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21st European, Mediterranean, and Middle Eastern Conference on Information systems, EMCIS 2024

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

ISBN: (纸本)9783031813214

This paper reviews the application of Large Language Models (LLMs) in addressing the growing sophistication of cybersecurity threats. By evaluating numerous studies, we delineate the role of LLMs in improving the detection, analysis, and response to cyber threats in real-time. Our findings demonstrate that LLMs significantly enhance the efficiency and accuracy of cybersecurity measures compared to traditional models. However, their deployment in cybersecurity frameworks involves critical considerations around ethics, privacy, and security. This review provides a succinct overview of LLMs’ current capabilities and practical implementations within the cybersecurity landscape, identifies research gaps, and suggests future research directions. The insights presented here highlight the vital contribution of LLMs to the advancement of cybersecurity solutions and adapting to evolving threat environments. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Cyber attacks

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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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Bidirectional LSTM-Based Energy Consumption Forecasting:Advancing AI-Driven Cloud Integration for Cognitive City Energy Management

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computers, Materials & Continua 2025年第5期83卷 2907-2926页

作者： Sheik Mohideen Shah Meganathan Selvamani Mahesh Thyluru Ramakrishna Surbhi Bhatia Khan Shakila Basheer Wajdan Al Malwi Mohammad Tabrez Quasim Department of Computer Science and Engineering Srinivasa Ramanujan CentreSASTRA Deemed UniversityKumbakonam612001India Department of Computer Science and Engineering Faculty of Engineering and TechnologyJAIN(Deemed-to-be University)Bangalore562112India School of Science Engineering and EnvironmentUniversity of SalfordManchesterM54WTUK University Centre for Research and Development Chandigarh UniversityMohali140413PunjabIndia Centre for Research Impact and Outcome Chitkara University Institute of Engineering and TechnologyChitkara UniversityRajpura140401PunjabIndia Department of Information Systems College ofComputer and Information SciencePrincessNourah bintAbdulrahmanUniversityP.O.Box 84428Riyadh11671Saudi Arabia College of Computer Science Informatics and Computer Systems Department King Khalid UniversityAbha61421Saudi Arabia Department of Computer Science and Artificial Intelligence College of Computing and Information TechnologyUniversity of BishaBisha61922Saudi Arabia

Efficient energy management is a cornerstone of advancing cognitive cities,where AI,IoT,and cloud computing seamlessly integrate to meet escalating global energy *** this context,the ability to forecast electricity consumption with precision is vital,particularly in residential settings where usage patterns are highly variable and *** study presents an innovative approach to energy consumption forecasting using a bidirectional Long Short-Term Memory(LSTM)*** a dataset containing over twomillionmultivariate,time-series observations collected froma single household over nearly four years,ourmodel addresses the limitations of traditional time-series forecasting methods,which often struggle with temporal dependencies and non-linear *** bidirectional LSTM architecture processes data in both forward and backward directions,capturing past and future contexts at each time step,whereas existing unidirectional LSTMs consider only a single temporal *** design,combined with dropout regularization,leads to a 20.6%reduction in RMSE and an 18.8%improvement in MAE over conventional unidirectional LSTMs,demonstrating a substantial enhancement in prediction accuracy and *** to existing models—including SVM,Random Forest,MLP,ANN,and CNN—the proposed model achieves the lowest MAE of 0.0831 and RMSE of 0.2213 during testing,significantly outperforming these *** results highlight the model’s superior ability to navigate the complexities of energy usage patterns,reinforcing its potential application in AI-driven IoT and cloud-enabled energy management systems for cognitive *** integrating advanced machine learning techniqueswith IoT and cloud infrastructure,this research contributes to the development of intelligent,sustainable urban environments.

