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Enhanced Fire Detection for Industrial and Environmental safety Using IoT-Integrated vision sensors and Residual Clutch Attention Network and Depthwise Distinguishable Convolutional Neural Networks

Enhanced Fire Detection for Industrial and Environmental Saf...

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Intelligent Data Communication Technologies and Internet of Things (IDCIoT), International Conference on

作者： D.Christy sujatha Priyanshu singh C. vallikannu Ravi varman skanda M G T. Gavaskar Department of Software Engineering Periyar Maniammai Institute of Science & Technology Thanjavur Tamil Nadu India Department of Computer Science and Engineering R.V college of engineering Bangalore Karnataka India Department of Humanities and Sciences Rajalakshmi Engineering College Chennai Tamil Nadu India Department of Electrical and Electronics Engineering Karpagam academy of Higher Education Coimbatore Tamil Nadu India Department of Industrial and Production Engineering SJCE JSS Science and Technology University Mysore Karnataka India Department of Mechanical Engineering St. Joseph's College of Engineering Chennai Tamil Nadu India

ISBN: (数字)9798331527549

ISBN: (纸本)9798331527556

Fire detection is an important factor in improved safety in industrial and environmental conditions. The conventional flame detectors, largely relying on heat and smoke detectors, have drawbacks of time lag, low sensitivity, and low coverage when compared with our requirement in large industrial scenes and vast natural terrains vulnerable to fire occurrence. These systems are highly unsuitable for dynamic and high-risk situations as they require a higher accuracy and a faster reaction time for the alarms to be efficient as is now the case due to the aforementioned reasons. In an attempt to remedy these limitations, this research introduces a fire detection system using IoT and vision sensors that combines a RCAN with DDCNN. This architecture is tuned using the Crayfish Optimization Algorithm (COA), which seeks to improve model parameter to improve response time and accuracy. by incorporating feedbacks from sensors placed at its different parts and reading from the environment, the RCAN-DDCNN model can quickly identify the fire patterns and respond by activating the sprinkler systems as well as setting off alarms. The proposed system presents a superior option for fire detecting because it is scalable and adaptive and it provides high degree of precision, making it an optimal development in fire detection technology for industry and environment. The introduced approach attains higher accuracy as 99%.

关键词： support vector machines Accuracy vision sensors sensor systems stability analysis safety Convolutional neural networks Time factors Optimization smoke detectors

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Integration of Continuous Improvement Approach in sustainable E-Government services Towards Citizen satisfaction in Health sector Development of Conceptual Model

Integration of Continuous Improvement Approach in Sustainabl...

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Automation and Computation (AUTOCOM), International Conference on

作者： R. Anandhakrishnan D. vimala s. vasantha Department of Biotechnology V.S.B. College of Engineering Karur Tamil Nadu India School of Management Studies Vels Institute of Science Technology & Advanced Studies(VISTAS) Chennai Tamil Nadu India Department of MBA Saveetha Engineering College Chennai Tamil Nadu India

ISBN: (数字)9798331542375

ISBN: (纸本)9798331542382

E-government success relies on high-quality e-services. Understanding e-government service quality is crucial for improving existing and future implementations. As the globe advances in communication and information technology, study on e-government services especially in health sector since covid-19 is gaining momentum. This study introduces a conceptual model for measuring the impact of continuous improvement approach on sustainable e-government services in health sector. The suggested model identifies five stream dimensions to assess the quality of e-government services based on a survey of relevant literature. This article focuses on the five dimension of continuous improvement approach the efficiency, accessibility, and sustainability of e-government services. This research provides a conceptual model for integrating continuous improvement approach to promote sustainable long-term e-government services in the health care area. Governments may use AI-driven analytics, predictive modeling, and automation to improve administrative procedures, personalise healthcare services, and enable data-driven decision-making. The concept of continuous improvement approach is central to the model, in which AI algorithms continuously analyze data from various sources, such as electronic health records, patient feedback and performance metrics. The outcome of the conceptual model is to identify the effect of integrating continuous improvement approach in the healthcare sector to achieve sustainable e-government services that optimizes resource allocation, improves service quality, and fosters innovation for Citizen satisfaction. This research adds to the literature on e-government and healthcare management by presenting a new conceptual model that combines ideas of continuous improvement. Future study should concentrate on empirical validation and implementation methodologies to determine the suggested model's usefulness and scalability in real-world scenarios. Overall, incorpor

