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22nd International Learning and Technology Conference, L and T 2025

作者： Qaisar, Saeed Mian Subasi, Abdulhamit Effat University Electrical and Computer Engineering Department Jeddah21478 Saudi Arabia Institute of Biomedicine Faculty of Medicine University of Turku Turku20520 Finland College of Engineering Effat University Department of Computer Science Jeddah21478 Saudi Arabia

ISBN: (纸本)9798331532215

Contemporary power solutions are revolutionizing the existing trend in residential and transportation services. Mobile realizations, in particular, are advancing based on innovative mobile power supply solutions. Batteries are essential to several key applications, such as electric vehicles (EVs), satellites, and mobile phones. Additionally, batteries are elementary parts of the energy storage for distributed renewable microgrids. The are just a few applications based on the batteries-based power supply. Li-ion batteries are frequently used because of their exceptional qualities, which include endurance, high power density, and compact size. Besides these notable benefits the Li-Ion batteries are expensive. To effectively compensate for this high initial cost, the "battery management systems"(BMSs) are used. These solutions are effective in maximizing the Li-Ion battery life and also guarantee a safer operation. Based on the advantages of modular design, the contemporary Li-Ion batteries can be comprised of thousands of individual cells. It results in complex and sophisticated BMSs and can render a notable overhead in terms of power consumption. The focus of this research is on enhancing the existing Li-Ion BMSs by incorporating novel event-based feature mining and machine/ensemble learning techniques. The event-based feature extractor mines pertinent information and promises significant real-time compression. In order to predict the capacity of Li-Ion cells, the produced feature set is then processed utilizing robust machine/ensemble learning algorithms, and the performance of considered regressors is compared. A real Li-Ion battery dataset is used to demonstrate the applicability. For the case of a bagging-based ensemble regressor, the method obtained a correlation coefficient of 0.9996. © 2025 IEEE.

关键词： Lithium-ion batteries

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A Comprehensive Survey on Digital Twin: Focusing on Security Threats and Requirements

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IEEE Access 2025年 13卷 73362-73390页

作者： Mun, Hyeran Han, Kyusuk Damiani, Ernesto Yeun, Hyun Ku Kim, Tae-Yeon Martino, Luigi Yeun, Chan Yeob Khalifa University Center for Cyber-Physical Systems Department of Computer Science Abu Dhabi United Arab Emirates Abu Dhabi United Arab Emirates Higher Colleges of Technology School of Engineering and Technology Dubai United Arab Emirates Khalifa University Department of Civil and Environmental Engineering Abu Dhabi United Arab Emirates

Digital Twin (DT) aims to seamlessly replicate physical objects or processes in virtual environments, garnering attention for supporting diverse intelligent management services such as monitoring, analysis, and control. Integration with technologies like artificial intelligence (AI), big data, edge computing, and the Internet of Things (IoT) has propelled the development of DTs. However, the DT may not only introduce new potential security flaws but also inherit existing vulnerabilities from the technologies they incorporate and interact with due to their integration with diverse technologies. Despite its growing impact, research addressing the security concerns of DTs remains insufficient and still in its early stages. In this article, we provide a comprehensive and in-depth review of the current state of DTs, focusing on their security aspects. We first depict an overview of DTs, including the definition of the DT and various DT applications, and then present the architecture for DTs. Subsequently, we conduct a detailed examination of security attacks and threats across the functional layers of the DT architecture. In contrast to the previous surveys on DTs, we derive security properties and security functional requirements (SFRs) based on the Common Criteria (CC) standard for mitigating these attacks and threats. We also introduce technologies that enable the achievement of SFRs with a future research perspective. By addressing these critical security aspects, our article enhances the security and trustworthiness of DTs, contributing to their safe and reliable deployment in various domains. © 2013 IEEE.

关键词： Cyber attacks

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UAV -Based Object Tracking Using Siamese Networks and Kalman Filtering

UAV -Based Object Tracking Using Siamese Networks and Kalman...

