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Enhancing Human Action Recognition with Adaptive Hybrid Deep Attentive Networks and Archerfish Optimization

computers, Materials & Continua 2024年第9期80卷 4791-4812页

作者： Ahmad Yahiya Ahmad Bani Ahmad Jafar Alzubi Sophers James Vincent Omollo Nyangaresi Chanthirasekaran Kutralakani Anguraju Krishnan Department of Accounting and Finance Faculty of BusinessMiddle East UniversityAmman11831Jordan Faculty of Engineering Al-Balqa Applied UniversitySalt19117Jordan Department of Mathematics Kongunadu College of Engineering and Technology(Autonomous)TholurpattiTrichy621215India Department of Computer Science and Software Engineering Jaramogi Oginga Odinga University of Science and TechnologyBondo210-40601Kenya Department of Electronics and Communication Engineering Saveetha School of EngineeringSaveetha Institute of Medical and Technical SciencesChennai602105India Department of Electronics and Communication Engineering Saveetha Engineering College(Autonomous)Chennai602105India Department of Computer Science and Engineering Kongunadu College of Engineering and Technology(Autonomous)TholurpattiTrichy621215India

In recent years,wearable devices-based Human Activity Recognition(HAR)models have received significant *** developed HAR models use hand-crafted features to recognize human activities,leading to the extraction of basic *** images captured by wearable sensors contain advanced features,allowing them to be analyzed by deep learning algorithms to enhance the detection and recognition of human *** lighting and limited sensor capabilities can impact data quality,making the recognition of human actions a challenging *** unimodal-based HAR approaches are not suitable in a real-time ***,an updated HAR model is developed using multiple types of data and an advanced deep-learning ***,the required signals and sensor data are accumulated from the standard *** these signals,the wave features are *** the extracted wave features and sensor data are given as the input to recognize the human *** Adaptive Hybrid Deep Attentive Network(AHDAN)is developed by incorporating a“1D Convolutional Neural Network(1DCNN)”with a“Gated Recurrent Unit(GRU)”for the human activity recognition ***,the Enhanced Archerfish Hunting Optimizer(EAHO)is suggested to fine-tune the network parameters for enhancing the recognition *** experimental evaluation is performed on various deep learning networks and heuristic algorithms to confirm the effectiveness of the proposed HAR *** EAHO-based HAR model outperforms traditional deep learning networks with an accuracy of 95.36,95.25 for recall,95.48 for specificity,and 95.47 for precision,*** result proved that the developed model is effective in recognizing human action by taking less ***,it reduces the computation complexity and overfitting issue through using an optimization approach.

关键词： Human action recognition multi-modal sensor data and signals adaptive hybrid deep attentive network enhanced archerfish hunting optimizer 1D convolutional neural network gated recurrent units

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An Empirical Study of Unanswered Python-Related Questions on Stack Overflow

An Empirical Study of Unanswered Python-Related Questions on...

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2024 International Conference on Information Technology Research and Innovation, ICITRI 2024

作者： Nugroho, Yusuf Sulistyo Halim, Salsabil Aisyah Abdul Islam, Syful Kurniawan, Yogiek Indra Erlina, Tati Universitas Muhammadiyah Surakarta Informatics Engineering Faculty of Communication and Informatics Surakarta Indonesia BSMRSTU Computer Science and Engineering Faculty of Engineering Gopalganj Bangladesh Universitas Jenderal Soedirman Informatics Engineering Faculty of Engineering Purwokerto Indonesia Universitas Andalas Computer Engineering Faculty of Information Technology Padang Indonesia

ISBN: (纸本)9798350376210

As a high-level programming language, Python supports user-friendly coding and system integration. It is not only used for data analytics but is also applied in software development. Although it has several benefits, users have various issues while running Python code. When programmers face issues, they often use Stack Overflow (SO) to get assistance for their problems. However, many Pythonrelated questions on SO are not responded to and remain unanswered. Thus, in this paper, we analyzed these unanswered questions to identify potential weaknesses of user and discussion characteristics. We applied a mixed-methods approach, including quantitative and qualitative analysis of user reputation, question types, and discussion topics. Our analysis shows that unanswered questions are mostly from low-reputation users (72.52%), followed by mid-reputation users (17.93%), and high-reputation users (9.5 5%). The distribution of the unanswered question types shows that low-reputation users ask more 'how' type of question (38.90%) followed by mid and high-reputation users. In contrast, the 'what' and 'why' questions are mostly posted by high-reputation users, followed by mid and low-reputation users. Our word-cloud analysis suggests a correlation between user reputation and the question topics. We acknowledge that there are limitations in our study, include potential misclassifications due to the time of posting and biases in manual labeling, which may affect the accuracy of our findings. This study provides insights into unanswered questions posted by the community, open up potential areas for future work such as improving support mechanisms and community engagement. © 2024 IEEE.

