Closing the loop: A systematic review of artificial intelligence in circular e-waste management Tala Jano, Aya Nabil Sayed, Md Mosarrof Hossen, Christos Sardianos, Ridha Hamila, Faycal Bensaali, Iraklis Varlamis, George Dimitrakopoulos Waste Management, 2026 The proliferation of technological advancements, knitted with volatile consumption patterns and poor end-of-life management of discarded electronics, is currently outpacing sustainability transitions, putting increasing strain on finite material resources and heightening ecological vulnerability. This, in turn, has made electronic waste a stealth contributor to climate change with adverse impacts on the environment, economy, and society at large. This reality underscores the urgent need for a strategic shift from linear waste-disposal methods to circular pathways, where Artificial Intelligence (AI) can build more sustainable feedback loops. At the nexus of AI and circular e-waste management, this study systematically reviews 147 articles from 2019 to October 2025. The analysis reveals a steady increase in AI adoption, particularly in deep learning-based detection and classification applications. To structure the evidence from the literature, a six-tier taxonomy is proposed, encompassing AI methods, lifecycle stages, data, waste types, limitations, challenges, and future pathways and opportunities. Beyond technical interventions, systemic and operational barriers that demand strategic levers to address regulatory ambiguities, legislative gaps, managerial inefficiencies, and logistical fragmentation are elucidated. These challenges underpin data availability and generalizability, as well as the lack of standardization, interoperability gaps, and barriers to the ethical and regulatory adoption of AI. In practice, these constraints limit the development of uncertainty-aware electronic waste systems capable of functioning under realistic operational dynamics. To this end, the paper reframes AI-based systems from terminal sinks to regenerative loops, aligning technological progress with sustainable electronic waste management.
Illicit Object Detection in X-Ray Imaging Using Deep Learning Techniques: A Comparative Evaluation Jorgen Cani, Christos Diou, Spyridon Evangelatos, Vasileios Argyriou, Panagiotis Radoglou-Grammatiki, Panagiotis Sarigiannidis, Iraklis Varlamis, Georgios Th. Papadopoulos IEEE Access, 2026 Automated X-ray inspection is crucial for efficient and unobtrusive security screening in various public settings. However, challenges such as object occlusion/overlap, variations in the physical properties of the items of interest, diversity in the types of X-ray scanning devices used, and limited training data hinder accurate and reliable detection of illicit items. Despite the large body of research works in the field, the reported experimental evaluation is often incomplete, while the derived outcomes are frequently conflicting. In order to shed light on the research landscape of this field and to facilitate further research, a systematic, detailed, and thorough comparative evaluation study of recent Deep Learning (DL)-based methods for X-ray object detection is conducted in this work. For achieving this, a comprehensive evaluation framework is developed, composed of the following building blocks: a) Six of the most recent, large-scale and widely used public datasets for X-ray illicit item detection (namely, OPIXray, CLCXray, SIXray, EDS, HiXray, and PIDray), b) Ten different state-of-the-art object detection schemes, covering all main categories present in the literature, including generic Convolutional Neural Network (CNN), custom (X-ray-specific) CNN, generic transformer and generic hybrid CNN-transformer architectures, and c) Various detection (mAP<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">50</sup> and mAP<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">50:95</sup> mean Average Precision (mAP)) and time/computational-complexity (inference time (ms), parameter size (M), and computational load (GFLOPS)) performance metrics. A thorough analysis of the computed experimental results leads to the extraction of critical observations and detailed insights, emphasizing on the following key aspects: a) Overall behavior of the various object detection schemes, b) Object-level detection performance investigation, c) Dataset-specific observations, d) Statistical variance and empirical reliability examination, and e) Time efficiency and computational complexity analysis. In order to support reproducibility of the reported experimental results and to promote research in the field, the evaluation framework code and model weights are publicly available at https://github.com/jgenc/xray-comparative-evaluation, while a broad set of precomputed results (for all employed detectors and datasets) can be found at https://jgenc.github.io/ceasefire_demo/#/.
The role of energy consumption prediction in Green Orchestration Ioannis Korontanis, Ioannis Kontopoulos, Konstantinos Tserpes, Iraklis Varlamis Maiot 2025 Proceedings of the 2025 International Workshop on Middleware for Autonomous Aiot Systems in the Computing Continuum, 2025 This paper introduces a specialized profiler for energy consumption prediction and proposes a Conceptual Green Orchestration Pipeline that utilizes the profiler as middleware. The system's architecture integrates a standard orchestrator (like Kubernetes) with a local ONNX model registry, targeting edge devices for deployment. Crucially, the profiler demonstrated strong performance, achieving 89% accuracy in selecting the optimal deployment device for resource-intensive models, a significant improvement over the 33.4% accuracy seen with random placement.
