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AI-Driven Smart Healthcare for Society 5.0

作者： Praveen Kumar Patidar Navdeep Kaur Amit Vajpayee Pradeepta Kumar Sarangi Alok Kumar Agrawal Department of Computer Science and Engineering Parul Institute of Technology Parul University Vadodara Gujarat Chitkara University College of Nursing Chitkara University Himachal Pradesh India Parul Institute of Engineering and Technology Parul University Vadodara Gujarat India Chitkara University School of Engineering and Technology Chitkara University Himachal Pradesh Chitkara University School of Engineering and Technology Chitkara University HP India

ISBN: (数字)9798331536336

ISBN: (纸本)9798331536343

Recent advancements in machine learning (ML) and artificial intelligence (AI) have greatly enhanced the detection and diagnosis of healthcare issues, including chronic kidney disease (CKD). CKD, also known as chronic kidney failure, involves the gradual deterioration of kidney function, which is essential for filtering waste and excess fluids from the blood. As the disease progresses, harmful fluids, electrolytes, and toxins can accumulate, posing serious health risks. The term "chronic" indicates the slow progression of this condition, which is seeing rising incidence globally. Patients with severely impaired kidney function typically survive without intervention for only about 18 days, resulting in urgent demand for kidney transplants and dialysis treatments. In addressing CKD, this study employs seven different ML algorithms to analyze kidney disease data and identify the most effective model. Many popular models such as Random Forest and Decision Tree models have been proved as useful tools. This study evaluates six distinct machine learning algorithms to analyze kidney disease data and identify the most effective model. The findings indicate that the LG Boost model achieved an impressive accuracy rate of 98.3%. Additionally, Stochastic Gradient Boosting and XGBoost models also have performed very well with an impressive accuracy of 97.5%.

关键词： Analytical models Accuracy Machine learning algorithms Fluids Stochastic processes Chronic kidney disease Data models Decision trees Random forests Diseases

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IOT based wearable sensor system architecture for classifying human activity

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Measurement: Sensors 2025年 39卷

作者： Mahalakshmi, V. Kumar, Pramod Bhende, Manisha Keshta, Ismail Rathod, Swatiben Yashvantbhai Ramesh, Janjhyam Venkata Naga Department of Computer Science College of Engineering and computer science Jazan University Jazan45142 Saudi Arabia Computer science and Technology Ganga Institute of Technology and Management Kablana Haryana Jhajjar124104 India Dr. D. Y. Patil Vidyapeeth Pune India Dr. D. Y. Patil School of Science & Technology Tathawade Pune India Computer Science and Information Systems Department College of Applied Sciences AlMaarefa University Riyadh Saudi Arabia Instrumentation And Control Department Vishwakrma Government Engineer College Chandkheda Ahmedabad382424 India Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Vaddeswaram Guntur Dist. Andhra Pradesh 522302 India

Human Activity Recognition (HAR) has applications in diverse fields, including sports management and behavior classification. Existing HAR methods can be categorized into three main approaches: camera-based, wearable sensor-based, and Wi-Fi sensing-based. Camera-based methods suffer from privacy concerns, while wearable sensor-based methods face limitations in battery longevity and continuous monitoring. Wi-Fi sensing methods mitigate privacy and battery issues but rely on costly Intel 5300 network cards or software-defined radio (SDR) platforms, limiting scalability. This paper presents a cost-effective IoT-based human activity recognition system using ESP32, leveraging its Wi-Fi sensing capabilities. The proposed system follows a structured workflow: (i) channel state information (CSI) is extracted from ESP32 modules, (ii) data preprocessing is performed using Hampel and Gaussian filters for noise and outlier removal, (iii) dimensionality reduction is achieved through Principal Component Analysis (PCA) and Discrete Wavelet Transform (DWT), and (iv) activity classification is conducted using Dynamic Time Warping (DTW) and the K-Nearest Neighbors (KNN) algorithm. Experimental evaluations demonstrate that the proposed system achieves an average recognition accuracy of 98.6 % across six human activities, comparable to high-end Intel 5300-based HAR systems, while significantly reducing hardware costs and improving ease of deployment. © 2025

关键词： Discrete wavelet transforms

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Flow-Level Bandwidth Allocation on P4 Tofino Switch with In-Network DRL Inference

Flow-Level Bandwidth Allocation on P4 Tofino Switch with In-...