关键词： Python

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Artificial intelligence in physiological characteristics recognition for internet of things authentication

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Digital communications and Networks 2024年第3期10卷 740-755页

作者： Zhimin Zhang Huansheng Ning Fadi Farha Jianguo Ding Kim-Kwang Raymond Choo School of Computer and Communication Engineering University of Science and Technology BeijingBeijing100083China Beijing Engineering Research Center for Cyberspace Data Analysis and Applications Beijing100083China Faculty of Informatics Engineering Aleppo UniversityAleppoSyria Department of Computer Science Blekinge Institute of Technology37179KarlskronaSweden Department of Information Systems and Cyber Security University of Texas at San AntonioSan AntonioTX78249-0631USA

Effective user authentication is key to ensuring equipment security,data privacy,and personalized services in Internet of Things(IoT)***,conventional mode-based authentication methods(e.g.,passwords and smart cards)may be vulnerable to a broad range of attacks(e.g.,eavesdropping and side-channel attacks).Hence,there have been attempts to design biometric-based authentication solutions,which rely on physiological and behavioral *** characteristics need continuous monitoring and specific environmental settings,which can be challenging to implement in ***,we can also leverage Artificial Intelligence(AI)in the extraction and classification of physiological characteristics from IoT devices processing to facilitate ***,we review the literature on the use of AI in physiological characteristics recognition pub-lished after *** use the three-layer architecture of the IoT(i.e.,sensing layer,feature layer,and algorithm layer)to guide the discussion of existing approaches and their *** also identify a number of future research opportunities,which will hopefully guide the design of next generation solutions.

关键词： Physiological characteristics recognition Artificial intelligence Internet of things Biological-driven authentication

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Privacy preserving dynamic data release based on non-synonymous diverse anatomy

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International Journal of Security and Networks 2023年第2期18卷 75-90页

作者： Yan, Yan Eyeleko, Anselme Herman Mahmood, Adnan Sun, Zichao Dong, Zhuoyue Xu, Fei School of Computer and Communication Lanzhou University of Technology Lanzhou730050 China School of Computing Faculty of Science and Engineering Macquarie University SydneyNSW2109 Australia

The publishing and using of big data brought unprecedented convenience to users. However, it also results in the disclosure of personal privacy information. In order to mitigate the privacy leakage risk of sensitive information during dynamic data updating, this paper envisages a non-synonymous diverse anatomy method for the privacy preserving dynamic data publishing. The envisaged method inherits the advantages of the traditional anatomy method, retains the availability of the original data to the greatest extent, and avoids the loss of information caused by the generalisation process. A series of indicators are designed to evaluate the synonymous linkage between non-numerical sensitive values. A novel grouping mechanism is further proposed to achieve l-diversity anatomy by combining the concept of synonymous linkage and synonymous entropy with the dynamic update procedure. Experimental analysis suggests that the envisaged method can provide better privacy protection effect on the published data. © 2023 Inderscience Enterprises Ltd.

关键词： Sensitive data

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Exploring Large Scale Docker Image Vulnerability and Storage Performance Analysis Using High Performance Container-Based Docker Environment

Exploring Large Scale Docker Image Vulnerability and Storage...

