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检索条件"任意字段=3rd IEEE Intelligent Data Acquisition and Advanced Computing Systems"

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Research on the Application of Cloud computing-based Dual-Machine Hot Standby System in Railway Signal Safety Management 3

Research on the Application of Cloud Computing-based Dual-Ma...

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3rd International Conference on Integrated Circuits and Communication systems, ICICACS 2025

作者： Ji, Zhe Wang, Ding Liu, Bing Liu, Yiming Research and Design Institute Co. Ltd Shenzhen China China Academy of Railway Sciences Co. Ltd Beijing China

ISBN: (纸本)9798331508456

This paper investigates the application of cloud computing-based dual-computer hot standby computer security platform in railway signalling security management, aiming to enhance the reliability and security of railway signalling system. With the increasing importance of railway transport to modern society, the safety of railway signal system has become a key factor for public safety and social stability. This paper first analyses the security challenges faced by railway signalling systems and explores the potential of dual-machine hot-standby systems in improving system availability and security. Then, this paper details the design and implementation of a cloud-based dual-machine hot standby architecture, including system architecture, signal monitoring module, data encryption module, security audit module, and event response module. Through experimental validation, this paper compares the performance of the cloud-based dual-machine hot-standby architecture with that of the traditional single-machine system in terms of failure recovery time, resource utilisation and system response time, and the results show that the dual-machine hot-standby architecture has significant advantages in these key performance indicators. Finally, the paper discusses the limitations of the research and proposes directions for future research, including cost-benefit analysis, resource optimisation, technology adaptation research and intelligent development, with a view to providing a more efficient and intelligent solution for railway signal safety management. © 2025 ieee.

关键词： Subway signal systems

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A data-Driven Analysis on the Impact of Instagram on Loneliness, Happiness, and Social Comparison 3

A Data-Driven Analysis on the Impact of Instagram on Lonelin...

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3rd International Conference on intelligent systems, advanced computing, and Communication, ISACC 2025

作者： Acharya, Sushma Shreshta, Ganga Saru, Sabina Kumari Bhandari, Amrita Pandey, Shailesh Bahadur Everest Engineering College Department of Computer Science and Engineering Kathmandu Nepal

ISBN: (纸本)9798331523893

Instagram is an actively used social media platform among the 18-24 age group worldwide. People in this age group are especially vulnerable to mental disorders and suffer health consequences. The paper presents the findings from a data-driven examination of the associations between active and passive uses of Instagram, social comparison, loneliness and happiness among engineering undergraduates in Nepal. Clustering analysis shows that a student with low engagement on Instagram is associated with passive use (browsing feeds), but significant hours scattered throughout the day due to Instagram use. Meanwhile, a student with high engagement spends at least an hour in a single session. Bayesian network analysis finds that Instagram use is independent, and other variables are influenced by it. This supports the hypothesis that Instagram could be shaping psychological experiences and mental health outcomes. We also found that the shaping is modest but significant. Such a modest but potentially significant causal relationship was found between high Instagram use and high levels of social comparisons. Similarly, a potentially important relationship was found between high levels of social comparisons and high levels of loneliness. Our findings support the observation that social comparison orientation plays a mediating role. Interestingly, happiness neither influences nor influenced by the level of Instagram use. In the paper, we detail the computational techniques we have used, present interesting results, and discuss the model's findings. This research highlights the impact of social media use among undergraduates in a unique cultural context like Nepal, contributing to the limited body of research on social media's influence in South Asia. © 2025 ieee.

关键词： Bayesian network clustering decision tree Instagram subjective well-being

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data Privacy and Security in Healthcare systems Using Blockchain: A Review 3

Data Privacy and Security in Healthcare Systems Using Blockc...

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3rd International Conference on intelligent systems, advanced computing, and Communication, ISACC 2025

作者： Barman, Bijit Sarmah, Satyajit Gauhati University Dept. of Information Technology Guwahati India

ISBN: (数字)9798331523893

ISBN: (纸本)9798331523909

In healthcare, blockchain can enable secure and transparent management of electronic medical records (EMRs), enabling shared connectivity and data security across different representatives or stakeholders while preserving and securing patient confidential data, accuracy, and precision. An evidence-based affirmation can increase trust among patients, vendors, and regulators by reducing hazards related to illegal access & data breaches. Blockchain can overcome centralized systems that do not have access to it. This review intended to study how to preserve and secure healthcare data against external attacks. For these, a blockchain-based distributed system of storing electronic medical records (EMRs) is viewed where transactions will be kept in InterPlanetary File System (IPFS). This will ensure the security of EMRs and ease of sharing among the different stakeholders. © 2025 ieee.

