data-element_type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t\t\t<div class=\"elementor-element elementor-element-590295e elementor-position-left elementor-vertical-align-top elementor-widget elementor-widget-image-box\" data-id=\"590295e\" data-element_type=\"widget\" data-widget_type=\"image-box.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<div class=\"elementor-image-box-wrapper\"><figure class=\"elementor-image-box-img\"><a href=\"https:\/\/doi.org\/10.3390\/technologies11010032\" target=\"_blank\"><img width=\"827\" height=\"397\" src=\"https:\/\/mlv.cs.duth.gr\/wp-content\/uploads\/publications\/pubs_icons\/Identifying Historic Buildings over Time through Image Matching.png\" class=\"elementor-animation-grow attachment-full size-full\" alt=\"\" loading=\"lazy\" \/><\/a><\/figure><div class=\"elementor-image-box-content\"><h5 class=\"elementor-image-box-title\"><a href=\"https:\/\/doi.org\/10.3390\/technologies11010032\" target=\"_blank\">Identifying Historic Buildings over Time through Image Matching<\/a><\/h5><p class=\"elementor-image-box-description\">K.A. Tychola, S. Chatzistamatis, E. Vrochidou, G.E. Tsekouras, and G.A. Papakostas <br>\nTechnologies, vol. 11, no. 1, p. 32<br>\n<\/p><\/div><\/div>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-240972f elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"240972f\" data-element_type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-c9b9254\" data-id=\"c9b9254\" data-element_type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t\t\t<div class=\"elementor-element elementor-element-9b7393a elementor-position-left elementor-vertical-align-top elementor-widget elementor-widget-image-box\" data-id=\"9b7393a\" data-element_type=\"widget\" data-widget_type=\"image-box.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<div class=\"elementor-image-box-wrapper\"><figure class=\"elementor-image-box-img\"><a href=\"https:\/\/doi.org\/10.3390\/app13042443\" target=\"_blank\"><img width=\"1326\" height=\"522\" src=\"https:\/\/mlv.cs.duth.gr\/wp-content\/uploads\/publications\/pubs_icons\/Reinforcement Learning in Game Industry - Review, Prospects and Challenges.png\" class=\"elementor-animation-grow attachment-full size-full\" alt=\"\" loading=\"lazy\" \/><\/a><\/figure><div class=\"elementor-image-box-content\"><h5 class=\"elementor-image-box-title\"><a href=\"https:\/\/doi.org\/10.3390\/app13042443\" target=\"_blank\">Reinforcement Learning in Game Industry - Review, Prospects and Challenges<\/a><\/h5><p class=\"elementor-image-box-description\">K. Souchleris, G.K. Sidiropoulos, and G.A. Papakostas <br>\nApplied Sciences, vol. 13, no. 4, p. 2443<br>\n<\/p><\/div><\/div>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-16042b6 elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"16042b6\" data-element_type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-269ef9d\" data-id=\"269ef9d\" data-element_type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t\t\t<div class=\"elementor-element elementor-element-e326809 elementor-position-left elementor-vertical-align-top elementor-widget elementor-widget-image-box\" data-id=\"e326809\" data-element_type=\"widget\" data-widget_type=\"image-box.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<div class=\"elementor-image-box-wrapper\"><figure class=\"elementor-image-box-img\"><a href=\"https:\/\/doi.org\/10.1007\/978-981-19-6634-7_19\" target=\"_blank\"><img width=\"549\" height=\"168\" src=\"https:\/\/mlv.cs.duth.gr\/wp-content\/uploads\/publications\/pubs_icons\/Machine Learning as a Service (MLaaS) - An Enterprise Perspective.png\" class=\"elementor-animation-grow attachment-full size-full\" alt=\"\" loading=\"lazy\" \/><\/a><\/figure><div class=\"elementor-image-box-content\"><h5 class=\"elementor-image-box-title\"><a href=\"https:\/\/doi.org\/10.1007\/978-981-19-6634-7_19\" target=\"_blank\">Machine Learning as a Service (MLaaS) - An Enterprise Perspective<\/a><\/h5><p class=\"elementor-image-box-description\">I. Grigoriadis, E. Vrochidou, I. Tsiatsiou, G.A. Papakostas <br>\nProceedings of International Conference on Data Science and Applications, pp. 261-273, Kolkata, India<br>\n<\/p><\/div><\/div>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-b1f7e3e elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"b1f7e3e\" data-element_type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-7c4d68c\" data-id=\"7c4d68c\" data-element_type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t\t\t<div class=\"elementor-element elementor-element-da1df8f elementor-widget elementor-widget-spacer\" data-id=\"da1df8f\" data-element_type=\"widget\" data-widget_type=\"spacer.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<div class=\"elementor-spacer\">\n\t\t\t<div class=\"elementor-spacer-inner\"><\/div>\n\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-87b6040 elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"87b6040\" data-element_type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-f44c6ba\" data-id=\"f44c6ba\" data-element_type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t\t\t<div class=\"elementor-element elementor-element-328eec7 elementor-widget elementor-widget-heading\" data-id=\"328eec7\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<h3 class=\"elementor-heading-title elementor-size-default\">2022<\/h3>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-e82273c elementor-widget elementor-widget-heading\" data-id=\"e82273c\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<h3 class=\"elementor-heading-title elementor-size-default\">Total: 25 (#Journals: 17, #Conferences: 8, #Book Chapters: 0, #Books: 0)<\/h3>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-d32d796 elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"d32d796\" data-element_type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-c87fe18\" data-id=\"c87fe18\" data-element_type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t\t\t<div class=\"elementor-element elementor-element-b64d409 elementor-position-left elementor-vertical-align-top elementor-widget elementor-widget-image-box\" data-id=\"b64d409\" data-element_type=\"widget\" data-widget_type=\"image-box.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<div class=\"elementor-image-box-wrapper\"><figure class=\"elementor-image-box-img\"><a href=\"https:\/\/doi.org\/10.1109\/3ICT56508.2022.9990841\" target=\"_blank\"><img width=\"2956\" height=\"1663\" src=\"https:\/\/mlv.cs.duth.gr\/wp-content\/uploads\/publications\/pubs_icons\/The Effects of Fully Connected Layers Adjustment for Lightweight Convolutional Neural Networks.png\" class=\"elementor-animation-grow attachment-full size-full\" alt=\"\" loading=\"lazy\" \/><\/a><\/figure><div class=\"elementor-image-box-content\"><h5 class=\"elementor-image-box-title\"><a href=\"https:\/\/doi.org\/10.1109\/3ICT56508.2022.9990841\" target=\"_blank\">The Effects of Fully Connected Layers Adjustment for Lightweight Convolutional Neural Networks<\/a><\/h5><p class=\"elementor-image-box-description\">E. Nerantzis, A. Kazakis, G. Symeonidis, and G.A. Papakostas<br>\n2022 International Conference on Innovation and Intelligence for Informatics, Computing, and Technologies (3ICT), pp. 50-57, Sakheer, Bahrain<br>\n<\/p><\/div><\/div>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-297859f elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"297859f\" data-element_type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-65239ca\" data-id=\"65239ca\" data-element_type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t\t\t<div class=\"elementor-element elementor-element-2ad32e8 elementor-position-left elementor-vertical-align-top elementor-widget elementor-widget-image-box\" data-id=\"2ad32e8\" data-element_type=\"widget\" data-widget_type=\"image-box.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<div class=\"elementor-image-box-wrapper\"><figure class=\"elementor-image-box-img\"><a href=\"https:\/\/doi.org\/10.3390\/a15110391\" target=\"_blank\"><img width=\"962\" height=\"347\" src=\"https:\/\/mlv.cs.duth.gr\/wp-content\/uploads\/publications\/pubs_icons\/Convolutional Neural Networks_ A Roundup and Benchmark of Their Pooling Layer Variants.png\" class=\"elementor-animation-grow attachment-full size-full\" alt=\"\" loading=\"lazy\" \/><\/a><\/figure><div class=\"elementor-image-box-content\"><h5 class=\"elementor-image-box-title\"><a href=\"https:\/\/doi.org\/10.3390\/a15110391\" target=\"_blank\">Convolutional Neural Networks: A Roundup and Benchmark of Their Pooling Layer Variants<\/a><\/h5><p class=\"elementor-image-box-description\">N.-I. Galanis, P. Vafiadis, K.-G. Mirzaev, and G.A. Papakostas<br>\nAlgorithms, vol. 15, no. 11,  p. 391<br>\n<\/p><\/div><\/div>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-978d157 elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"978d157\" data-element_type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-b9e0db4\" data-id=\"b9e0db4\" data-element_type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t\t\t<div class=\"elementor-element elementor-element-e36e7b3 elementor-position-left elementor-vertical-align-top elementor-widget elementor-widget-image-box\" data-id=\"e36e7b3\" data-element_type=\"widget\" data-widget_type=\"image-box.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<div class=\"elementor-image-box-wrapper\"><figure class=\"elementor-image-box-img\"><a href=\"https:\/\/doi.org\/10.1007\/978-981-19-3035-5_67\" target=\"_blank\"><img width=\"580\" height=\"455\" src=\"https:\/\/mlv.cs.duth.gr\/wp-content\/uploads\/publications\/pubs_icons\/Machine Learning for Cloud Resources Management\u2014An Overview.png\" class=\"elementor-animation-grow attachment-full size-full\" alt=\"\" loading=\"lazy\" \/><\/a><\/figure><div class=\"elementor-image-box-content\"><h5 class=\"elementor-image-box-title\"><a href=\"https:\/\/doi.org\/10.1007\/978-981-19-3035-5_67\" target=\"_blank\">Machine Learning for Cloud Resources Management\u2014An Overview<\/a><\/h5><p class=\"elementor-image-box-description\">V.N. Tsakalidou, P. Mitsou, and G.A. Papakostas<br>\nComputer Networks and Inventive Communication Technologies. Lecture Notes on Data Engineering and Communications Technologies, vol. 141, pp. 903-915<br>\n<\/p><\/div><\/div>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-06d7dd6 elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"06d7dd6\" data-element_type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-0303453\" data-id=\"0303453\" data-element_type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t\t\t<div class=\"elementor-element elementor-element-4fd57e1 elementor-position-left elementor-vertical-align-top elementor-widget elementor-widget-image-box\" data-id=\"4fd57e1\" data-element_type=\"widget\" data-widget_type=\"image-box.