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2025 International Conference on Multi-Agent systems for Collaborative Intelligence, ICMSCI 2025

作者： Ramanan, S.V. Rajasekar, M. Shanmugapriya, G. Sujitha, R. Akshya, J. Choudhry, Mani Deepak P Pg Institute of Technology Department of Electronics and Communication Engineering Coimbatore India Vsb college of Engineering Department of Computer Science and Engineering Technical Campus Coimbatore India Dr.N G.P Institute of Technology Department of Computer Science and Business Systems Coimbatore India Christ The King Engineering College Department of Information Technology Coimbatore India Srm Institute of Science and Technology Department of Computational Intelligence Chennai India Srm Institute of Science and Technology Department of Computing Technologies Chennai India

ISBN: (纸本)9798331509828

The ability to match features and keep track of objects in changing dynamic environments is still an important problem, particularly due to varying noise levels, diverse datasets, and high dimensional feature spaces. Euclidean Distance and Cosine Similarity based matching are existing methods that commonly do not generalize well, particularly in the case of cross domain scenarios, where noise and motion dynamics are powerful factors to limit the accuracy of tracking. However, Deep Metric Learning approaches can extract robust features at the cost of computational efficiency and are unsuitable for real-time applications. This research proposes a Siamese Network framework to solve these problems by using robust feature matching and multi-object tracking. Based on that, the proposed framework uses a dual-branch network architecture in which feature embeddings extracted from paired inputs are mapped through the same shared latent space. Furthermore, the adaptive learning rate leads to efficient convergence during the training. Experimental results show that the Siamese Network not only achieves better accuracy than the existing methods and the more such as Deep Metric Learning but also performs significantly more efficient computation. With an accuracy of 90.2 % at a precision of 88.5%, the framework's inference time is much faster than that of Deep Metric Learning at 2.0ms. Furthermore, the proposed method performs well on the generalization over datasets with a 15% improvement of cross-domain matching accuracy on Euclidean Distance. By striking this balance between accuracy, efficiency and adaptability, it shows its suitability for dynamic and noisy environments for real-time tracking. The proposed method fills existing gaps and achieves a new state of the art for feature matching and tracking performance. © 2025 IEEE.

关键词： Object tracking

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An improved and decentralized/distributed healthcare framework for disabled people through AI models

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Alexandria engineering Journal 2025年 125卷 441-448页

作者： Rathee, Geetanjali Garg, Sahil Kaddoum, Georges Alzanin, Samah M. Hassan, Mohammad Mehedi Department of Computer Science and Engineering Netaji Subhas University of Technology Dwarka Sector-3 New Delhi110078 India Electrical Engineering Department École de technologie supérieure MontréalQC H3C 1K3 Canada Centre for Research Impact & Outcome Chitkara University Institute of Engineering and Technology Chitkara University Punjab Rajpura140401 India Department of Computer Science College of Computer Engineering and Sciences Prince Sattam bin Abdulaziz University Al-Kharj11942 Saudi Arabia Department of Information Systems College of Computer and Information Sciences King Saud University Riyadh11543 Saudi Arabia King Salman Center for Disability Research Riyadh11614 Saudi Arabia

Access to adequate healthcare is critical for everyone, but people with disabilities often face considerable challenges in receiving reliable and timely medical treatment. The Vision 2030 plan in Saudi Arabia intends to change the healthcare system by incorporating new technologies that increase accessibility, efficiency, and service delivery. However, current healthcare systems continue to suffer from delays, inefficient data processing, and accessibility concerns, especially for the visually impaired. This study proposes a more decentralized healthcare system that uses artificial intelligence (AI) and machine learning (ML) models to improve healthcare services for individuals with disabilities. The system achieves real-time data processing, reduces latency, and enhances decision-making accuracy by combining federated learning and zero-shot architectures. Furthermore, smart technologies such as the Internet of Things (IoT) and natural language processing (NLP) provide seamless data collection and analysis, allowing healthcare practitioners to provide prompt and personalized treatment. The suggested solution solves crucial issues such as inefficiencies in data processing, delays in obtaining medical information, and limits in current healthcare processes. This platform improves impaired people's freedom and mobility by delivering remote healthcare solutions using AI-powered diagnostics and real-time monitoring. This study contributes to a more inclusive and efficient healthcare system in Saudi Arabia by bridging the gap between technology and accessibility, which aligns with the Vision 2030 objective of providing fair healthcare services to everyone. © 2025 The Authors

