@conference {180, title = {Cross-Domain Recommendations Using Attention and~Multitask Learning}, booktitle = {Artificial Intelligence Applications and Innovations}, year = {2025}, publisher = {Springer Nature Switzerland}, organization = {Springer Nature Switzerland}, address = {Limassol}, abstract = {In this study, we propose a novel deep learning method for cross-domain recommendations that effectively combines attention mechanisms, autoencoders, and multitask learning. Our approach leverages multiple datasets from diverse domains and incorporates domain-specific encoders, a shared self-attention mechanism, and a multilayer perceptron (MLP) to capture both intra-domain and inter-domain relationships. By jointly modeling these interactions, we improve recommendation accuracy across domains. Experimental results using the MovieLens dataset demonstrate that our proposed cross-domain recommendation system outperforms traditional approaches including matrix factorization, standard MLPs, and self-attention-based baselines.}, isbn = {978-3-031-96228-8}, doi = {https://doi.org/10.1007/978-3-031-96228-8_29}, author = {Papachronopoulos, Gerasimos and Gkillas, Alexandros and Kosmopoulos, Dimitrios}, editor = {Maglogiannis, Ilias and Iliadis, Lazaros and Andreou, Andreas and Papaleonidas, Antonios} } @article {184, title = {A cross-domain recommender system using deep coupled autoencoders}, journal = {ACM Transactions on Recommender Systems}, year = {2025}, abstract = {Long-standing data sparsity and cold-start constitute thorny and perplexing problems for the recommendation systems. Cross-domain recommendation as a domain adaptation framework has been utilized to effectively address these challenging issues, by exploiting information from multiple domains. In this study, an item-level relevance cross-domain recommendation task is explored, where two related domains, that is, the source and the target domain contain common items. Additionally, a user-level relevance scenario is considered, where the two related domains contain common users. In light of these scenarios, two novel coupled autoencoder-based deep learning methods are proposed for cross-domain recommendation. The first method aims to simultaneously learn a pair of autoencoders in order to reveal the intrinsic representations in the source and target domains, along with a coupled mapping function to model the non-linear relationships between these representations. The second method is derived based on a new joint regularized optimization problem, which employs two autoencoders to generate in a deep and non-linear manner the user and item-latent factors, while at the same time a data-driven function is learnt to map the latent factors across domains. Extensive numerical experiments are conducted illustrating the superior performance of our proposed methods compared to several state-of-the-art cross-domain recommendation frameworks for the extreme cold start scenario.}, author = {Alexandros Gkillas and Dimitrios Kosmopoulos} } @article {166, title = {Cost-efficient coupled learning methods for recovering near-infrared information from RGB signals: Application in precision agriculture}, journal = {Computers and Electronics in Agriculture}, volume = {209}, year = {2023}, abstract = {[size= 16px; box-sizing: border-box; margin: 0px; padding: 0px; caret-color: $\#$2e2e2e; color: $\#$2e2e2e; font-family: ElsevierGulliver, Georgia, {\textquoteright}Times New Roman{\textquoteright}, STIXGeneral, {\textquoteright}Cambria Math{\textquoteright}, Arial, Helvetica, {\textquoteright}Lucida Sans Unicode{\textquoteright}, {\textquoteright}Microsoft Sans Serif{\textquoteright}, {\textquoteright}Segoe UI Symbol{\textquoteright}, {\textquoteright}Arial Unicode MS{\textquoteright}, serif][url=https://www.sciencedirect.com/topics/computer-science/multispectral-imaging]Multispectral imaging[/url] and the derived [/size][url=https://www.sciencedirect.com/topics/agricultural-and-biological-sciences/spectral-analysis]spectral analysis[/url] offer useful tools for revealing beneficial information for a variety of applications, e.g., precision agriculture, [url=https://www.sciencedirect.com/topics/computer-science/medical-imaging]medical imaging[/url] and [url=https://www.sciencedirect.com/topics/computer-science/autonomous-driving]autonomous driving[/url]. Contrary to mainstream RGB cameras that can capture information derived only from three bands within the visible spectrum, the multispectral cameras can offer better spectral resolution by utilizing the underlying information in the visible and the near-infrared spectrum. However, the cost of the multispectral cameras is very high and their mobility is limited due to their weight and their need for special hardware equipment. Considering