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Visual Analytics and Data Visualization (ICVADV), International Conference on

作者： L Sherin Beevi Ponsy R K Sathia Bhama J Albert Vijayan Joe Prathap P. M. W Vinil Dani J. Premalatha Department of Computer Science and Engineering R.M.D. Engineering College Kavaraipettai INDIA Department of Computer Technology Madras Institute of Technology Anna University Chromepet Amaala Utilities PPP Larsen & Toubro Limited Soudi Arabia Department of EEE St. Peter's College of Engg.and Tech. Chennai INDIA Department of Artificial Intelligence and Data Science Dhanalakshmi Srinivasan College of Engg. and Tech. Mamallapuram

ISBN: (数字)9798331521394

ISBN: (纸本)9798331521400

Proposed work describes an optimal framework for improving supply chain security and delivery efficiency by combining Ant Colony Optimization (ACO) and blockchain technology. Inconvenient path design is a primary trouble in last-mile delivery, causing delays and wasting resources. This study seeks to address the above issue by optimizing delivery schedules, guaranteeing that many purchasers in the same geographic area are handled by a single delivery person, hence eliminating redundant trips and delivery times. Proposed technique is driven by real-world inefficiencies seen in e-commerce systems, such as the case of two independent things bought to the same address but delivered at different times by different couriers on the same day. ACO is used to find the best delivery route, guaranteeing that all consumers in a particular area receive their items simultaneously. The framework incorporates blockchain technology to improve security, transparency, and traceability during the delivery process. Furthermore, advanced cybersecurity measures such as Root of Trust (RoT), Physically Unclonable Functions (PUFs), and Software Bill of Materials (SBOM) are used to secure data integrity and enable third-party monitoring. By combining ACO for route optimization with blockchain for safe transaction processing, this system not only improves shipping time as well as asset efficiency, but it also protects supply chain cybersecurity. The suggested approach provides a scalable and reliable framework for future smart logistics and e-commerce applications, assuring operational efficiency and security.

关键词： Ant colony optimization Visual analytics Supply chains Bills of materials Physical unclonable function Software Real-time systems Blockchains Electronic commerce Software measurement

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Zak-OTFS for Identification of Linear Time-Varying Systems

arXiv

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arXiv 2025年

作者： Nisar, Danish Mohammed, Saif Khan Hadani, Ronny Chockalingam, Ananthanarayanan Calderbank, Robert Department of Electrical Engineering Indian Institute of Technology Delhi India Department of Mathematics University of Texas Austin United States Cohere Technologies Inc. CA United States Department of Electrical and Communication Engineering Indian Institute of Science Bangalore India Department of Electrical and Computer Engineering Duke University United States IIT Delhi India

Linear time-varying (LTV) systems model radar scenes where each reflector/target applies a delay, Doppler shift and complex amplitude scaling to a transmitted waveform. The receiver processes the received signal using the transmitted signal as a reference. The self-ambiguity function of the transmitted signal captures the cross-correlation of delay and Doppler shifts of the transmitted waveform. It acts as a blur that limits resolution, at the receiver, of the delay and Doppler shifts of targets in close proximity. This paper considers resolution of multiple targets and compares performance of traditional chirp waveforms with the Zak-OTFS waveform. The self-ambiguity function of a chirp is a line in the delay-Doppler domain, whereas the self-ambiguity function of the Zak-OTFS waveform is a lattice. The advantage of lattices over lines is better localization, and we show lattices provide superior noise-free estimation of the range and velocity of multiple targets. When the delay spread of the radar scene is less than the delay period of the Zak-OTFS modulation, and the Doppler spread is less than the Doppler period, we describe how to localize targets by calculating cross-ambiguities in the delay-Doppler domain. We show that the signal processing complexity of our approach is superior to the traditional approach of computing cross-ambiguities in the continuous time / frequency domain. Copyright © 2025, The Authors. All rights reserved.

