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检索条件"机构=Dpt. of Computer Science and Biomedical Informatics"

共 56 条记录，以下是1-10 订阅

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A Reward Modulated Spiked Timing Depended Plasticity inspired algorithm applied on a MultiLayer Perceptron 27

A Reward Modulated Spiked Timing Depended Plasticity inspire...

引用

27th Pan-Hellenic Conference on Progress in Computing and informatics with International Participation, PCI 2023

作者： Giannakas, Georgios Sapounaki, Maria Tsoukas, Vasileios Kakarountas, Athanasios Dpt. of Computer Science and Biomedical Informatics University of Thessaly Lamia Greece

ISBN: (纸本)9798400716263

The creation of a framework in which traditional Machine Learning and neuromorphic algorithms compete to solve a shared Reinforcement Learning environment is presented in this work. In addition, this configuration allows the exploitation of modern and widely-used Machine Learning libraries. The PyTorch framework is used to investigate the expanded capabilities and potential of training an action-critic network pair comprised of specialised units using a custom learning algorithm. The policy and value networks utilised in this context are fully interconnected MultiLayer Perceptrons. The training procedure employs two distinct algorithms: an algorithm inspired by Reward Modulated Spiked Timing Dependent Plasticity and the conventional Back Propagation technique. A comparative evaluation and analysis of the findings is performed. © 2023 Owner/Author.

关键词： Reinforcement learning

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A Review of Homomorphic Encryption and its Contribution to the Sector of Health Services 27

A Review of Homomorphic Encryption and its Contribution to t...

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27th Pan-Hellenic Conference on Progress in Computing and informatics with International Participation, PCI 2023

作者： Roumpies, Fotios Kakarountas, Athanasios Dpt. of Computer Science and Biomedical Informatics University of Thessaly Lamia Greece

ISBN: (纸本)9798400716263

Homomorphic encryption is a groundbreaking cryptographic method that has made giant contributions to healthcare by addressing the urgent need for steady and privacy-keeping information analysis and sharing. This encryption approach permits information to be processed while nonetheless in its encrypted form, permitting healthcare businesses to perform complex computations on confidential patient information without compromising character privacy or data protection. It paved the way for secure cloud-based facts storage, sharing, and collaborative healthcare research, facilitating advancements in fact-driven selection-making, customized medicinal drugs, and remote affected person tracking. Homomorphic encryption has emerged as a vital enabler of innovation by maintaining the confidentiality of affected personal information while enabling meaningful analysis. © 2023 Owner/Author.

关键词： Data privacy

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Smart Delivery for Goods Exploiting ML Algorithms 26

Smart Delivery for Goods Exploiting ML Algorithms

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26th Pan-Hellenic Conference on informatics, PCI 2022

作者： Boumpa, Eleni Tsoukas, Vasileios Chioktour, Vasileios Kalafati, Maria Spathoulas, Georgios Kakarountas, Athanasios Dpt. of Computer Science and Biomedical Informatics University of Thessaly Lamia Greece

ISBN: (纸本)9781450398541

This paper presents the work conducted on providing an efficient solution to the Vehicle Routing Problem considering all stages of a delivery process. The results are promising when compared to real-life data and depict the benefits of the application of ML algorithms in all stages of the process. A platform capable to monitor the various stages of the process was developed, and a number of ML models were incorporated to provide the best solution to a multiobjective problem. The results indicate on average a maximum of 25% distance saving, and a maximum of 14% regarding the total delivery time. © 2022 ACM.

关键词： Fleet operations

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PaperMill Detection in Scientific Content 18

PaperMill Detection in Scientific Content

引用

18th International Workshop on Semantic and Social Media Adaptation and Personalization, SMAP 2023

作者： Razis, Gerasimos Anagnostopoulos, Konstantinos Metaxas, Omiros Stefanidis, Stefanos-Dimitrios Zhou, Hong Anagnostopoulos, Ioannis LLC Atypon Systems CA United States University of Thessaly Computer Science and Biomedical Informatics Dpt. Lamia Greece