the aforementioned limitations, we propose two low-cost and efficient methods to infer detailed [url=https://www.sciencedirect.com/topics/computer-science/spectral-information]spectral information[/url] outside the visible spectrum range by employing only an RGB camera. The proposed methods require significantly less training data, containing approximately [size= 16px; box-sizing: border-box; margin: 0px; padding: 0px; caret-color: $\#$2e2e2e; color: $\#$2e2e2e; font-family: ElsevierGulliver, Georgia, {\textquoteright}Times New Roman{\textquoteright}, STIXGeneral, {\textquoteright}Cambria Math{\textquoteright}, Arial, Helvetica, {\textquoteright}Lucida Sans Unicode{\textquoteright}, {\textquoteright}Microsoft Sans Serif{\textquoteright}, {\textquoteright}Segoe UI Symbol{\textquoteright}, {\textquoteright}Arial Unicode MS{\textquoteright}, serif]99.8\%[/size][size= 16px; box-sizing: border-box; margin: 0px; padding: 0px; caret-color: $\#$2e2e2e; color: $\#$2e2e2e; font-family: ElsevierGulliver, Georgia, {\textquoteright}Times New Roman{\textquoteright}, STIXGeneral, {\textquoteright}Cambria Math{\textquoteright}, Arial, Helvetica, {\textquoteright}Lucida Sans Unicode{\textquoteright}, {\textquoteright}Microsoft Sans Serif{\textquoteright}, {\textquoteright}Segoe UI Symbol{\textquoteright}, {\textquoteright}Arial Unicode MS{\textquoteright}, serif] less parameters compared to the competing [url=https://www.sciencedirect.com/topics/computer-science/deep-learning]deep learning[/url] approaches and can be deployed on various edge devices with computational and power constraints, e.g., mobile phones or unmanned drones for addressing problems in precision agriculture under real-field settings. Extensive numerical results demonstrate the efficacy of the proposed models to reconstruct images outside the visible spectrum. Additionally, the reconstructed images can be utilized to estimate the [url=https://www.sciencedirect.com/topics/agricultural-and-biological-sciences/normalized-difference-vegetation-index]normalized difference vegetation index[/url] (NDVI), which can reveal valuable information concerning the health of the monitored plants without the need of a multispectral camera.[/size]}, author = {Alexandros Gkillas and Dimitrios Kosmopoulos and Kostas Berberidis} } @article {164, title = {A Multispectral Dataset for the Detection of Tuta absoluta and Leveillula taurica in Tomato Plants}, journal = {Smart Agricultural Technology}, year = {2022}, pages = {100146}, abstract = {Tomato (Solanum lycopersicum) is one of the most important vegetables for human nutrition and its cultivation employs amounts of resources worldwide. However, tomato cultivation is plagued by several diseases and pests that increase production cost and introduce additional environmental and health risks due to pesticide use. Timely disease and pest detection is of high importance for tomato crop output and the environment, since plant protection input can be optimized. Here, we present a dataset of multispectral images (RGB and NIR) of tomato plants, at various stages of infection with Tuta absoluta and Leveillula taurica, which to our knowledge is unique. The dataset comprised of 263 images collected from a real greenhouse. Additionally, we applied a baseline Faster-RCNN object detector for the localization and classification lesions. Our experiments include (i) a version for the RGB channels and (ii) a custom backbone architecture version for feature fusion using the same Faster-RCNN head. Lastly, based on the detector{\textquoteright}s output, we compute an \>0.9 F1-score on binary classification, while mAP 18.5\% and mAP 20.2\% on detection, highlight the added value of NIR spectral bands for detecting these diseases.}, keywords = {Faster-RCNN, Feature fusion, Leaf Miner, Plant Disease Dataset, Powdery Mildew}, issn = {2772-3755}, doi = {https://doi.org/10.1016/j.atech.2022.100146}, url = {https://www.sciencedirect.com/science/article/pii/S2772375522001101}, author = {P.S. Georgantopoulos and D. Papadimitriou and C. Constantinopoulos and T. Manios and I.N. Daliakopoulos and D. Kosmopoulos} } @article {154, title = {A cross-domain recommender system using deep coupled autoencoders}, year = {2021}, month = {8 December 2021}, abstract = {Long-standing data sparsity and cold-start constitute thorny and perplexing problems for the recommendation systems. Cross-domain recommendation as a domain adaptation framework has been utilized to efficiently address these challenging issues, by exploiting information from multiple domains. In this study, an item-level relevance cross-domain recommendation task is explored, where two related domains, that is, the source and the target domain contain common items without sharing sensitive information regarding