关键词： Chirp modulation

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Deep Learning Techniques for Intrusion Detection Systems in Healthcare Environments

Deep Learning Techniques for Intrusion Detection Systems in ...

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International Conference on Communication Systems and Network Technologies (CSNT)

作者： Mahfooz Alam Zubair Ashraf Priya Singh Bishwajeet Pandey Kaleemur Rehman Laura Aldasheva Department of Master of Computer Applications G. L. Bajaj Institute of Technology and Management Greater Noida India College of Computing and Information Technology University of Bisha Bisha Kingdom of Saudi Arabia Department of Computer Science Institute of Engineering & Technology Dr. B. R. Ambedkar University Khandri Agra India Department of CSE-AI G. L. Bajaj Institute of Technology and Management Greater Noida India Department of IS and CS Astana IT University Astana Kazakhstan

ISBN: (数字)9798331531935

ISBN: (纸本)9798331531942

Nowadays, healthcare systems face ever-increasing cyber security threats due to the sensitive nature of patient data and the proliferation of IoT-enabled medical devices. Traditional Intrusion Detection Systems (IDS) struggle to adapt to healthcare environments' dynamic and heterogeneous data. Deep learning techniques, with their ability to analyze complex and high-dimensional data, have emerged as a promising approach to enhancing IDS performance. This paper explores the application of deep learning methods for intrusion detection in healthcare, focusing on their advantages, challenges, and practical implementations. Topics include neural network architectures, feature engineering for medical and network data, and real-time intrusion detection in resource-constrained environments. The goal is to provide a roadmap for leveraging deep learning to ensure robust and scalable security for healthcare systems.

关键词： Deep learning Performance evaluation Medical devices High dimensional data Neural networks Intrusion detection Medical services Real-time systems Security Long short term memory

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Synergistic Integration of 5G-enabled Cloud Native Infrastructures With Advanced Technologies for Urban Safety Enhancement

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IEEE Transactions on Consumer Electronics 2025年

作者： Sharma, Ashutosh Sharma, Amit Raju, Kola Narasimha Keshta, Ismail Guo, Kai Henan University of Science and Technology Business School Henan China University of Petroleum and Energy Studies School of Computer Science Dehradun India INSA CVL PRISME Laboratory University of Orleans France Loire Valley Institute for Advanced Studies LE STUDIUM Orlèans France Chitkara University Chitkara University Institute of Engineering and Technology Punjab India Visakhapatnam India AlMaarefa University College of Applied Sciences Riyadh Saudi Arabia

Urban safety remains an important concern in modern cities, which makes it necessary for advanced approaches to address the progressing challenge such as fire risk assessment and security enhancement. This study explores the important integration of 5G-enabled cloud-native infrastructures with the Internet of Things (IoT), and Artificial Intelligence (AI) algorithm for empowering urban safety. Kubernetes is used as an essential component of the Cloud Native Computing Foundation (CNCF) ecosystem, for managing scalable and resilient infrastructures. This integration of 5G-enabled systems to cloud-native infrastructures proposes an efficient solution that offers high-speed connectivity, low latency, and the scalability required for real-time data processing and communication. In the proposed work, IoT devices are strategically deployed across urban areas to achieve comprehensive coverage and facilitate real-time data collection and monitoring, ideally integrated with Kubernetes-managed clusters to ensure data transmission and processing. Machine Learning (ML) and Deep Learning (DL) based model is trained on the collected data from the field, using Kubernetes for scalable and automated deployment providing real-time data analysis, accurate prediction, and decision-making. The proposed 5G integrated framework provides proactive risk management and enhanced emergency response strategies. The integration of 5G-enabled systems led to a significant reduction in latency, typically to 20 milliseconds, enhancing real-time performance and decision-making processes. This study introduces a framework designed to reduce latency to 20 ms and improve data transmission rates by 25%. Real-world applications, including fire detection and traffic management, demonstrated the systems effectiveness, achieving sensitivity and recall rates of 97% and 96%, respectively. These results underscore the potential of the proposed framework to enhance urban safety while addressing scalability and in

关键词： 5G mobile communication systems

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Advancing IC Fault Localization and Diagnosis Through Machine Learning

Advancing IC Fault Localization and Diagnosis Through Machin...