关键词： Blockchain data privacy Ethereum InterPlanetary File System

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Multi-Agent systems for Autonomous IoT Network Management Using Distributed Reinforcement Learning 3

Multi-Agent Systems for Autonomous IoT Network Management Us...

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3rd International Conference on intelligent systems, advanced computing, and Communication, ISACC 2025

作者： Neelamegam, G. Venkatesan, Rajaram Ramya, S.R. Ramya, R.S. Akshya, J. Sundarrajan, M. Choudhry, Mani Deepak VSB College of Engineering and Technology Department of Computer Science and Engineering Tamil Nadu Coimbatore India Builders Engineering College Department of Artificial Intelligence and Data Science Tamil Nadu Tirupur India Dr NGP Institute of Technology Department of Computer Science and Engineering Tamil Nadu Coimbatore India Sri Ramakrishna Engineering College Department of Computer Science and Engineering Tamil Nadu Coimbatore India SRM Institute of Science and Technology Department of Computational Intelligence Tamil Nadu Chennai India SRM Institute of Science and Technology Department of Networking and Communications Tamil Nadu Chennai India SRM Institute of Science and Technology Department of Computing Technologies Tamil Nadu Chennai India

ISBN: (纸本)9798331523893

The ever-growing complexity of IoT networks ignited by their wide scale adoption in applications such as smart cities, the industrial automation, and health care, compelled to develop sophisticated yet resource efficient and scalable network management solution. Centralized systems are traditional in architecture, which leads to higher latency and bottlenecks, while decentralized systems can distribute workloads however the lack of adaptability to real time, along with the difficulty of extracting maximum resource usage, are limitations. In recent years, reinforcement learning (RL) based approaches have taken shape as a promising alternative to facilitate the automated learning and optimisation of task management. However, there are two challenges for existing RL based methods: slow convergence, inefficient priority assignment of tasks, and poor coordination amongst agents in the case of dynamic, large scale IoT environment. To solve these challenges, in this research we propose a distributed reinforcement learning based multi agent framework for managing IoT network. The framework also integrates a task prioritization mechanism to dynamically choose an optimal task allocation solution. Further validation was performed in a simulated IoT environment where real world inspired datasets were used. The proposed framework demonstrated 30% more task throughput than centralized, 25% less latency than decentralized, and 40% more energy efficiency than current RL based systems. The system was further validated using advanced visualizations such as anomaly detection maps and resource allocation efficiency graphs, demonstrating its capacity to manage dynamic load of tasks and resource allocation. The proposed framework addresses the critical limitations of current methods, through scalable, adaptive, and energy efficient framework for managing IoT network, and accordingly helps advancing the intelligent and autonomous IoT systems. © 2025 ieee.

关键词： Distributed Reinforcement Learning IoT Network Management Multi-Agent systems Resource Optimization

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Cloud-Based AI Robotics for Precision Pruning of Fruit Trees to Improve Sustainable Farming 3

Cloud-Based AI Robotics for Precision Pruning of Fruit Trees...

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3rd International Conference on intelligent systems, advanced computing, and Communication, ISACC 2025

作者： Ilambirai, R.C. Barkathulla, A. Mahalakshmi, G. Babu, J. Jagan Prabu, K. Srinivasan, C. SRM Institute of Science and Technology Department of Electrical and Electronics Engineering Tamil Nadu Chennai India Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology School of Computing Department of Computer Science and Engineering Tamil Nadu Chennai India Chennai Institute of Technology Department of Computer Science and Engineering Tamil Nadu Chennai India R.M.D Engineering College Department of Electronics and Communication Engineering Tamil Nadu Chennai India Gojan School of Business and Technology Department of Electronics and Communication Engineering Tamil Nadu Chennai India Saveetha University Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Computer Science and Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798331523893

The crucial agricultural practice of precision pruning fruit trees greatly impacts fruit production yield and quality. Traditional pruning techniques take a lot of time and money because they need skilled workers and are labor-intensive. It suggests a novel cloud-based AI robotics system intended to precisely prune fruit trees to address these issues. This system uses cutting-edge robotics fitted with Convolutional Neural Networks (CNNs) to carry out precise and effective tasks related to pruning. Cloud computing integration makes it possible to process and make real-time decisions, improving the system's flexibility in responding to changing tree conditions and pruning needs. The system's primary parts are robotic arms for accurate cuts, various sensors for analyzing tree structures, and CNN models for maximizing pruning techniques. Early field trials show noticeable gains in operational efficiency and pruning accuracy compared to conventional techniques. This study demonstrates how cloud-integrated AI robotics can transform agricultural practices and improve fruit farming's sustainability and productivity. © 2025 ieee.

