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OptiMobileX: Optimizing Deep Transfer Learning Model for Accurate Human Posture Recognition Using a Deep Feature Fusion Technique

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IEEE Sensors Journal 2025年第6期25卷 9759-9766页

作者： Ogundokun, Roseline Oluwaseun Damasevicius, Robertas Maskeliunas, Rytis Kaunas University of Technology Department of Software Engineering Kaunas44249 Lithuania Kaunas University of Technology Centre of Real Time Computer Systems Kaunas44249 Lithuania

Human posture recognition (HPR) has garnered growing interest given the possibility of its use in various applications, including healthcare and sports fitness. Interestingly, achieving accurate pose recognition on mobile devices with very little computing power is still tricky. The precise identification of human posture on mobile devices with constrained computational resources is of utmost importance to bring about significant advancements in healthcare, fitness, and technology engagement. By facilitating real-time monitoring and providing personalized solutions, this technology improves accessibility and well-being, revolutionizing our interaction with digital applications. In this work, we used the deep feature concatenation (DFC) mechanism to combine features extracted from input images using two modern pretrained DTL models, MobileNetV2 and Xception, which led to our proposed OptiMobileX: A deep transfer learning (DTL) model based on a DFC of the MobileNetV2 and Xception models for the overall improvement of the identification capability of this posture. The classification accuracy of 96.42% is achieved by the OptiMobileX model, demonstrating the superiority of the proposed model over MobileNetV2, Xception, AlexNet, and InceptionV3, which only manage to obtain 88.17%, 92.47%, 90.32%, and 73.48% accuracy, respectively. The OptiMobileX model exhibits notable capabilities but faces significant limitations. Inadequate details on detecting inappropriate postures and a lack of information on required computing resources hinder the study's comprehensiveness and practical implementation. The absence of insufficient discussion on dataset biases and a lack of information on the model's generalization capacity create obstacles, raising concerns about its fairness, applicability, and real-world utility. © 2001-2012 IEEE.

关键词： Deep learning

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EEMLCR: Energy-Efficient Machine Learning-Based Clustering and Routing for Wireless Sensor Networks

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IEEE Access 2025年 13卷 70849-70871页

作者： Akram, Muhammad Ullah Bazai, Sibghat Imran Ghafoor, Muhammad Akram, Saira Ilyas, Qazi Mudassar Mehmood, Abid Iqbal, Sajid Asim Rafique, Muhammad Department of Software Engineering Quetta87300 Pakistan Department of Computer Engineering Quetta87300 Pakistan Pakistan Television Corporation Department of Engineering Lahore44000 Pakistan King Faisal University College of Computer Sciences and Information Technology Department of Information Systems Al Hofuf31982 Saudi Arabia King Faisal University College of Business Administration Department of Management Information Systems Al Hofuf31982 Saudi Arabia

Wireless Sensor Networks (WSNs) offer a powerful technology for sensing and transmitting data across vast geographical regions. However, limitations inherent to WSNs, such as low-power sensor units, communication constraints, and limited processing capabilities, can significantly impact their lifespan. To address these limitations and enhance the energy efficiency of WSNs, it is often necessary to divide sensors into clusters and establish routing to conserve energy. Machine learning algorithms can potentially automate these processes, minimizing energy consumption and extending network lifetime. This research investigates the application of machine learning algorithms, specifically Q-learning and K-means clustering, to propose the Energy-Efficient Machine Learning-based Clustering and Routing (EEMLCR) method for WSNs. This method facilitates cluster formation and routing path selection. The proposed method is compared with the well-established LEACH algorithm and two multi-hop variants, DMHT LEACH and EDMHT LEACH to validate its effectiveness. Our experimental results demonstrate the effectiveness of EEMLCR compared to LEACH and its multi-hop variants (DMHT LEACH and EDMHT LEACH). After 600 rounds in networks comprising 400 nodes, EEMLCR showed significant improvements in key performance metrics. These include increased alive nodes, reduced average energy consumption, higher remaining energy levels, and improved packet reception. Additionally, we compared EEMLCR with recent state-of-the-art algorithms such as EECDA and CMML, where our method demonstrated comparable or superior performance in terms of network lifetime and energy efficiency. By optimizing clustering and routing strategies, WSNs can reduce energy consumption, leading to more efficient utilization of the limited energy resources available to sensor nodes. The primary objective of this research is to contribute to the development of energy-efficient WSNs by leveraging machine learning algorithms for dat