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Multi-Agent Systems for Collaborative Intelligence (ICMSCI), International Conference on

作者： P. Rajasekar Alok Kumar Prema Latha V Jayapal N Boddu Soundarya Santhoshi K. Karthik Mathiyalagan Sivaramkumar Department of Data Science and Business Systems School of Computing SRM Institute of Science and Technology Kattankulathur Tamil Nadu India Department of Electrical and Electronics Engineering Guru Gobind Singh Educational Society Technical Campus Bokaro Jharkhand India Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Vaddeswaram Andhra Pradesh India Department of Electronics and Communication Engineering Kongunadu College of Engineering and Technology Trichy Tamil Nadu India Department of Mechanical Engineering S.R.K.R Engineering College Bhimavaram Andhra Pradesh India Department of Electronics and Communication Engineering Velammal College of Engineering and Technology Madurai Tamil Nadu India Department of BIOSCIENCES Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences (SIMATS) Saveetha University Chennai India

ISBN: (数字)9798331509828

ISBN: (纸本)9798331509835

Fault localization and diagnosis of Integrated Circuits (ICs) are essential for maintaining dependability in contemporary electronic systems. This research presents a sophisticated system utilizing Data Augmentation, Wrapper-based feature selection, and deep learning to improve the precision of defect identification and localization. Data Augmentation methods, such as synthetic fault data generation via SMOTE, rectify dataset imbalances, facilitating the model's generalization across various fault scenarios. Wrapper-based feature selection utilizes Recursive Feature Elimination (RFE) to discern the most significant characteristics, including timing delays, voltage levels, and signal patterns, so diminishing dimensionality while preserving essential information. The framework is centred around a deep learning model that integrates Convolutional Neural Networks (CNNs) for spatial fault detection and Long Short-Term Memory (LSTM) networks for temporal analysis. Experimental findings indicate a notable enhancement in fault localization precision, less false positives, and improved model interpretability. This method offers a scalable and resilient alternative for integrated circuit defect diagnosis, tackling the increasing complexity of contemporary electronic systems.

关键词： Location awareness Deep learning Fault detection Feature extraction Data augmentation Electrical fault detection Data models Convolutional neural networks Integrated circuit modeling Long short term memory

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An Integrated IoT and Wireless System for Energy Optimization and Real-Time Monitoring Towards Sustainable and Efficient Building Management in Smart Buildings

An Integrated IoT and Wireless System for Energy Optimizatio...

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2025 International Conference on Intelligent Control, Computing and Communications, IC3 2025

作者： Singh, Someet Malik, Praveen Kumar Chauhan, Ashish Singh Kumar, Manoj Yamsani, Nagendar Singh, Parulpreet School of Electronics and Electrical Engineering Lovely Professional University Punjab Phagwara India Uttaranchal University Uttarakhand Dehradun India Mlr Institute of Technology Department of Electronics and Communication Engineering Hyderabad India School of Computer Science and Artificial Intelligence Sr University Warangal India

ISBN: (纸本)9798331527495

At a time when urbanization is increasing, and the world's energy need is growing, energy efficiency in building elements has become a key factor in the environment impact mitigation and operational cost reduction strategies. Building management is governed by traditional operational features that are often inadequate to contemporary occupation patterns, infrastructural demands, and environmental changes. Consequently, there is a growing tendency towards the implementation of integrated systems such as autonomous management systems which can oversee energy use and other resources without sacrificing comfort and safety. Cloud storage and wireless technologies have now made it possible to improve energy management like never before. By making use of such technologies, smart building systems are able to receive, transmit and monitor data from many networks in real time. This capability further enhances the control of energy-demanding devices and processes such as heating, ventilation, and lighting, which are controlled and manipulated by preset schedules and manual settings. In addition to this, predictive maintenance and detection of inefficiencies is possible due to real time data analysis trends, usage history and anticipation of external consumption forces. © 2025 IEEE.