关键词： Agricultural Automation Fruit Trees Real-Time data Processing Sensor Integration Sustainable Farming

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data Security Protection System in Human-Machine-Object Fusion intelligent Demand Engineering System 3

Data Security Protection System in Human-Machine-Object Fusi...

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3rd International Conference on Integrated Circuits and Communication systems, ICICACS 2025

作者： Qi, Yongjun Li, Chenggao Tang, Hailin Shen, Junxuan Guangdong Baiyun University Faculty of Megadata and Computing Guangdong Guangzhou China School of Information and Communication Technology Mongolian University of Science and Technology Ulaanbaatar Mongolia

ISBN: (纸本)9798331508456

In the engineering system of human-machine physical intelligence requirements, the multi-source and high complexity of data pose significant security risks. Therefore, this article designs a comprehensive data security protection system. Firstly, this article chooses a dynamic encryption strategy based on role-based access control. Then, this article combines differential privacy algorithm to construct a privacy protection model to ensure the privacy of data during sharing and processing. Subsequently, this article proposes a trusted data transmission protocol. Finally, this article introduces federated learning techniques to further improve the security and robustness of data in distributed scenarios. The experimental results show that the system performs well in terms of data security, privacy protection, and processing efficiency. Especially under high-intensity attacks, the system successfully detected and prevented approximately 98% of tampering. In the above data conclusions, the research method has significant advantages in encryption performance, achieving a better balance between privacy protection and data accuracy, demonstrating strong application prospects. © 2025 ieee.

关键词： Requirements engineering

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A Hybrid Framework for Secure Group Communication Using Quantum-Classical Cryptography and Reinforcement Learning 3

A Hybrid Framework for Secure Group Communication Using Quan...

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3rd International Conference on intelligent systems, advanced computing, and Communication, ISACC 2025

作者： Renisha, P.S. Rudra, Bhawana National Institute of Technology Department of Information Technology Karnataka Surathkal India

ISBN: (纸本)9798331523893

This research work describes a framework for secured and effective way of group interaction incorporating classical cryptography and quantum communication technique. This framework employs a classical cryptographic methods for group logistics like as transmitting of messages and membership management while employing quantum communication strategy for secure key distribution and related authentication. In order to increase the flexibility and data-security of the proposed system further, a supervisor learning based mechanism is incorporated into the framework where reinforcement learning will employed in controlling the interaction of the protocols and the decision-making processes in an active manner. This combination of classical, quantum and supervisor learning strategies gives a solution to the issues of scalability, efficiency, effective and timely actionable response to increasing cyber threats especially in the era of quantum computing. The framework is highly effective and secured for group interaction in distributed network infrastructures. It will be a leading the approach for advanced cryptographic mechanism in the future. © 2025 ieee.

关键词： authentication classical cryptography distributed systems hybrid framework key distribution machine learning quantum communication quantum computing reinforcement learning secure group communication

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An intelligent and Dynamic Replication Scheme for Reliable Cloud Environment 3

An Intelligent and Dynamic Replication Scheme for Reliable C...

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3rd International Conference on Integrated Circuits and Communication systems, ICICACS 2025

作者： Damodhar, M. Rao, Ch.D.V. Subba Sri Venkateswara University College of Engineering Department of Computer Science and Engineering Tirupati India

