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2025 Design, Automation and Test in Europe Conference, DATE 2025

作者： Besias, Spyridon Sertaridis, Ilias Afentaki, Florentia Balaskas, Konstantinos Zervakis, Georgios University of Patras Department of Computer Engineering & Informatics Patras Greece

ISBN: (纸本)9783982674100

Printed Electronics (PE) provide a mechanically flexible and cost-effective solution for machine learning (ML) circuits, compared to silicon-based technologies. However, due to large feature sizes, printed classifiers are limited by high power, area, and energy overheads, which restricts the realization of battery-powered systems. In this work, we design sequential printed bespoke Support Vector Machine (SVM) circuits that adhere to the power constraints of existing printed batteries while minimizing energy consumption, thereby boosting battery life. Our results show 6.5x energy savings while maintaining higher accuracy compared to the state of the art. © 2025 EDAA.

关键词： Support vector machines

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A Mobility-Aware Reinforcement Learning Proactive Solution for State Data Migration in Edge Computing 29

A Mobility-Aware Reinforcement Learning Proactive Solution f...

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29th IEEE International Workshop on computer Aided Modeling and Design of Communication Links and Networks, CAMAD 2024

作者： Dritsas, Elias Ramantas, Kostas Verikoukis, Christos Iquadrat Barcelona Spain University of Patras Department of Computer Engineering and Informatics Patras Greece Industrial Systems Institute Athena Research and Innovation Center Patras Greece

ISBN: (纸本)9798350377644

To handle the demands of modern applications for storage, computing, low latency, and bandwidth, various services are offloaded from the cloud to edge servers, bringing them closer to end-users. This shift in computing resources closer to the users is known as Edge Computing whose services are highly affected by users' mobility. As users move and change geographical locations, the services provided by an edge server may be compromised or become distant from the user, leading to a decrease in the quality of service (QoS) for applications. Service migration is a common approach to ensure that essential services remain close to users, thereby improving the application QoS. The synergy of Edge Computing with Artificial Intelligence (AI) can open up new capabilities for the Lifecycle Management (LCM) of applications, besides the adoption of the innovative microservice-based architecture for building them and, especially, stateful to ensure seamless service continuity and minimal latency during user's mobility. The leverage of AI-based LCM of such applications can significantly benefit edge computing by automating and optimising, in terms of resource allocation, the process of decision-making during deployment and run-time operation of microservices running on edge devices. In this direction, we elaborate a mobility-induced proactive migration mechanism empowered by efficient Deep Learning (DL) and Reinforcement Learning (RL) algorithms to identify the optimal edge node for application and state data migration without violating Service Level Agreement - SLA (e.g., latency) and ensure efficient resource usage at the edge. © 2024 IEEE.

关键词： Edge Computing Life-Cycle-Management Migration Reinforcement Learning Trajectory Prediction

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Validating State-of-the-Art Virtual Keyboard Simulators with Empirical Data 24

Validating State-of-the-Art Virtual Keyboard Simulators with...

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Proceedings of the 28th Pan-Hellenic Conference on Progress in Computing and informatics

作者： Ioulia Simou Andreas Komninos John Garofalakis Computer Engineering & Informatics Department University of Patras Patras Greece

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XMeta-OS: A Conceptual Framework for Unified Resource Management in Distributed Edge-Cloud Environments 3rd

XMeta-OS: A Conceptual Framework for Unified Resource Manag...

