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Low-Cost Federated Broad Learning for Privacy-Preserved Knowledge Sharing in the RIS-Aided Internet of Vehicles

engineering 2024年第2期33卷 178-189页

作者： Xiaoming Yuan Jiahui Chen Ning Zhang Qiang(John)Ye Changle Li Chunsheng Zhu Xuemin Sherman Shen Qinhuangdao Branch Campus Northeastern UniversityQinhuangdao 066004China State Key Laboratory of Integrated Services Networks&the Research Institute of Smart Transportation Xidian UniversityXi’an 710071China Department of Electrical and Computer Engineering University of WindsorWindsorON N9B 3P4Canada Department of Electrical and Software Engineering University of CalgaryCalgaryAB T2N 1N4Canada SUSTech Institute of Future Networks Southern University of Science and TechnologyShenzhen 518055China Department of Electrical and Computer Engineering University of WaterlooWaterlooON N2L 3G1Canada

High-efficiency and low-cost knowledge sharing can improve the decision-making ability of autonomous vehicles by mining knowledge from the Internet of Vehicles(IoVs).However,it is challenging to ensure high efficiency of local data learning models while preventing privacy leakage in a high mobility *** order to protect data privacy and improve data learning efficiency in knowledge sharing,we propose an asynchronous federated broad learning(FBL)framework that integrates broad learning(BL)into federated learning(FL).In FBL,we design a broad fully connected model(BFCM)as a local model for training client *** enhance the wireless channel quality for knowledge sharing and reduce the communication and computation cost of participating clients,we construct a joint resource allocation and reconfigurable intelligent surface(RIS)configuration optimization framework for *** problem is decoupled into two convex *** to improve the resource scheduling efficiency in FBL,a double Davidon–Fletcher–Powell(DDFP)algorithm is presented to solve the time slot allocation and RIS configuration *** on the results of resource scheduling,we design a reward-allocation algorithm based on federated incentive learning(FIL)in FBL to compensate clients for their *** simulation results show that the proposed FBL framework achieves better performance than the comparison models in terms of efficiency,accuracy,and cost for knowledge sharing in the IoV.

关键词： Knowledge sharing Internet of Vehicles Federated learning Broad learning Reconfigurable intelligent surfaces Resource allocation

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Health-Care Monitoring of Patient using CNN based Model in Internet of Things 1

Health-Care Monitoring of Patient using CNN based Model in I...

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1st IEEE International Conference on Applied Intelligence and Sustainable Computing, ICAISC 2023

作者： Parameshachari, B.D. Hemalatha, K.L. Rahebi, Javad Baker, Mohammed Rashad Upendra Roy, B.P. Nitte Meenakshi Institute of Technology Department of Electronics and Communications Engineering Bengaluru India Sri Krishna Institute of Technology Department of Information Science and Engineering Bangalore India Istanbul Topkapi University Department of Software Engineering Turkey University of Kirkuk College of Computer Science and Information Technology Department of Software Kirkuk Iraq Channabasaveshwara Institute of Technology Gubbi Department of Electronics and Communication Engineering Tumkur India

ISBN: (纸本)9798350323795

There is rising public concern about exposure to radiofrequency (RF) electromagnetic fields (EMF) as more and more wireless communications become concentrated in everyday living surroundings. There are a number of obstacles, primarily originating from infrastructure expenses, but recent technology breakthroughs are shifting attention to Internet of Things (IoT) devices that enable automatic and continuous real-time EMF monitoring. The Internet of Things (IoT) has made the world a better place by expanding the capabilities of telemedicine and allowing for more precise remote monitoring of patients. Improved healthcare facilities for both patients and doctors/hospitals are only one way that IoT is making a significant impact in the medical field. Five main parts make up the proposed system: patient data gathering, primary report creation, hospital care, pharmacy care, and diagnostics. This study presents an Internet of Things (IoT)-based healthcare scheme accomplished of real-time nursing of energetic signs and environmental factors for discrete patients. Five sensors, including a heart-rate monitor, a body temperature monitor, a room temperature monitor, a carbon monoxide monitor, and a carbon dioxide monitor, are utilised to collect data from the hospital setting. Medical personnel receive patient updates through a portal and use this data for further diagnosis and care. The success of the system demonstrates that the built prototype is ideal for healthcare monitoring. This study has investigated unique approaches to the automatic construction of convolutional neural network (CNN) topologies, and it has also developed a new method for diagnosis. The results of the trials prove that the recommended algorithm achieves improved than the state-of-the-art techniques. © 2023 IEEE.

关键词： Diagnosis

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Advanced Multi-Spectral Image Processing Techniques for Enhanced Remote Sensing and Comprehensive Environmental Monitoring in Diverse Ecosystems

Advanced Multi-Spectral Image Processing Techniques for Enha...