关键词： Deep learning bidirectional LSTM energy consumption forecasting time-series analysis predictive modeling machine learning in energy management

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Deep learning for cerebral vascular occlusion segmentation: A novel ConvNeXtV2 and GRN-integrated U-Net framework for diffusion-weighted imaging

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Neuroscience 2025年 574卷 42-53页

作者： Ince, Suat Kunduracioglu, Ismail Algarni, Ali Bayram, Bilal Pacal, Ishak Department of Radiology University of Health Sciences Van Education and Research Hospital Van 65000 Turkey Department of Computer Engineering Faculty of Engineering Igdir University Igdir 76000 Turkey Department of Informatics and Computer Systems College of Computer Science King Khalid University Abha 61421 Saudi Arabia Department of Neurology University of Health Sciences Van Education and Research Hospital Van 65000 Turkey Department of Electronics and Information Technologies Faculty of Architecture and Engineering Nakhchivan State University Nakhchivan AZ 7012 Azerbaijan

Cerebral vascular occlusion is a serious condition that can lead to stroke and permanent neurological damage due to insufficient oxygen and nutrients reaching brain tissue. Early diagnosis and accurate segmentation are critical for effective treatment planning. Due to its high soft tissue contrast, Magnetic Resonance Imaging (MRI) is commonly used for detecting these occlusions such as ischemic stroke. However, challenges such as low contrast, noise, and heterogeneous lesion structures in MRI images complicate manual segmentation and often lead to misinterpretations. As a result, deep learning-based computer-Aided Diagnosis (CAD) systems are essential for faster and more accurate diagnosis and treatment methods, although they can sometimes face challenges such as high computational costs and difficulties in segmenting small or irregular lesions. This study proposes a novel U-Net architecture enhanced with ConvNeXtV2 blocks and GRN-based Multi-Layer Perceptrons (MLP) to address these challenges in cerebral vascular occlusion segmentation. This is the first application of ConvNeXtV2 in this domain. The proposed model significantly improves segmentation accuracy, even in low-contrast regions, while maintaining high computational efficiency, which is crucial for real-world clinical applications. To reduce false positives and improve overall accuracy, small lesions (≤5 pixels) were removed in the preprocessing step with the support of expert clinicians. Experimental results on the ISLES 2022 dataset showed superior performance with an Intersection over Union (IoU) of 0.8015 and a Dice coefficient of 0.8894. Comparative analyses indicate that the proposed model achieves higher segmentation accuracy than existing U-Net variants and other methods, offering a promising solution for clinical use. © 2025 International Brain Research Organization (IBRO)

关键词： Brain stroke diagnosis Deep learning Health Medical image processing U-Net

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TCIP: Transformed Colour Intensity Profiles analysis for fault detection in power line insulators

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Multimedia Tools and Applications 2025年第8期84卷 4503-4534页

作者： Tomaszewski, Michal Gasz, Rafal Kasana, Singara Singh Osuchowski, Jakub Singh, Simranjit Zator, Slawomir Department of Computer Science Faculty of Electrical Engineering Automatic Control and Informatics Opole University of Technology Prószkowska 76 Opole45-758 Poland Department of Computer Science and Information Technology Central University of Haryana Haryana Mahendergarh123031 India Department of Information Technology Dr B. R. Ambedkar National Institute of Technology Punjab Jalandhar India Department of Safety Engineering and Technical Systems Faculty of Production Engineering and Logistics Opole University of Technology Prószkowska 76 Opole45-758 Poland

In response to the escalating demand for electricity, the aging process and inherent failures in power lines have become unavoidable challenges in their operational integrity. This research addresses the imperative need for a robust method to detect damage in power insulator disc images, introducing a novel feature extraction technique grounded in color intensity profile transformation and ensemble learning. For the purposes of the research, a specific dataset was developed based on publicly available images of power insulators. The collection consists of 9232 images of insulator discs which included 2184 images of damaged discs. The discernible damages include breakages and burnouts. Two distinct analysis variants were employed. In the first variant (V1), two classification groups were selected: disc with damages and disc without damages. In the second variant (V2), three classification groups were selected: discs without damage, broken discs, and burnout discs. The described research was divided into two successive stages. In the first of them (S1), the transformed color intensity profiles for the prepared data set were built and various machine learning models for their classification were trained and evaluated. In the next stage (S2), previously developed, highest efficient machine learning models, were used to make the detection of various damaged types of overhead line insulator discs. For the first stage of research high-performance results and a high value of the F1 score parameters were obtained (F1 score = 0.999 for the first variant (S1V1) and F1 score = 0.992 for the second variant (S1V2). In the second stage of research and the first variant (S2V1), the highest result of the F1 score was 0.932 and for the second variant (S2V2) F1 score was 0.930. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： Feature extraction