关键词： Sensor nodes

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Forest Fire Prevention and Detection System 5

Forest Fire Prevention and Detection System

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5th IEEE International Conference on Power, Electronics and computer Applications, ICPECA 2025

作者： Jayakody, Lasitha Edirisinghe, Chamathka Senasinghe, Tharush Dissanayake, Shaminda Gamage, Narmada Pandithage, Dinithi Wijayaraja, Jaliya L. Sri Lanka Institute of Information Dept. of Computer Systems Engineering Malabe Sri Lanka

ISBN: (纸本)9798331533694

Forest fires pose significant threats to both the environment and human life, necessitating the development of advanced detection and prevention systems. In this study, we propose an integrated IoT (Internet of Things)-based system for early detection and management of forest fires. The system incorporates a range of sensors including heat, gas, and thermal imaging sensors, along with IoT technology for real-Time monitoring and communication. Utilizing image processing techniques and (ML) Machine Learning algorithms, the system can accurately detect fires, predict their spreading patterns, and analyze environmental conditions. Key features include the deployment of disposable heat sensors for temperature monitoring, IoT sensors for gas detection and weather monitoring, and a centralized processing node for data analysis. Alerts are sent to nearby authorities and residents, while automated firefighting devices are activated to suppress fires until backup arrives. Experimental results demonstrate the effectiveness of the proposed system in early detection and prevention of forest fires. © 2025 IEEE.

关键词： Temperature sensors

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Risk Assessment of computer Network Information Security in the Context of Artificial Intelligence 14th

Risk Assessment of Computer Network Information Security in ...

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14th International Conference on Frontier Computing, FC 2024

作者： Sun, Chenyao Computer Systems Engineering University of Glasgow GlasgowG128QQ United Kingdom

ISBN: (纸本)9789819623907

This paper is based on artificial intelligence environment, combined with artificial neural network to quantify the evaluation index. By connecting the number of nodes to determine the input signal dimension to, from the fuzzy matrix and fuzzy affiliation matrix, calculate the network information security risk data, network information security risk assessment and analysis. The artificial intelligence network framework security risk processing rate is as high as 100%, and the average time consumed is above 14 s in all cases. The training error accuracy is 0.72, and the expected output is stable at 0.68. Meticulous protection of computer network security, improve network information security, so as to improve their own network security awareness. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Artificial Intelligence Evaluation Index Fuzzy Matrix Security Risk Processing Signal Dimension

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UAV-Assisted Zero Knowledge Model Proof for Generative AI: A Multi-Agent Deep Reinforcement Learning Approach

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IEEE Internet of Things Journal 2025年第10期12卷 13441-13454页

作者： Hao, Min Shang, Chen Wang, Siming Jiang, Wenchao Nie, Jiangtian Singapore University of Technology and Design Pillar of Information Systems Technology and Design 487372 Singapore Nanyang Technological University School of Computer Science and Engineering 638788 Singapore

As more users seek generative AI models to enhance work efficiency, generative AI and Model-as-a-Service will drive transformative changes and upgrades across all industries. However, when users utilize generative AI models provided by the service provider, they cannot be certain that the model's quality matches the provider's claims. Considering the need to protect intellectual property, the service provider will not disclose model details for user verification. To this end, we take the Internet of Vehicles as research background, proposing a zero knowledge model proof architecture based on UAVs. We also introduce a multi-agent reinforcement learning algorithm to optimize the verification process. In specific, we first propose a verification scheme for the key operations of generative adversarial networks based on non-interactive zero knowledge proof. The zero knowledge proof architecture ensures that model parameters cannot be stolen during the verification process. After that, we propose an age of verification (AoV) metric to ensure the timeliness and freshness of zero knowledge proof. We also construct a trade-off optimization problem between the energy consumption of UAV as a verifier and the AoV of edge servers as service providers, and transform the problem based on Lyapunov optimization theory. Following that, we propose an enhanced multi-agent proximal policy optimization algorithm to enable the collaborative verification of edge servers by multiple UAVs. The algorithm simulation results demonstrate that the reward value of our proposed algorithm is over 10% higher than that of the standard algorithm, with a faster and more stable overall convergence speed. Additionally, the zero knowledge proof performance test results indicate that the verification delay in our proposed architecture is less than 500ms during the verification phase, meeting practical requirements. © 2025 IEEE.