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<div class=\"elementor-image-box-wrapper\"><figure class=\"elementor-image-box-img\"><a href=\"https:\/\/doi.org\/10.3390\/ijms232012272\" target=\"_blank\"><img width=\"991\" height=\"566\" src=\"https:\/\/mlv.cs.duth.gr\/wp-content\/uploads\/publications\/pubs_icons\/Omics Data and Data Representations for Deep Learning-Based Predictive Modeling.png\" class=\"elementor-animation-grow attachment-full size-full\" alt=\"\" loading=\"lazy\" \/><\/a><\/figure><div class=\"elementor-image-box-content\"><h5 class=\"elementor-image-box-title\"><a href=\"https:\/\/doi.org\/10.3390\/ijms232012272\" target=\"_blank\">Omics Data and Data Representations for Deep Learning-Based Predictive Modeling<\/a><\/h5><p class=\"elementor-image-box-description\">S. Tsimenidis, E. Vrochidou, and G.A. Papakostas<br>\nInternational Journal of Molecular Science, vol. 23 no. 20, p. 12272<br>\n<\/p><\/div><\/div>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-3bf97ee elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"3bf97ee\" data-element_type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-12bafe3\" data-id=\"12bafe3\" data-element_type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t\t\t<div class=\"elementor-element elementor-element-50ca08a elementor-position-left elementor-vertical-align-top elementor-widget elementor-widget-image-box\" data-id=\"50ca08a\" data-element_type=\"widget\" data-widget_type=\"image-box.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<div class=\"elementor-image-box-wrapper\"><figure class=\"elementor-image-box-img\"><a href=\"https:\/\/doi.org\/10.3390\/electronics11203289\" target=\"_blank\"><img width=\"799\" height=\"719\" src=\"https:\/\/mlv.cs.duth.gr\/wp-content\/uploads\/publications\/pubs_icons\/Towards Robotic Marble Resin Application_ Crack Detection on Marble Using Deep Learning.png\" class=\"elementor-animation-grow attachment-full size-full\" alt=\"\" loading=\"lazy\" \/><\/a><\/figure><div class=\"elementor-image-box-content\"><h5 class=\"elementor-image-box-title\"><a href=\"https:\/\/doi.org\/10.3390\/electronics11203289\" target=\"_blank\">Towards Robotic Marble Resin Application: Crack Detection on Marble Using Deep Learning<\/a><\/h5><p class=\"elementor-image-box-description\">E. Vrochidou, G.K. Sidiropoulos, A.G. Ouzounis, A. Lampoglou, I. Tsimperidis, G.A. Papakostas, I.T. Sarafis, V. Kalpakis, and A. Stamkos<br>\nElectronics, vol. 11 no. 20, p. 3289<br>\n<\/p><\/div><\/div>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-722be72 elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"722be72\" data-element_type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-f9e6876\" data-id=\"f9e6876\" data-element_type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t\t\t<div class=\"elementor-element elementor-element-fef8c17 elementor-position-left elementor-vertical-align-top elementor-widget elementor-widget-image-box\" data-id=\"fef8c17\" data-element_type=\"widget\" data-widget_type=\"image-box.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<div class=\"elementor-image-box-wrapper\"><figure class=\"elementor-image-box-img\"><a href=\"https:\/\/doi.org\/10.3390\/biomedicines10102545\" target=\"_blank\"><img width=\"977\" height=\"370\" src=\"https:\/\/mlv.cs.duth.gr\/wp-content\/uploads\/publications\/pubs_icons\/How Resilient Are Deep Learning Models in Medical Image Analysis The Case of the Moment-Based Adversarial Attack (Mb-AdA).png\" class=\"elementor-animation-grow attachment-full size-full\" alt=\"\" loading=\"lazy\" \/><\/a><\/figure><div class=\"elementor-image-box-content\"><h5 class=\"elementor-image-box-title\"><a href=\"https:\/\/doi.org\/10.3390\/biomedicines10102545\" target=\"_blank\">How Resilient Are Deep Learning Models in Medical Image Analysis? The Case of the Moment-Based Adversarial Attack (Mb-AdA)<\/a><\/h5><p class=\"elementor-image-box-description\">T.V. Maliamanis, K.D. Apostolidis, and G.A. Papakostas<br>\nBiomedicines, vol. 10 no. 10, p. 2545<br>\n<\/p><\/div><\/div>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-b2b8fcd elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"b2b8fcd\" data-element_type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-c91d108\" data-id=\"c91d108\" data-element_type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t\t\t<div class=\"elementor-element elementor-element-895b382 elementor-position-left elementor-vertical-align-top elementor-widget elementor-widget-image-box\" data-id=\"895b382\" data-element_type=\"widget\" data-widget_type=\"image-box.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<div class=\"elementor-image-box-wrapper\"><figure class=\"elementor-image-box-img\"><a href=\"https:\/\/doi.org\/10.3390\/computers11090133\" target=\"_blank\"><img width=\"1331\" height=\"523\" src=\"https:\/\/mlv.cs.duth.gr\/wp-content\/uploads\/publications\/pubs_icons\/On Predicting Soccer Outcomes in the Greek League Using Machine Learning.png\" class=\"elementor-animation-grow attachment-full size-full\" alt=\"\" loading=\"lazy\" \/><\/a><\/figure><div class=\"elementor-image-box-content\"><h5 class=\"elementor-image-box-title\"><a href=\"https:\/\/doi.org\/10.3390\/computers11090133\" target=\"_blank\">On Predicting Soccer Outcomes in the Greek League Using Machine Learning<\/a><\/h5><p class=\"elementor-image-box-description\">M.-C. Malamatinos, E. Vrochidou, and G.A. Papakostas<br>\nComputers, vol. 101 no. 9, p. 133<br>\n<\/p><\/div><\/div>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-fd7da53 elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"fd7da53\" data-element_type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-9e98421\" data-id=\"9e98421\" data-element_type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t\t\t<div class=\"elementor-element elementor-element-9269c26 elementor-position-left elementor-vertical-align-top elementor-widget elementor-widget-image-box\" data-id=\"9269c26\" data-element_type=\"widget\" data-widget_type=\"image-box.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<div class=\"elementor-image-box-wrapper\"><figure class=\"elementor-image-box-img\"><a href=\"https:\/\/doi.org\/10.3390\/s22176364\" target=\"_blank\"><img width=\"6588\" height=\"4481\" src=\"https:\/\/mlv.cs.duth.gr\/wp-content\/uploads\/publications\/pubs_icons\/Biometrics_ Going 3D.jpg\" class=\"elementor-animation-grow attachment-full size-full\" alt=\"\" loading=\"lazy\" \/><\/a><\/figure><div class=\"elementor-image-box-content\"><h5 class=\"elementor-image-box-title\"><a href=\"https:\/\/doi.org\/10.3390\/s22176364\" target=\"_blank\">Biometrics: Going 3D<\/a><\/h5><p class=\"elementor-image-box-description\">G.G. Samatas and G.A. Papakostas<br>\nSensors, vol. 22, no. 17, p. 6364<br>\n<\/p><\/div><\/div>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-907c56d elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"907c56d\" data-element_type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-f045075\" data-id=\"f045075\" data-element_type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t\t\t<div class=\"elementor-element elementor-element-0d1a3dc elementor-position-left elementor-vertical-align-top elementor-widget elementor-widget-image-box\" data-id=\"0d1a3dc\" data-element_type=\"widget\" data-widget_type=\"image-box.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<div class=\"elementor-image-box-wrapper\"><figure class=\"elementor-image-box-img\"><a href=\"https:\/\/doi.org\/10.1109\/AIC55036.2022.9848929\" target=\"_blank\"><img width=\"871\" height=\"287\" src=\"https:\/\/mlv.cs.duth.gr\/wp-content\/uploads\/publications\/pubs_icons\/An Update on Cooking Recipe Generation with Machine Learning and Natural Language Processing.png\" class=\"elementor-animation-grow attachment-full size-full\" alt=\"\" loading=\"lazy\" \/><\/a><\/figure><div class=\"elementor-image-box-content\"><h5 class=\"elementor-image-box-title\"><a href=\"https:\/\/doi.org\/10.1109\/AIC55036.2022.9848929\" target=\"_blank\">An Update on Cooking Recipe Generation with Machine Learning and Natural Language Processing<\/a><\/h5><p class=\"elementor-image-box-description\">N.-I. Galanis and G.A. Papakostas<br>\n2022 IEEE World Conference on Applied Intelligence and Computing (AIC), pp. 739-744, Sonbhadra, India<br>\n<\/p><\/div><\/div>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-3eb9d4e elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"3eb9d4e\" data-element_type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-625b43c\" data-id=\"625b43c\" data-element_type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t\t\t<div class=\"elementor-element elementor-element-42419df elementor-position-left elementor-vertical-align-top elementor-widget elementor-widget-image-box\" data-id=\"42419df\" data-element_type=\"widget\" data-widget_type=\"image-box.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<div class=\"elementor-image-box-wrapper\"><figure class=\"elementor-image-box-img\"><a href=\"https:\/\/doi.org\/10.1109\/IWSSIP55020.2022.9854435\" target=\"_blank\"><img width=\"327\" height=\"245\" src=\"https:\/\/mlv.cs.duth.gr\/wp-content\/uploads\/publications\/pubs_icons\/Visual Quality Inspection of Pomegranate Crop Using a Novel Dataset and Deep Learning1.png\" class=\"elementor-animation-grow attachment-full size-full\" alt=\"\" loading=\"lazy\" \/><\/a><\/figure><div class=\"elementor-image-box-content\"><h5 class=\"elementor-image-box-title\"><a href=\"https:\/\/doi.org\/10.1109\/IWSSIP55020.2022.9854435\" target=\"_blank\">Visual Quality Inspection of Pomegranate Crop Using a Novel Dataset and Deep Learning<\/a><\/h5><p class=\"elementor-image-box-description\">A. Koufatzis, E. Vrochidou, and G.A. Papakostas<br>\n29th International Conference on Systems, Signals and Image Processing (IWSSIP), pp. 1-4, Sofia, Bulgaria<br>\n<\/p><\/div><\/div>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-81066b4 elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"81066b4\" data-element_type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-c56201e\" data-id=\"c56201e\" data-element_type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t\t\t<div class=\"elementor-element elementor-element-75e09b6 elementor-position-left elementor-vertical-align-top elementor-widget elementor-widget-image-box\" data-id=\"75e09b6\" data-element_type=\"widget\" data-widget_type=\"image-box.