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3rd International Conference on Advances in Computing Research, ACR 2025

作者： Zacharioudakis, Eleftherios Kakoulli, Elena Evripidou, Salomi Intelligent Systems Laboratory Department of Computer Science Neapolis University Pafos Pafos Cyprus National Technical University of Ukraine Igor Sikorsky Kyiv Polytechnic Institute Kyiv Ukraine Electrical Engineering and Computer Engineering and Informatics Cyprus University of Technology Limassol Cyprus

ISBN: (纸本)9783031876462

This paper introduces XMeta-OS, a meta-operating system specially designed with Linux as its foundation to unify and optimize resource management for the distributed edge-cloud when dynamic use of GPU resources is possible. This approach offer a new GPU abstraction layer and dynamic edge-cloud allocation model that offer enhanced flexibility and resource utilization over existing systems like Kubernetes and OpenStack. By using likely sensitive data, the model applied cloud-pushed deployment model. XMeta-OS manages computer resources in real-time. Therefore, fulfilling high performance various needs. It allocates CPU and GPU in real-time to workloads requiring massive parallel processing, such as machine learning, scientific simulations, real-time data analytics, and transportation tasks. Fabrication in the laboratory of an edge-cloud environment can help to assess the scalability, fault tolerance and efficiency of the system. It showcases the overall architectural design of XMeta-OS. Changes to the kernel and security solutions for safe and effective distribution are suggested. This article presents the design of the XMeta-OS. The future of XMeta-OS entails usage and testing with which we see latency, energy, computation and other such measurements in labs. XMeta-OS is a solution to cost performance, emerging application, large-scale edge-cloud deployments, and distributed GPU-enabled infrastructures. Meta-Operating System in Edge-Cloud Continuum for dynamic resource management of GPU. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Cluster computing

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An Energy-Efficient Protocol for Internet of Things Based Wireless Sensor Networks

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computers, Materials & Continua 2023年第5期75卷 2397-2412页

作者： Mohammed Mubarak Mustafa Ahmed Abelmonem Khalifa Korhan Cengiz Nikola Ivkovic Department of Information Technology Sri Krishna College of Engineering&TechnologyCoimbatoreIndia Department of Networking and Security College of Information TechnologyUniversity of FujairahFujairahUAE Switching and Traffic Department National Telecommunication InstituteCairoEgypt Department of Computer Engineering Istinye UniversityZeytinburnuIstanbulTurkey Faculty of Organization and Informatics University of ZagrebVarazdinCroatia

The performance of Wireless Sensor Networks(WSNs)is an important fragment of the Internet of Things(IoT),where the current WSNbuilt IoT network’s sensor hubs are enticing due to their critical *** grouping hubs,a clustering convention offers a useful solution for ensuring energy-saving of hubs andHybridMedia Access Control(HMAC)during the course of the ***,current grouping standards suffer from issues with the grouping structure that impacts the exhibition of these conventions *** this investigation,we recommend an Improved Energy-Proficient Algorithm(IEPA)for HMAC throughout the lifetime of the WSN-based *** consecutive segments are *** the covering of adjusted clusters,an ideal number of clusters is determined ***,fair static clusters are shaped,based on an updated calculation for fluffy cluster heads,to reduce and adapt the energy use of the sensor *** heads(CHs)are,ultimately,selected in optimal locations,with the pivot of the cluster heads working among cluster ***,the proposed convention diminishes and balances the energy utilization of hubs by improving the grouping structure,where the IEPAis reasonable for systems that need a long *** assessment results demonstrate that the IEPA performs better than existing conventions.

关键词： Energy consumption improved energy-proficient algorithm internet of things wireless sensor network

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Apply Combined Model Between VGG16 and LightGBM on Weather Image Recognition

Apply Combined Model Between VGG16 and LightGBM on Weather I...

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2023 RIVF International Conference on Computing and Communication Technologies, RIVF 2023

作者： Quy Nam, Tran Syamsuddin, Irfan Posts and Telecommunications Institute of Technology Hanoi Viet Nam Politeknik Negeri Ujung Department of Informatics & Computer Engineering Pandang Indonesia

ISBN: (纸本)9798350315844

This research implements hybrid deep learning network models for weather image classification. The study proposes to apply a combined model, namely VGG16-LightGBM. In its architecture model, a pre-train convolutional neural network (CNN) name as VGG16 is employed for feature extraction of images and the LightGBM algorithm is used to make classification. The results on accuracy of proposed models were compared with four other models, namely Xception, Inception V3,Vgg19, Vgg16 which are all implemented by transfer learning mechanism on the same dataset. The experimental results proved that the VGG16-LightGBM gives the best performance with the highest accuracy of 81,28%, outperforms the transfer learning technique of other 4 pre-train models in the problem of weather image classification. © 2023 IEEE.