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2024 International Conference on System, Computation, Automation and Networking, ICSCAN 2024

作者： Kavya Sri, B. Mahalakshmi, K. Anna University Faculty of Information and Communication Engineering Chennai India Kit - Kalaignarkarunanidhi Institute of Technology Department of Computer Science and Engineering Coimbatore India

ISBN: (纸本)9798331510022

In modern cloud environments, Docker containers have become a cornerstone for efficient application deployment, enabling microservices architectures and seamless scalability. However, large-scale Docker environments present significant challenges, particularly in terms of security vulnerabilities and storage performance. This paper explores vulnerability analysis and storage performance optimization in high-performance, container-based Docker environments. Docker swarm is a container orchestration platform which could be very useful for maintaining the docker containers at scale even at distributed environment, these tools also provide us a great features for managing the rolling updates and health check in the lifecycle of containers. We perform a comprehensive evaluation of Docker image vulnerabilities using automated scanning tools, assessing common vulnerabilities and exposures (CVEs) across large image repositories. Additionally, we investigate storage performance bottlenecks and analyse the impact of various storage drivers on container efficiency. Our findings offer critical insights into balancing performance and security in high-scale Docker deployments, proposing strategies for mitigating risks and enhancing resource management. The results guide container orchestration decisions for enterprises aiming to deploy secure, high-performance systems. © 2024 IEEE.

关键词： Bottles

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Extended-Attention Applied in Selected Stages 7

Extended-Attention Applied in Selected Stages

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7th International Conference on Pattern Recognition and Artificial Intelligence, PRAI 2024

作者： Zhang, Zeyu Todo, Yuki Kanazawa University Division of Electrical Engineering and Computer Science Kanazawa Japan Kanazawa University Faculty of Electrical Information and Communication Engineering Kanazawa Japan

ISBN: (纸本)9798350350890

The Self-attention mechanism was widely applied in computer Vision (CV) field. However, while Self-attention mechanism brings high accuracy to Transformer, it also brings a huge amount of calculation. To further enhance accuracy and decrease computational load, in this paper, two refinements were implemented. Firstly, two matrices were introduced to enhance the extraction of image information for Extended-attention method. The capacity of Transformer was enhanced by these matrices to capture crucial information in complex tasks, the final accuracy and generalization performance were significantly improved. Secondly, the impact of implementing the aforementioned Self-attention mechanism at various stages of the Transformer is investigated. Intriguingly, our findings reveal that selectively implementing this method at specific stages leads to a significant enhancement in the model's performance. © 2024 IEEE.

关键词： Deep learning Different Stages Self-attention Transformer

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Privacy-Secure and Decentralized Biometric Authentication Models Using Federated Learning Frameworks

Privacy-Secure and Decentralized Biometric Authentication Mo...

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2024 International Conference on System, Computation, Automation and Networking, ICSCAN 2024

作者： Mathivanan, P. Mahalakshmi, K. Anna University Faculty of Information and Communication Engineering Chennai India Kit - Kalaignarkarunanidhi Institute of Technology Department of Computer Science and Engineering Coimbatore India

ISBN: (纸本)9798331510022

A biometric identifier, in contrast to other identifiers used for authentication, is a quantitative evaluation of an individual's physical attributes that is successfully used to confirm or validate the identification. Because of its remarkable and consistent texture variation, iris recognition is thought to be the most dependable biometric recognition. Concerns about security, authentication, and identification are growing in importance across all domains as technology advances daily. High-security applications use these unique patterns for iris recognition. To increase the security of biometric applications without jeopardizing individual privacy, this research suggests a novel FL technique for privacy-preserving iris detection. Traditional biometric systems rely on centralised data storage and processing, which threatens data security and misuse. Federated Learning: decentralized model training that allows multiple federated clients (like mobile devices or edge servers) to collaboratively train a global model without sharing their raw iris data. We explore how this architecture can be applied to iris detection systems, maintaining accuracy while mitigating privacy risks. Our method ensures that iris features are learned locally on individual devices, and only encrypted model updates are shared with the central server. To strengthen privacy, we incorporate differential privacy and secure aggregation techniques to prevent data leakage during model training. Experimental results demonstrate the effectiveness of the proposed framework in maintaining high detection accuracy, low communication overhead, and robust privacy guarantees. © 2024 IEEE.