关键词： Analytical models Technological innovation Automation Medical services Predictive models Data models vaccines Continuous improvement sustainable development Electronic government

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Theoretical bound and Compensation for Residual Error Probability of GRAND-CRC-based Functional safety Communication

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IEEE Transactions on Communications 2025年

作者： Zhan, Ming Pang, Zhibo Wu, Fang Zhou, Qiang Zhang, shiqing Zhang, Jianwu Yu, Kan Taizhou University College of Electronic and Information Engineering Zhejiang Taizhou318000 China ABB Corporate Research Forskargränd 7 VästeråsSE-721 78 Sweden School of Electrical Engineering and Computer Science Department of Intelligent Systems StockholmSE-114 28 Sweden Southwest University College of Economics and Management Chongqing400715 China Hangzhou Dianzi University College of Telecommunication Engineering Hangzhou310018 China La Trobe University Bendigo Campus School of Engineering and Mathematical Sciences Department of Computer Science and Information Technology Edwards Rd Flora HillVIC3552 Australia

In modern Industrial Internet of Things (IIoT) ecosystems, functional safety protocols are increasingly utilized to transmit safety protocol data unit (sPDU). Integrating the universal guessing random additive noise decoding (GRAND) algorithm with cyclic redundancy check (CRC)-coded sPDU can minimize sPDU retransmissions. However, it introduces residual error probability (REP) degradation that requires careful consideration. Using the IEC 61784-3 standard and CRC assumptions, we derive a closed-form REP evaluation formula specific to sPDU length and maximum error correction capability. Our analysis reveals that, under the worst industrial conditions and for an sPDU length of 128 bits, the REP performance degrades by approximately 2:2 × 102 times when the CRC signature length is 24 bits and up to one bit is guessing decoded. This degradation becomes more pronounced with increased error correction capability. To address this, we propose to compensate the REP degradation by adopting longer CRC signature. This paper provides a theoretical framework for adopting the GRAND algorithm in functional safety communication, setting a foundation for enhancing reliability in IIoT applications. © 1972-2012 IEEE.

关键词： Decoding

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Quantum-Driven Hybrid Network Architecture Leveraging 5G for High-speed and Resilient Data Transmission

Quantum-Driven Hybrid Network Architecture Leveraging 5G for...

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Mobile Computing and sustainable Informatics (ICMCsI), International Conference on

作者： Kusuma Ashok Kumar R. Mohandas sajja suneel M.R. Mahalakshmi Avinash Kumar Manmohan singh Department of Physics B V Raju Institute of Technology Telangana state India Electronics and Communication Engineering Chennai Institute of Technology Chennai Department of CSE (Data Science) Institute of Aeronautical Engineering Hyderabad Telangana India Department of ECE Sri Muthukumaran Institute of Technology Chennai Tamil Nadu Department of Computer Science and Engineering IES College of Technology Bhopal

ISBN: (数字)9798331522667

ISBN: (纸本)9798331522674

The integration of quantum communication principles with 5G technology presents a groundbreaking approach to addressing the growing demand for high-speed, secure, and resilient communication systems. This paper proposes a Quantum-Driven Hybrid Network Architecture that leverages Quantum Key Distribution (QKD) for unparalleled security and combines it with 5G's ultra-fast data transmission capabilities. The architecture integrates key components such as QKD modules, 5G infrastructure, and edge computing, organized into a layered framework comprising quantum, network control, and application layers. simulation results demonstrate significant improvements in throughput (up to 23.5%), latency reduction (up to 25%), and error rate reduction in high-noise environments (from 0.25% to 0.05%). The proposed system also exhibits enhanced security, achieving a 100% success rate in mitigating eavesdropping and man-in-the-middle attacks. Using resource-efficient mechanisms, the hybrid network achieves an overall accuracy of 97.8% in secure and resilient data transmission. This work validates the potential of quantum-enhanced 5G systems to meet the challenges of future communication networks.