ISBN: (纸本)9798350327717

Researchers often come under pressure when facing the ever-increasing demand to produce a progressive number of publications, resorting to hiring the services of paper mills. These are unofficial, and often illegitimate, organizations providing ready-made questionable research components and services, posing a threat to the research integrity, scientific ecosystem, and publishers. Identifying paper mill material is a challenging and laborious process, while the increasing number of Artificial Intelligence services generating human-like text obstructs this process. The purpose of this paper is to contribute to the research integrity domain by proposing the PaperMill Detection manuscript screening framework. By leveraging contextual signals, it measures the probability of a document being the result of a paper mill organization or generated by Artificial Intelligence. The combination of these signals can facilitate the detection of questionable scientific content. Our evaluation has revealed that the proposed approach outperforms other open-source and commercial solutions in all examined evaluation metrics, achieving an F1 score of 0.97. © 2023 IEEE.

关键词： Artificial intelligence

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Self-Supervised Graph Representation Learning for In-The-Wild Wearable and Smartphone based Emotion Recognition

Self-Supervised Graph Representation Learning for In-The-Wil...

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2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Ziogas, Ioannis Hadjileontiadis, Leontios J. Khandoker, Ahsan H. Al Shehhi, Aamna Dpt. of Biomedical Eng. and Biotechnology Khalifa University of Science and Technology Abu Dhabi United Arab Emirates Dpt. of Electrical and Computer Eng. Aristotle University of Thessaloniki Thessaloniki Greece

ISBN: (纸本)9798350368741

Wearable and smartphone-based emotion recognition (WER) remains a challenging setting in affective computing, due to the notorious difficulty and bias associated with in-the-wild label collection. The high inter-and intra-subject emotional variability motivates us to explore WER modeling through graph node classification in a limited resources learning scheme powered by Self-Supervised Learning (SSL) graph masking augmentation tasks. We employ a subgraph sampling approach during training, utilizing labeled and unlabeled data, along with supervised, semi-supervised, and SSL mechanisms in a multi-task inductive graph neural network architecture. Our evaluations on K-EmoPhone through leave-one-group-out cross-validation in the binary arousal and valence tasks yield average accuracy gains of 4.3% and 7.8%, compared to the full resource setting, utilizing only 20% and 25% of the labels, respectively. Our model analysis sheds light on the relation of SSL graph augmentations to emotional arousal and valence and justifies the approach of SSL-driven subgraph training for in-the-wild WER. © 2025 IEEE.

关键词： Graph Masking Graph Neural Networks Self-Supervised Learning Wearable Emotion Recognition

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A Review of Homomorphic Encryption and its Contribution to the Sector of Health Services 23

A Review of Homomorphic Encryption and its Contribution to t...

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Proceedings of the 27th Pan-Hellenic Conference on Progress in Computing and informatics

作者： Fotios Roumpies Athanasios Kakarountas Dpt. of Computer Science and Biomedical Informatics University of Thessaly Greece

ISBN: (纸本)9798400716263

关键词： Healthcare Homomorphic Encryption data security digital libraries secure data sharing

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A Reward Modulated Spiked Timing Depended Plasticity inspired algorithm applied on a MultiLayer Perceptron 23

A Reward Modulated Spiked Timing Depended Plasticity inspire...

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Proceedings of the 27th Pan-Hellenic Conference on Progress in Computing and informatics

作者： Georgios Giannakas Maria Sapounaki Vasileios Tsoukas Athanasios Kakarountas Dpt. of Computer Science and Biomedical Informatics University of Thessaly Greece

ISBN: (纸本)9798400716263

关键词： Agent Bio & Nature Inspired Computing Machine Learning PyTorch Reinforcement Learning Reward Modulated Spiked Timing Depended Plasticity autograd

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Leveraging protein-protein interactions in phenotype prediction through graph neural networks 21

Leveraging protein-protein interactions in phenotype predict...