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<div class=\"elementor-image-box-wrapper\"><figure class=\"elementor-image-box-img\"><a href=\"https:\/\/doi.org\/10.3390\/digital2030022\" target=\"_blank\"><img width=\"720\" height=\"440\" src=\"https:\/\/mlv.cs.duth.gr\/wp-content\/uploads\/publications\/pubs_icons\/On 3D Reconstruction Using RGB-D Cameras .png\" class=\"elementor-animation-grow attachment-full size-full\" alt=\"\" loading=\"lazy\" \/><\/a><\/figure><div class=\"elementor-image-box-content\"><h5 class=\"elementor-image-box-title\"><a href=\"https:\/\/doi.org\/10.3390\/digital2030022\" target=\"_blank\">On 3D Reconstruction Using RGB-D Cameras <\/a><\/h5><p class=\"elementor-image-box-description\">K.A. Tychola, I. Tsimperidis, and G.A. Papakostas<br>\nDigital, vol. 2, no. 3, p. 401-423<br>\n<\/p><\/div><\/div>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-c73ba24 elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"c73ba24\" data-element_type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-c8e94a7\" data-id=\"c8e94a7\" data-element_type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t\t\t<div class=\"elementor-element elementor-element-4fe0f9a elementor-position-left elementor-vertical-align-top elementor-widget elementor-widget-image-box\" data-id=\"4fe0f9a\" data-element_type=\"widget\" data-widget_type=\"image-box.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<div class=\"elementor-image-box-wrapper\"><figure class=\"elementor-image-box-img\"><a href=\"https:\/\/doi.org\/10.3390\/jimaging8070191\" target=\"_blank\"><img width=\"983\" height=\"637\" src=\"https:\/\/mlv.cs.duth.gr\/wp-content\/uploads\/publications\/pubs_icons\/Hand-Crafted and Learned Feature Aggregation for Visual Marble Tiles Screening .jpg\" class=\"elementor-animation-grow attachment-full size-full\" alt=\"\" loading=\"lazy\" \/><\/a><\/figure><div class=\"elementor-image-box-content\"><h5 class=\"elementor-image-box-title\"><a href=\"https:\/\/doi.org\/10.3390\/jimaging8070191\" target=\"_blank\">Hand-Crafted and Learned Feature Aggregation for Visual Marble Tiles Screening <\/a><\/h5><p class=\"elementor-image-box-description\">G.K. Sidiropoulos, A.G. Ouzounis, G.A. Papakostas, A. Lampoglou, I.T. Sarafis, A. Stamkos, and G. Solakis<br>\nImaging, vol. 8, no. 7, p. 191<br>\n<\/p><\/div><\/div>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-d652276 elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"d652276\" data-element_type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-edd1628\" data-id=\"edd1628\" data-element_type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t\t\t<div class=\"elementor-element elementor-element-857247a elementor-position-left elementor-vertical-align-top elementor-widget elementor-widget-image-box\" data-id=\"857247a\" data-element_type=\"widget\" data-settings=\"{&quot;_animation&quot;:&quot;none&quot;}\" data-widget_type=\"image-box.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<div class=\"elementor-image-box-wrapper\"><figure class=\"elementor-image-box-img\"><a href=\"https:\/\/doi.org\/10.1109\/ICAAIC53929.2022.9792817\" target=\"_blank\"><img width=\"1159\" height=\"515\" src=\"https:\/\/mlv.cs.duth.gr\/wp-content\/uploads\/publications\/pubs_icons\/Machine Vision for Grape Cluster Quality Assessment.png\" class=\"elementor-animation-grow attachment-full size-full\" alt=\"\" loading=\"lazy\" \/><\/a><\/figure><div class=\"elementor-image-box-content\"><h5 class=\"elementor-image-box-title\"><a href=\"https:\/\/doi.org\/10.1109\/ICAAIC53929.2022.9792817\" target=\"_blank\">Machine Vision for Grape Cluster Quality Assessment<\/a><\/h5><p class=\"elementor-image-box-description\">T. Kalampokas, E. Vrochidou, and G.A. Papakostas<br>\n2022 International Conference on Applied Artificial Intelligence and Computing (ICAAIC), Salem, India<br>\n<\/p><\/div><\/div>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-0197dfe elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"0197dfe\" data-element_type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-4223c8d\" data-id=\"4223c8d\" data-element_type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t\t\t<div class=\"elementor-element elementor-element-0edd069 elementor-position-left elementor-vertical-align-top elementor-widget elementor-widget-image-box\" data-id=\"0edd069\" data-element_type=\"widget\" data-widget_type=\"image-box.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<div class=\"elementor-image-box-wrapper\"><figure class=\"elementor-image-box-img\"><a href=\"https:\/\/doi.org\/10.1155\/2022\/1008491\" target=\"_blank\"><img width=\"601\" height=\"250\" src=\"https:\/\/mlv.cs.duth.gr\/wp-content\/uploads\/publications\/pubs_icons\/A Survey on Deep Learning for Building Load Forecasting.png\" class=\"elementor-animation-grow attachment-full size-full\" alt=\"\" loading=\"lazy\" \/><\/a><\/figure><div class=\"elementor-image-box-content\"><h5 class=\"elementor-image-box-title\"><a href=\"https:\/\/doi.org\/10.1155\/2022\/1008491\" target=\"_blank\">A Survey on Deep Learning for Building Load Forecasting<\/a><\/h5><p class=\"elementor-image-box-description\">I. Patsakos, E. Vrochidou, and G.A. Papakostas<br>\nMathematical Problems in Engineering, vol. 2022, Article ID 1008491<br>\n<\/p><\/div><\/div>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-4ae056b elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"4ae056b\" data-element_type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-4d97cd2\" data-id=\"4d97cd2\" data-element_type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t\t\t<div class=\"elementor-element elementor-element-3866683 elementor-position-left elementor-vertical-align-top elementor-widget elementor-widget-image-box\" data-id=\"3866683\" data-element_type=\"widget\" data-widget_type=\"image-box.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<div class=\"elementor-image-box-wrapper\"><figure class=\"elementor-image-box-img\"><a href=\"https:\/\/doi.org\/10.3390\/jimaging8060155\" target=\"_blank\"><img width=\"458\" height=\"250\" src=\"https:\/\/mlv.cs.duth.gr\/wp-content\/uploads\/publications\/pubs_icons\/Digital Watermarking as an Adversarial Attack on Medical Image Analysis with Deep Learning.png\" class=\"elementor-animation-grow attachment-full size-full\" alt=\"\" loading=\"lazy\" \/><\/a><\/figure><div class=\"elementor-image-box-content\"><h5 class=\"elementor-image-box-title\"><a href=\"https:\/\/doi.org\/10.3390\/jimaging8060155\" target=\"_blank\">Digital Watermarking as an Adversarial Attack on Medical Image Analysis with Deep Learning<\/a><\/h5><p class=\"elementor-image-box-description\">K.D. Apostolidis and G.A. Papakostas<br>\nImaging, vol. 8, no. 6, p. 155<br>\n<\/p><\/div><\/div>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-acf6fc7 elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"acf6fc7\" data-element_type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-6e851fd\" data-id=\"6e851fd\" data-element_type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t\t\t<div class=\"elementor-element elementor-element-3b134a1 elementor-position-left elementor-vertical-align-top elementor-widget elementor-widget-image-box\" data-id=\"3b134a1\" data-element_type=\"widget\" data-widget_type=\"image-box.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<div class=\"elementor-image-box-wrapper\"><figure class=\"elementor-image-box-img\"><a href=\"https:\/\/doi.org\/10.1109\/CCWC54503.2022.9720902\" target=\"_blank\"><img width=\"1021\" height=\"384\" src=\"https:\/\/mlv.cs.duth.gr\/wp-content\/uploads\/publications\/pubs_icons\/MLOps - Definitions, Tools and Challenges.png\" class=\"elementor-animation-grow attachment-full size-full\" alt=\"\" loading=\"lazy\" \/><\/a><\/figure><div class=\"elementor-image-box-content\"><h5 class=\"elementor-image-box-title\"><a href=\"https:\/\/doi.org\/10.1109\/CCWC54503.2022.9720902\" target=\"_blank\">MLOps - Definitions, Tools and Challenges<\/a><\/h5><p class=\"elementor-image-box-description\">G. Symeonidis, E. Nerantzis, A. Kazakis, and G.A. Papakostas<br>\n2022 IEEE 12th Annual Computing and Communication Workshop and Conference (CCWC), Las Vegas, NV, USA, pp. 453-460<br>\n<\/p><\/div><\/div>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-3e86a64 elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"3e86a64\" data-element_type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-0be1cf7\" data-id=\"0be1cf7\" data-element_type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t\t\t<div class=\"elementor-element elementor-element-91465b1 elementor-position-left elementor-vertical-align-top elementor-widget elementor-widget-image-box\" data-id=\"91465b1\" data-element_type=\"widget\" data-widget_type=\"image-box.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<div class=\"elementor-image-box-wrapper\"><figure class=\"elementor-image-box-img\"><a href=\"https:\/\/doi.org\/10.3390\/info13050235\" target=\"_blank\"><img width=\"503\" height=\"437\" src=\"https:\/\/mlv.cs.duth.gr\/wp-content\/uploads\/publications\/pubs_icons\/Text Classification Using Intuitionistic Fuzzy Set Measures - An Evaluation Study1.png\" class=\"elementor-animation-grow attachment-full size-full\" alt=\"\" loading=\"lazy\" \/><\/a><\/figure><div class=\"elementor-image-box-content\"><h5 class=\"elementor-image-box-title\"><a href=\"https:\/\/doi.org\/10.3390\/info13050235\" target=\"_blank\">Text Classification Using Intuitionistic Fuzzy Set Measures - An Evaluation Study<\/a><\/h5><p class=\"elementor-image-box-description\">G.K. Sidiropoulos, N. Diamianos, K.D. Apostolidis, and G.A. Papakostas<br>\nInformation, vol. 13, no. 5, p. 235<br>\n<\/p><\/div><\/div>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-bf12b31 elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"bf12b31\" data-element_type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-dad43ab\" data-id=\"dad43ab\" data-element_type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t\t\t<div class=\"elementor-element elementor-element-26a6fae elementor-position-left elementor-vertical-align-top elementor-widget elementor-widget-image-box\" data-id=\"26a6fae\" data-element_type=\"widget\" data-widget_type=\"image-box.