关键词： Smart homes

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Integrated RFID System for Intralogistics Operations with Industrial Mobile Robots

Integrated RFID System for Intralogistics Operations with In...

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IEEE International Conference on Autonomous Robot Systems and Competitions (ICARSC)

作者： Fábio Daniel Pacheco Paulo M. Rebelo Ricardo B. Sousa Manuel F. Silva Hélio S. Mendonça INESC TEC - Institute for Systems and Computer Engineering Technology and Science Porto Portugal ISEP Polytechnic of Porto Porto Portugal University of Trás-os-Montes and Alto Douro (UTAD) Vila Real Portugal Faculty of Engineering University of Porto (FEUP) Porto Portugal

ISBN: (数字)9798331538606

ISBN: (纸本)9798331538613

Radio-Frequency IDentification (RFID) technologies automate the identification of objects and persons, having several applications in retail, manufacturing, and intralogistics sectors. Several works explore the application of RFID systems in robotics and intralogistics, focusing on locating robots, tags, and inventory management. This paper addresses the challenge of intralogistics cargo trolleys communicating their characteristics to an autonomous mobile robot through an RFID system. The robot must know the trolley's relative pose to avoid collisions with the surroundings. As a result, the passive tag on the cargo communicates information to the robot, including the base footprint of the trolley. The proposed RFID system includes the development of a controller board to interact with the frontend integrated circuit of an external antenna onboard the industrial mobile robot. Experimental results assess the system's readability distance in two distinct environments and with two different antenna modules. All the code and documentation are available in a public repository.

关键词： Wireless communication Wireless sensor networks Service robots Robot sensing systems Sensors Mobile robots Object recognition Collision avoidance Radiofrequency identification Autonomous robots

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Plausible Deniable Medical Image Encryption by Large Language Models and Reversible Content-Aware Strategy

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IEEE Journal of Biomedical and Health Informatics 2025年 PP卷 PP页

作者： Wu, Yirui Liu, Xinfu Cascone, Lucia Nappi, Michele Wan, Shaohua Hohai University Key Laboratory of Water Big Data Technology Ministry of Water Resources Nanjing210096 China Hohai University College of Computer Science and Software Engineering Nanjing210096 China University of Salerno Department of Computer Science SA Fisciano Italy University of Electronic Science and Technology of China Shenzhen Institute for Advanced Study Shenzhen518110 China

There is a rising concern about healthcare system security, where data loss could bring lots of damages to patients and hospitals. As a promising encryption method for medical images, DNA encoding own characteristics of high speed, parallelism computation, minimal storage, and unbreakable cryptosystems. Inspired by the idea of involving Large Language Models(LLMs) to improve DNA encoding, we propose a medical image encryption method with LLM-enhanced DNA encoding, which consists of LLM enhancing module and content-aware permutation&diffusion module. Regarding medical images generally have plain backgrounds with low-entropy pixels, the first module compresses pixels into highly compact signals with features of probabilistic varying and plausibly deniability, serving as another LLM-based layer of defense against privacy breaches before DNA encoding. The second module not only adds permutation by randomly sampling from a redundant correlation between adjacent pixels to break the internal links between pixels but also performs a DNAbased diffusion process to greatly increase the complexity of cracking. Experiments on ChestXray-14, COVID-CT and fcon-1000 datasets show that the proposed method outperforms all comparative methods in sensitivity, correlation and entropy. © 2025 IEEE.