PEPPER: Profiling-based Edge Placement and Partitioning for Deep Learning Execution Ioannis Korontanis, Ioannis Kontopoulos, Athina Zacharia, Antonios Makris, Christos Chronis, Maria Pateraki, Konstantinos Tserpes, Iraklis Varlamis Iot 2025 Proceedings of the 15th International Conference on the Internet of Things 2025, 2025 Unlocking the full potential of AI at the edge requires overcoming the fundamental challenge of running complex models efficiently on devices with limited computational power. In this work, the challenge of optimizing the deployment of deep learning models in resource-constrained environments is addressed. A novel pipeline is proposed for profiling and partitioning ONNX models, to enhance inference efficiency across heterogeneous hardware platforms. Optimal split points within the deep learning models are identified through the application of Tarjan’s Bridge-Finding Algorithm, and the inference times of the models are predicted per device based on the respective characteristics and CPU load. For the prediction of inference times, the XGBoost algorithm is employed. The effectiveness of the proposed approach is validated through experiments conducted on real-world edge devices, demonstrating that highly efficient and adaptable deployment of complex deep learning models can be achieved in such environments.
A randomized clinical trial of a dietary intervention and mental health associations in adults with increased genetic risk for obesity Maria Kafyra, Ioanna Panagiota Kalafati, Iraklis Varlamis, Andriana. C. Kaliora, Panagiotis Moulos, George V. Dedoussis Scientific Reports, 2025 Nutrigenetic parameters influence quality of life (QoL) by highlighting how genetic variations affect weight management and allow for personalized dietary recommendations that enhance health status. The present study investigates the effect of a dietary intervention on the SF-12 physical (SF-PCS-12) and mental components (SF-MCS-12) of adults with overweight and obesity, at high genetic risk for increased Body Mass Index (BMI). Data from 80 participants were analyzed, all of whom were randomized at baseline to follow either a high-protein or high-carbohydrate hypocaloric diet for three months. QoL was measured using the SF-12 questionnaire, with self-reported assessments conducted at baseline and at the end of each intervention month. Differences pre- and post- intervention were assessed using the Wilcoxon signed-rank test. Additionally, 10 BMI-related variants were investigated for potential effects on the observed differences in SF-PCS-12 and SF-MCS-12, via linear regressions using the Plink software. Overall, no statistically significant differences were observed in SF-PCS-12 and SF-MCS-12 scores post-intervention. However, while SF-PCS-12 showed less improvement in high-risk individuals, participants with high-BMI risk alleles demonstrated a notable increase in SF-MCS-12 scores. This finding suggests that dietary interventions may positively impact mental health, even in individuals with a heightened genetic risk for obesity.
Differentiable Physics Training Method for Robot Motion Planning Laura Leja, Guntis Vilnis Strazds, Christos Chronis, Iraklis Varlamis, Kārlis Freivalads IFAC Papersonline, 2025 Robotic manipulation in dynamic and unstructured environments remains a challenging problem, particularly for tasks that require precise interaction with moving targets. This work presents a robotic motion planning algorithm using differentiable simulation, where a robotic arm is trained to perform peg insertion tasks in an industrial setting where the insertion target is moving. Successfully performing such tasks requires real-time tracking of the target and continuous adaptation of the robot’s trajectory. To achieve this, we train a neural network controller to generate actions along the trajectory, using differentiable physics to enable end-to-end optimization. The optimization objectives and non-penetration constraints are expressed in a differentiable way to perform gradient backpropagation through the entire simulation. Initial experimental results demonstrate that the proposed training approach is both fast and stable, yielding precise robot positioning and smooth trajectories, exceeding the results obtained with a more traditional Reinforcement Learning baseline.
Applied AI for Adaptive Robotic Conveyor-Feeding Guntis V. Strazds, Katrīna Viltrake, Gergely Hollósi, Laura Leja, Arnis Priedītis, Diāna Dupļevska, Makss Meiers, Christos Chronis, Iraklis Varlamis, George Dimitrakopoulos, Karlis Freivalds IFAC Papersonline, 2025 This paper presents a modular AI-driven robotic system for dynamic production line tasks, integrating real-time vision, adaptive motion planning, and making use of LLM-based code generation to facilitate reconfiguration for new products or tasks. The architecture of the overall system is described, as are design considerations and initial evaluation results for AI-based components for bottle pose estimation, tracking of moving sockets into which bottles need to be inserted, and training of a neural network for dynamic robot-motion control.