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<div class=\"elementor-image-box-wrapper\"><figure class=\"elementor-image-box-img\"><a href=\"https:\/\/doi.org\/10.3390\/computers11050061\" target=\"_blank\"><img width=\"1653\" height=\"2338\" src=\"https:\/\/mlv.cs.duth.gr\/wp-content\/uploads\/publications\/pubs_icons\/Robustly Effective Approaches on Motor Imagery-Based Brain Computer Interfaces.png\" class=\"elementor-animation-grow attachment-full size-full\" alt=\"\" loading=\"lazy\" \/><\/a><\/figure><div class=\"elementor-image-box-content\"><h5 class=\"elementor-image-box-title\"><a href=\"https:\/\/doi.org\/10.3390\/computers11050061\" target=\"_blank\">Robustly Effective Approaches on Motor Imagery-Based Brain Computer Interfaces<\/a><\/h5><p class=\"elementor-image-box-description\">S.S. Moumgiakmas and G.A. Papakostas<br>\nComputers, vol. 11, no. 5, p. 61<br>\n<\/p><\/div><\/div>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-99a51db elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"99a51db\" data-element_type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-36d71b9\" data-id=\"36d71b9\" data-element_type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t\t\t<div class=\"elementor-element elementor-element-98f0971 elementor-position-left elementor-vertical-align-top elementor-widget elementor-widget-image-box\" data-id=\"98f0971\" data-element_type=\"widget\" data-widget_type=\"image-box.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<div class=\"elementor-image-box-wrapper\"><figure class=\"elementor-image-box-img\"><a href=\"https:\/\/doi.org\/10.1007\/978-981-16-9573-5_41\" target=\"_blank\"><img width=\"581\" height=\"631\" src=\"https:\/\/mlv.cs.duth.gr\/wp-content\/uploads\/publications\/pubs_icons\/Computer Vision in Autoimmune Diseases Diagnosis - Current Status and Perspectives.png\" class=\"elementor-animation-grow attachment-full size-full\" alt=\"\" loading=\"lazy\" \/><\/a><\/figure><div class=\"elementor-image-box-content\"><h5 class=\"elementor-image-box-title\"><a href=\"https:\/\/doi.org\/10.1007\/978-981-16-9573-5_41\" target=\"_blank\">Computer Vision in Autoimmune Diseases Diagnosis \u2014 Current Status and Perspectives<\/a><\/h5><p class=\"elementor-image-box-description\">V.N. Tsakalidou, P. Mitsou, and G.A. Papakostas<br>\nComputational Vision and Bio-Inspired Computing, pp. 571-586<br>\n<\/p><\/div><\/div>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-311063d elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"311063d\" data-element_type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-0e71806\" data-id=\"0e71806\" data-element_type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t\t\t<div class=\"elementor-element elementor-element-cde0fe6 elementor-position-left elementor-vertical-align-top elementor-widget elementor-widget-image-box\" data-id=\"cde0fe6\" data-element_type=\"widget\" data-widget_type=\"image-box.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<div class=\"elementor-image-box-wrapper\"><figure class=\"elementor-image-box-img\"><a href=\"https:\/\/doi.org\/10.1007\/978-3-030-96878-6_9\" target=\"_blank\"><img width=\"345\" height=\"316\" src=\"https:\/\/mlv.cs.duth.gr\/wp-content\/uploads\/publications\/pubs_icons\/Moment Transform-Based Compressive Sensing.jpg\" class=\"elementor-animation-grow attachment-full size-full\" alt=\"\" loading=\"lazy\" \/><\/a><\/figure><div class=\"elementor-image-box-content\"><h5 class=\"elementor-image-box-title\"><a href=\"https:\/\/doi.org\/10.1007\/978-3-030-96878-6_9\" target=\"_blank\">Moment Transform-Based Compressive Sensing in Image Processing<\/a><\/h5><p class=\"elementor-image-box-description\">T. Kalampokas and G.A. Papakostas<br>\nSystems, Signals and Image Processing, 96-107<br>\n<\/p><\/div><\/div>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-8534261 elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"8534261\" data-element_type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-c67f4d7\" data-id=\"c67f4d7\" data-element_type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t\t\t<div class=\"elementor-element elementor-element-0f27bc4 elementor-position-left elementor-vertical-align-top elementor-widget elementor-widget-image-box\" data-id=\"0f27bc4\" data-element_type=\"widget\" data-widget_type=\"image-box.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<div class=\"elementor-image-box-wrapper\"><figure class=\"elementor-image-box-img\"><a href=\"https:\/\/doi.org\/10.3390\/designs6010017\" target=\"_blank\"><img width=\"711\" height=\"413\" src=\"https:\/\/mlv.cs.duth.gr\/wp-content\/uploads\/publications\/pubs_icons\/Inertial Measurement Units (IMUs) in Mobile Robots over the Last Five Years_ A Review.jpg\" class=\"elementor-animation-grow attachment-full size-full\" alt=\"\" loading=\"lazy\" \/><\/a><\/figure><div class=\"elementor-image-box-content\"><h5 class=\"elementor-image-box-title\"><a href=\"https:\/\/doi.org\/10.3390\/designs6010017\" target=\"_blank\">Inertial Measurement Units (IMUs) in Mobile Robots over the Last Five Years: A Review<\/a><\/h5><p class=\"elementor-image-box-description\">G.G. Samatas and T.P. Pachidis<br>\nDesigns, vol. 6, no. 1, p. 17<br>\n<\/p><\/div><\/div>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-fe07a1b elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"fe07a1b\" data-element_type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-65fa4d3\" data-id=\"65fa4d3\" data-element_type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t\t\t<div class=\"elementor-element elementor-element-c7c7990 elementor-position-left elementor-vertical-align-top elementor-widget elementor-widget-image-box\" data-id=\"c7c7990\" data-element_type=\"widget\" data-widget_type=\"image-box.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<div class=\"elementor-image-box-wrapper\"><figure class=\"elementor-image-box-img\"><a href=\"https:\/\/doi.org\/10.3390\/machines10020129\" target=\"_blank\"><img width=\"550\" height=\"424\" src=\"https:\/\/mlv.cs.duth.gr\/wp-content\/uploads\/publications\/pubs_icons\/Computer Vision in Self-Steering Tractors.jpg\" class=\"elementor-animation-grow attachment-full size-full\" alt=\"\" loading=\"lazy\" \/><\/a><\/figure><div class=\"elementor-image-box-content\"><h5 class=\"elementor-image-box-title\"><a href=\"https:\/\/doi.org\/10.3390\/machines10020129\" target=\"_blank\">Computer Vision in Self-Steering Tractors<\/a><\/h5><p class=\"elementor-image-box-description\">E. Vrochidou, D. Oustadakis, A. Kefalas, G.A. Papakostass<br>\nMachines, vol. 10, no. 2, p. 129<br>\n<\/p><\/div><\/div>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-4359462 elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"4359462\" data-element_type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-9c82531\" data-id=\"9c82531\" data-element_type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t\t\t<div class=\"elementor-element elementor-element-c98a86a elementor-position-left elementor-vertical-align-top elementor-widget elementor-widget-image-box\" data-id=\"c98a86a\" data-element_type=\"widget\" data-widget_type=\"image-box.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<div class=\"elementor-image-box-wrapper\"><figure class=\"elementor-image-box-img\"><a href=\"https:\/\/doi.org\/10.3390\/info13020064\" target=\"_blank\"><img width=\"902\" height=\"461\" src=\"https:\/\/mlv.cs.duth.gr\/wp-content\/uploads\/publications\/pubs_icons\/Fsmpy_ A Fuzzy Set Measures Python Library.jpg\" class=\"elementor-animation-grow attachment-full size-full\" alt=\"\" loading=\"lazy\" \/><\/a><\/figure><div class=\"elementor-image-box-content\"><h5 class=\"elementor-image-box-title\"><a href=\"https:\/\/doi.org\/10.3390\/info13020064\" target=\"_blank\">Fsmpy: A Fuzzy Set Measures Python Library<\/a><\/h5><p class=\"elementor-image-box-description\">G.K. Sidiropoulos, K.D. Apostolidis, N. Damianos, and G.A. Papakostas<br>\nInformation 2022, 13(2), p. 64<br>\n<\/p><\/div><\/div>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-be36ff7 elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"be36ff7\" data-element_type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-61e0a02\" data-id=\"61e0a02\" data-element_type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t\t\t<div class=\"elementor-element elementor-element-e1e5c36 elementor-position-left elementor-vertical-align-top elementor-widget elementor-widget-image-box\" data-id=\"e1e5c36\" data-element_type=\"widget\" data-widget_type=\"image-box.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<div class=\"elementor-image-box-wrapper\"><figure class=\"elementor-image-box-img\"><a href=\"https:\/\/doi.org\/10.3390\/s22020621\" target=\"_blank\"><img width=\"562\" height=\"498\" src=\"https:\/\/mlv.cs.duth.gr\/wp-content\/uploads\/publications\/pubs_icons\/Behavioral Data Analysis of Robot-Assisted Autism Spectrum Disorder (ASD) Interventions Based on Lattice Computing Techniques.jpg\" class=\"elementor-animation-grow attachment-full size-full\" alt=\"\" loading=\"lazy\" \/><\/a><\/figure><div class=\"elementor-image-box-content\"><h5 class=\"elementor-image-box-title\"><a href=\"https:\/\/doi.org\/10.3390\/s22020621\" target=\"_blank\">Behavioral Data Analysis of Robot-Assisted Autism Spectrum Disorder (ASD) Interventions Based on Lattice Computing Techniques<\/a><\/h5><p class=\"elementor-image-box-description\">C. Lytridis, V.G. Kaburlasos, C. Bazinas, G.A. Papakostas, G. Sidiropoulos, V.