关键词： 5G mobile communication scalability computer architecture Network architecture Throughput Quantum key distribution Data communication Resource management Quantum networks Edge computing

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FLes: A Federated Learning-Enhanced semantic Communication Framework for Mobile AIGC-Driven Human Digital Twins

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IEEE Network 2025年

作者： Okegbile, samuel D. Gao, Haoran Talabi, Oluwasegun Cai, Jun Yi, Changyan Niyato, Dusit shen, Xuemin University of the Fraser Valley School of Computing AbbotsfordBCV2S 7M8 Canada Concordia University Department of Electrical and Computer Engineering MontrealQCH3G 1M8 Canada Nanjing University of Aeronautics and Astronautics College of Computer Science and Technology Jiangsu Nanjing211106 China Nanyang Technological University College of Computing and Data Science Singapore University of Waterloo Department of Electrical and Computer Engineering Canada

Innovative mobile artificial intelligence-generated content (AIGC) can support the evolution and updating processes of virtual twins (vTs) in human digital twin (HDT) systems. With a reliable and efficient automatic data generation process, the requirement for a timely physical-to-virtual synchronization in HDT can be satisfied. While such an AIGC-enabled HDT system can facilitate modelling high fidelity vTs, generating content that represents the true states in the physical environment and providing timely customized services, it may suffer from a poor understanding of contexts, a lack of creativity, and various security and privacy concerns. In this paper, we propose a novel framework, which integrates federated learning (FL) and semantic communication (semCom) to enhance performance in the AIGC-enabled HDT system while improving accuracy and convergence properties. First, we present a holistic architectural framework for the proposed FL-enhanced semCom (FLes) solution for mobile AIGC-enabled HDT systems and discuss its design requirements and challenges. We later present key technologies necessary to realize the FLes solution, followed by elaborating on important technical issues to suggest future directions. Experimental results demonstrate that FLes not only facilitates reliable and personalized content generation but also shows better performance when compared to existing solutions. © 1986-2012 IEEE.

关键词： Federated learning

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Autonomous vehicle Traffic Flow Optimization Using AI and 5G Communication Networks

Autonomous Vehicle Traffic Flow Optimization Using AI and 5G...

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Device Intelligence, Computing and Communication Technologies, (DICCT), International Conference on

作者： Jayant shekhar Rama Prabha K. P s. Prashanth Umamaheswari K Rahul s G K. Gayathri Department of CSE Sharda School of Engineering and Technology Sharda University Greater Noida UP India School of Computer Science and Engineering Vellore Institute of Technology Chennai Tamil Nadu India Department of Aeronautical Engineering Excel Engineering College Namakkal Tamil Nadu India Department of Information Technology V.S.B. Engineering College Karur Tamilnadu India Department of Electronics and Communication Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai Tamil Nadu India Department of Artificial Intelligence and Data Science St. Joseph's Institute of Technology Chennai Tamil Nadu India

ISBN: (数字)9798331543358

ISBN: (纸本)9798331543365

Real-time optimization of autonomous vehicle (Av) traffic flow using 5G communication networks and AI algorithms could overcome the limitations of static traffic enforcement, which uses outdated technologies to manage fluctuating traffic demands. It allows dynamic flow. such commodities cannot adapt in real time because to their rigorous handling requirements, which disrupts traffic dynamics and creates construction delays and traffic congestion. The study suggests transmitting real-time vehicle status data to nearest intelligent crossings via vehicular wireless networks (vWNs). These intersections enhance traffic flow and reduce 5G network congestion using AI algorithms. Predictive analytics will help Avs change routes while on the road to maximize travel efficiency. It finds that peak congestion, average route duration, and fuel use reduce vehicle CO2 emissions by 66.7%, 22%, and 15%, respectively. It determined that the proposed system was a viable design route for a deployable smart city transportation solution because it outperformed traditional techniques in economic and environmental criteria.