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2024 IEEE International Conference on Recent Advances in Science and engineering Technology, ICRASET 2024

作者： Jagannathan, J. Ponmalar, A. Pandiarajan, Rajkumar Sudha, I. Ramesh, P.S. Vellore Institute of Technology School of Computer Science Engineering and Information Systems Department of Software and Systems Engineering Katpadi Vellore India R.M.K. Engineering College Tamilnadu Chennai India School of Computing Kalasalingam Academy Of Research and Education Department of Information and Technology Krishnankoil Virudhunagar India Saveetha Institute of Medical and Technical Sciences Simats University Saveetha School of Engineering Department of Computer Science and Engineering Tamil Nadu Chennai India Vel Tech Rangarajan Dr.Sagunthala R&d Institute of Science and Technology Department of Computer Science and Engineering Tamilnadu Chennai India

ISBN: (纸本)9798350388602

The research is aimed at improvement of multi-spectral image analysis for remote sensing and monitoring the environment at various ecosystems. A combined spectral imagery encompasses several spectrums at a time compared to single spectral imagery that takes only one spectrum at an instance or at a given area to cover all these spectrums. The study employs digital high resolution satellite images and ground based data to quantify the vegetation vigour, water quality, soil characteristics and changes in land cover. Features, indexes, and classification routines are employed to process the data that is collected with the aid of avionics and other peripherals. This provincial level modelling highlights important gains in environmental status, which includes the general improvement in vegetation condition, water quality as well as physical properties of the soil in the province. This study focuses on the complementary possibilities of integration of multi-spectral imaging with other remote sensing technologies and the high performance of data processing. These techniques play significant roles if used to give current and comprehensive information in the management and conservation of the environment as highlighted in the study. © 2024 IEEE.

关键词： data analysis environmental monitoring image processing techniques machine learning Multi-spectral imaging remote sensing soil properties vegetation health water quality

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Autonomous Number Plate Detection System for Vehicle Identification and Tracking 2

Autonomous Number Plate Detection System for Vehicle Identif...

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2nd International Conference on Recent Trends in Microelectronics, Automation, Computing, and Communications Systems, ICMACC 2024

作者： Nareddy, Neha Sri Polneni, Abhinaya Baithi, Rekha Kunchamwar, Pratyusha Suvarna, Sunil Kumar Battu, Durgabhavani Budda, Naveena Agarwal, Vikas Munimanda, Premchander AGVs Group Hyderabad India Malla Reddy Engineering College for Women Department of Information Technology Hyderabad India Ideabytes Software India Private Limited Hyderabad India School of Computer Science Georgia Tech Atlanta United States School of Computer Science and Engineering Malla Reddy Engineering College for Women Hyderabad India

ISBN: (纸本)9798350366570

The autonomous number plate detection system plays a crucial role in modern transportation, law enforcement, and security infrastructure. Technical approaches using artificial intelligence, machine learning, and computer vision are actively researched to achieve ease of maintenance through real-time vehicle detection. The recent development has drastically improved the accuracy of object detection and recognition systems in deep neural networks. Using these techniques, vehicle number plates can easily be detected autonomously for vehicle identification and tracking. Here, we have used a custom-trained TensorFlow-based number plate detection method to autonomously detect vehicles using images captured by a smartphone, or surveillance camera. It showed an impressive mAP @ 0.5 of 95%, and demonstrated limitations in long-distance detection, leading us to designate it as the optimal vehicle detection model for accuracy. We designed and developed an autonomous number plate detection system for vehicle identification and tracking, which includes web-based, android-based, and embedded-based applications. In addition, we evaluated the proposed autonomous number plate detection system with real-time images and videos. The proposed system achieved real-time vehicle detection and tracking, boasting a commendable 90% detection accuracy. © 2024 IEEE.

关键词： Deep neural networks

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Deployment of Polar Codes for Mission-Critical Machine-Type Communication Over Wireless Networks

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computers, Materials & Continua 2022年第4期71卷 573-592页

作者： Najib Ahmed Mohammed Ali Mohammed Mansoor Rodina Binti Ahmad Saaidal Razalli Bin Azzuhri Department of Software Engineering Faculty of Computer Science and Information TechnologyUniversity of Malaya50603Kuala Lumpur Malaysia 2Department of Computer System&TechnologyFaculty of Computer Science and Information TechnologyUniversity of Malaya50603Kuala LumpurMalaysia