-A. Nikopoulou, V. Holeva, M. Papadopoulou, and A. Evangeliou<br>\nSensors, vol. 22, no. 2, p. 621<br>\n<\/p><\/div><\/div>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-8dd46cc elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"8dd46cc\" data-element_type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-c3cd010\" data-id=\"c3cd010\" data-element_type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t\t\t<div class=\"elementor-element elementor-element-da13954 elementor-position-left elementor-vertical-align-top elementor-widget elementor-widget-image-box\" data-id=\"da13954\" data-element_type=\"widget\" data-widget_type=\"image-box.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<div class=\"elementor-image-box-wrapper\"><figure class=\"elementor-image-box-img\"><a href=\"https:\/\/doi.org\/10.3390\/app12010010\" target=\"_blank\"><img width=\"433\" height=\"266\" src=\"https:\/\/mlv.cs.duth.gr\/wp-content\/uploads\/publications\/pubs_icons\/Rheumatoid Arthritis Diagnosis_ Deep Learning vs. Humane.jpg\" class=\"elementor-animation-grow attachment-full size-full\" alt=\"\" loading=\"lazy\" \/><\/a><\/figure><div class=\"elementor-image-box-content\"><h5 class=\"elementor-image-box-title\"><a href=\"https:\/\/doi.org\/10.3390\/app12010010\" target=\"_blank\">Rheumatoid Arthritis Diagnosis: Deep Learning vs. Humane<\/a><\/h5><p class=\"elementor-image-box-description\">G.P. Avramidis, M.P. Avramidou, and G.A. Papakostas<br>\nApplied Sciences, vol. 12, no. 1, p. 10<br>\n<\/p><\/div><\/div>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-fc448f5 elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"fc448f5\" data-element_type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-2b3c235\" data-id=\"2b3c235\" data-element_type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t\t\t<div class=\"elementor-element elementor-element-1b18e33 elementor-widget elementor-widget-spacer\" data-id=\"1b18e33\" data-element_type=\"widget\" data-widget_type=\"spacer.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<div class=\"elementor-spacer\">\n\t\t\t<div class=\"elementor-spacer-inner\"><\/div>\n\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-3c659bd elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"3c659bd\" data-element_type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-6ef67a7\" data-id=\"6ef67a7\" data-element_type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t\t\t<div class=\"elementor-element elementor-element-2d40d04 elementor-widget elementor-widget-heading\" data-id=\"2d40d04\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<h3 class=\"elementor-heading-title elementor-size-default\">2021<\/h3>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-e7fd5f8 elementor-widget elementor-widget-heading\" data-id=\"e7fd5f8\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<h3 class=\"elementor-heading-title elementor-size-default\">Total: 27 (#Journals: 14, #Conferences: 12, #Book Chapters: 1, #Books: 0) <\/h3>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-f1d55cd elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"f1d55cd\" data-element_type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-02b4ebc\" data-id=\"02b4ebc\" data-element_type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t\t\t<div class=\"elementor-element elementor-element-a603811 elementor-position-left elementor-vertical-align-top elementor-widget elementor-widget-image-box\" data-id=\"a603811\" data-element_type=\"widget\" data-widget_type=\"image-box.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<div class=\"elementor-image-box-wrapper\"><figure class=\"elementor-image-box-img\"><a href=\"https:\/\/doi.org\/10.1109\/PIC53636.2021.9687023\" target=\"_blank\"><img width=\"150\" height=\"135\" src=\"https:\/\/mlv.cs.duth.gr\/wp-content\/uploads\/publications\/pubs_icons\/Computer Vision for Astronomical Image Analysis1.jpg\" class=\"elementor-animation-grow attachment-thumbnail size-thumbnail\" alt=\"\" loading=\"lazy\" \/><\/a><\/figure><div class=\"elementor-image-box-content\"><h5 class=\"elementor-image-box-title\"><a href=\"https:\/\/doi.org\/10.1109\/PIC53636.2021.9687023\" target=\"_blank\">Computer Vision for Astronomical Image Analysis<\/a><\/h5><p class=\"elementor-image-box-description\">S. Karypidou, I. Georgousis, and G.A. Papakostas<br>\n2021 IEEE International Conference on Progress in Informatics and Computing (PIC), Shanghai, China, pp. 94-101<br>\n<\/p><\/div><\/div>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-6a9f026 elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"6a9f026\" data-element_type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-cc605aa\" data-id=\"cc605aa\" data-element_type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t\t\t<div class=\"elementor-element elementor-element-7b99fd9 elementor-position-left elementor-vertical-align-top elementor-widget elementor-widget-image-box\" data-id=\"7b99fd9\" data-element_type=\"widget\" data-widget_type=\"image-box.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<div class=\"elementor-image-box-wrapper\"><figure class=\"elementor-image-box-img\"><a href=\"https:\/\/doi.org\/10.1155\/2021\/5567489\" target=\"_blank\"><img width=\"150\" height=\"111\" src=\"https:\/\/mlv.cs.duth.gr\/wp-content\/uploads\/publications\/pubs_icons\/Refined Color Texture Classification Using CNN and Local Binary Pattern.jpg\" class=\"elementor-animation-grow attachment-thumbnail size-thumbnail\" alt=\"\" loading=\"lazy\" \/><\/a><\/figure><div class=\"elementor-image-box-content\"><h5 class=\"elementor-image-box-title\"><a href=\"https:\/\/doi.org\/10.1155\/2021\/5567489\" target=\"_blank\">Refined Color Texture Classification Using CNN and Local Binary Pattern<\/a><\/h5><p class=\"elementor-image-box-description\">K.M Hosny, T. Magdy, N.A. Lashin, K. Apostolidis, and G.A. Papakostas<br>\nMathematical Problems in Engineering, vol. 2021, Article ID 5567489<br>\n<\/p><\/div><\/div>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-3d40ba9 elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"3d40ba9\" data-element_type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-9af2228\" data-id=\"9af2228\" data-element_type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t\t\t<div class=\"elementor-element elementor-element-8bd30c0 elementor-position-left elementor-vertical-align-top elementor-widget elementor-widget-image-box\" data-id=\"8bd30c0\" data-element_type=\"widget\" data-widget_type=\"image-box.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<div class=\"elementor-image-box-wrapper\"><figure class=\"elementor-image-box-img\"><a href=\"https:\/\/doi.org\/10.1109\/ICSPIS53734.2021.9652176\" target=\"_blank\"><img width=\"150\" height=\"107\" src=\"https:\/\/mlv.cs.duth.gr\/wp-content\/uploads\/publications\/pubs_icons\/2021_ICSPIS.png\" class=\"elementor-animation-grow attachment-thumbnail size-thumbnail\" alt=\"\" loading=\"lazy\" \/><\/a><\/figure><div class=\"elementor-image-box-content\"><h5 class=\"elementor-image-box-title\"><a href=\"https:\/\/doi.org\/10.1109\/ICSPIS53734.2021.9652176\" target=\"_blank\">Evaluating Convolutional Neural Networks for No-Reference Image Quality Assessment<\/a><\/h5><p class=\"elementor-image-box-description\">K.D. Apostolidis, T. Polyzos, I. Grigoriadis and G.A. Papakostas<br>\n4th International Conference on Signal Processing and Information Security (ICSPIS), pp. 68-71<br>\n<\/p><\/div><\/div>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-528a0ed elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"528a0ed\" data-element_type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-2d89471\" data-id=\"2d89471\" data-element_type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t\t\t<div class=\"elementor-element elementor-element-e385443 elementor-position-left elementor-vertical-align-top elementor-widget elementor-widget-image-box\" data-id=\"e385443\" data-element_type=\"widget\" data-widget_type=\"image-box.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<div class=\"elementor-image-box-wrapper\"><figure class=\"elementor-image-box-img\"><a href=\"https:\/\/doi.org\/10.3390\/app11188318\" target=\"_blank\"><img width=\"150\" height=\"101\" src=\"https:\/\/mlv.cs.duth.gr\/wp-content\/uploads\/publications\/pubs_icons\/Machine Learning in Discriminating Active Volcanoes of the Hellenic Volcanic Arc.jpg\" class=\"elementor-animation-grow attachment-thumbnail size-thumbnail\" alt=\"\" loading=\"lazy\" \/><\/a><\/figure><div class=\"elementor-image-box-content\"><h5 class=\"elementor-image-box-title\"><a href=\"https:\/\/doi.org\/10.3390\/app11188318\" target=\"_blank\">Machine Learning in Discriminating Active Volcanoes of the Hellenic Volcanic Arc<\/a><\/h5><p class=\"elementor-image-box-description\">A.G. Ouzounis and G.A. Papakostas<br>\nApplied Sciences, vol. 11, no. 18, p. 8318<br>\n<\/p><\/div><\/div>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-4e1ac6f elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"4e1ac6f\" data-element_type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-6377669\" data-id=\"6377669\" data-element_type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t\t\t<div class=\"elementor-element elementor-element-594bb95 elementor-position-left elementor-vertical-align-top elementor-widget elementor-widget-image-box\" data-id=\"594bb95\" data-element_type=\"widget\" data-widget_type=\"image-box.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<div class=\"elementor-image-box-wrapper\"><figure class=\"elementor-image-box-img\"><a href=\"https:\/\/doi.org\/10.3390\/horticulturae7090282\" target=\"_blank\"><img width=\"150\" height=\"108\" src=\"https:\/\/mlv.cs.duth.gr\/wp-content\/uploads\/publications\/pubs_icons\/Machine Vision for Ripeness Estimation in Viticulture Automation.jpg\" class=\"elementor-animation-grow attachment-thumbnail size-thumbnail\" alt=\"\" loading=\"lazy\" \/><\/a><\/figure><div class=\"elementor-image-box-content\"><h5 class=\"elementor-image-box-title\"><a href=\"https:\/\/doi.org\/10.3390\/horticulturae7090282\" target=\"_blank\">Machine Vision for Ripeness Estimation in Viticulture Automation<\/a><\/h5><p class=\"elementor-image-box-description\">E. Vrochidou, C. Bazinas, M. Manios, G. A. Papakostas, T.P. Pachidis, and V.G. Kaburlasos<br>\nHorticulturae, vol. 7, no. 9, p. 282<br>\n<\/p><\/div><\/div>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-19ec8b2 elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"19ec8b2\" data-element_type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-e8e1be8\" data-id=\"e8e1be8\" data-element_type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t\t\t<div class=\"elementor-element elementor-element-bb4f594 elementor-position-left elementor-vertical-align-top elementor-widget elementor-widget-image-box\" data-id=\"bb4f594\" data-element_type=\"widget\" data-widget_type=\"image-box.