关键词： Electronic health record

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Neutrosophic Sets and systems: A Decade of Scientific Contribution and Growth

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Neutrosophic Sets and systems 2025年 81卷 438-465页

作者： Saqlain, Muhammad Merigó, José M. Kumam, Poom Department of Mathematics King Mongkut’s University of Technology Thonburi Bangkok10140 Thailand School of Computer Science Faculty of Engineering and Information Technology University of Technology Sydney 81 Broadway UltimoNSW2007 Australia

Neutrosophic Sets and systems (NSS) has become an important Journal for neutrosophic theory and its applications in uncertainty modeling and decision sciences. In 2023, NSS celebrated its 10th anniversary, marking a decade of contributions to scientific literature. Motivated by this event, this paper provides a critical bibliometric analysis of NSS’s evolution as a leading academic journal in neutrosophy. This study explores the publication trends, citation impact, most productive authors, institutions, and global collaboration networks of NSS. The study maps bibliographic coupling, keyword co-occurrence and co-citation analysis using data from the Scopus database, secondary search using NSS webpage, and visualization tools like the VOSviewer. The findings identify Neutrosophic Sets, Decision-Making, and Fuzzy Sets as the most frequently studied keywords that highlight the increasing integration of neutrosophic approaches into multi-criteria decision-making (MCDM) and soft computing. The results indicate the most significant areas of research that are topology, soft set theory, hypersoft sets, and uncertainty modeling. The findings highlight the NSS increasing international collaboration, with contributions from America, Middle East, and Asia. Additionally, the analysis explores NSS’s alignment with sustainable development goals (SDGs), revealing notable contributions to good health and well-being (SDG 3), quality education (SDG 4), and peace, justice, and strong institutions (SDG 16). These contributions highlight how neutrosophic sets and systems is valuable across many researchers, from public health to governance to education. Overall, this paper serves as a fundamental study for researchers looking to explore emerging trends and novel applications of neutrosophic theories across diverse scientific and practical domains. © 2025, University of New Mexico. All rights reserved.

关键词： Bibliographies

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Linear Convergent Distributed Nash Equilibrium Seeking With Compression

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IEEE Transactions on Automatic Control 2025年第5期70卷 3316-3323页

作者： Chen, Xiaomeng Wu, Yuchi Yi, Xinlei Huang, Minyi Shi, Ling Hong Kong University of Science and Technology Department of Electronic and Computer Engineering Hong Kong Shanghai University School of Mechatronic Engineering and Automation Shanghai200444 China College of Electronics and Information Engineering Tongji University Department of Control Science and Engineering Shanghai201804 China Massachusetts Institute of Technology Lab for Information & Decision Systems CambridgeMA02139 United States Carleton University School of Mathematics and Statistics OttawaONK1S 5B6 Canada

Information compression techniques are majorly employed to reduce communication cost over peer-to-peer links. In this article, we investigate distributed Nash equilibrium (NE) seeking problems in a class of noncooperative games over directed graphs with information compression. To improve communication efficiency, a compressed distributed NE seeking (C-DNES) algorithm is proposed to obtain an NE for games, where the differences between decision vectors and their estimates are compressed. The proposed algorithm is compatible with a general class of compressors, including both unbiased and biased compressors. Moreover, our approach only requires the adjacency matrix of the directed graph to be row-stochastic, in contrast to past works that relied on balancedness or specific global network parameters. It is shown that C-DNES not only inherits the advantages of conventional distributed NE algorithms, achieving linear convergence rate for games with restricted strongly monotone mappings, but also saves communication costs in terms of transmitted bits. Finally, numerical simulations illustrate the advantages of C-DNES in saving communication cost by an order of magnitude under different compressors. © 1963-2012 IEEE.