Preface Proceedings 28th Pan Hellenic Conference on Progress in Computing and Informatics with International Participation PCI 2024, 2025
Generating Job Recommendations Based on User Personality and Gallup Tests Shakhmar Sarsenbay, Asset Kabdiyev, Iraklis Varlamis, Christos Sardianos, Cemil Turan, Bobir Razhametov, Yermek Kazym Algorithms, 2025 This paper introduces a novel approach to job recommendation systems by incorporating personality traits evaluated through the Gallup CliftonStrengths assessment, aiming to enhance the traditional matching process beyond skills and qualifications. Unlike broad models like the Big Five, Gallup’s CliftonStrengths assesses 34 specific talents (e.g., ‘Analytical’, ‘Empathy’), enabling finer-grained, actionable job matches. While existing systems focus primarily on hard skills, this paper argues that personality traits—such as those measured by the Gallup test—play a crucial role in determining career satisfaction and long-term job retention. The proposed approach offers a more granular and actionable method for matching candidates with job opportunities that align with their natural strengths. Leveraging Gallup tests, we develop a job-matching approach that identifies personality traits and integrates them with recommendation algorithms to generate a list of the most suitable specializations for the user. By utilizing a GPT-4 model to process job descriptions and rank relevant personality traits, the system generates more personalized recommendations that account for both hard and soft skills. The empirical experiments demonstrate that this integration can improve the accuracy and relevance of job recommendations, leading to better career outcomes. The paper contributes to the field by offering a comprehensive framework for personality-based job matching and validating its effectiveness, paving the way for a more holistic approach to recruitment and talent management.
Continual learning for energy management systems: A review of methods and applications, and a case study Aya Nabil Sayed, Yassine Himeur, Iraklis Varlamis, Faycal Bensaali Applied Energy, 2025 An intelligent system must incrementally acquire, update, accumulate, and exploit knowledge to navigate the real world’s intricacies. This trait is frequently referred to as Continual Learning (CL), and it can be limited by catastrophic forgetting, a phenomenon in which learning a new task acutely reduces the system’s performance on prior tasks. Numerous strategies have been developed to address this issue, as CL is essential for developing Artificial Intelligence (AI) systems that adapt to dynamic environments. This study examines the practical applications of CL, concentrating on energy management systems and their integration with Deep Learning (DL) models. Energy management systems are strategies and methods for monitoring, controlling, and optimizing energy use within a system or organization. The literature is systematically analyzed, highlighting methods such as replay techniques, regularization strategies, and architectural adaptations that address the challenges of catastrophic forgetting. Moreover, the review encompasses various energy-related applications, including non-intrusive load monitoring, demand-side management, fault/anomaly detection, load forecasting/prediction, and renewable energy integration. Additionally, a case study on anomaly detection in energy systems is conducted, comparing different CL approaches. The case study findings aim to bridge the gap between theoretical advancements and real-world applications, providing insights and guidelines for implementing CL in diverse fields. Finally, this survey identifies key challenges that impede the deployment of CL and suggests potential directions to enhance its implementation in the energy management sector. • Providing the first review of Continual Learning (CL) for energy management systems. • Conducting a case study comparing various CL methods for energy anomaly detection. • Identifying key challenges that could hinder the deployment of CL in the energy domain. • Exploring future directions, e.g., meta-learning, federated CL, and efficient task allocation.