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<div class=\"elementor-image-box-wrapper\"><figure class=\"elementor-image-box-img\"><a href=\"https:\/\/doi.org\/10.3390\/electronics10172132\" target=\"_blank\"><img width=\"150\" height=\"84\" src=\"https:\/\/mlv.cs.duth.gr\/wp-content\/uploads\/publications\/pubs_icons\/A Survey on Adversarial Deep Learning Robustness in Medical Image Analysis1.png\" class=\"elementor-animation-grow attachment-thumbnail size-thumbnail\" alt=\"\" loading=\"lazy\" \/><\/a><\/figure><div class=\"elementor-image-box-content\"><h5 class=\"elementor-image-box-title\"><a href=\"https:\/\/doi.org\/10.3390\/electronics10172132\" target=\"_blank\">A Survey on Adversarial Deep Learning Robustness in Medical Image Analysis<\/a><\/h5><p class=\"elementor-image-box-description\">K.D. Apostolidis, and G.A. Papakostas<br>\nElectronics, vol. 10, no. 17, p. 2132<br>\n<\/p><\/div><\/div>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-bf3f858 elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"bf3f858\" data-element_type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-a30da41\" data-id=\"a30da41\" data-element_type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap\">\n\t\t\t\t\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-8c2d989 elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"8c2d989\" data-element_type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-6b8e821\" data-id=\"6b8e821\" data-element_type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t\t\t<div class=\"elementor-element elementor-element-fb60907 elementor-position-left elementor-vertical-align-top elementor-widget elementor-widget-image-box\" data-id=\"fb60907\" data-element_type=\"widget\" data-widget_type=\"image-box.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<div class=\"elementor-image-box-wrapper\"><figure class=\"elementor-image-box-img\"><a href=\"https:\/\/doi.org\/10.1109\/IEMCON53756.2021.9623255\" target=\"_blank\"><img width=\"150\" height=\"97\" src=\"https:\/\/mlv.cs.duth.gr\/wp-content\/uploads\/publications\/pubs_icons\/2021_IEMCON_Vi4m.png\" class=\"elementor-animation-grow attachment-thumbnail size-thumbnail\" alt=\"\" loading=\"lazy\" \/><\/a><\/figure><div class=\"elementor-image-box-content\"><h5 class=\"elementor-image-box-title\"><a href=\"https:\/\/doi.org\/10.1109\/IEMCON53756.2021.9623255\" target=\"_blank\">Exploiting Deep Metric Learning for Mable Quality Assessment with Small and Imbalanced Image Data<\/a><\/h5><p class=\"elementor-image-box-description\">G.K. Sidiropoulos, A.G. Ouzounis, G.A. Papakostas, I.T. Sarafis, A. Stamkos, V. Kalpakis and G. Solakis<br>\nIEEE 12th Annual Information Technology, Electronics and Mobile Communication Conference (IEMCON), <br> pp. 0266-0269\n<\/p><\/div><\/div>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-aad8d59 elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"aad8d59\" data-element_type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-13a9c0e\" data-id=\"13a9c0e\" data-element_type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t\t\t<div class=\"elementor-element elementor-element-a4d67a4 elementor-position-left elementor-vertical-align-top elementor-widget elementor-widget-image-box\" data-id=\"a4d67a4\" data-element_type=\"widget\" data-widget_type=\"image-box.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<div class=\"elementor-image-box-wrapper\"><figure class=\"elementor-image-box-img\"><a href=\"https:\/\/doi.org\/10.1109\/ICDS53782.2021.9626726\" target=\"_blank\"><img width=\"138\" height=\"150\" src=\"https:\/\/mlv.cs.duth.gr\/wp-content\/uploads\/publications\/pubs_icons\/2021_ICDS_Vi4m.png\" class=\"elementor-animation-grow attachment-thumbnail size-thumbnail\" alt=\"\" loading=\"lazy\" \/><\/a><\/figure><div class=\"elementor-image-box-content\"><h3 class=\"elementor-image-box-title\"><a href=\"https:\/\/doi.org\/10.1109\/ICDS53782.2021.9626726\" target=\"_blank\">Marble Quality Assessment with Deep Learning Regression<\/a><\/h3><p class=\"elementor-image-box-description\">A.G. Ouzounis, G. Taxopoulos, G.A. Papakostas, I.T. Sarafis, A. Stamkos and G. Solakis<br>Fifth International Conference On Intelligent Computing in Data Sciences (ICDS), pp. 1-5<\/p><\/div><\/div>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-22a24d2 elementor-position-left elementor-vertical-align-top elementor-widget elementor-widget-image-box\" data-id=\"22a24d2\" data-element_type=\"widget\" data-widget_type=\"image-box.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<div class=\"elementor-image-box-wrapper\"><figure class=\"elementor-image-box-img\"><a href=\"https:\/\/doi.org\/10.1109\/CCWC51732.2021.9376086\" target=\"_blank\"><img width=\"150\" height=\"146\" src=\"https:\/\/mlv.cs.duth.gr\/wp-content\/uploads\/publications\/pubs_icons\/2021_CCWC2021-Vi4m.png\" class=\"elementor-animation-grow attachment-thumbnail size-thumbnail\" alt=\"\" loading=\"lazy\" \/><\/a><\/figure><div class=\"elementor-image-box-content\"><h3 class=\"elementor-image-box-title\"><a href=\"https:\/\/doi.org\/10.1109\/CCWC51732.2021.9376086\" target=\"_blank\">Texture Analysis for Machine Learning Based Marble Tiles Sorting<\/a><\/h3><p class=\"elementor-image-box-description\">G.K. Sidiropoulos, A.G. Ouzounis, G.A. Papakostas, I.T. Sarafis, A. Stamkos and G. Solakis,<br>IEEE 11th Annual Computing and Communication Workshop and Conference (CCWC), pp. 0045-0051<\/p><\/div><\/div>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-c43bb84 elementor-position-left elementor-vertical-align-top elementor-widget elementor-widget-image-box\" data-id=\"c43bb84\" data-element_type=\"widget\" data-widget_type=\"image-box.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<div class=\"elementor-image-box-wrapper\"><figure class=\"elementor-image-box-img\"><a href=\"https:\/\/doi.org\/10.5220\/0010517001010108\" target=\"_blank\"><img width=\"115\" height=\"150\" src=\"https:\/\/mlv.cs.duth.gr\/wp-content\/uploads\/publications\/pubs_icons\/2021_DeLTA-Vi4m.png\" class=\"elementor-animation-grow attachment-thumbnail size-thumbnail\" alt=\"\" loading=\"lazy\" \/><\/a><\/figure><div class=\"elementor-image-box-content\"><h3 class=\"elementor-image-box-title\"><a href=\"https:\/\/doi.org\/10.5220\/0010517001010108\" target=\"_blank\">Interpretable Deep Learning for Marble Tiles Sorting <\/a><\/h3><p class=\"elementor-image-box-description\">\u0391. 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Solakis<br>2nd International Conference on Deep Learning Theory and Applications (DeLTA), pp. 101-108<\/p><\/div><\/div>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-388b7d6 elementor-position-left elementor-vertical-align-top elementor-widget elementor-widget-image-box\" data-id=\"388b7d6\" data-element_type=\"widget\" data-widget_type=\"image-box.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<div class=\"elementor-image-box-wrapper\"><figure class=\"elementor-image-box-img\"><a href=\"https:\/\/doi.org\/10.1109\/CCWC51732.2021.9376134\" target=\"_blank\"><img width=\"150\" height=\"150\" src=\"https:\/\/mlv.cs.duth.gr\/wp-content\/uploads\/publications\/pubs_icons\/2021_CCWC_Tziridis.png\" class=\"elementor-animation-grow attachment-thumbnail size-thumbnail\" alt=\"\" loading=\"lazy\" \/><\/a><\/figure><div class=\"elementor-image-box-content\"><h3 class=\"elementor-image-box-title\"><a href=\"https:\/\/doi.org\/10.1109\/CCWC51732.2021.9376134\" target=\"_blank\">EEG Signal Analysis for Seizure Detection Using Recurrence Plots and Tchebichef Moments<\/a><\/h3><p class=\"elementor-image-box-description\">K. Tziridis, T. Kalampokas and G.A. 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Papakostas<br>\nInternational Journal of Psychological and Mathematics, vol. 9, no. 16, p. 1854<br>\n<\/p><\/div><\/div>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-dca96b3 elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"dca96b3\" data-element_type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-3422165\" data-id=\"3422165\" data-element_type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t\t\t<div class=\"elementor-element elementor-element-f9ff1b7 elementor-position-left elementor-vertical-align-top elementor-widget elementor-widget-image-box\" data-id=\"f9ff1b7\" data-element_type=\"widget\" data-widget_type=\"image-box.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<div class=\"elementor-image-box-wrapper\"><figure class=\"elementor-image-box-img\"><a href=\"https:\/\/doi.org\/10.3390\/fi13080200\" target=\"_blank\"><img width=\"150\" height=\"84\" src=\"https:\/\/mlv.cs.duth.gr\/wp-content\/uploads\/publications\/pubs_icons\/Computer Vision for Fire Detection on UAVs\u2014From Software to Hardware.jpg\" class=\"elementor-animation-grow attachment-thumbnail size-thumbnail\" alt=\"\" loading=\"lazy\" \/><\/a><\/figure><div class=\"elementor-image-box-content\"><h5 class=\"elementor-image-box-title\"><a href=\"https:\/\/doi.org\/10.3390\/fi13080200\" target=\"_blank\">Computer Vision for Fire Detection on UAVs\u2014From Software to Hardware<\/a><\/h5><p class=\"elementor-image-box-description\">S.S. Moumgiakmas, G.G. Samatas, and G.A. Papakostas<br>\nFuture Internet, vol. 13, no. 8, p. 200<br>\n<\/p><\/div><\/div>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-e09e4f7 elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"e09e4f7\" data-element_type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-4ac21aa\" data-id=\"4ac21aa\" data-element_type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t\t\t<div class=\"elementor-element elementor-element-f282a11 elementor-position-left elementor-vertical-align-top elementor-widget elementor-widget-image-box\" data-id=\"f282a11\" data-element_type=\"widget\" data-widget_type=\"image-box.