关键词： Stochastic systems

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The Role of AI and Machine Learning in Cancer Diagnosis and Treatment 4

The Role of AI and Machine Learning in Cancer Diagnosis and ...

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4th International Conference on Sentiment Analysis and Deep Learning, ICSADL 2025

作者： Gawande, Tejas Verma, Prateek Datta Meghe Institute of Higher Education and Research Faculty of Engineering and Technology Department of Computer Science and Engineering Maharashtra Wardha442001 India Datta Meghe Institute of Higher Education and Research Faculty of Engineering and Technology Department of Artifical Intelligence and Machine Learning Maharashtra Wardha442001 India

ISBN: (纸本)9798331523923

AI together with ML technology now provides detailed rapid medical data evaluation to enhance cancer diagnosis and treatment methods. Advanced algorithms installed in these technologies help medical staff identify cancer indicators which human experts commonly miss leading to better diagnostic accuracy. AI-powered Magnetic Resonance Imaging (MRI) and Computed Tomography (CT) scan analysis allows doctors to perform quicker and more efficient medical diagnosis. The usage of ML models enables medical professionals to detect prognostic trace elements thus they can enhance treatment choices by analyzing tumor cell biological classification groups. AI actively participates in protein and gene targeting together with clinical trial planning to move personal cancer treatment forward. AI applications face multiple obstacles that stop their effective adoption in the field of oncology. The majority of present-day barriers to AI implementation in oncology stem from problems with data privacy together with algorithmic biases and model validation methods while the high complexity of systems and decreasing model interpretability specifically hinder usage. The success of AI models depends on extensive and diverse datasets although the actual availability of relevant data sets combined with standardization procedures continues to be difficult. Medical diagnostic applications where AI performs its decision-making work present important transparency issues that create doubts about its effectiveness. The analysis of data privacy through federated learning and XAI for interpretability and transfer learning for efficiency and deep learning for accuracy improvement are recent solutions being researched to resolve these problems. The primary aim of this research explores how AI and ML influence cancer diagnosis together with treatment methods while studying present challenges alongside suggested methods to boost their operational performance. The combination of better data governance toget

关键词： Federated learning

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Document Summarizer: A Machine Learning approach to PDF summarization

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Procedia computer science 2025年 252卷 1025-1036页

作者： Rama Krishna Peddarapu Sudagoni Anogna M N S Sri Harshini J Sireesha K Guna Rathna Department of Computer Science and Engineering“ Faculty of Computer Sciences and Engineering VNR Vignana Jyothi Institute of Engineering and Technology Hyderabad Telangana India Department of Computer Science and Engineering VNR Vignana Jyothi Institute of Engineering and Technology Hyderabad Telangana India

To address the need for summarizing and extracting information efficiently, this paper highlights the growing challenge posed by the increasing number of PDF files. Reading lengthy documents is a tedious and time-consuming task. To save time and quickly comprehend the key points of a PDF, a PDF summarizer tool has been developed to tackle these issues. In today’s professional environments, gathering and managing data from documents is critical. This article introduces an innovative solution called DocSum, which automates the process of summarizing extracted data. Powered by *** Core, the DocSum system allows users to upload PDF documents for processing and receive concise summaries in return. The system features a user-friendly interface that encourages interaction and engagement, utilizing AI and machine learning techniques to streamline document handling. Users can request specific summaries, enabling efficient document management workflows. By empowering users to seamlessly interact with vast amounts of information, DocSum enhances productivity and explores new ways to optimize document management. This solution is ideal for professionals seeking to stay informed and manage data effectively, while also keeping track of advancements in document handling technologies across multiple industries. It is not only helpful for professionals but also helpful for students who need quick revision or examinations, teachers to gain brief knowledge on the concepts.