关键词： Image classification

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AI-Based Wearable Sensing: A Technological Intervention to Physiological Monitoring and Early Detection of Pandemic Diseases

AI-Based Wearable Sensing: A Technological Intervention to P...

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2024 International Conference on Information and Communication Technology for Development for Africa, ICT4DA 2024

作者： Tito, Stanley Leonard Banzi, Jamal F. Mbeya University of Science and Technology Department of Computer Science and Engineering Mbeya Tanzania United Republic of Sokoine University of Agriculture Department of Informatics and Information Technology Morogoro Tanzania United Republic of

ISBN: (纸本)9798331527792

The COVID-19 pandemic has drastically left the world with a profound impact that will resonate for years to come. The devastating loss of lives, the dramatic decline in economic output, and the disruption of social interactions have all contributed to a sudden shift in our global landscape. Monitoring COVID-19 symptoms has become essential in controlling the spread of the virus and preventing hospitalizations. This paper presents an innovative solution that leverages advanced wearable technology integrated with cutting-edge artificial intelligence (AI) algorithms to monitor biometric signals associated with the presence of COVID-19 and other pandemic diseases. For persistent monitoring, the system is designed to acquire biometric signals such as temperature, heart rate, and blood oxygen saturation from wearable devices, which are considered as input to the long short-term memory (LSTM) network. The model will then reconstruct the signals, process them, and predict the health status of the patient with respect to the COVID-19 pandemic. Experiments were conducted to evaluate the performance of the system using two parameters i.e. temperature and heart rates. The system's performance was compared to a certified medical station, and both heart rate and temperature parameter tests obtained good correlation (R=0. 73 and R=0. 96 respectively). This indicates the feasibility of the proposed system in early prediction of the presence of COVID-19, representing a crucial step forward in our efforts to build a more resilient and prepared world to navigate the challenges posed by the ever-present threat of future epidemics and pandemics. © 2024 IEEE.

关键词： Heart

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Pathogenicity Prediction of Single Amino Acid Variants With Machine Learning Model Based on Protein Structural Energies

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IEEE/ACM Transactions on Computational Biology and Bioinformatics 2023年第1期20卷 606-615页

作者： Wu, Tzu-Hsuan Lin, Peng-Chan Chou, Hsin-Hung Shen, Meng-Ru Hsieh, Sun-Yuan National Cheng Kung University Institute of Medical Informatics Tainan701 Taiwan National Cheng Kung University Hospital Department of Computer Science and Information Engineering Department of Internal Medicine Tainan704 Taiwan National Chi Nan University Department of Computer Science and Information Engineering Nantou County54516 Taiwan National Cheng Kung University College of Medicine Department of Obstetrics and Gynecology Department of Pharmacology Tainan701 Taiwan National Cheng Kung University Institute of Manufacturing Information Systems and Institute of Medical Informatics Department of Computer Science and Information Engineering Tainan701 Taiwan

The most popular tools for predicting pathogenicity of single amino acid variants (SAVs) were developed based on sequence-based techniques. SAVs may change protein structure and function. In the context of van der Waals force and disulfide bridge calculations, no method directly predicts the impact of mutations on the energies of the protein structure. Here, we combined machine learning methods and energy scores of protein structures calculated by Rosetta Energy Function 2015 to predict SAV pathogenicity. The accuracy level of our model (0.76) is higher than that of six prediction tools. Further analyses revealed that the differential reference energies, attractive energies, and solvation of polar atoms between wildtype and mutant side-chains played essential roles in distinguishing benign from pathogenic variants. These features indicated the physicochemical properties of amino acids, which were observed in 3D structures instead of sequences. We added 16 features to Rhapsody (the prediction tool we used for our data set) and consequently improved its performance. The results indicated that these energy scores were more appropriate and more detailed representations of the pathogenicity of SAVs. © 2004-2012 IEEE.