Machine learning for sustainable development in electronics Iraklis Varlamis, Ilias Panagiotopoulos, Christos Chronis, George Dimitrakopoulos, Faycal Bensaali Harnessing Automation and Machine Learning for Resource Recovery and Value Creation from Waste to Value, 2025
Public Space Security Management Using Digital Twin Technologies Stylianos Zindros, Christos Chronis, Panagiotis Radoglou-Grammatikis, Vasileios Argyriou, Panagiotis Sarigiannidis, Iraklis Varlamis, Georgios Th. Papadopoulos International Conference on Digital Signal Processing DSP, 2025
X-Ray Illicit Object Detection Using Hybrid CNN-Transformer Neural Network Architectures Jorgen Cani, Christos Diou, Spyridon Evangelatos, Panagiotis Radoglou-Grammatikis, Vasileios Argyriou, Panagiotis Sarigiannidis, Iraklis Varlamis, Georgios Th. Papadopoulos Proceedings of the IEEE International Conference on Big Data Computing Service and Applications Bigdataservice, 2025
TRIFFID: Autonomous Robotic Aid For Increasing First Responders Efficiency Jorgen Cani, Panagiotis Koletsis, Konstantinos Foteinos, Ioannis Kefaloukos, Lampros Argyriou, Manolis Falelakis, Iván Del Pino, Angel Santamaria-Navarro, Martin Čech, Ondřej Severa, Alessandro Umbrico, Francesca Fracasso, Andre A Orlandini, Dimitrios Drakoulis, Evangelos Markakis, Iraklis Varlamis, Georgios Th. Papadopoulos Eeite 2025 6th International Conference in Electronic Engineering and Information Technology, 2025
From STEAM to Machine: Emissions control in the shipping 4.0 era Dimitrios Kaklis, Takis J. Varelas, Iraklis Varlamis, Pavlos Eirinakis, George Giannakopoulos, Constantine V. Spyropoulos Sname 8th International Symposium on Ship Operations Management and Economics Some 2023, 2023
Preface to the Proceedings of the 1st International Workshop on Computational Intelligence for Process Mining (CI4PM 2022) and 1st International Workshop on Pervasive Artificial Intelligence (PAI 2022) Ceur Workshop Proceedings, 2023
Developing a news classifier for Greek using BERT George Gkolfopoulos, Iraklis Varlamis 7th South East Europe Design Automation Computer Engineering Computer Networks and Social Media Conference Seeda Cecnsm 2022, 2022
AI-as-a-Service Toolkit for Human-Centered Intelligence in Autonomous Driving Valerio De Caro, Saira Bano, Achilles Machumilane, Alberto Gotta, Pietro Cassara, Antonio Carta, Rudy Semola, Christos Sardianos, Christos Chronis, Iraklis Varlamis, Konstantinos Tserpes, Vincenzo Lomonaco, Claudio Gallicchio, Davide Bacciu 2022 IEEE International Conference on Pervasive Computing and Communications Workshops and Other Affiliated Events Percom Workshops 2022, 2022
sustAGE 1.0 – First Prototype, Use Cases, and Usability Evaluation Adria Mallol, Iraklis Varlamis, Maria Pateraki, Manolis Lourakis, Georgios Athanassiou, Michail Maniadakis, Konstantinos Papoutsakis, Thodoris Papadopoulos, Anastasia Semertzidou, Nicholas Cummins, Bj Schuller, Ion Karolos, Christos Pikridas, Petros Patias, Spyros Vantolas, Leonidas Kallipolitis, Frank Werner, Antonio Ascolese, Vito Nitti Applied Human Factors and Ergonomics International, 2022
Effect of Mastiha supplementation on NAFLD: The MAST4HEALTH Randomised, Controlled Trial Charalampia Amerikanou, Stavroula Kanoni, Andriana C. Kaliora, Angela Barone, Mladen Bjelan, Giuseppe D'Auria, Aristea Gioxari, María José Gosalbes, Sofia Mouchti, Maria G. Stathopoulou, Beatriz Soriano, Stefan Stojanoski, Rajarshi Banerjee, Maria Halabalaki, Eleni V. Mikropoulou, Aimo Kannt, John Lamont, Carlos Llorens, Fernando Marascio, Miriam Marascio, Francisco J. Roig, Ilias Smyrnioudis, Iraklis Varlamis, Sophie Visvikis‐Siest, Milan Vukic, Natasa Milic, Milica Medic‐Stojanoska, Lucia Cesarini, Jonica Campolo, Amalia Gastaldelli, Panos Deloukas, Maria Giovanna Trivella, M. Pilar Francino, George V. Dedoussis, MAST4HEALTH consortium Molecular Nutrition and Food Research, 2021
Micro-moment-based Interventions for a Personalized Support of Healthy and Sustainable Ageing at Work: Development and Application of a Context-sensitive Recommendation Framework Icete International Conference on E Business and Telecommunication Networks International Joint Conference on Computational Intelligence, 2021
A Survey of UAS Technologies to Enable Beyond Visual Line Of Sight (BVLOS) Operations International Conference on Vehicle Technology and Intelligent Transport Systems Vehits Proceedings, 2021