Mission critical Machine-type Communication(mcMTC),also referred to as Ultra-reliable Low Latency Communication(URLLC),has become a research *** is primarily characterized by communication that provides ultra-high reliability and very low latency to concurrently transmit short commands to a massive number of connected *** the reduction in physical(PHY)layer overhead and improvement in channel coding techniques are pivotal in reducing latency and improving reliability,the current wireless standards dedicated to support mcMTC rely heavily on adopting the bottom layers of general-purpose wireless standards and customizing only the upper *** mcMTC has a significant technical impact on the design of all layers of the communication protocol *** this paper,an innovative bottom-up approach has been proposed for mcMTC applications through PHY layer targeted at improving the transmission reliability by implementing ultra-reliable channel coding scheme in the PHY layer of IEEE 802.11a standard bearing in mind short packet transmission *** achieve this aim,we analyzed and compared the channel coding performance of convolutional codes(CCs),low-density parity-check(LDPC)codes,and polar codes in wireless network on the condition of short data packet *** Viterbi decoding algorithm(VA),logarithmic belief propagation(Log-BP)algorithm,and cyclic redundancy check(CRC)successive cancellation list(SCL)(CRC-SCL)decoding algorithm were adopted to CC,LDPC codes,and polar codes,***,a new PHY layer for mcMTC has been *** reliability of the proposed approach has been validated by simulation in terms of Bit error rate(BER)and packet error rate(PER)***-to-noise ratio(SNR).The simulation results demonstrate that the reliability of IEEE 802.11a standard has been significantly improved to be at PER=10−5 or even better with the implementation of polar *** results also show that the general-purpose wireless net

关键词： Machine-type communication mission-critical applications MTC URLLC M2M wireless networks polar codes

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Incremental Instance Segmentation for Cluttered Baggage Threat Detection

Incremental Instance Segmentation for Cluttered Baggage Thre...

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2023 IEEE International Conference on Computational Intelligence and Virtual Environments for Measurement Systems and Applications, CIVEMSA 2023

作者： Nasim, Ammara Velayudhan, Divya Ahmed, Abdelfatah Hassan Hassan, Taimur Akcay, Samet Akram, Muhammad Usman Werghi, Naoufel National University of Sciences and Technology Department of Computer and Software Engineering Pakistan Khalifa University KUCARS and C2PS Department of Electrical Engineering and Computer Science United Arab Emirates Abu Dhabi University Department of Electrical and Computer Engineering Abu Dhabi United Arab Emirates Intel Corporation RandD Division United Kingdom

ISBN: (纸本)9781665434454

Identification of contraband items from highly oc-cluded baggage of air travelers is a challenging task even for human experts with very high experience. Many researchers have been working rigorously to develop computer vision-based techniques for baggage screening through X-ray images. Nu-merous machine learning and deep learning-based frameworks have been proposed by researchers in the last two decades. However, all of these techniques face limitations in segmenting prohibited items from highly occluded and cluttered baggage. In this paper, we propose a novel framework based on semantic segmentation to automatically detect concealed prohibited items from X-ray baggage scans. Furthermore, to detect different overlapping instances of the same contraband item, we propose an instance-Aware segmentation model that enables the semantic segmentation model to identify multiple instances of the same threat category through incremental learning without requiring additional overhead. The proposed framework is computationally lighter compared to other similar approaches as it requires min-imal training examples and leverages previous knowledge. The proposed model has outperformed state-of-The-Art instance seg-mentation techniques when tested on publicly available GDXray and SIXray datasets, giving mean average precision scores of 0.50 and 0.47, respectively. In addition, the proposed framework leads other instance segmentation baseline models in terms of mean inference time. © 2023 IEEE.

关键词： Semantic Segmentation

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Strategy Masking: A Method for Guardrails in Value-based Reinforcement Learning Agents

arXiv

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arXiv 2025年

作者： Keane, Jonathan Keyser, Sam Kedziora, Jeremy Department of Computer Science and Software Engineering Milwaukee School of Engineering MilwaukeeWI United States

The use of reward functions to structure AI learning and decision making is core to the current reinforcement learning paradigm;however, without careful design of reward functions, agents can learn to solve problems in ways that may be considered "undesirable" or "unethical. Without thorough understanding of the incentives a reward function creates, it can be difficult to impose principled yet general control mechanisms over its behavior. In this paper, we study methods for constructing guardrails for AI agents that use reward functions to learn decision making. We introduce a novel approach, which we call strategy masking, to explicitly learn and then suppress undesirable AI agent behavior. We apply our method to study lying in AI agents and show that it can be used to effectively modify agent behavior by suppressing lying post-training without compromising agent ability to perform effectively. © 2025, CC BY.

