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Secure and Trustworthy Data Management Mechanism for Dance-Consumer Electronics in AIoT

IEEE Transactions on Consumer Electronics 2024年第1期71卷 1970-1979页

作者： Wei, Hua Miao, Junfeng Lv, Jianhui Chen, Chien-Ming Kumari, Saru Amoon, Mohammed Xinxiang University Xinxiang453003 China University of Science and Technology Beijing School of computer and Communication Engineering Beijing100083 China Peng Cheng Laboratory Shenzhen518057 China Nanjing University of Information Science and Technology School of Artificial Intelligence China Chaudhary Charan Singh University Department of Mathematics Meerut India King Saud University Community College Department of Computer Science Riyadh11437 Saudi Arabia

The integration of Artificial Intelligence (AI) with the Internet of Things (IoT) marks a considerable advancement in data collection and utilization across various sectors. In the realm of complex scenarios, dance-consumer electronics (DCE) employ this technology to provide detailed and trustworthy documentation and three-dimensional digital capture of dancers' movements. However, the handling of sensitive physiological data poses significant security and trustworthiness risks in such consumer electronics, including potential breaches that could be exploited for location tracking and behavior prediction. To address the challenges of encrypted traffic detection and the need for trustworthiness in IoT, this paper introduces the AI-Enhanced Anonymous Traffic Filtering Framework, which features terminal anonymization. This framework employs the Inception-SelfAttBiGRU model to filter malicious traffic and includes a credibility-based filtering mechanism prior to this module to reduce network load and enhance system efficiency for those consumer electronics. The evaluation concentrated on two main areas: assessing the access credibility-based filtering mechanism's ability to identify and block malicious DCE endpoints and evaluating the effectiveness of the encrypted traffic classification technique. Using simulated environments and public datasets, this comprehensive assessment demonstrated that the dual-layer protection mechanism effectively detects and blocks malicious traffic, thereby enhancing system security and maintaining network integrity. © 2024 IEEE.

关键词： Information management

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Unsupervised Domain Adaptation Methods for Causal Correlation

Unsupervised Domain Adaptation Methods for Causal Correlatio...

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2023 International Annual Conference on Complex Systems and Intelligent science, CSIS-IAC 2023

作者： Zhou, Liyuan Dou, Quansheng Tang, Huanling Li, Xiujuan Liu, Yanchao College of Information and Electronic Engineering Shandong Technology and Business University Yantai China School of Computer Science and Technology Shandong Technology and Business University Yantai China School of Computer Science and Technology Kashi University Xinjiang China

ISBN: (纸本)9798350309003

Unsupervised domain adaptation methods enhance the ability of models to generalize by minimizing the distributional disparities between the source and target domains through the transfer of knowledge from different domains and tasks, utilizing both labeled and unlabeled data. However, a challenge arises due to the presence of pseudo-correlations between certain 'confounders' and category labels, leading to a decrease in model prediction accuracy. To tackle this issue, this paper introduces the Causal Correlation Reweighting Module (CCRM). The CCRM samples features related to the labels and features independent of the labels, evaluates their independence, applies reweighting to the label-related features, and reduces the pseudo-correlation between the label-independent features and category labels. This approach results in improved model generalization performance and enhances the model's ability to handle unsupervised domain adaptation tasks. The proposed method is validated on widely-used domain adaptive datasets such as Digits, Office-Home and VisDA-2017, demonstrating higher accuracy compared to previous domain adaptation models. © 2023 IEEE.

关键词： Knowledge management

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AI-Enhanced Cognitive Therapy: Personalized Guidance via Adaptive Agents with Voice Analysis and Stress Detection 9

AI-Enhanced Cognitive Therapy: Personalized Guidance via Ada...

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9th International Conference on Communication and Electronics Systems, ICCES 2024

作者： Venkataraman, Sudharshan Uma, G. Kannan, V. Devi, V. Lalitha Vellore Institute of Technology Tamilnadu Vellore India Srimad Andavan Arts and science College Dept of Computer Science Tamilnadu Trichy India K.Ramakrishnan College of Engineering Tamilnadu Trichy India

ISBN: (纸本)9798350377972

This research presents a novel framework for AI-Enhanced Automated Cognitive Therapy (AECT) that integrates cutting-edge technologies to provide personalized and effective mental health support. AECT leverages refined Transformer-based conversational agents,advanced voice analysis,and physiological signal processing to offer a comprehensive therapeutic experience. The proposed system incorporates deep learning models for voice analysis and facial emotion recognition,achieving high accuracy in identifying emotional subtleties and stress levels. By combining these advanced technologies,AECT provides a personalized and engaging therapeutic experience,demonstrating the potential to significantly impact mental well-being and improve therapeutic outcomes worldwide. © 2024 IEEE.