关键词： Federated learning

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Computational Investigation of Multiband EMNZ Metamaterial Absorber for Terahertz Applications

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computers, Materials & Continua 2022年第5期71卷 3905-3920页

作者： Ismail Hossain Md Samsuzzaman Mohd Hafiz Baharuddin Norsuzlin Binti Mohd Sahar Mandeep Singh Jit Singh Mohammad Tariqul Islam Space Science Center Universiti Kebangsaan Malaysia43600UKM BangiSelangorMalaysia Department of Computer and Communication Engineering Faculty of Computer Science and EngineeringPatuakhali Science and Technology UniversityBangladesh Department of Electrical Electronic and Systems EngineeringFaculty of Engineering and Built EnvironmentUniversiti Kebangsaan MalaysiaMalaysia

This study presents an Epsilon Mu near-zero(EMNZ)nanostructured metamaterial absorber(NMMA)for visible regime *** resonator and dielectric layers are made of tungsten(W)and quartz(fused),where the working band is expanded by changing the resonator layer’s *** to perfect impedance matching with plasmonic resonance characteristics,the proposed NMMA structure is achieved an excellent absorption of 99.99%at 571 THz,99.50%at 488.26 THz,and 99.32%at 598 THz *** absorption mechanism is demonstrated by the theory of impedance,electric field,and power loss density distributions,*** geometric parameters are explored and analyzed to show the structure’s performance,and a near-field pattern is used to explain the absorption mechanism at the resonance frequency *** numerical analysis method describes that the proposed structure exhibited more than 80%absorbability between 550 and 900 *** computer Simulation Technology(CST Microwave Studio 2019)software is used to design the proposed ***,CSTHFSS interference is validated by the simulation data with the help of the finite element method(FEM).The proposed NMMA structure is also exhibits glucose concentration sensing capability as *** the proposed broadband absorber may have a potential application in THz sensing,imaging(MRI,thermal,color),solar energy harvesting,light modulators,and optoelectronic devices.

关键词： Metamaterial absorber terahertz applications multiband EM near zero nanostructured visible regime applications

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Necessity of emergent materials development for air filtration to increase air quality 16

Necessity of emergent materials development for air filtrati...

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16th International Conference on Electronics, computers and Artificial Intelligence, ECAI 2024

作者： Ahmed, Omar Oproescu, Mihai Iana, Gabriel-Vasile National University of Science and Technology Politehnica Bucharest Science and Materials Engineering Doctoral School Romania Piteti University Centre National University of Science and Technology Politehnica Bucharest Faculty of Electronics Communication and Computer Science Pitesti Romania National University of Science and Technology Politehnica Bucharest Faculty of Electronics Communication and Computer Science Romania

ISBN: (纸本)9798350371154

Air quality is a major public health issue globally. Air pollution is responsible for millions of premature deaths annually and can cause a range of health problems, including respiratory diseases. The need to develop emergent materials and new technologies to increase air quality offers a series of opportunities due to the benefits offered. Emerging materials can be used to create more efficient air filters, air quality monitoring sensors and catalysts to reduce emissions. This article summarizes the main legislative regulations for reducing pollution, as well as emerging materials that play a role in increasing air purity. The main methods of increasing air quality are highlighted: nanoremediation and nanofiltration, mentioning their main disadvantages. © 2024 IEEE.

关键词： Air quality

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Dual-Mode Resonant Sectorial Monopole Antenna with Stable Backfire Gain

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Chinese Journal of Electronics 2024年第6期33卷 1478-1486页

作者： Feiyan JI Heng ZHANG Xiuqiong XING Wenjun LU Lei ZHU Jiangsu Key Laboratory of Wireless Communications Nanjing University of Posts and Telecommunications Key Laboratory of Wireless Sensor Network & Communication Shanghai Institute of Microsystem and Information Technology Chinese Academy of Sciences Department of Electrical and Computer Engineering Faculty of Science and Technology University of Macau

A novel design approach to wideband, dual-mode resonant monopole antenna with stable, enhanced backfire gain is advanced. The sectorial monopole evolves from a linear, 0.75-wavelength electric prototype monopole under wideband dual-mode resonant operation. As theoretically predicted by the two resonant modes TE3/5,1and TE9/5,1within a 150° radiator, the operation principle is revealed at first. As have been numerically demonstrated and experimentally validated at 2.4-GHz band, the designed antenna exhibits a wide impedance bandwidth over 90.1%（i.e., 2.06–5.44 GHz）, in which the stable gain bandwidth in the backfire,-x-direction（θ = 90°, φ = 180°） with peak value of 3.2 dBi and fluctuation less than 3 dB is up to 45.3%（i.e., 3.74–5.44 GHz）. It is concluded that the stable wideband backfire gain frequency response should be owing to the high-order resonant mode in the unique sectorial monopole antennas.

关键词： Fluctuations Azimuth Prototypes Metals Resonance Frequency measurement Broadband antennas Impedance Gain Wideband

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