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Empowering the adoption of blockchain-based cryptocurrency as a payment method: A user-friendly educational approach

IET Blockchain

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IET Blockchain 2025年第1期5卷

作者： Alrehaili, Ahmed White, Martin Beloff, Natalia University of Sussex School of Engineering and Informatics Informatics Brighton United Kingdom Islamic University of Madinah College of Computing and Information Systems Computer Science Department Medina Saudi Arabia

Organizations are adopting technological innovations to transform payment systems due to challenges with traditional methods, such as slow speed and high fees. These challenges have prompted a shift towards blockchain-based cryptocurrencies. However, cryptocurrency adoption for payments remains limited, especially in Saudi Arabia. This study adapts the technology acceptance model to explore cryptocurrency adoption through the blockchain-based cryptocurrency as a payment method in Saudi Arabia (BCAP-SA) model. Factors within the model are assessed using an experimental vignette-task methodology and surveys. A key component is an educational package, offering comprehensive materials to explain blockchain technology. The findings confirm the reliability of surveys. Most model factors are statistically significant in influencing users’ intention to use cryptocurrency. The study finds that perceived ease of use, perceived usefulness, and perceived trust significantly impact participants’ intentions. Additionally, low transaction fees and age are the most influential factors on the technology acceptance model's core constructs. Statistical analysis indicates that decentralization and anonymity were insignificant and thus excluded from the revised BCAP-SA model. These findings highlight the potential to enhance cryptocurrency adoption in Saudi Arabia. The study's insights can guide strategies to promote wider cryptocurrency usage in the region. © 2025 The Author(s). IET Blockchain published by John Wiley & Sons Ltd on behalf of The Institution of Engineering and Technology.

关键词： blockchain platforms blockchains cryptocurrencies models and analysis

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A model of an intelligent clustering system with an external module for the architecture of RTOS with intensive changes of states regarding their flexibility and balancing 1

A model of an intelligent clustering system with an external...

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1st International Workshop on Advanced Applied Information Technologies, AdvAIT 2024

作者： Kozelskiy, Oleksandr Kashtalian, Antonina Stetsyuk, Vasyl Martiniuk, Dmytro Sachenko, Anatoliy Khmelnitsky National University Instytutska street 11 Khmelnitsky29016 Ukraine Kazimierz Pulaski University of Technology and Humanities Department of Informatics Radom Poland Research Institute for Intelligent Computer Systems West Ukrainian National University Ternopil Ukraine

The article considers the creation of a model of a clustering system with an external module for the architecture of OSRCH with intensive changes of states, which concerns their flexibility and load. An overview of modern approaches to clustering and load balancing in real-time operating systems was performed, and their advantages and disadvantages were identified. The study is aimed at identifying the main problems that need to be solved in order to increase the efficiency of clustering and improve load balancing in the real conditions of the operation of real-time operating systems. The main challenges arising in the context of ensuring high performance of such systems are considered. The obtained results can be used to develop new methods that increase the efficiency of resource management and ensure the stability of real-time operating systems. © 2024 Copyright for this paper by its authors.

关键词： flexibility enhancement operating system resource management RTOS task scheduler

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NFT-based Data Provenance for AI Transparency in Enterprise Information systems 16

NFT-based Data Provenance for AI Transparency in Enterprise ...