关键词： Chatbots

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Incentive Mechanism of Selfish Nodes Based on Energy Optimization and Game Theory

Incentive Mechanism of Selfish Nodes Based on Energy Optimiz...

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2024 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2024

作者： Dong, XiangJia Cheng, Yuan Seah, Winston K. G. Zhang, Feng Xu, Gang College of Computer Science Inner Mongolia University Engineering Research Center of Ecological Big Data Ministry of Education Hohhot China School of Engineering & Computer Science Victoria University of Wellington Wellington New Zealand School of Computer and Information Technology Shanxi University Taiyuan China

ISBN: (纸本)9781665410205

With the proliferation of mobile intelligent terminals, opportunistic networks have attracted widespread attention as a complementary technology to multi-network convergence. Different from traditional wireless networks, message delivery in opportunistic networks does not rely on a fixed infrastructure, but rather storing messages in a cache and utilizing the movement and encounters of nodes to relay messages. However, in practical application scenarios, nodes have limited storage space and energy and will easily exhibit selfishness. An increase in the number of selfish nodes will drastically degrade the performance of the network. To solve the problem of significant network performance degradation when the number of selfish nodes is high, this paper proposes an Incentive mechanism of Selfish nodes based on Energy optimization and Game Theory (ISEGT). The mechanism abstracts the process of forwarding messages by nodes into a bargaining game process, and selectively forwards messages based on nodes' remaining energy and other circumstances. The experimental results show that the ISEGT mechanism can motivate selfish nodes to actively participate in message forwarding, which improves the success rate of message delivery and the survival rate of nodes, and optimizes the overall performance of the network. © 2024 IEEE.

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Blockchain Based Voting System for Secure and Transparent Electoral Processes 10

Blockchain Based Voting System for Secure and Transparent El...

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10th International Conference on Advanced Computing and Communication Systems, ICACCS 2024

作者： Sumathy, S. Murugan, S. Karthick Latha, J. Mageshwari, G. Andrews, Allen Joseph Gilbert Kumar, M. Hema Sir Theagaraya College-Shift Ii Pg Department of Computer Science Tamilnadu Chennai India R. M. D. Engineering College Department of Computer Science and Engineering Tamilnadu Thiruvallur India R. M. K. Engineering College Department of Computer Science and Engineering Tamilnadu Tiruvallur India R.M.K. College of Engineering and Technology Department of Artificial Intelligence and Data Science Tamilnadu Tiruvallur India Deloitte consulting Llp 200 Sterling road MechanicsburgPA United States Sona College of Technology Department of Ece Tamil Nadu Salem India

ISBN: (纸本)9798350384369

This study describes an innovative approach to blockchain-based voting systems. The proposed method guarantees the integrity, security, and resistance to manipulation of elections conducted on a decentralized network through the utilization of smart contracts and cryptographic principles. Emphasizing participant confidentiality, vote integrity, and real-time auditability, the experimental outcomes demonstrate the system's scalability, dependability, and security. Within the domain of theoretical democratic discourse, presents a prospective scenario in which voting methods based on blockchain technology. The results indicate that the approach can be easily implemented on a large scale, representing a significant milestone in the struggle for democratic, secure, and transparent voting procedures. With highly efficient, boasting a transaction speed of 500 TPS, scalability to accommodate over a thousand users, and a block confirmation time of less than 10s. Encryption efficiency and digital signature dependability are indicators of the system's high level of security. Equipped with an accuracy rate that is 97.2%. © 2024 IEEE.

关键词： Decentralized finance

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Machine Learning Approaches for IC Fault Localization and Diagnosis 9

Machine Learning Approaches for IC Fault Localization and Di...