关键词： 5G mobile communication smart cities Heuristic algorithms Real-time systems Artificial intelligence vehicle dynamics Autonomous vehicles Optimization Traffic congestion Intelligent sensors

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Optimizing English Lexical Databases with bERT and Reinforcement Learning

Optimizing English Lexical Databases with BERT and Reinforce...

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Advances in Electrical, Computing, Communication and sustainable Technologies (ICAECT), International Conference on

作者： v. saranya G.R.K. Murthy Purnachandra Rao Alapati M. Mythili K. swarnamughi b Kiran bala Department of English Panimalar Engineering college Chennai India Department of B.S&H (English) Narasaraopeta Engineering College Narasaraopet India Department of English Prasad V Potluri Siddhartha Institute of Technology Andhra Pradesh India Department of English Nandha Engineering College(Autonomous) Erode India Department of English Sathyabama Institute of Science And Technology Chennai India Department of AI & DS K.Ramakrishnan College of Engineering Trichy India

ISBN: (数字)9798350356236

ISBN: (纸本)9798350356243

The evolution of natural language processing (NLP) has significantly transformed the way this study analyzes and understand language. This study presents a novel approach to generating lexical databases for English language sources using AI-powered methods, specifically leveraging bERT and reinforcement learning. Traditional methods for lexical database creation often rely on manual compilation or semi-automated techniques, which can be time-consuming and may lack comprehensive coverage of linguistic nuances. These existing approaches struggle with scalability and adaptability, particularly when addressing evolving language use and the inclusion of rare or domain-specific terms. The Open English WordNet dataset serves as a foundational resource, providing rich lexical relationships and contextual information necessary for effective database generation. Our proposed method integrates bERT for context-aware embedding generation and reinforcement learning for continuous optimization of lexical extraction and enrichment processes. This innovative combination not only enhances the accuracy of detecting synonyms, antonyms, hypernyms, and partof-speech tags but also ensures that the system learns from user feedback, leading to ongoing refinement. The advantages of this method include improved precision and recall, rapid processing times, and the ability to adapt to new linguistic data dynamically. Ultimately, this study demonstrates that AI-driven approaches can significantly enhance the quality and usability of lexical databases, paving the way for more sophisticated applications in natural language processing and linguistics.

关键词： Databases scalability semantics Reinforcement learning bidirectional control Linguistics Transformers Natural language processing Encoding Usability

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Detection of Emergency vehicles in Traffic and Assign Traffic Free Path Using Deep Learning

Detection of Emergency Vehicles in Traffic and Assign Traffi...

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sentiment Analysis and Deep Learning (ICsADL), International Conference on

作者： sriharsha vikruthi Tejesh Reddy singasani v T Ram Pavan Kumar M P v v s D Nagendrudu Ch Raghavendra R. sahith Dept of CSE B V Raju Institute of Technology Narsapur Medak Telangana India University of Cumberlands Lousiville KY Dept of Computer Science Kakaraparti Bhavanarayana College Vijayawada Andhrapradesh India Department of Artificial Intelligence and Machine Learning Aditya Univeristy Suramplaem India Dept of CSE(Data Science) KKR & KSR Institute of Technology and Sciences Guntur Andhra Pradesh Dept of Computer Science and Engineering CVR College of Engineering Hyderabad Telangana India

ISBN: (数字)9798331523923

ISBN: (纸本)9798331523930

Too much of Traffic jams are in a highly populated country. sometimes emergency vehicles such as fire-fighter and ambulance get stuck in traffic, putting lives at threat in many cases. Priority should be given to special vehicles and their way should be cleared. However, traffic police find it difficult or sometimes impossible to handle with it. Emergency vehicles have visual and audio warning devices help them to overtake traffic by demanding as little space as possible from other vehicles. Many death happen due to delay in doctors, ambulance and fire engine vehicles while reaching their destinations. The traditional systems didn't provide path free route accurately. Therefore, by using detection of emergency vehicles in traffic and assign traffic free path using deep learning system, emergency vehicles can get free path or fast route by observing the traffic cameras. so, those emergency vehicles can reach the location on time by using RNN system. Therefore, the main objective is to improve this system for better results interms of accuracy, detection rate and precision.