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International Conference on Artificial Intelligence in Information and Communication (ICAIIC)

作者： Muhammad Irfan Hang Hu Myung J. Lee Arslan Qadeer Yang G. Kim Kazi Ahmed Daiki Nobayashi Department of Electrical Engineering City College of New York New York NY USA School of Computing & Data Science Wentworth Institute of Technology Boston MA USA Department of Computer Engineering Technology NYCCT Brooklyn NY USA Department of Electrical & Computer Engineering Technology NYIT New York NY USA Department of Electrical and Electronic Engineering Kyushu Institute of Technology Fukuoka Japan

ISBN: (数字)9798331507022

ISBN: (纸本)9798331507039

The Software-Defined Networking (SDN) paradigm has significantly improved network efficiency by integrating machine learning (ML) capabilities into the control plane (CP). This integration allows adaptive management of network resources in response to dynamic traffic conditions. However, the significant geographical distance between the CP and data plane (DP) causes considerable round-trip latency, measured in milliseconds, which poses a major challenge to time-sensitive traffic. To address this challenge, our research proposes a novel in-network Reinforcement Learning (RL) inference framework. This framework extends programmability from the CP to the DP, enabling timely and precise control of network resources to meet the stringent Quality of Service (QoS) requirements of mission-critical applications. The in-network RL inference is implemented using match-action tables within the Protocol Independent Switch Architecture (PISA) framework in the DP and validated through hardware deployment of protocol-independent packet processors (P4). To allocate bandwidth precisely based on QoS requirements, a P4 meter extern is used to differentiate the unique demands of individual traffic flows. Our enhanced deep-deterministic policy gradient (eDDPG)-based RL inference achieves superior performance with minimal processing overhead, reducing latency by 88.7% and jitter by 89.1 % compared to systems without RL inference.

关键词： Protocols Program processors Process control Switches Quality of service Reinforcement learning Channel allocation Jitter Network resource management Software defined networking

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Smart Antennas and Machine Learning: Enhancing Beamforming in Wireless Communications

Smart Antennas and Machine Learning: Enhancing Beamforming i...

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Pervasive Computational Technologies (ICPCT), International Conference on

作者： A V V Sudhakar Upasana Sharma K. Visala K. Suresh Kumar Chirag Arora Kuldeep Chouhan Department of EEE School of Engineering SR University Warangal Telangana India Department of Electronics and Communication Engineering ABES Engineering College Ghaziabad UP Computer Science Engineering Vignan's Institute of Information Technology Vishakapatnam Andhra Pradesh MBA Department Panimalar Engineering College Varadarajapuram Chennai Department of Electronics and Communication Engineering KIET Group of Institutions Ghaziabad U.P Department of Computer Science and Applications Sharda School of Engineering and Technology Sharda University Greater Noida India

ISBN: (数字)9798331508685

ISBN: (纸本)9798331519476

One of the most effective and widely used technological advancements for increasing capacity, enhancing quality, and expanding coverage in wireless communications systems is the smart antenna. Smart antennas are essential for increasing the effectiveness and dependability of wireless communication systems because they make use of cutting-edge signal processing techniques. Smart antennas' beamforming capability, which concentrates signal transmission and reception in particular directions, increases signal strength, lowers interference, and expands network capacity. As wireless networks become more complicated, machine learning (ML) has become a potent tool for beamforming optimisation. ML algorithms can proactively adjust antenna characteristics by using real-time data, learning from fluctuations in the environment to optimise performance. In order to improve beamforming capabilities, this research investigates the incorporation of ML with smart antenna systems. We go over several ML techniques, such as supervised and reinforcement learning, for resource allocation, user localisation, and signal-to-noise ratio (SNR) optimisation. Additionally, we draw attention to how deep learning might enhance spatial filtering, facilitating more effective communication in 5G and beyond. The potential direction for next-generation wireless communication networks is presented by the convergence of ML and smart antennas.