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<div class=\"elementor-image-box-wrapper\"><figure class=\"elementor-image-box-img\"><a href=\"http:\/\/www.ijmerr.com\/index.php?m=content&#038;c=index&#038;a=show&#038;catid=196&#038;id=1630\" target=\"_blank\"><img width=\"150\" height=\"143\" src=\"https:\/\/mlv.cs.duth.gr\/wp-content\/uploads\/publications\/pubs_icons\/Vision-based Vineyard Trunk Detection and its Integration into a Grapes Harvesting Robot.jpg\" class=\"elementor-animation-grow attachment-thumbnail size-thumbnail\" alt=\"\" loading=\"lazy\" \/><\/a><\/figure><div class=\"elementor-image-box-content\"><h5 class=\"elementor-image-box-title\"><a href=\"http:\/\/www.ijmerr.com\/index.php?m=content&#038;c=index&#038;a=show&#038;catid=196&#038;id=1630\" target=\"_blank\">Vision-based Vineyard Trunk Detection and its Integration into a Grapes Harvesting Robot<\/a><\/h5><p class=\"elementor-image-box-description\">E. Badeka, T. Kalampokas, E. Vrochidou, K. Tziridis, G.A. Papakostas, T.P. Pachidis, V.G. Kaburlasos<br>\nInternational Journal of Mechanical Engineering and Robotics Research, vol. 10, no. 7, pp. 374-385<br>\n<\/p><\/div><\/div>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-f3d5c69 elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"f3d5c69\" data-element_type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-1b0c2a8\" data-id=\"1b0c2a8\" data-element_type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t\t\t<div class=\"elementor-element elementor-element-7ea62da elementor-position-left elementor-vertical-align-top elementor-widget elementor-widget-image-box\" data-id=\"7ea62da\" data-element_type=\"widget\" data-widget_type=\"image-box.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<div class=\"elementor-image-box-wrapper\"><figure class=\"elementor-image-box-img\"><a href=\"https:\/\/doi.org\/10.1007\/978-3-030-79150-6_53\" target=\"_blank\"><img width=\"150\" height=\"63\" src=\"https:\/\/mlv.cs.duth.gr\/wp-content\/uploads\/publications\/pubs_icons\/2021_\u0391\u0399\u0391\u0399.png\" class=\"elementor-animation-grow attachment-thumbnail size-thumbnail\" alt=\"\" loading=\"lazy\" \/><\/a><\/figure><div class=\"elementor-image-box-content\"><h5 class=\"elementor-image-box-title\"><a href=\"https:\/\/doi.org\/10.1007\/978-3-030-79150-6_53\" target=\"_blank\">Machine Learning Meets Natural Language Processing - The Story so Far<\/a><\/h5><p class=\"elementor-image-box-description\">N.-I. Galanis, P. Vafiadis, K.-G. Mirzaev and G.A. Papakostas <br>IFIP International Conference on Artificial Intelligence Applications and Innovations, pp.\n673-686<\/p><\/div><\/div>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-b868f85 elementor-position-left elementor-vertical-align-top elementor-widget elementor-widget-image-box\" data-id=\"b868f85\" data-element_type=\"widget\" data-widget_type=\"image-box.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<div class=\"elementor-image-box-wrapper\"><figure class=\"elementor-image-box-img\"><a href=\"https:\/\/doi.org\/10.3390\/electronics10121398\" target=\"_blank\"><img width=\"97\" height=\"150\" src=\"https:\/\/mlv.cs.duth.gr\/wp-content\/uploads\/publications\/pubs_icons\/Social Robots in Special Education_ A Systematic Review1.jpg\" class=\"elementor-animation-grow attachment-thumbnail size-thumbnail\" alt=\"\" loading=\"lazy\" \/><\/a><\/figure><div class=\"elementor-image-box-content\"><h5 class=\"elementor-image-box-title\"><a href=\"https:\/\/doi.org\/10.3390\/electronics10121398\" target=\"_blank\">Social Robots in Special Education: A Systematic Review<\/a><\/h5><p class=\"elementor-image-box-description\">G.A. Papakostas, G.K. Sidiropoulos, C.I. Papadopoulou, E. Vrochidou, V.G. Kaburlasos, M.T. Papadopoulou, V. Holeva, V.-A. Nikopoulou, and N. Dalivigkas<br>\nElectronics, vol. 10, no. 12, p. 1398<br>\n<\/p><\/div><\/div>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-63a2cae elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"63a2cae\" data-element_type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-9025664\" data-id=\"9025664\" data-element_type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t\t\t<div class=\"elementor-element elementor-element-b9358eb elementor-position-left elementor-vertical-align-top elementor-widget elementor-widget-image-box\" data-id=\"b9358eb\" data-element_type=\"widget\" data-widget_type=\"image-box.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<div class=\"elementor-image-box-wrapper\"><figure class=\"elementor-image-box-img\"><a href=\"https:\/\/doi.org\/10.1155\/2021\/9955212\" target=\"_blank\"><img width=\"150\" height=\"73\" src=\"https:\/\/mlv.cs.duth.gr\/wp-content\/uploads\/publications\/pubs_icons\/Estimating Children Engagement Interacting with Robots in Special Education Using Machine Learning1.jpg\" class=\"elementor-animation-grow attachment-thumbnail size-thumbnail\" alt=\"\" loading=\"lazy\" \/><\/a><\/figure><div class=\"elementor-image-box-content\"><h5 class=\"elementor-image-box-title\"><a href=\"https:\/\/doi.org\/10.1155\/2021\/9955212\" target=\"_blank\">Estimating Children Engagement Interacting with Robots in Special Education Using Machine Learning<\/a><\/h5><p class=\"elementor-image-box-description\">G.A. Papakostas, G.K. Sidiropoulos, C. Lytridis, C. Bazinas, V.G. Kaburlasos, E. Kourampa, E. Karageorgiou, P. Kechayas and M.T. Papadopoulou<br>\nMathematical Problems in Engineering, vol. 2021, Article ID 9955212, 10 pages<br>\n<\/p><\/div><\/div>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-aa5b318 elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"aa5b318\" data-element_type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-9f5bd38\" data-id=\"9f5bd38\" data-element_type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t\t\t<div class=\"elementor-element elementor-element-8928f78 elementor-position-left elementor-vertical-align-top elementor-widget elementor-widget-image-box\" data-id=\"8928f78\" data-element_type=\"widget\" data-widget_type=\"image-box.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<div class=\"elementor-image-box-wrapper\"><figure class=\"elementor-image-box-img\"><a href=\"https:\/\/doi.org\/10.1016\/j.compag.2021.106220\" target=\"_blank\"><img width=\"145\" height=\"150\" src=\"https:\/\/mlv.cs.duth.gr\/wp-content\/uploads\/publications\/pubs_icons\/Grape stem detection using regression convolutional neural networks.jpg\" class=\"elementor-animation-grow attachment-thumbnail size-thumbnail\" alt=\"\" loading=\"lazy\" \/><\/a><\/figure><div class=\"elementor-image-box-content\"><h5 class=\"elementor-image-box-title\"><a href=\"https:\/\/doi.org\/10.1016\/j.compag.2021.106220\" target=\"_blank\">Grape stem detection using regression convolutional neural networks<\/a><\/h5><p class=\"elementor-image-box-description\">\u03a4. Kalampokas, \u0415. Vrochidou, G.A. Papakostas, T. Pachidis, V.G. Kaburlasos<br>\nComputers and Electronics in Agriculture, vol. 186, p. 106220<br>\n<\/p><\/div><\/div>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-46c6285 elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"46c6285\" data-element_type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-f77a6a7\" data-id=\"f77a6a7\" data-element_type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t\t\t<div class=\"elementor-element elementor-element-4134d03 elementor-position-left elementor-vertical-align-top elementor-widget elementor-widget-image-box\" data-id=\"4134d03\" data-element_type=\"widget\" data-widget_type=\"image-box.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<div class=\"elementor-image-box-wrapper\"><figure class=\"elementor-image-box-img\"><a href=\"https:\/\/doi.org\/10.3390\/jimaging7050089\" target=\"_blank\"><img width=\"150\" height=\"87\" src=\"https:\/\/mlv.cs.duth.gr\/wp-content\/uploads\/publications\/pubs_icons\/Feature Extraction for Finger-Vein-Based Identity Recognition.jpg\" class=\"elementor-animation-grow attachment-thumbnail size-thumbnail\" alt=\"\" loading=\"lazy\" \/><\/a><\/figure><div class=\"elementor-image-box-content\"><h5 class=\"elementor-image-box-title\"><a href=\"https:\/\/doi.org\/10.3390\/jimaging7050089\" target=\"_blank\">Feature Extraction for Finger-Vein-Based Identity Recognition<\/a><\/h5><p class=\"elementor-image-box-description\">G.K. Sidiropoulos, P. Kiratsa, P. Chatzipetrou, and G.A. Papakostas<br>\nJournal of Imaging, vol. 7, no. 5, p. 89<br>\n<\/p><\/div><\/div>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-8013c03 elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"8013c03\" data-element_type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-cdf8073\" data-id=\"cdf8073\" data-element_type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t\t\t<div class=\"elementor-element elementor-element-d02724f elementor-position-left elementor-vertical-align-top elementor-widget elementor-widget-image-box\" data-id=\"d02724f\" data-element_type=\"widget\" data-widget_type=\"image-box.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<div class=\"elementor-image-box-wrapper\"><figure class=\"elementor-image-box-img\"><a href=\"https:\/\/doi.org\/10.3390\/math9091063\" target=\"_blank\"><img width=\"150\" height=\"57\" src=\"https:\/\/mlv.cs.duth.gr\/wp-content\/uploads\/publications\/pubs_icons\/Brain Signals Classification Based on Fuzzy Lattice Reasoning.jpg\" class=\"elementor-animation-grow attachment-thumbnail size-thumbnail\" alt=\"\" loading=\"lazy\" \/><\/a><\/figure><div class=\"elementor-image-box-content\"><h5 class=\"elementor-image-box-title\"><a href=\"https:\/\/doi.org\/10.3390\/math9091063\" target=\"_blank\">Brain Signals Classification Based on Fuzzy Lattice Reasoning<\/a><\/h5><p class=\"elementor-image-box-description\">E. Vrochidou, C. Lytridis, C. Bazinas, G.A. Papakostas, H. Wagatsuma, and V.G. Kaburlasos<br>\nMathematics, vol. 9, no. 9, p. 1063<br>\n<\/p><\/div><\/div>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-8a0d4c0 elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"8a0d4c0\" data-element_type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-3f87298\" data-id=\"3f87298\" data-element_type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t\t\t<div class=\"elementor-element elementor-element-5cf20ad elementor-position-left elementor-vertical-align-top elementor-widget elementor-widget-image-box\" data-id=\"5cf20ad\" data-element_type=\"widget\" data-widget_type=\"image-box.