关键词： Reinforcement learning

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Application of the Servqual Model in the E-cosmetics Sector 24th

Application of the Servqual Model in the E-cosmetics Sector

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24th International Symposium for Production Research, ISPR 2024

作者： Köse, Nilüfer Zeren, Belkis İclal Çiçeklidağ, Paşa Gürbüzer, Selman Süha Sahin, Merve Department of Industrial Engineering Istanbul Sabahattin Zaim University Istanbul Turkey Computer Science and Software Engineer The University of Western Australia Perth Australia Department of Management Ibn Haldun University Istanbul Turkey

ISBN: (纸本)9783031835827

This study utilizes the SERVQUAL model to conduct a comprehensive analysis of the impact of service quality on customer satisfaction and loyalty within Türkiye’s rapidly expanding e-cosmetics sector. As the industry experiences substantial growth due to digital transformation and changing consumer behaviors, the importance of high service quality in maintaining customer loyalty has never been more critical. This research collected data from 95 participants through meticulously designed online surveys conducted between May and June 2024, employing an adapted SERVQUAL survey to assess service quality across five key dimensions: Tangibles, Reliability, Responsiveness, Assurance, and Empathy. Utilizing advanced statistical methods with SmartPLS 4.0, the study explores both the direct and indirect effects of these dimensions on customer satisfaction (CS) and customer loyalty (CL). The findings reveal that while tangibles, reliability, and assurance have strong positive impacts on customer satisfaction, other factors such as product quality, brand image, and user interface design also significantly affect customer satisfaction and loyalty. This study provides actionable insights into how e-cosmetics companies can enhance their service quality to better meet consumer expectations, thereby fostering sustained customer satisfaction and loyalty. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： User experience

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Ensemble-Based Robust Model for Accurate Driving Range Estimation of EVs Leveraging Big Data

Ensemble-Based Robust Model for Accurate Driving Range Estim...

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IEEE International Energy Conference and Exhibition (EnergyCon)

作者： Samira Hosseini Abdulsalam Yassine Thangarajah Akilan Department of Electrical and Computer Engineering Department of Software Engineering Lakehead University Thunder Bay Canada

In the emergence of greener transportation, electric vehicles (EVs) play an important role, where the accurate pre-diction of the driving range is pivotal for alleviating driver range anxiety, serving as a foundation for spatial planning, operational strategies, and efficient charging infrastructure management. This study addresses the challenge of limited driving range in EVs and introduces an ensemble-based machine learning (ML) model for predicting the vehicle's driving range. By employing big data collected from the real world, this work investigates the appropriateness of various ML models, including extreme learning model (ELM), extreme gradient boosting (XGBoost), multiple linear regression (MLR), multilayer perceptron (MLP), deep MLP, random forests (RF), AdaBoost, and support vector regression (SVR) for the problem stated above. Extensive experimental analysis proves that the ensemble framework that exploits MLR and XGBoost predictors surmounts the existing solutions. It achieves an R2 score greater than 0.9 on both training and testing data subsets, exhibiting no overfitting issues, and boasting acceptable inference time. The findings offer a compelling solution for enhancing the estimation of EV driving range for practical applications.

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A Novel Attention-based Explainable Deep Learning Framework Towards Medical Image Classification 7

A Novel Attention-based Explainable Deep Learning Framework ...

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7th International Symposium on Innovative Approaches in Smart Technologies, ISAS 2023

作者： Muoka, Gladys W. Yi, Ding Ukwuoma, Chiagoziem Chima Martin, Moindi Duke Aydin, Ahmet Arif Al-Antari, Mugahed A. University of Electronic Science and Technology of China School of Information and Software Engineering Chengdu China Chengdu University of Technology Sichuan College of Nuclear Technology and Automation Engineering Chengdu China Unicaf University Lilongwe Department of Computer Science Lilongwe Malawi Inönü University Faculty of Engineering Department of Computer Engineering Turkey Sejong University College of Software & Convergence Technology Daeyang AI Center Department of Artificial Intelligence Seoul Korea Republic of

ISBN: (纸本)9798350383065

Deep learning applications for medical image classification have shown remarkable promise, particularly incorporating attention-based neural networks. This is particularly relevant in medical imaging, where the integration of Artificial Intelligence assists with various imaging tasks, including classification, segmentation, and detection. Deep learning is revolutionizing medical research and playing a significant role in advancing personalized clinical treatment. However, the lack of interpretability in these models presents a significant obstacle to their adoption in clinical practice. Therefore, there is a growing need for a comprehensive understanding of artificial intelligence systems and their internal mechanisms, capabilities, and limitations, which is the focus of the field of explainable AI. This study proposes a novel attention-based explainable deep learning framework for medical image classification tasks, including Covid-19, breast cancer (BreakHis), lung cancer (LC2500), and Retinal optical coherence tomography (OCT). The proposed framework recorded overall accuracies of 98% (Covid-19 Radiography), 95% (BreakHis), 99.8% (LC2500), and 95%(OCT). For visual analysis of the outcomes, we employ and use the LIME, SHAP, and ELI-5 to analyze the achieved results. The study's primary goal is to bridge the gap between the high performance achieved by attention-based models and the necessity for transparency and interpretability in medical image diagnostics. © 2023 IEEE.

关键词： Optical tomography

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