关键词： Image enhancement

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Buckling analysis of laminated multiphase composite plates under in-plane loading using data-driven soft computing

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engineering Analysis with Boundary Elements 2025年 176卷

作者： Topal, Umut Ly, Duy-Khuong Vu, Ho-Nam Nguyen-Thoi, T. Department of Civil Engineering Faculty of Technology Karadeniz Technical University Trabzon Turkey Laboratory for Computational Mechanics Institute for Computational Science and Artificial Intelligence Van Lang University Ho Chi Minh City Viet Nam Faculty of Civil Engineering School of Technology Van Lang University Ho Chi Minh City Viet Nam Laboratory for Applied and Industrial Mathematics Institute for Computational Science and Artificial Intelligence Van Lang University Ho Chi Minh City Viet Nam Faculty of Mechanical-Electrical and Computer Engineering School of Technology Van Lang University Ho Chi Minh City Viet Nam

Laminated multiphase composite plates reinforced with carbon nanotubes (CNTs) and carbon fibers in an epoxy matrix offer excellent mechanical performance for lightweight, high-strength applications. This study focuses on their buckling behavior under in-plane loads by developing a soft computing framework that couples isogeometric analysis (IGA) with Murakami's zigzag theory for layerwise displacement representation and a deep feedforward neural network for data-driven prediction. A set of numerical simulations is conducted to generate training and validation data across various laminate thicknesses, fiber orientations, CNT/carbon fiber volume fractions, and boundary conditions. These simulation results are split into training and validation subsets, ensuring the evaluation of the machine learning model's accuracy in predicting critical buckling loads. The trained predictive models are then evaluated against reference solutions to confirm their accuracy. After verifying the models’ reliability, the framework is employed to carry out a detailed parametric investigation, assessing key parameters that affect the buckling response of laminated multiphase composites. © 2025 Elsevier Ltd

关键词： Buckling loads

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Success History Adaptive Competitive Swarm Optimizer with Linear Population Reduction: Performance benchmarking and application in eye disease detection

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computers in Biology and Medicine 2025年 186卷 109587-109587页

作者： Zhong, Rui Wang, Zhongmin Hussien, Abdelazim G. Houssein, Essam H. Al-Shourbaji, Ibrahim Elseify, Mohamed A. Yu, Jun Information Initiative Center Hokkaido University Sapporo Japan College of Tropical Crops Yunnan Agricultural University Yunnan China Department of Computer and Information Science Linköping University Linköping Sweden Faculty of Science Fayoum University Faiyum Egypt Faculty of Computers and Information Minia University Minia Egypt Department of Electrical and Electronics Engineering Jazan Jazan University Jazan Saudi Arabia Department of Computer Science University of Hertfordshire Hatfield United Kingdom Department of Electrical Engineering Faculty of Engineering Al-Azhar University Qena Egypt Institute of Science and Technology Niigata University Niigata Japan

Eye disease detection has achieved significant advancements thanks to artificial intelligence (AI) techniques. However, the construction of high-accuracy predictive models still faces challenges, and one reason is the deficiency of the optimizer. This paper presents an efficient optimizer named Success History Adaptive Competitive Swarm Optimizer with Linear Population Reduction (L-SHACSO). Inspired by the effective success history adaptation scheme and linear population reduction strategy in Differential Evolution (DE), we introduce these techniques into CSO to enable the automatic and intelligent adjustment of hyper-parameters during optimization thereby balancing exploration and exploitation across different phases. To thoroughly investigate the performance of L-SHACSO, we conduct extensive numerical experiments on CEC2017, CEC2020, CEC2022, and eight engineering problems. State-of-the-art optimizers including jSO and L-SHADE-cnEpSin and recently proposed metaheuristic algorithms (MAs) such as RIME and the Parrot Optimizer (PO) are employed as competitors. Experimental results confirm the superiority of L-SHACSO across various optimization tasks. Furthermore, we integrate L-SHACSO into DenseNet and Extreme Learning Machine (ELM) and propose DenseNet-L-SHACSO-ELM for eye disease detection, where the features extracted by the pre-trained DenseNet are fed into L-SHACSO-optimized ELM for classification. Experiments on public datasets confirm the feasibility and effectiveness of our proposed model, which has great potential in real-world scenarios. The source code of this research is available at https://***/RuiZhong961230/L-SHACSO. © 2024 Elsevier Ltd

关键词： Swarm intelligence

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