Dependable Integration Concepts for Human-Centric AI-Based Systems Georg Macher, Siranush Akarmazyan, Eric Armengaud, Davide Bacciu, Calogero Calandra, Herbert Danzinger, Patrizio Dazzi, Charalampos Davalas, Maria Carmela De Gennaro, Angela Dimitriou, Juergen Dobaj, Maid Dzambic, Lorenzo Giraudi, Sylvain Girbal, Dimitrios Michail, Roberta Peroglio, Rosaria Potenza, Farank Pourdanesh, Matthias Seidl, Christos Sardianos, Konstantinos Tserpes, Jakob Valtl, Iraklis Varlamis, Omar Veledar Lecture Notes in Computer Science Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics, 2021
Real-time personalised energy saving recommendations Christos Sardianos, Christos Chronis, Iraklis Varlamis, George Dimitrakopoulos, Yassine Himeur, Abdullah Alsalemi, Faycal Bensaali, Abbes Amira Proceedings IEEE Congress on Cybermatics 2020 IEEE International Conferences on Internet of Things Ithings 2020 IEEE Green Computing and Communications Greencom 2020 IEEE Cyber Physical and Social Computing Cpscom 2020 and IEEE Smart Data Smartdata 2020, 2020
REHAB-C: Recommendations for Energy HABits Change Christos Sardianos, Iraklis Varlamis, George Dimitrakopoulos, Dimosthenis Anagnostopoulos, Abdullah Alsalemi, Faycal Bensaali, Yassine Himeur, Abbes Amira Future Generation Computer Systems, 2020
Data Analytics, Automations, and Micro-Moment Based Recommendations for Energy Efficiency Christos Sardianos, Iraklis Varlamis, Christos Chronis, George Dimitrakopoulos, Yassine Himeur, Abdullah Alsalemi, Faycal Bensaali, Abbes Amira Proceedings 2020 IEEE 6th International Conference on Big Data Computing Service and Applications Bigdataservice 2020, 2020
A model for predicting room occupancy based on motion sensor data Christos Sardianos, Iraklis Varlamis, Christos Chronis, George Dimitrakopoulos, Yassine Himeur, Abdullah Alsalemi, Faycal Bensaali, Abbes Amira 2020 IEEE International Conference on Informatics Iot and Enabling Technologies Iciot 2020, 2020
Stop-and-move sequence expressions over semantic trajectories Yenier Torres Izquierdo, Grettel Monteagudo García, Marco A. Casanova, Luiz André P. Paes Leme, Christos Sardianos, Konstantinos Tserpes, Iraklis Varlamis, Lívia C. Ruback Rodrigues International Journal of Geographical Information Science, 2020
Boosting domestic energy efficiency through accurate consumption data collection Abdullah Alsalemi, Mona Ramadan, Faycal Bensaali, Abbes Amira, Christos Sardianos, Iraklis Varlamis, George Dimitrakopoulos Proceedings 2019 IEEE Smartworld Ubiquitous Intelligence and Computing Advanced and Trusted Computing Scalable Computing and Communications Internet of People and Smart City Innovation Smartworld Uic Atc Scalcom Iop SCI 2019, 2019
The effect of global and local influence models on the quality of recommendations Nikolaos Mantas, Malamati Louta, Magdalini Eirinaki, Iraklis Varlamis, George Karetsos Proceedings of the 2019 International Symposium on Performance Evaluation of Computer and Telecommunication Systems Spects 2019 Part of Summersim 2019 Multiconference, 2019
SIMULATING APPLIANCE-BASED POWER CONSUMPTION RECORDS FOR ENERGY EFFICIENCY AWARENESS Energy Proceedings, 2019
Biosensors and Internet of Things in smart healthcare applications: challenges and opportunities Maria Pateraki, Konstantinos Fysarakis, Vangelis Sakkalis, Georgios Spanoudakis, Iraklis Varlamis, Michail Maniadakis, Manolis Lourakis, Sotiris Ioannidis, Nicholas Cummins, Björn Schuller, Evangelos Loutsetis, Dimitrios Koutsouris Wearable and Implantable Medical Devices Applications and Challenges, 2019
Extracting user habits from google maps history logs Christos Sardianos, Iraklis Varlamis, Grigoris Bouras Proceedings of the 2018 IEEE ACM International Conference on Advances in Social Networks Analysis and Mining Asonam 2018, 2018
Document clustering as a record linkage problem Nikiforos Pittaras, George Giannakopoulos, Leonidas Tsekouras, Iraklis Varlamis Proceedings of the ACM Symposium on Document Engineering 2018 Doceng 2018, 2018
Detecting search and rescue missions from AIS data Iraklis Varlamis, Konstantinos Tserpes, Christos Sardianos Proceedings IEEE 34th International Conference on Data Engineering Workshops Icdew 2018, 2018