the users{\textquoteright} behavior, and thus avoiding the leak of user privacy. In light of this scenario, two novel coupled autoencoder-based deep learning methods are proposed for cross-domain recommendation. The first method aims to simultaneously learn a pair of autoencoders in order to reveal the intrinsic representations of the items in the source and target domains, along with a coupled mapping function to model the non-linear relationships between these representations, thus transferring beneficial information from the source to the target domain. The second method is derived based on a new joint regularized optimization problem, which employs two autoencoders to generate in a deep and non-linear manner the user and item latent factors, while at the same time a data-driven function is learnt to map the item-latent factors across domains. Extensive numerical experiments on two publicly available benchmark datasets are conducted illustrating the superior performance of our proposed methods compared to several state-of-the-art cross-domain recommendation frameworks.}, author = {Alexandros Gkillas and Dimitrios Kosmopoulos} } @proceedings {147, title = {A Method for Recovering Near Infrared Information from RGB Measurements with Application in Precision Agriculture}, journal = {European Signal Processing Conference}, year = {2021}, pages = {616-620}, publisher = {IEEE}, edition = {IEEE}, abstract = {[size= 13.008px]In this work we develop a cost-efficient coupled dictionary learning based method for reconstructing multispectral images using only a single RGB commercial camera, without requiring the sensitivity function of the camera sensor. Considering the very high cost, the acquisition time and reduced mobility of multispectral cameras we claim that this is a very attractive option. In contrast to other approaches, the proposed method is not limited only to spectral bands inside the visible spectrum, but it also considers an even more challenging task, that is the reconstruction of spectral bands outside the visible range closer to the near-infrared wavelengths of the spectrum. Extensive experiments with real data demonstrate the effectiveness and applicability of the proposed method in the precision agriculture domain. To this end, we calculate one of the most widely used vegetation indices, the normalized difference vegetation index (NVDI), which may be used for plant health monitoring.[/size]}, author = {A. Gkillas and D. Kosmopoulos and C. Constantinopoulos and D. Ampeliotis and K. Berberidis} } @article {26, title = {Multimodal and ontology-based fusion approaches of audio and visual processing for violence detection in movies}, journal = {Expert Systems with Applications}, volume = {38}, year = {2011}, pages = {14102 - 14116}, keywords = {Knowledge representation, Learning, Movie, Multimodal fusion, Ontology, Reasoning, Violence}, issn = {0957-4174}, doi = {10.1016/j.eswa.2011.04.219}, author = {Thanassis Perperis and Theodoros Giannakopoulos and Alexandros Makris and D. Kosmopoulos and Sofia Tsekeridou and Stavros J. Perantonis and Sergios Theodoridis} } @article {9, title = {Online classification of visual tasks for industrial workflow monitoring}, journal = {Neural Networks}, volume = {24}, year = {2011}, pages = {852{\textendash}860}, keywords = {Activity recognition, ESN, Fusion, Genetic algorithm, HMM, Workflow}, issn = {0893-6080}, author = {Athanasios Voulodimos and D. Kosmopoulos and Veres, Galina and Helmut Grabner and Van Gool, Luc and Varvarigou, T.A.} } @conference {101, title = {Audio-Visual Fusion for Detecting Violent Scenes in Videos}, booktitle = {Artificial Intelligence: Theories, Models and Applications, 6th Hellenic Conference on AI, SETN 2010, Athens, Greece, May 4-7, 2010. Proceedings}, year = {2010}, publisher = {Springer}, organization = {Springer}, author = {Theodoros Giannakopoulos and Alexandros Makris and Dimitrios I. Kosmopoulos and Stavros J. Perantonis and Sergios Theodoridis} } @proceedings {8, title = {Robust Workflow Recognition Using Holistic Features and Outlier-Tolerant Fused Hidden Markov Models}, journal = {International Conference on Artificial Neural Networks}, volume = {1}, year = {2010}, pages = {551-560}, author = {Athanasios Voulodimos and Helmut Grabner and D. Kosmopoulos and Van Gool, Luc and Varvarigou, T.A.} } @conference {85, title = {Violence content classification using audio features}, booktitle = {Proceedings of the 4th Helenic conference on Advances in Artificial Intelligence}, year = {2006}, publisher = {Springer-Verlag}, organization = {Springer-Verlag}, address = {Berlin, Heidelberg}, isbn = {3-540-34117-X, 978-3-540-34117-8}, author = {Giannakopoulos, Theodoros and Kosmopoulos, Dimitrios and Aristidou, Andreas and Theodoridis, Sergios} }