关键词： Proteins

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Job Resumes Recommendation using Integration of Fuzzy Discernibility Matrix Feature Selection and Convolutional Neural Network Multi-label Text Classification

Informatica (Slovenia)

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Informatica (Slovenia) 2025年第13期49卷 49-58页

作者： Hayami, Regiolina Yusoff, Nooraini Daud, Kauthar Mohd Fatma, Yulia Department of Informatics Engineering Faculty Computer Science Universitas Muhammadiyah Riau Indonesia Faculty of Data Science and Computing Universiti Malaysia Kelantan 16100 Malaysia Center for Artificial Intelligence Technology Faculty of Information Science and Technology Universiti Kebangsaan Malaysia 43600 Malaysia

The manual analysis of job resumes poses specific challenges, including the time-intensive process and the high likelihood of human error, emphasizing the need for automation in content-based recommendations. Recent advancements in deep learning, particularly Convolutional Neural Networks (CNN) for Multi-label Text Classification (MLTC), offer a promising solution for addressing these challenges through artificial intelligence. While CNN is renowned for its robust feature extraction capabilities, it faces specific challenges such as managing high-dimensional data and poor data interpretability. To address these limitations, this study employs the Fuzzy Discernibility Matrix (FDM) feature selection technique to determine the relevance of skills for each job vacancy. FDM assigns weights to the features of each job category and ranks their relevance based on the highest scores, which are then utilized in the MLTC CNN model. The integration of FDM and MLTC CNN serves as the foundation for generating content-based recommendations derived from key features in job resumes. This study produces a content-based job recommendation system that displays the top three job categories, accompanied by explanations of the skills supporting each selected category. These recommendations also consider cosine similarity values between analyzed items and the integrated results of FDM and MLTC CNN. The application of FDM for feature weighting has been proven to enhance multi-label text classification outcomes by providing better insights during the feature selection process. With a recall of 97.26%, precision of 94.81%, and accuracy of 98.58%, the MLTC model integrating FDM feature selection and CNN demonstrates robust performance characteristics in content-based job recommendation tasks. © 2025 Slovene Society Informatika. All rights reserved.

关键词： Convolutional neural networks

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DLMetaChain: An IoT Data Lake Architecture Based on the Blockchain 13

DLMetaChain: An IoT Data Lake Architecture Based on the Bloc...

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13th International Conference on Information, Intelligence, Systems and Applications, IISA 2022

作者： Pingos, Michalis Christodoulou, Panayiotis Andreou, Andreas Cyprus University of Technology Computer Engineering and Informatics Department of Electrical Engineering Limassol Cyprus Neapolis University Department of Computer Science Pafos Cyprus

ISBN: (数字)9781665463904

ISBN: (纸本)9781665463904

Nowadays, the IoT ecosystem is evolving rapidly, with multiple heterogeneous sources producing high volumes of data and processes transforming this data into meaningful or 'smart' information. These volumes of data, including IoT data, need to be stored in repositories that can host raw, unprocessed, relational and non-relational types of data, such as Data Lakes. Due to the weakness of metadata management, security & access control is one of the main challenges of Big Data storage architectures as Data Lakes can be replaced without oversight of the contents. Recently, the Blockchain technology has been introduced as an effective solution to build trust between different entities, where trust is either nonexistent or unproven, and to address security and privacy concerns. In this paper we introduce DLMetaChain, an extended Data Lake metadata mechanism that consists of data from heterogeneous data sources which interact with IoT data. The extended mechanism mainly focuses on developing an architecture to ensure that the data in the Data Lake is not modified or altered by taking into advantage the capabilities of the Blockchain. © 2022 IEEE.

关键词： Smart contract

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