Parallelization of large-scale drug-protein binding experiments Antonios Makris, Dimitrios Michail, Iraklis Varlamis, Chronis Dimitropoulos, Konstantinos Tserpes, George Tsatsaronis, Joachim Haupt, Mark Sawyer Proceedings 2017 International Conference on High Performance Computing and Simulation Hpcs 2017, 2017
A graph-based text similarity measure that employs named entity information Institute of Informatics, Telecommunications, N.C.S.R. “Demokritos”, Greece, Leonidas Tsekouras, Iraklis Varlamis, Department of Informatics, Telematics, Harokopio University of Athens, Greece, George Giannakopoulos, Institute of Informatics, Telecommunications, N.C.S.R. “Demokritos”, Greece International Conference Recent Advances in Natural Language Processing Ranlp, 2017
PRO-Fit: Exercise with friends Saumil Dharia, Vijesh Jain, Jvalant Patel, Jainikkumar Vora, Rizen Yamauchi, Magdalini Eirinaki, Iraklis Varlamis Proceedings of the 2016 IEEE ACM International Conference on Advances in Social Networks Analysis and Mining Asonam 2016, 2016
A tool for the visualisation of public opinion Konstantinos Soulis, Iraklis Varlamis, Andreas Giannakoulopoulos, Filippos Charatsev International Journal of Electronic Governance, 2013
PONTE: A context-aware approach for automated clinical trial protocol design Persdb 2012 6th International Workshop on Personalized Access Profile Management and Context Awareness in Databases in Conjunction with VLDB 2012, 2012
Word sense disambiguation as an integer linear programming problem Vicky Panagiotopoulou, Iraklis Varlamis, Ion Androutsopoulos, George Tsatsaronis Lecture Notes in Computer Science Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics, 2012
Efficient community detection using power graph analysis George Tsatsaronis, Matthias Reimann, Iraklis Varlamis, Orestis Gkorgkas, Kjetil Nørvåg International Conference on Information and Knowledge Management Proceedings, 2011
A knowledge-based semantic kernel for text classification Jamal Abdul Nasir, Asim Karim, George Tsatsaronis, Iraklis Varlamis Lecture Notes in Computer Science Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics, 2011
How to become a group leader? Or modeling author types based on graph mining George Tsatsaronis, Iraklis Varlamis, Sunna Torge, Matthias Reimann, Kjetil Nørvåg, Michael Schroeder, Matthias Zschunke Lecture Notes in Computer Science Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics, 2011
Scalable semantic annotation of text using lexical and Web resources Elias Zavitsanos, George Tsatsaronis, Iraklis Varlamis, Georgios Paliouras Lecture Notes in Computer Science Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics, 2010
Semanticrank: Ranking keywords and sentences using semantic graphs Coling 2010 23rd International Conference on Computational Linguistics Proceedings of the Conference, 2010
Preface Anargyros Chryssanthou, Ioannis Apostolakis, Iraklis Varlamis Certification and Security in Health Related Web Applications Concepts and Solutions, 2010
Quality of content in Web 2.0 applications Iraklis Varlamis Lecture Notes in Computer Science Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics, 2010
Semantic relatedness hits bibliographic data George Tsatsaronis, Iraklis Varlamis, Sofia Stamou, Kjetil Nørvåg, Michalis Vazirgiannis International Conference on Information and Knowledge Management Proceedings, 2009
Crossing the Ts and closing the tags: Improving webstandards compliance in open source e-learning platforms 8th European Conference on Elearning 2009 Ecel 2009, 2009
Omiotis: A thesaurus-based measure of text relatedness George Tsatsaronis, Iraklis Varlamis, Michalis Vazirgiannis, Kjetil Nørvåg Lecture Notes in Computer Science Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics, 2009
Word sense disambiguation with semantic networks George Tsatsaronis, Iraklis Varlamis, Michalis Vazirgiannis Lecture Notes in Computer Science Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics, 2008
Bridging XML-Schema and relational databases. A system for generating and manipulating relational databases using valid XML documents Proceedings of the ACM Symposium on Document Engineering, 2001
Web document searching using enhanced hyperlink semantics based on XML Proceedings of the International Database Engineering and Applications Symposium Ideas, 2001
Distributed virtual reality authoring interfaces for the WWW IEEE International Conference on Multi Media and Expo, 2000