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9th International Conference on science, technology, engineering and Mathematics, ICONSTEM 2024

作者： Saidala, Ravi Kumar Dwivedi, Yagya Dutta Aparna, P. Prasad, S. J. Suji Dineshkumar, S. Hemalatha, R. Alliance University Department of Computer Science Engineering Karnataka Bangalore560076 India Institute of Aeronautical Engineering Department of Aeronautical Engineering Telangana Hyderabad500043 India Rmk College of Engineering And Technology Department of Computer Science And Engineering Tamil Nadu Puduvoyal601206 India Kongu Engineering College Department of Electronics And Instrumentation Engineering Tamil Nadu Erode638060 India M.Kumarasamy College of Engineering Department of Electrical And Electronics Engineering Tamil Nadu Karur639113 India St. Joseph's College of Engineering Omr Department of Computer Science And Engineering Tamil Nadu Chennai600119 India

ISBN: (纸本)9798350365092

The conducted researchprovide a machine learning-driven method for thermal testing of integrated circuits. This method involves comparing the current temperature distribution with a reference distribution that is suitable for the energy state of the system. Furthermore, the research explores a methodology for positioning temperature sensors. This placement approach is advised for utilization in the identification and troubleshooting of issues with integrated circuits (ICs). Our failure localization method utilizes the average temperatures in several sub- areas of the integrated circuit, together with the interconnections between those temperatures. This research provides the simulation results for the sensor placement technique, fault detection, and localization. The results on the productivity of the testing approach are presented utilizing statistical methodologies. © 2024 IEEE.

关键词： Timing circuits

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A Systematic Literature Review of Machine Learning and Deep Learning Approaches for Spectral Image Classification in Agricultural Applications Using Aerial Photography

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computers, Materials & Continua 2024年第3期78卷 2967-3000页

作者： Usman Khan Muhammad Khalid Khan Muhammad Ayub Latif Muhammad Naveed Muhammad Mansoor Alam Salman A.Khan Mazliham Mohd Su’ud College of Computing and Information Sciences Karachi Institute of Economics and TechnologyKarachi75190Pakistan Faculty of Computing and Informatics(FCI) Multimedia UniversityCyberjaya63100Malaysia Faculty of Computing Riphah International UniversityIslamabad46000Pakistan Faculty of Engineering and Information Technology School of Computer ScienceUniversity of Technology SydneySydneyAustralia

Recently,there has been a notable surge of interest in scientific research regarding spectral *** potential of these images to revolutionize the digital photography industry,like aerial photography through Unmanned Aerial Vehicles(UAVs),has captured considerable *** encouraging aspect is their combination with machine learning and deep learning algorithms,which have demonstrated remarkable outcomes in image *** a result of this powerful amalgamation,the adoption of spectral images has experienced exponential growth across various domains,with agriculture being one of the prominent *** paper presents an extensive survey encompassing multispectral and hyperspectral images,focusing on their applications for classification challenges in diverse agricultural areas,including plants,grains,fruits,and *** meticulously examining primary studies,we delve into the specific agricultural domains where multispectral and hyperspectral images have found practical ***,our attention is directed towards utilizing machine learning techniques for effectively classifying hyperspectral images within the agricultural *** findings of our investigation reveal that deep learning and support vector machines have emerged as widely employed methods for hyperspectral image classification in ***,we also shed light on the various issues and limitations of working with spectral *** comprehensive analysis aims to provide valuable insights into the current state of spectral imaging in agriculture and its potential for future advancements.

关键词： Machine learning deep learning unmanned aerial vehicles multi-spectral images image recognition object detection hyperspectral images aerial photography

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Quantum Computing's Double-Edged Sword Unravelling the Vulnerabilities in Quantum Key Distribution for Enhanced Network Security

Quantum Computing's Double-Edged Sword Unravelling the Vulne...

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2023 IEEE International Conference on Next Generation Electronics, NEleX 2023

作者： Anantraj, I. Umarani, B. Karpagavalli, C. Usharani, C. Lakshmi, S. Jothi Sri Krishna College of Engineering and Technology Department of Computer Science and Business Systems Coimbatore India Kongunadu College of Engineering and Technology Department of Electronics and Communication Engineering Namakkal India Ramco Institute of Technology Department of Artificial Intelligence and Data Science Rajapalayam India