关键词： Wireless communication Array signal processing OFDM Transmitting antennas Signal processing algorithms Interference Resource management Antennas Optimization Signal to noise ratio

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Advancements in cheating detection algorithms in FPS games

Advancements in cheating detection algorithms in FPS games

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2024 International Conference on computer Application and Information Security, ICCAIS 2024

作者： Xu, Ziwen Yu, Ruotong Liang, Yan Chen, Zihan Wan, Ye School of Computer Science and Technology Tongji University Shanghai 201804 China Faculty of Science and Technology Beijing Normal University-Hong Kong Baptist University United International College Guangdong Zhuhai 519087 China Luddy School of Informatics Computing and Engineering Indiana University of Bloomington Indiana Bloomington 47405 IN United States Guangdong Guangya High School Guangdong Guangzhou 510000 China School of Computer Science and Technology Northeastern University Hebei Qinhuangdao 066000 China

ISBN: (纸本)9781510689312

Over the past two decades, the increasing variety of cheating techniques in FPS games has led to the development of cheating detection algorithms. This literature review provides a chronological review of advancements in cheating detection techniques. Early methods, such as avatar trajectory-based detection, focused on behavioral analysis and identified bots by analyzing movement patterns. A remote anomaly-based system called Fides was presented to detect client-side manipulations with client-server architectures. With the emergence of machine learning, a variety of supervised learning methods have been introduced into the field of game detection to achieve real-time detection. More recent studies have been more innovative. Traps like the “bait-target” technique were introduced to detect cheating mechanisms that mimic human behavior. Methods combining deep learning and computer vision were proposed to detect complex cheating based on visual images. Overall, this review shows the diversity of cheat detection algorithms that have evolved to maintain the fairness of competitive gaming environments. © The Authors.

关键词： Cheating detection FPS games machine learning

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Comprehensive Analysis of Machine Learning Approaches for Phishing Website Detection

Comprehensive Analysis of Machine Learning Approaches for Ph...

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Cognitive Computing in engineering, Communications, sciences and Biomedical Health Informatics (IC3ECSBHI), International Conference on

作者： Pragna K P Suhani Shetty Ashwini Kodipalli Ushasree. A Shridhar B. Devamane Roshan Joy Martis Dept. of Electronics and Communication Engineering Global Academy Of Technology Bengaluru Karnataka India Dept. of Artificial Intelligence & Data Science Global Academy of Technology Bengaluru Karnataka India Dept. of Computer Science & Engineering Gokaraju Lailavathi Engineering College Hyderabad India Department of Computer Science School of Computing Amrita Vishwavidyapeetam Mysuru Dept. of Electronics & Communication Engineering Manipal Institute of Technology Bengaluru Manipal Academy of Higher Education Manipal India

ISBN: (数字)9798331518523

ISBN: (纸本)9798331518530

Website phishing, a notable cybercrime, involves deceiving individuals by impersonating legitimate websites. URL spoofing, a common technique in website phishing, involves creating website names. The primary objective of this paper is to accurately differentiate between legitimate and phishing sites using relevant URL attributes and different binary classification machine learning algorithms. This study compares simple, bagging, and boosting algorithms across performance analysis metrics. The best-performing models are selected for an ensemble approach. Additionally, various hyperparameter tuning techniques are assessed for the Random Forest Classifier, and Explainable AI methods like LIME and SHAP are used to interpret findings. The experiment yields optimal parameters, achieving 89. % accuracy with an ensemble model with K-Nearest Neighbors, Decision Tree, XG Boost, and Cat Boost. These findings highlight the significance of ensemble models and hyperparameter tuning in machine learning.