RECENT SCHOLAR PUBLICATIONS
Closing the loop: A systematic review of artificial intelligence in circular e-waste management T Jano, AN Sayed, MM Hossen, C Sardianos, R Hamila, F Bensaali, ... Waste Management 214, 115392 , 2026 2026 Citations: 2
Illicit object detection in x-ray imaging using deep learning techniques: A comparative evaluation J Cani, C Diou, S Evangelatos, V Argyriou, P Radoglou-Grammatikis, ... IEEE Access , 2026 2026 Citations: 8
An open-source and open-design robot for STEM education in K-12 I Varlamis, C Chronis, C Sofianopoulou, E Papageorgiou Social robots and artificial intelligence in education: Integrating AI in K … , 2026 2026 Citations: 5
The role of energy consumption prediction in Green Orchestration I Korontanis, I Kontopoulos, K Tserpes, I Varlamis Proceedings of the Middleware for Autonomous AIoT Systems in the Computing … , 2025 2025
Legal Assistance in Low-Resource Languages: Evaluating RAG and Fine-Tuned LLMs for Greek e-Governance M Tsourma, D Michail, I Varlamis, A Drosou, D Tzovaras 2025 3rd International Conference on Foundation and Large Language Models … , 2025 2025 Citations: 1
PEPPER: Profiling-based Edge Placement and Partitioning for Deep Learning Execution I Korontanis, I Kontopoulos, A Zacharia, A Makris, C Chronis, M Pateraki, ... Proceedings of the 15th International Conference on the Internet of Things … , 2025 2025
Performance Analysis of Filter Pruning Methods for Edge Classification Tasks A Stefanidou, I Kontopoulos, K Tserpes, I Varlamis 2025 IEEE Intelligent Mobile Computing (MobileCloud), 51-58 , 2025 2025 Citations: 1
X-ray illicit object detection using hybrid CNN-transformer neural network architectures J Cani, C Diou, S Evangelatos, P Radoglou-Grammatikis, V Argyriou, ... 2025 IEEE 11th International Conference on Big Data Computing Service and … , 2025 2025 Citations: 6
Visual hand gesture recognition with deep learning: A comprehensive review of methods, datasets, challenges and future research directions K Foteinos, M Linardakis, P Radoglou-Grammatikis, V Argyriou, ... arXiv preprint arXiv:2507.04465 , 2025 2025 Citations: 10
Public space security management using digital twin technologies S Zindros, C Chronis, P Radoglou-Grammatikis, V Argyriou, ... 2025 25th International Conference on Digital Signal Processing (DSP), 1-5 , 2025 2025 Citations: 3
Classification of Greek News Articles by Text Type Using Open Large Language Models I Veneti, I Varlamis, GT Papadopoulos 2025 6th International Conference in Electronic Engineering & Information … , 2025 2025
TRIFFID: Autonomous Robotic Aid For Increasing First Responders Efficiency J Cani, P Koletsis, K Foteinos, I Kefaloukos, L Argyriou, M Falelakis, ... 2025 6th International Conference in Electronic Engineering & Information … , 2025 2025 Citations: 10
Generating Job Recommendations Based on User Personality and Gallup Tests S Sarsenbay, A Kabdiyev, I Varlamis, C Sardianos, C Turan, ... Algorithms 18 (5), 275 , 2025 2025 Citations: 6
State of play and future directions in industrial computer vision AI standards A Stefanidou, P Radoglou-Grammatikis, V Argyriou, P Sarigiannidis, ... 2025 IEEE Conference on Artificial Intelligence (CAI), 1528-1533 , 2025 2025
A randomized clinical trial of a dietary intervention and mental health associations in adults with increased genetic risk for obesity M Kafyra, IP Kalafati, I Varlamis, AC Kaliora, P Moulos, GV Dedoussis Scientific reports 15 (1), 14188 , 2025 2025 Citations: 1
Messy Data in Education: Enhancing Data Science Literacy Through Real-World Datasets in a Master’s Program I Varlamis Education Sciences 15 (4), 500 , 2025 2025 Citations: 8
Continual learning for energy management systems: A review of methods and applications, and a case study AN Sayed, Y Himeur, I Varlamis, F Bensaali Applied Energy 384, 125458 , 2025 2025 Citations: 23
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Applications of knowledge distillation in remote sensing: A survey Y Himeur, N Aburaed, O Elharrouss, I Varlamis, S Atalla, W Mansoor, ... Information Fusion 115, 102742 , 2025 2025 Citations: 28
Deploying Large AI Models on Micro-Electronics with RISC-V: Federated Learning for Energy Monitoring and Robotics C Chronis, I Varlamis, K Tserpes, G Dimitrakopoulos, F Bensaali 10th International Congress on Information and Communication Technology … , 2025 2025