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<div class=\"elementor-image-box-wrapper\"><figure class=\"elementor-image-box-img\"><a href=\"https:\/\/doi.org\/10.3390\/electronics10091056\" target=\"_blank\"><img width=\"113\" height=\"150\" src=\"https:\/\/mlv.cs.duth.gr\/wp-content\/uploads\/publications\/pubs_icons\/An Autonomous Grape-Harvester Robot_ Integrated System Architecture.jpg\" class=\"elementor-animation-grow attachment-thumbnail size-thumbnail\" alt=\"\" loading=\"lazy\" \/><\/a><\/figure><div class=\"elementor-image-box-content\"><h5 class=\"elementor-image-box-title\"><a href=\"https:\/\/doi.org\/10.3390\/electronics10091056\" target=\"_blank\">An Autonomous Grape-Harvester Robot: Integrated System Architecture<\/a><\/h5><p class=\"elementor-image-box-description\">E. Vrochidou, K. Tziridis, A. Nikolaou, T. Kalampokas, G.A. Papakostas, T.P. Pachidis, S. Mamalis, S. Koundouras, and V.G. Kaburlasos<br>\nElectronics, vol. 10, no. 9, p. 1056<br>\n<\/p><\/div><\/div>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-2fcc690 elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"2fcc690\" data-element_type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-ddeb9bf\" data-id=\"ddeb9bf\" data-element_type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t\t\t<div class=\"elementor-element elementor-element-9118482 elementor-position-left elementor-vertical-align-top elementor-widget elementor-widget-image-box\" data-id=\"9118482\" data-element_type=\"widget\" data-widget_type=\"image-box.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<div class=\"elementor-image-box-wrapper\"><figure class=\"elementor-image-box-img\"><a href=\"https:\/\/doi.org\/10.3390\/electronics10091066\" target=\"_blank\"><img width=\"150\" height=\"150\" src=\"https:\/\/mlv.cs.duth.gr\/wp-content\/uploads\/publications\/pubs_icons\/New Image Encryption Algorithm Using Hyperchaotic System and Fibonacci Q-Matrix.jpg\" class=\"elementor-animation-grow attachment-thumbnail size-thumbnail\" alt=\"\" loading=\"lazy\" \/><\/a><\/figure><div class=\"elementor-image-box-content\"><h5 class=\"elementor-image-box-title\"><a href=\"https:\/\/doi.org\/10.3390\/electronics10091066\" target=\"_blank\">New Image Encryption Algorithm Using Hyperchaotic System and Fibonacci Q-Matrix<\/a><\/h5><p class=\"elementor-image-box-description\">K.M. Hosny, S.T. Kamal, M.M. Darwish, and G.A. Papakostas<br>\nElectronics, vol. 10, no. 9, p. 1066<br>\n<\/p><\/div><\/div>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-6a33d27 elementor-position-left elementor-vertical-align-top elementor-widget elementor-widget-image-box\" data-id=\"6a33d27\" data-element_type=\"widget\" data-widget_type=\"image-box.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<div class=\"elementor-image-box-wrapper\"><figure class=\"elementor-image-box-img\"><a href=\"https:\/\/doi.org\/10.1109\/AIIoT52608.2021.9454237\" target=\"_blank\"><img width=\"150\" height=\"129\" src=\"https:\/\/mlv.cs.duth.gr\/wp-content\/uploads\/publications\/pubs_icons\/2021_WorldAIoT2021-Edge.png\" class=\"elementor-animation-grow attachment-thumbnail size-thumbnail\" alt=\"\" loading=\"lazy\" \/><\/a><\/figure><div class=\"elementor-image-box-content\"><h5 class=\"elementor-image-box-title\"><a href=\"https:\/\/doi.org\/10.1109\/AIIoT52608.2021.9454237\" target=\"_blank\">Remote Crop Sensing with IoT and AI on the Edge<\/a><\/h5><p class=\"elementor-image-box-description\">P. Savvidis and G.A. Papakostas<br>\n IEEE World AI IoT Congress (AIIoT),  pp. 0048-0054<br>\n<\/p><\/div><\/div>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-f29aa33 elementor-position-left elementor-vertical-align-top elementor-widget elementor-widget-image-box\" data-id=\"f29aa33\" data-element_type=\"widget\" data-widget_type=\"image-box.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<div class=\"elementor-image-box-wrapper\"><figure class=\"elementor-image-box-img\"><a href=\"https:\/\/doi.org\/10.1109\/AIIoT52608.2021.9454173\" target=\"_blank\"><img width=\"141\" height=\"150\" src=\"https:\/\/mlv.cs.duth.gr\/wp-content\/uploads\/publications\/pubs_icons\/2021_WorldAIoT2021-PredMain.png\" class=\"elementor-animation-grow attachment-thumbnail size-thumbnail\" alt=\"\" loading=\"lazy\" \/><\/a><\/figure><div class=\"elementor-image-box-content\"><h5 class=\"elementor-image-box-title\"><a href=\"https:\/\/doi.org\/10.1109\/AIIoT52608.2021.9454173\" target=\"_blank\">Predictive Maintenance - Bridging Artificial Intelligence and IoT<\/a><\/h5><p class=\"elementor-image-box-description\">G.G. Samatas, S.S. Moumgiakmas and G.A. Papakostas<br>\n IEEE World AI IoT Congress (AIIoT),  pp. 0413-0419<br>\n<\/p><\/div><\/div>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-7e18627 elementor-position-left elementor-vertical-align-top elementor-widget elementor-widget-image-box\" data-id=\"7e18627\" data-element_type=\"widget\" data-widget_type=\"image-box.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<div class=\"elementor-image-box-wrapper\"><figure class=\"elementor-image-box-img\"><a href=\"https:\/\/doi.org\/10.1109\/AIIoT52608.2021.9454230\" target=\"_blank\"><img width=\"150\" height=\"112\" src=\"https:\/\/mlv.cs.duth.gr\/wp-content\/uploads\/publications\/pubs_icons\/2021_WorldAIoT2021-MachineBio.png\" class=\"elementor-animation-grow attachment-thumbnail size-thumbnail\" alt=\"\" loading=\"lazy\" \/><\/a><\/figure><div class=\"elementor-image-box-content\"><h5 class=\"elementor-image-box-title\"><a href=\"https:\/\/doi.org\/10.1109\/AIIoT52608.2021.9454230\" target=\"_blank\">Machine Biometrics - Towards Identifying Machines in a Smart City Environment<\/a><\/h5><p class=\"elementor-image-box-description\">G.K. Sidiropoulos and G.A. Papakostas<br>\n IEEE World AI IoT Congress (AIIoT),  pp. 0197-0201<br>\n<\/p><\/div><\/div>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-25f1ad2 elementor-position-left elementor-vertical-align-top elementor-widget elementor-widget-image-box\" data-id=\"25f1ad2\" data-element_type=\"widget\" data-widget_type=\"image-box.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<div class=\"elementor-image-box-wrapper\"><figure class=\"elementor-image-box-img\"><a href=\"https:\/\/doi.org\/10.1117\/12.2587268\" target=\"_blank\"><img width=\"150\" height=\"110\" src=\"https:\/\/mlv.cs.duth.gr\/wp-content\/uploads\/publications\/pubs_icons\/2021_ICMV.png\" class=\"elementor-animation-grow attachment-thumbnail size-thumbnail\" alt=\"\" loading=\"lazy\" \/><\/a><\/figure><div class=\"elementor-image-box-content\"><h5 class=\"elementor-image-box-title\"><a href=\"https:\/\/doi.org\/10.1117\/12.2587268\" target=\"_blank\">DOME-T: Adversarial Computer Vision Attack on Deep Learning Models Based on Tchebichef Image Moments<\/a><\/h5><p class=\"elementor-image-box-description\">T. Maliamanis and G.A. Papakostas<br>\nProc. SPIE 11605, Thirteenth International Conference on Machine Vision, 116050D<br>\n<\/p><\/div><\/div>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-99a0cc7 elementor-position-left elementor-vertical-align-top elementor-widget elementor-widget-image-box\" data-id=\"99a0cc7\" data-element_type=\"widget\" data-widget_type=\"image-box.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<div class=\"elementor-image-box-wrapper\"><figure class=\"elementor-image-box-img\"><a href=\"https:\/\/doi.org\/10.1016\/B978-0-12-821777-1.00004-5\" target=\"_blank\"><img width=\"150\" height=\"109\" src=\"https:\/\/mlv.cs.duth.gr\/wp-content\/uploads\/publications\/pubs_icons\/2021_BC_Maliamanis.png\" class=\"elementor-animation-grow attachment-thumbnail size-thumbnail\" alt=\"\" loading=\"lazy\" \/><\/a><\/figure><div class=\"elementor-image-box-content\"><h5 class=\"elementor-image-box-title\"><a href=\"https:\/\/doi.org\/10.1016\/B978-0-12-821777-1.00004-5\" target=\"_blank\">Machine Learning Vulnerability in Medical Imaging<\/a><\/h5><p class=\"elementor-image-box-description\">T. Maliamanis and G.A. Papakostas<br>\nChapter 3 - Editor(s): Pardeep Kumar, Yugal Kumar, Mohamed A. Tawhid,\nIn Intelligent Data-Centric Systems,\nMachine Learning, Big Data, and IoT for Medical Informatics,\nAcademic Press,\n2021,\nPages 53-70,\nISBN 9780128217771,<br>\n<\/p><\/div><\/div>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t","protected":false},"excerpt":{"rendered":"<p>2026 Total: 17 (#Journals: 10, #Conferences: 5, #Book Chapters: 2, #Books: 0) Fuzzy Cognitive Maps for Interpretable AI-Based Modeling of Student Engagement and Academic Grit E. Kytidou, E. Vrochidou, and G.A. Papakostas Engineering Applications of Artificial International Journal of Human\u2013Computer Interaction, p. 1-20. A Large Language Model-Based Multi-Agent Framework for Sustainable Industrial Design V. Alevizos,&hellip;&nbsp;<a href=\"https:\/\/mlv.cs.duth.gr\/index.php\/publications\/\" class=\"\" rel=\"bookmark\">Read More &raquo;<span class=\"screen-reader-text\">Publications<\/span><\/a><\/p>\n","protected":false},"author":1,"featured_media":0,"parent":0,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"page-templates\/template-pagebuilder-full-width.php","meta":{"_mi_skip_tracking":false,"neve_meta_sidebar":"","neve_meta_container":"","neve_meta_enable_content_width":"","neve_meta_content_width":0,"neve_meta_title_alignment":"","neve_meta_author_avatar":"","neve_post_elements_order":"","neve_meta_disable_header":"","neve_meta_disable_footer":"","neve_meta_disable_title":"on"},"_links":{"self":[{"href":"https:\/\/mlv.cs.duth.gr\/index.php\/wp-json\/wp\/v2\/pages\/20"}],"collection":[{"href":"https:\/\/mlv.cs.duth.gr\/index.php\/wp-json\/wp\/v2\/pages"}],"about":[{"href":"https:\/\/mlv.cs.duth.gr\/index.php\/wp-json\/wp\/v2\/types\/page"}],"author":[{"embeddable":true,"href":"https:\/\/mlv.cs.duth.gr\/index.php\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/mlv.cs.duth.gr\/index.php\/wp-json\/wp\/v2\/comments?post=20"}],"version-history":[{"count":1417,"href":"https:\/\/mlv.cs.duth.gr\/index.php\/wp-json\/wp\/v2\/pages\/20\/revisions"}],"predecessor-version":[{"id":3878,"href":"https:\/\/mlv.cs.duth.gr\/index.php\/wp-json\/wp\/v2\/pages\/20\/revisions\/3878"}],"wp:attachment":[{"href":"https:\/\/mlv.cs.duth.gr\/index.php\/wp-json\/wp\/v2\/media?parent=20"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}