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21st IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology, CIBCB 2024

作者： Smeriglio, Riccardo Rosell-Mirmi, Joana Radeva, Petia Abante, Jordi Universitat de Barcelona Dpt. of Physics Spain Politecnico di Torino Control and Computer Engineering Dpt. Turin Italy Headache Research Group Vall d'Hebron Institut de Recerca Catalonia Barcelona Spain Universitat de Barcelona Institute of Neurosciences Dpt. of Mathematics & Computer Science Catalonia Barcelona Spain Laboratory of Stem Cells & Regenerative Medicine Creatio Production & Validation Center of Advanced Therapies Spain Universitat de Barcelona Institute of Neurosciences Dpt. of Biomedical Sciences Spain Dpt. of Mathematics & Computer Science Catalonia Barcelona Spain

ISBN: (纸本)9798350356632

Current genotype-to-phenotype models, such as polygenic risk scores, only account for linear relationships between genotype and phenotype and ignore epistatic interactions, limiting the complexity of the diseases that can be properly characterized. Protein-protein interaction networks have the potential to improve the performance of the models. Moreover, interactions at the protein level can have profound implications in understanding the genetic etiology of diseases and, in turn, for drug development. In this article, we propose a novel approach for phenotype prediction based on graph neural networks (GNNs) that naturally incorporates existing protein interaction networks into the model. As a result, our approach can naturally discover relevant epistatic interactions. We assess the potential of this approach using simulations and comparing it to linear and other non-linear approaches. We also study the performance of the proposed GNN-based methods in predicting Alzheimer's disease, one of the most complex neurodegenerative diseases, where our GNN approach outperform state of the art methods. In addition, we show that our proposal is able to discover critical interactions in the Alzheimer's disease. Our findings highlight the potential of GNNs in predicting phenotypes and discovering the underlying mechanisms of complex diseases. © 2024 IEEE.

关键词： Prediction models

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FraMark: A Blockchain Marketplace for a 5G Network Management using Fractional NFTs 7

FraMark: A Blockchain Marketplace for a 5G Network Managemen...

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7th World Symposium on Communication Engineering, WSCE 2024

作者： Michoulis, George Tsourdinis, Kyros Niotis, George Kyranou, Konstantinos Fountoukidis, Eleutherios Lagkas, Thomas Argyriou, Vasileios Sarigiannidis, Panagiotis Psannis, Konstantinos E. Goudos, Sotirios K. Sidroco Holdings Ltd R&D Dpt. Thessaloniki Greece Sidroco Holdings Ltd R&D Dpt. Nicosia Cyprus Democritus University of Thrace Dpt. of Computer Science Kavala Greece University of Western Macedonia Dpt. of Electrical & Computer Engineering Kozani Greece Kingston University London Kingston Upon Thames Dpt. of Networks and Digital Media Surrey United Kingdom University of Macedonia Department of Applied Informatics Thessaloniki Greece ELEDIA@AUTH School of Physics Aristotle University of Thessaloniki Thessaloniki Greece

ISBN: (纸本)9798331542825

This paper presents FraMark, a blockchain-based framework for 5G network management that utilizes Hyperledger Fabric and fractional Non-Fungible Tokens (NFTs) to optimize resource allocation. We introduce a comprehensive system that fractionalizes digital assets to represent 5G network resources, enhancing efficiency and allocation flexibility. Our framework addresses the challenges of 5G network management by ensuring low latency, high transaction throughput, and secure resource trading. We conduct experiments to evaluate the performance of our approach, including CPU and memory utilization, latency analysis, and block size assessment. The results demonstrate the effectiveness of FraMark in improving network resource utilization and transaction efficiency, validating our framework's claims. By leveraging blockchain technology and innovative resource slicing techniques, FraMark paves the way for dynamic and efficient 5G network management. © 2024 IEEE.

关键词： 5G mobile communication systems

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Prototype Haptic Feedback Device for Robot-Assisted Minimally Invasive Surgery 6

Prototype Haptic Feedback Device for Robot-Assisted Minimall...

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6th South-East Europe Design Automation, computer Engineering, computer Networks and Social Media Conference, SEEDA-CECNSM 2021

作者： Zagori, Sofia I. Kakarountas, Athanasios P. Dpt. of Computer Science and Biomedical Informatics University of Thessaly Lamia Greece

ISBN: (纸本)9781665427425

Robot-Assisted Minimally Invasive Surgery (RAMIS) is an innovation that has benefited hundreds of thousands of patients to date. However, despite their many advantages, those robots are not as popular as expected. Many health professionals are hesitant to use them since they don't provide as much feedback as to their human senses. Haptic feedback is crucial for the smooth conduction of the operation. Our goal was to develop a low-cost sensing system for surgical robots, which will add to their attraction and make them more appealing to surgeons and patients. © 2021 IEEE

关键词： Feedback

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