关键词： Deep learning space vehicles visualization sentiment analysis Recurrent neural networks Accuracy Law enforcement Roads Medical services Automobiles

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Optimizing Federated semantic Learning in Distributed AIGC-Enabled Human Digital Twins: A Multi-Criteria and Multi-shard User selection Framework

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IEEE Transactions on Mobile Computing 2025年

作者： Okegbile, samuel D. Gao, Haoran Talabi, Oluwasegun Cai, Jun Niyato, Dusit shen, Xuemin Concordia University Department of Electrical and Computer Engineering MontrealQCH3G 1M8 Canada University of the Fraser Valley School of Computing AbbotsfordBCV2S 7M8 Canada Nanyang Technological University College of Computing and Data Science Singapore University of Waterloo Department of Electrical and Computer Engineering Canada

Artificial intelligence-generated content (AIGC) has been proposed as a solution to meet the requirements of ultra-reliable, secure, and privacy-preserving connectivity in human digital twin (HDT) networks. In such an AIGC-enhanced HDT, contents representing the true statuses of physical twins are generated in the virtual environment for the immediate update and evolution of the corresponding virtual twins (vTs). However, adopting a distributed AIGC in HDT presents several challenges, including the need for personalized vTs, data privacy concerns, and insufficient contextual understanding. This paper introduces a multi-layer federated semantic learning framework to address these challenges, incorporating batch learning to meet the training requirements for semantic-channel encoders and decoders. Furthermore, we introduce a novel user association framework to maximize the overall system performance under shard formation constraints. We then formulate a long-term joint optimization problem for user selection over finite learning periods. A novel Lyapunov-based online optimization strategy was proposed to mitigate the impact of time-varying and unpredictable training conditions. Additionally, we introduce a multi-arm bandit-based method and a context-centric user selection approach to solve the optimization problem. The results demonstrate that the proposed user association framework addresses the limitations of existing approaches, thereby improving the overall performance of the multi-shard AIGC-enhanced HDT. © 2025 IEEE.

关键词： Federated learning

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Transforming Hospital visitor screening with Cloud-Powered Deep Learning for safety and security

Transforming Hospital Visitor Screening with Cloud-Powered D...

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IEEE students' Conference on Electrical, Electronics and computer science (sCEECs)

作者： D. Antony Joseph Rajan sivanesan T M N. Mohankumar M. srinivasan Audithan sivaraman s. velmurugan Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Chennai Tamil Nadu India Department of Computer Science and Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai Tamil Nadu India Symbiosis Institute of Technology Symbiosis International (Deemed University) Pune India Department of Information Technology P. S. V College of Engineering and Technology Krishnagiri Tamilnadu Department of Information Technology Netaji Subhas University of Technology (Govt of NCTDelhi) Jafferpur New Delhi Department of Electronics and Communication Engineering T.J.S. Engineering College Tamil Nadu India

ISBN: (数字)9798331529833

ISBN: (纸本)9798331529840

This research describes a novel approach to hospital visitor screening utilizing cloud-powered deep learning using convolutional neural networks (CNNs). Our proposed approach will fill this need by improving threat detection accuracy and decreasing false positives. Our system can successfully detect possible dangers among visitors by using CNNs, renowned for their ability to perform image recognition tasks. Integrating cloud computing also makes scalability, real-time processing, and centralized administration possible across several hospital locations. This rigorous testing and evaluation technique improves hospital security requirements reliably and resiliently. It presents novel methods to transform visitor screening fundamentally, solving the challenge of keeping patients, staff, and facilities secure from evolving dangers. The proposed method greatly improves hospital security since it might increase safety and reduce dangers.

关键词： Deep learning Cloud computing Image recognition Accuracy Hospitals Threat assessment Real-time systems security Convolutional neural networks Testing

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