关键词： Uniform resource locators Machine learning algorithms Explainable AI Phishing Computational modeling Biological system modeling Nearest neighbor methods Performance analysis Tuning Random forests

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Block-FeST: Blockchain-Enhanced Federated Sparse Transformers for Privacy-Preserving RES Forecasting in Internet of Vehicles Systems

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IEEE Internet of Things Journal 2025年

作者： Hameed, Aroosa Danish, Syed Muhammad Ranjha, Ali Srivastava, Gautam Carleton University Department of Systems and Computer Engineering Ottawa Canada Algoma University School of Computer Science and Technology Brampton Canada University of the Fraser Valley School of Computing BC Canada Brandon University Department of Math and Computer Science BrandonMBR7A 6A9 Canada China Medical University Research Centre for Interneural Computing Taichung40402 Taiwan Chitkara University Institute of Engineering and Technology Chitkara University Centre for Research Impact and Outcome Punjab Rajpura140401 India

Internet of Vehicles (IoV) frameworks integrate smart vehicles, roads, network infrastructures, and users into one system, enhancing environmental awareness, increasing efficiency, and reducing accidents. Although IoV is widely adopted, it has resulted in an increase in global energy demand, necessitating more robust and reliable energy solutions. In order to meet this growing demand, both traditional and distributed energy generation technologies have been developed, particularly renewable energy sources (RES). In order to ensure seamless operation of smart vehicles and related infrastructure, integrating renewable energy into existing grid infrastructure is essential. This enables stable and efficient power supply to IoV systems, especially during time periods of high demand. As part of this shift, it is important to accurately forecast the energy generation load of individual prosumers—entities that both produce and consume energy—because of their intermittent and dynamic nature. Therefore, we propose Block-FeST, a blockchain-based Federated Learning (FL) framework designed to predict the energy generation patterns of RES prosumers while preserving their private and sensitive data. Within this Block-FeST framework, a Sparse Transformer model is used to forecast energy generation among prosumer clients. Additionally, blockchain technology is integrated into the Block-FeST framework to enable distributed aggregation and securely validate and record the local parameters shared by clients. The results indicate that Block-FeST is superior to the second-best baseline method, with improvements of 20.4% in the mean square error (MSE), 13.7% in mean absolute error (MAE) and 19.3% in root mean square error (RMSE) for a long sequence length of 128. © 2014 IEEE.

关键词： Federated learning

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Near-field Source Localization in 3-D Using Two Parallel Centrally Symmetric Unfold Coprime Array

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IEEE Transactions on Wireless Communications 2025年

作者： Chen, Hua Li, Junjie Yang, Songjie Liu, Wei Eldar, Yonina C. Yuen, Chau Ningbo University Faculty of Electrical Engineering and Computer Science Ningbo315211 China University of Electronic Science and Technology of China National Key Laboratory of Wireless Communications Chengdu610054 China The Hong Kong Polytechnic University Department of Electrical and Electronic Engineering Kowloon Hong Kong Weizmann Institute of Science Faculty of Mathematics and Computer Science Rehovot7610001 Israel Nanyang Technological University School of Electrical and Electronics Engineering Singapore639798 Singapore

Most near-field (NF) localization algorithms cannot deal with the underdetermined case, while those which can are computationally expensive due to employment of fourth-order cumulants. In this work, a low-complexity solution is provided for underdetermined three-dimensional (3-D) NF localization, by employing second-order statistics with a tailored array configuration named two parallel centrally symmetric unfold coprime (TPSC) array. Its implementation can be divided into three stages. Firstly, the proposed algorithm constructs two cross-correlation matrices based on the received array data, which eliminates the non-linear range-related information of NF signals. Secondly, covariance and vectorization operations are applied to these two cross-correlation matrices to form a virtual array with extended aperture. Finally, the two-dimensional (2-D) angle parameters are estimated by the sparse and parametric approach (SPA) and a phase retrieval operation, and then the one-dimensional (1-D) range parameter is achieved by the multiple signal classification (MUSIC) algorithm. One specific feature is that the estimated angle and range parameters are matched automatically. An analysis of the properties of the TPSC array is provided, and an optimal parameter configuration is derived, given that the total number of array elements is fixed. Simulation results demonstrate that the designed TPSC array can achieve underdetermined 3-D NF localization, and deliver enhanced estimation capabilities, surpassing those of established algorithms. © 2002-2012 IEEE.