关键词： Generative adversarial networks

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Oversampling-Enhanced Feature Fusion-Based Hybrid ViT-1DCNN Model for Ransomware Cyber Attack Detection

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computer Modeling in engineering & Sciences 2025年第2期142卷 1667-1695页

作者： Muhammad Armghan Latif Zohaib Mushtaq Saifur Rahman Saad Arif Salim Nasar Faraj Mursal Muhammad Irfan Haris Aziz Department of Computer and Information System Cleveland State UniversityOhio44115USA Department of Electrical Electronics and Computer SystemsCollege of Engineering and TechnologyUniversity of SargodhaSargodha40100Pakistan Electrical Engineering Department College of EngineeringNajran UniversityNajran61441Saudi Arabia Department of Mechanical Engineering College of EngineeringKing Faisal UniversityAl Ahsa31982Saudi Arabia Department of Mechanical Industrial and Energy System EngineeringUniversity of SargodhaSargodha40100Pakistan

Ransomware attacks pose a significant threat to critical infrastructures,demanding robust detection *** study introduces a hybrid model that combines vision transformer(ViT)and one-dimensional convolutional neural network(1DCNN)architectures to enhance ransomware detection *** common challenges in ransomware detection,particularly dataset class imbalance,the synthetic minority oversampling technique(SMOTE)is employed to generate synthetic samples for minority class,thereby improving detection *** integration of ViT and 1DCNN through feature fusion enables the model to capture both global contextual and local sequential features,resulting in comprehensive ransomware *** on the UNSW-NB15 dataset,the proposed ViT-1DCNN model achieved 98%detection accuracy with precision,recall,and F1-score metrics surpassing conventional *** approach not only reduces false positives and negatives but also offers scalability and robustness for real-world cybersecurity *** results demonstrate the model’s potential as an effective tool for proactive ransomware detection,especially in environments where evolving threats require adaptable and high-accuracy solutions.

关键词： Ransomware attacks cybersecurity vision transformer convolutional neural network feature fusion encryption threat detection

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Automatic emotion recognition using deep neural network

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Multimedia Tools and Applications 2025年 1-30页

作者： Sujatha, R. Chatterjee, Jyotir Moy Pathy, Baibhav Hu, Yu-Chen School of Computer Science Engineering and Information Systems Vellore Institute of Technology Vellore India Department of CSE Graphic Era University Dehradun India School of Electrical Engineering Vellore Institute of Technology Vellore India Department of Computer Science Tunghai University Taichung City Taiwan

Emotions are a vital semantic part of human correspondence. Emotions are significant for human correspondence as well as basic for human–computer cooperation. Viable correspondence between people is possibly achieved when both the importance and the emotion of the correspondence are perceived by all groups included. Understanding the significance of language has generally been concentrated on in natural language processing (NLP) as a semantic examination. In NLP, the text can be handled appropriately for classification. Emotion detection from facial emotion is the subfield of social signal processing applied in a wide assortment of regions, explicitly for human and PC collaboration. Many researchers have proposed various approaches, generally utilizing machine learning concepts. Automatic emotion recognition (AER) is significant for working with consistent intuitiveness between a person and a smart device toward fully acknowledging an intelligent society. Many researchers examined cross-lingual and multilingual speech emotion as a stage toward language-free emotion acknowledgment in natural speech. In the present work, we are proposing a deep learning-based AER system using four openly accessible datasets, namely Basic Arabic Vocal Emotions Dataset (BAVED), Acted Emotional Speech Dynamic Database (AESDD), Urdu written in Latin/Roman Script (URDU), and Toronto Emotional Speech Set (TESS), by utilizing the Jupyter notebook and a Python library for music and audio synthesis named Librosa. The experimental results exhibited that the proposed approach achieves better than the existing approaches, i.e., the accuracy of the proposed system with the URDU dataset is 96.24%, the TESS dataset is 99.10%, the AESDD dataset is 65.97%, and the BAVED dataset is 73.12%. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2025.