ISBN: (纸本)9798331508456

Recently, cloud computing research, especially Virtual Machine based replication schemes and their applications, were becoming prevalent. A cloud data center (DC) comprises of hosts with an enormous of virtual machines (VMs) where determining replica quantity and replication order is a tedious task to minimize cost and bandwidth. Majority of the existing dynamic replication techniques rely on budget constraints and server failure rate to guarantee customer requirements. But, there is a strong need to determine replication order of servers to retain the performance and system stability. In this paper, we suggest two different algorithms for better selection with proper and intelligent placement of replicas in the cloud region. The proposed algorithms towards dynamic replication are request based replica selection (RRS) and ant colony optimization (ACO). The foremost proposed algorithm was request based replica selection used for obtaining the best selected replicas depends upon the VM performance ranking and failure probability based on the task requirement. Conversely, the subsequent offered algorithm is ACO that is helpful to find the best location to place the designated replica depends on the least-cost distance metric, and the replicas availability in order to encompass and regulate with the entire network load and the link bottlenecks. The performance of proposed techniques using CloudSim was evaluated against various pro-active fault tolerance strategies. The simulation results gives that dynamic replica selection provides efficient task replication when compared with the existing pro-active algorithms. Additionally, ACO realizes high availability, low cost, and less bandwidth utilization. © 2025 ieee.

关键词： Ant colony optimization

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Improving Sleep Health Through data-Driven Machine Learning Insights 3

Improving Sleep Health Through Data-Driven Machine Learning ...

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3rd International Conference on intelligent systems, advanced computing, and Communication, ISACC 2025

作者： Kapoor, Ananya Suyal, Priyanka Bhardwaj, Rakhi Gola, Kamal Kumar Kumar, Mohit Verma, Aman College of Engineering Roorkee Dept. of CSE Roorkee India COER University Dept. of AI-ML Roorkee India COER University Dept. of CSE Roorkee India College of Engineering Roorkee Dept. of AI-ML Roorkee India

ISBN: (纸本)9798331523893

Sleep is a critical component of overall health and well-being, yet sleep disorders and poor sleep quality affect millions worldwide. Traditional approaches to sleep assessment, often reliant on self-reports and polysomnography, can be costly and impractical for long-term monitoring. Our approach includes Machine learning algorithms, including random forests, gradient boosting, and data visualization techniques, are employed to predict sleep stages and identify factors influencing sleep quality. Experimental results demonstrate the efficacy of the proposed models, achieving high accuracy in sleep stage classification and actionable insights for sleep improvement. This work underscores the potential of machine learning to revolutionize sleep health management by providing scalable, data-driven, and personalized solutions. © 2025 ieee.

关键词： component formatting insert style styling

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Empowering Leather Quality Assurance: Leveraging Convolutional Neural Networks for Precise Defect Detection and Classification 3

Empowering Leather Quality Assurance: Leveraging Convolution...

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3rd International Conference on intelligent systems, advanced computing, and Communication, ISACC 2025

作者： Logeswaran, K. Suresh, P. Savitha, S. Sah, Swati Kr, Prasanna Kumar Rajadevi, R. Vasugi, M. Sujit, B. Akilesh, T. Kongu Engineering College Center of Excellence in Real-Time Analytic Department of Artificial Intelligence Tamilnaud Erode India Vellore Institute of Technology Department of Database Systems Tamil Nadu Vellore India K.S.R College of Engineering Department of Computer Science and Engineering Tamil Nadu Tiruchengode India Sharda University School of Engineering and Technology Uttar Pradesh Greater Noida India Kongu Engineering College Department of Information Technology Tamilnaud Erode India Kongu Engineering College Department of Artificial Intelligence Tamilnaud Erode India

ISBN: (纸本)9798331523893

Ensuring the development of high-quality leather products in the industrial sector requires a strong focus on leather quality assurance. In this study, a unique method for enhancing leather quality assurance using Convolutional Neural Networks (CNNs) is presented. Our suggested solution is able to detect and classify leather defects with amazing accuracy and efficiency by utilizing deep learning. CNN integration for leather fault categorization and detection offers a more reliable and affordable solution, ensuring better product quality and satisfied customers. Existing methods for inspecting leather products often suffer from inconsistent quality control, subjective manual inspection processes, and the complexity of accurately identifying and classifying various types of leather defects. By addressing these challenges, the proposed system aims to empower leather manufacturers with a reliable and efficient solution for ensuring consistent product quality and reducing production costs. Convolutional Neural Networks (CNNs) are integrated to provide a highly automated and effective solution that overcomes these scaling challenges. The proposed CNN-based method uses deep learning algorithms to decode images, which allows the system to swiftly and precisely analyse enormous volumes of visual data. To make sure the CNN can differentiate between various defect classes and keep a low rate of false positives and false negatives, its accuracy, precision, recall, and F1 score should be carefully assessed. © 2025 ieee.

关键词： automation Convolutional Neural Networks (CNNs) deep learning defect classification defect detection image analysis Leather quality assurance manufacturing industry precision product quality control

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