MOST CITED SCHOLAR PUBLICATIONS
Fake news detection: A hybrid CNN-RNN based deep learning approach JA Nasir, OS Khan, I Varlamis International journal of information management data insights 1 (1), 100007 , 2021 2021 Citations: 826
Recommender systems for large-scale social networks: A review of challenges and solutions M Eirinaki, J Gao, I Varlamis, K Tserpes Future generation computer systems 78, 413-418 , 2018 2018 Citations: 266
SEWeP: using site semantics and a taxonomy to enhance the Web personalization process M Eirinaki, M Vazirgiannis, I Varlamis Proceedings of the ninth ACM SIGKDD international conference on Knowledge … , 2003 2003 Citations: 227
A survey of recommender systems for energy efficiency in buildings: Principles, challenges and prospects Y Himeur, A Alsalemi, A Al-Kababji, F Bensaali, A Amira, C Sardianos, ... Information Fusion 72, 1-21 , 2021 2021 Citations: 213
Text relatedness based on a word thesaurus G Tsatsaronis, I Varlamis, M Vazirgiannis Journal of Artificial Intelligence Research 37, 1-39 , 2010 2010 Citations: 204
Bridging XML-schema and relational databases: a system for generating and manipulating relational databases using valid XML documents I Varlamis, M Vazirgiannis Proceedings of the 2001 ACM Symposium on Document engineering, 105-114 , 2001 2001 Citations: 171
The present and future of standards for e-learning technologies I Varlamis, I Apostolakis Interdisciplinary Journal of E-Learning and Learning Objects 2 (1), 59-76 , 2006 2006 Citations: 164
Blockchain-based recommender systems: Applications, challenges and future opportunities Y Himeur, A Sayed, A Alsalemi, F Bensaali, A Amira, I Varlamis, M Eirinaki, ... Computer Science Review 43 , 2022 2022 Citations: 159
Blogrank: ranking weblogs based on connectivity and similarity features A Kritikopoulos, M Sideri, I Varlamis Proceedings of the 2nd international workshop on Advanced architectures and … , 2006 2006 Citations: 147
A Trust-Aware System for Personalized User Recommendations in Social Networks M Eirinaki, MD Louta, I Varlamis IEEE Transactions on Systems, Man, and Cybernetics: Systems 44 (4), 409 - 421 , 2014 2014 Citations: 144
The emergence of explainability of intelligent systems: Delivering explainable and personalized recommendations for energy efficiency C Sardianos, I Varlamis, C Chronis, G Dimitrakopoulos, A Alsalemi, ... International Journal of Intelligent Systems 36 (2), 656-680 , 2021 2021 Citations: 143
THESUS: Organizing Web document collections based on link semantics M Halkidi, B Nguyen, I Varlamis, M Vazirgiannis The VLDB Journal 12 (4), 320-332 , 2003 2003 Citations: 129
Biosensors and Internet of Things in smart healthcare applications: Challenges and opportunities M Pateraki, K Fysarakis, V Sakkalis, G Spanoudakis, I Varlamis, ... Wearable and implantable medical devices, 25-53 , 2020 2020 Citations: 120
The future of intelligent transport systems GJ Dimitrakopoulos, L Uden, I Varlamis Elsevier , 2020 2020 Citations: 110
Multimodal explainable artificial intelligence: A comprehensive review of methodological advances and future research directions N Rodis, C Sardianos, P Radoglou-Grammatikis, P Sarigiannidis, ... IEEE Access , 2024 2024 Citations: 109
SemanticRank: ranking keywords and sentences using semantic graphs G Tsatsaronis, I Varlamis, K Nørvåg Proceedings of the 23rd international conference on computational … , 2010 2010 Citations: 100
A survey on the use of large language models (llms) in fake news E Papageorgiou, C Chronis, I Varlamis, Y Himeur Future Internet 16 (8), 298 , 2024 2024 Citations: 94
A distributed framework for extracting maritime traffic patterns I Kontopoulos, I Varlamis, K Tserpes International Journal of Geographical Information Science 35 (4), 767-792 , 2021 2021 Citations: 86
Face mask detection in smart cities using deep and transfer learning: Lessons learned from the COVID-19 pandemic Y Himeur, S Al-Maadeed, I Varlamis, N Al-Maadeed, K Abualsaud, ... Systems 11 (2), 107 , 2023 2023 Citations: 82
Smart fusion of sensor data and human feedback for personalised energy-saving recommendations I Varlamis, C Sardianos, C Chronis, G Dimitrakopoulos, Y Himeur, ... Applied Energy 305, 117775 , 2021 2021 Citations: 82