关键词： Semantics

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Virtual-physical digital twin testbed for heterogeneous crowd operations

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Science China(Information Sciences) 2025年第5期68卷 404-406页

作者： Mingliang XU Zheng WANG Yang MENG Wencan LUO Hua WANG Shuo HE Chaochao LI Yibo GUO Yafei LI Pei LV School of Computer Science and Artificial Intelligence Zhengzhou University Engineering Research Center of Intelligent Swarm Systems Ministry of Education National Supercomputing Center in Zhengzhou

Heterogeneous crowd operations involve complex procedural subtasks performed by dynamic teams with diverse agent behaviors,tailored to specific task *** of such operations include carrier aircraft support,airport ground handling,and logistics *** a hybrid virtual-physical digital twin testbed for scenario generation and plan verification in heterogeneous crowd operations addresses the issues of low credibility in virtual simulations and the high costs associated with real-world *** is becoming increasingly important in practical applications.

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An integrated strategy of AEF attribute evaluation for reliable thunderstorm detection

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Digital Communications and Networks 2025年第1期11卷 234-245页

作者： Xu Yang Hongyan Xing Xinyuan Ji Xin Su Witold Pedrycz School of Electronics and Information Engineering Nanjing University of Information Science and TechnologyNanjing 210044China Department of Electrical and Computer Engineering University of AlbertaEdmonton AB T6G 2R3Canada College of Internet of Things Engineering Hohai UniversityChangzhou 213022China Systems Research Institute Polish Academy of Sciences00-901 WarsawPoland Department of Computer Engineering Faculty of Engineering and Natural SciencesIstinye UniversitySariyer/IstanbulTurkey

Thunderstorm detection based on the Atmospheric Electric Field(AEF)has evolved from time-domain models to space-domain *** is especially important to evaluate and determine the particularly Weather Attribute(WA),which is directly related to the detection reliability and *** this paper,a strategy is proposed to integrate three currently competitive WA's evaluation ***,a conventional evaluation method based on AEF statistical indicators is *** evaluation approaches include competing AEF-based predicted value intervals,and AEF classification based on fuzzy *** AEF attributes contribute to a more accurate AEF classification to different *** resulting dynamic weighting applied to these attributes improves the classification *** evaluation method is applied to evaluate the WA of a particular AEF,to obtain the corresponding evaluation *** integration in the proposed strategy takes the form of a score *** cumulative score levels correspond to different final WA *** imaging is performed to visualize thunderstorm activities using those AEFs already evaluated to exhibit thunderstorm *** results confirm that the proposed strategy effectively and reliably images thunderstorms,with a 100%accuracy of WA *** is the first study to design an integrated thunderstorm detection strategy from a new perspective of WA evaluation,which provides promising solutions for a more reliable and flexible thunderstorm detection.

关键词： Atmospheric electric field(AEF) Thunderstorm Attribute Fuzzy c-means Imaging

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Supervisory Control of Discrete Event systems Modeled With Labeled Petri Nets for Diagnosability Enforcement

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IEEE Transactions on Automation Science and engineering 2025年 22卷 13855-13870页

作者： Li, Chengzong Chen, Yufeng Mostafa, Almetwally M. Khalgui, Mohamed Qinghai University School of Energy and Electrical Engineering Xining810000 China Macau University of Science and Technology Institute of Systems Engineering Taipa China King Saud University College of Computer and Information Sciences Department of Information Systems Riyadh11543 Saudi Arabia German International University Faculty of Engineering Berlin13507 Germany

This paper proposes an active fault diagnosis method to enforce the diagnosability of discrete event systems using labeled Petri nets by constructing a diagnostic supervisor. For a non-diagnosable net model, its diagnosability is viewed as a control specification and addressed by using supervisory control techniques. First, an event-based monitor and a Petri net structure, referred to as data isolation arcs, are introduced to apply control specifications for labeled Petri nets. Then, a diagnostic supervisor reachability graph is generated to estimate the current state of the diagnoser. By analyzing the diagnostic supervisor reachability graph, an integer linear programming model is formulated to design a diagnostic supervisor, which can prevent the system from entering into any indeterminate cycle only by observing the occurrence of events. Finally, by the obtained diagnostic supervisor, the resulting net model is shown to be diagnosable. Some examples are presented to demonstrate the proposed method. © 2004-2012 IEEE.

关键词： Supervisory personnel

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