关键词： Multiple signal classification

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A lightweight approach for accurate detection of pipeline weld defects

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Nondestructive Testing and Evaluation 2025年

作者： Wang, Guansheng Luo, Renze Zeng, Xingjie Ni, Peng School of Computer Science and Software Engineering Southwest Petroleum University Sichuan Chengdu China School of Electrical Information Southwest Petroleum University Sichuan Chengdu China College of Computing Georgia Institute of Technology AtlantaGA United States

Pipeline welding is essential in oil and gas transport, where weld quality determines pipeline safety and reliability. Although deep neural networks excel at automated weld defect detection, many models are heavy and demand high computation, hindering real-time industrial use. We propose a lightweight weld defect detection framework based on an improved YOLOv8n architecture, incorporating three innovations. First, a FasterNet-inspired feature extraction and fusion module (C2f-FCG) preserves channel information while significantly reducing computational load. Second, a re-parameterisable GhostNet-based convolution module (RGE) generates redundant feature maps through cost-efficient operations, achieving substantial parameter compression and lower computation. Third, a detail-enhanced shared convolution detection head (DetectLSC) improves feature consistency and boosts detection accuracy. Experiments show that our approach reduces parameters and computational load by 63% and 51%, respectively, compared to the baseline, while increasing average detection accuracy by around 2%. Comparative tests on public datasets validate robust generalisation across diverse defect scenarios. Deployment on standard industrial GPUs confirms real-time inference speeds, highlighting its field practicality. These results demonstrate that our lightweight model effectively balances efficiency and accuracy, making it well-suited for real-time industrial weld defect detection. © 2025 Informa UK Limited, trading as Taylor & Francis Group.

关键词： Deep neural networks

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FSIW Cavity-Based Self-Packaging Filtering Slot Antennas with Wide-Stopband and High Frequency Selectivity

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IEEE Transactions on Antennas and Propagation 2025年

作者： Zhou, Xin Zhang, Gang Tam, Kam-Weng Pang, Ziyu Zhang, Zhuowei Lin, Huawei Hong, Jiasheng University of Macau Faculty of Science and Technology Department of Electrical and Computer Engineering China Nanjing Normal University School of Electrical and Automation Engineering Nanjing210046 China Nanjing Normal University Jiangsu Key Laboratory of 3D Printing Equipment and Manufacturing School of Electrical and Automation Engineering Nanjing210046 China Heriot-Watt University School of Engineering and Physical Sciences Department of Electrical Electronic and Computer Engineering EdinburghEH14 4AS United Kingdom

This article proposes a novel design approach for miniaturized, highly selective, self-packaged, and wide-stopband filtering slot antennas based on C- and T-type folded substrate integrated waveguide (C-/T-FSIW) cavities. Firstly, two forms of FSIW cavity are explored and analyzed. A new coupled C-type half-mode FSIW (C-HMFSIW) cavities are developed. A filtering antenna is proposed, employing a C-HMFSIW cavity for feeding and a slot-loaded C-FSIW cavity for radiation. By introducing cross-coupling between different resonators, a radiation null (RN) is achieved. To further enhance bandwidth and frequency selectivity, a new antenna configuration is developed using a C-HMFSIW cavity for feeding and a slot-loaded T-FSIW cavity for radiation. After loading the radiation slot, the T-FSIW cavity serves as two parts: one as the main radiator and the other as a parasitic radiator opposite to it. This configuration increases antenna bandwidth and generates an additional RN. Interestingly, adjusting the position of the radiation slot allows the RN to switch between the upper and lower stopbands. The antennas proposed in this work exhibit good stopband performance and compact size. Three filtering antenna prototypes are designed, fabricated, and measured, showing consistent results between measurements and simulations. © 1963-2012 IEEE.

关键词： Radiators

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