ISBN: (纸本)9798350319088

Quantum computing has emerged as both a boon and a bane in the realm of network security. This paper delves into the intriguing interplay between quantum computing and the vulnerabilities of Quantum Key Distribution (QKD) protocols, a cornerstone of secure quantum communication. While QKD offers unmatched security through the principles of quantum mechanics, it is not impervious to attacks in the age of rapidly advancing quantum computing. We examine these vulnerabilities, highlighting the challenges of ensuring the long-term security of quantum networks in a post-quantum world. Through a comprehensive exploration of threat models and the practicality of QKD, we uncover potential weaknesses in quantum network security. In particular, we investigate QKD's susceptibility to evolving quantum algorithms, hardware vulnerabilities, and post-processing security concerns. This work aims to provide a roadmap for understanding and addressing these vulnerabilities, ensuring the continued robustness of quantum communication networks, and fostering the development of post-quantum cryptographic solutions. As quantum computing advances, its implications for quantum network security are far-reaching. This study underlines the critical need for research and innovation in securing quantum communication. By unravelling the vulnerabilities within Quantum Key Distribution and exploring post-quantum cryptographic alternatives, this research contributes to the development of more resilient and secure quantum networks, safeguarding sensitive information and communications in an increasingly quantum-enabled world. © 2023 IEEE.

关键词： Quantum cryptography

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A Comprehensive Review of Bio-Medical Portable Devices for Blood Coagulation Detection Device 2

A Comprehensive Review of Bio-Medical Portable Devices for B...

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2nd DMIHER International Conference on Artificial Intelligence in Healthcare, Education and Industry, IDICAIEI 2024

作者： Bore, Joy Dhale, Trishul Kumar, Praveen Deshmukh, Gauri S. Faculty of Engineering and Technology Department of Computer Science and Medical Engineering Sawangi Meghe Maharashtra Wardha442001 India Yeshwantrao Chavan College of Engineering Department of Applied Chemistry Nagpur India

ISBN: (纸本)9798331528713

The paper provides a synoptic view of portable biomedical point-of-care devices for blood coagulation detection, emphasising the state-of-the-art technology adopted and its use in the medical industry. These devices give fast and precise results at the point of care and are indispensable for diagnosing coagulation and perceiving thrombotic states. It brought into focus the issues of monitoring blood coagulation across different areas of practice, such as the ICU, operative and perioperative services, and ambulatory care. This paper also highlights new trends in the development of sensors, miniaturisation and the integration of digital health solutions. Further, it explains how these devices are advantageous and disadvantageous in terms of speed, sample requirement, standardisation, calibration, and user-friendliness. Last but not least, the regulation and market uptake issue is discussed, focusing on how portable coagulation detection devices can revolutionise patient care and access to healthcare. Finally, the authors state that the paper should inspire further research to develop new solutions and collaboration to address obstacles and underpin advancements in the field. © 2024 IEEE.

关键词： Portable equipment

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Success Probability Analysis for Joint Sensing and Communications: A Determinantal Point Process Perspective

Success Probability Analysis for Joint Sensing and Communica...

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2024 International Conference on Integrated Circuits, Communication, and Computing Systems, ICIC3S 2024

作者： Tharaniya, R. Mukesh, T. Ka, Kameshwari Rajeshkumar, J. Ananthi, G. Thiagarajar College of Engineering Dept of Electronics & Communication Engineering Madurai India R.L.Jalappa Institude Of Technology Dept of Computer & Science Engineering Kodigehalli Karnataka Doddaballapur India

ISBN: (数字)9798350364088

ISBN: (纸本)9798350364088

In this paper, we present a novel framework for analyzing the success probability in Joint Sensing and Communication (JSAC) systems leveraging Determinantal Point Process (DPP). JSAC systems are integral in various applications such as cognitive radio networks and Internet of Things (IoT), where efficient utilization of spectrum resources is paramount. By employing DPP, which models the repulsion among sensing and communication nodes, we offer a rigorous closed form analysis of the success probability in such systems. Proposed the DPP concentrating on repulsive base stations and users, apply the analytically derived empty space function to the signal to interference plus noise ratio equation, and evaluate the results using the quasi-Monte Carlo integration approach in Multiple Input Multiple Output (MIMO) systems. Our framework provides insights into the tradeoffs between sensing performance and communication reliability, aiding in the design and optimization of JSAC networks for enhanced spectrum utilization and system efficiency. Through theoretical analysis and simulation results, we demonstrate the effectiveness of our approach in quantifying the success probability and its implications on system performance, thereby contributing to the advancement of JSAC technologies. © 2024 IEEE.

关键词： Signal to noise ratio

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