关键词： K Means Clustering Abstractive Extractive Sentence Transformer Embeddings

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Effect of Submerged Vegetation Density on Flow Hydrodynamics, Bulk Drag Coefficient, and Bed Friction

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Iranian Journal of science and Technology - Transactions of Civil engineering 2025年 1-9页

作者： Derakhshan, Sadegh Afzalimehr, Hossein Nazari-Sharabian, Mohammad Karakouzian, Moses Faculty of Civil Engineering Iran University of Science and Technology Tehran 16846-13114 Iran Department of Mathematics Engineering and Computer Science West Virginia State University Institute 25112 WV United States Department of Civil and Environmental Engineering and Construction University of Nevada Las Vegas Las Vegas 89154 NV United States

In a laboratory flume, experiments were conducted to explore bulk flow characteristics in channels with submerged aquatic vegetation patches of finite length and width with varying densities. The experiments aimed to assess the significance of precise drag coefficients and understand the impact of vegetation on bed friction. Wooden rods were used as surrogates for vegetation. Different vegetation densities were featured in six experimental setups. The Boundary Layer Characteristics Method (BLCM) was employed to predict bed shear stress, and a force balance approach was used for calculating bulk drag coefficients. It was found that an increase in vegetation density leads to a rise in the bulk drag coefficient. The presence of vegetation was observed to significantly alter the flow field, causing a redirection of flow from vegetated to unvegetated areas. Contrary to the common assumption in many studies that the bulk drag coefficient equals one, the findings from these experiments suggested this assumption might not be accurate. Additionally, the impact of vegetation on bed friction, often overlooked, was investigated. Two classic bed friction models, which do not account for flow field heterogeneities due to vegetation, and the BLCM, which considers these heterogeneities, were used to estimate bed friction. These estimates were then used in a flow resistance model to predict total friction, including both bed and vegetation friction. The effectiveness of the BLCM over the classic models suggests inaccuracies in the assumption that vegetation does not impact bed friction. © The Author(s), under exclusive licence to Shiraz University 2024.

关键词： Bed friction Boundary layer characteristics method Drag coefficient Force balance Submerged aquatic vegetation

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Secure Chat System: Harnessing the Power of Hybrid Encryption for Enhanced Security and Confidentiality 12th

Secure Chat System: Harnessing the Power of Hybrid Encryptio...

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12th International Conference on Recent Trends in Computing, ICRTC 2024

作者： Jerlin, M. Asha Shrivastav, Rochak Anusha, K. Devarajan, Ganesh Gopal Nagarajan, Senthil Murugan School of Computer Science and Engineering Vellore Institute of Technology Kelambakkam—Vandalur Rd Rajan Nagar Tamil Nadu Chennai600127 India Department of Computer Science and Engineering Faculty of Engineering and Technology SRM Institute of Science and Technology Delhi NCR Campus Delhi - Meerut Road Modinagar Uttar Pradesh Ghaziabad201204 India Department of Mathematics Faculty of Science Technology and Medicine University of Luxembourg Esch-Sur-Alzette Luxembourg

ISBN: (纸本)9789819788354

Strong and secure communication solutions are essential in a time when data breaches and privacy violations are becoming more common. The extensive design for a secure chat system is presented in this research study with the goal of protecting user privacy and guaranteeing the accuracy of data shared. The fundamental idea of this design is hybrid encryption, which carefully combines the strength of Rivest–Shamir–Adleman (RSA) asymmetric encryption with the effectiveness of Advanced Encryption Standard (AES) symmetric encryption. The system also includes advanced cryptographic features like Diffie–Hellman Key Exchange for symmetric key exchange and secure communication channels, Elliptic Curve Digital Signature Algorithm (ECDSA) for message authenticity, and HMAC-SHA-512 for message authentication. The secure distribution of RSA public keys is facilitated by Public Key Infrastructure (PKI), which enhances the confidentiality and authenticity of communication channels. JSON Web Tokens (JWT) are used to provide authentication, offering a scalable and secure method for user verification throughout login procedures. Furthermore, the system incorporates a bot element that proactively alerts users to occurrences related to entry and leave from chat rooms, hence enhancing transparency and privacy consciousness among users. The suggested architecture provides a solid answer to the problems of secure communication in modern digital settings by fusing these cryptographic primitives and features. This architecture has undergone extensive testing and validation, proving its effectiveness in protecting sensitive data and providing users with secure, frictionless communication experiences. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Authentication

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