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16th International Conference on ENTERprise Information systems, CENTERIS 2024 - 12th International Conference on Project MANagement, ProjMAN 2024 - 14th International Conference on Health and Social Care Information systems and Technologies, HCist 2024

作者： Verginadis, Yiannis Almpanoudis, Orestis Apostolou, Dimitris De Oliveira, Marcela T. Mentzas, Gregoris Department of Business Administration Athens University of Economics and Business Patission 76 Athens10434 Greece Institute of Communications and Computer Systems Iroon Polytechniou 9 Zografou15780 Greece Department of Informatics University of Piraeus Piraeus18534 Greece AA Delft2600 Netherlands

Enterprise Information systems have a long-established and crucial role for modern organizations, as they enable seamless integration and management of critical business processes, ensuring efficiency in operations, data accuracy, and enhanced decision-making capabilities. One of their most interesting emerging technologies refer to the use of Artificial Intelligence as they may seamlessly automate routine tasks, offer predictive analytics, and provide deep insights, ultimately leading to intelligent data-driven decisions and improved operational efficiency. Of course, this direction of work is accompanied by some important challenges that come from the opacity of certain AI models and their potential biases due to low-quality training data used. In this paper, we argue that such challenges can be mitigated by a novel framework able to integrate, in a transparent manner, quality-related metadata on datasets used for training the AI-enabled emerging technologies in the field of EIS systems. These metadata are minted as Non-Fungible Tokens (NFTs) over the blockchain. © 2025 The Author(s).

关键词： Data accuracy

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RAFA-Net: Region Attention Network for Food Items and Agricultural Stress Recognition

IEEE Transactions on AgriFood Electronics

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IEEE Transactions on AgriFood Electronics 2025年第1期3卷 121-133页

作者： Bera, Asish Krejcar, Ondrej Bhattacharjee, Debotosh Birla Institute of Technology and Science Department of Computer Science and Information Systems Pilani333031 India University of Hradec Kralove Center for Basic and Applied Science Faculty of Informatics and Management Hradec Kralove50003 Czech Republic Skoda Auto University Mlada Boleslav29301 Czech Republic Universiti Teknologi Malaysia Malaysia Japan International Institute of Technology Kuala Lumpur81310 Malaysia Jadavpur University Department of Computer Science and Engineering Kolkata700032 India

Deep convolutional neural networks (CNNs) have facilitated remarkable success in recognizing various food items and agricultural stress. A decent performance boost has been witnessed in solving the agro-food challenges by mining and analyzing region-based partial feature descriptors. Also, computationally expensive ensemble learning schemes fusing multiple CNNs have been studied in earlier works. This work proposes a region attention scheme for modeling long-range dependencies by building a correlation among different regions within an input image. The attention method enhances feature representation by learning the usefulness of context information from complementary regions. Spatial pyramidal pooling and average pooling pairs aggregate partial descriptors into a holistic representation. Both pooling methods establish spatial and channelwise relationships without incurring extra parameters. A context gating scheme is applied to refine the descriptiveness of weighted attentional features, which is relevant for classification. The proposed region attention network for food items and agricultural stress recognition method, dubbed RAFA-Net, has been experimented on three public food datasets, and has achieved state-of-the-art performances with distinct margins. The highest top-1 accuracy of RAFA-Net is 91.69%, 91.56%, and 96.97% on the UECFood-100, UECFood-256, and MAFood-121 datasets, respectively. In addition, better accuracies have been achieved on two benchmark agricultural stress datasets. The best top-1 accuracies on the Insect Pest (IP-102) and PlantDoc-27 plant disease datasets are 92.36%, and 85.54%, respectively;implying RAFA-Net's generalization capability. © 2023 IEEE.

关键词： Plant diseases

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A BASIS FOR HUMAN RESPONSIBILITY IN ARTIFICIAL INTELLIGENCE COMPUTATION

arXiv

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arXiv 2025年

作者： Calderonio, Vincenzo Institute of Legal Informatics and Judicial Systems Italian National Research Council Department of Computer Science University of Pisa Italy

Recent advancements in artificial intelligence have reopened the question about the boundaries of AI autonomy, particularly in discussions around artificial general intelligence (AGI) and its potential to act independently across varied purposes. This paper explores these boundaries through the analysis of the Alignment Research Center experiment on GPT-4 and introduces the Start Button Problem, a thought experiment that examines the origins and limits of AI autonomy. By examining the thought experiment and its counterarguments, it will be shown how in the need for human activation and purpose definition lies AI's inherent dependency on human-initiated actions, challenging the assumption of AI as an agent. Finally, the paper addresses the implications of this dependency on human responsibility, questioning the measure of the extension of human responsibility when using AI systems. © 2025, CC BY.

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