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1st IEEE International Conference on Advances in computing, Communication and Networking, ICAC2N 2024

作者： Shankar, Ajay Singh, Ankush Goswami, Kuldeep Gupta, Dhruv Galgotias University School of Computing Science and Engineering Greater Noida India

ISBN: (纸本)9798350356816

The analysis and analysis of cardiovascular sickness are vital clinical obligations to make sure Best to classification, this is helps provide right remedy to the affected character. Gadget analyzing programs within the medical area of interest have improved as they are able to recognize styles from statistics. Using gadget getting to know to Justify the cardiovascular sickness prevalence help misdiagnosis. This study makes a model that can predict heart disease to reduce deaths from heart disease. This paper presents a good integration method that can improve the accuracy of the group starting from Huang. Models used include random forest, decision trees classification, multilayer perceptron, and XG gain (XGB). Grid Search CV starts using the limitations of the Super Music Complete version to improve results. The proposed model was based on real-world data of 73,000 cases from Kaggle. The model was trained on 85.68% cutoff data and achieved the following new accuracy: decision tree: 86.34% (with crossover) and 87.58% (without matching), XG gain: 85.87% (with pass validation) and 87, 58% (without cross-validation), random forest: 87.58% (with pass-validation) and 86.34% (without cross-validation), multilayer perceptron: 87.18% (with pass-validation) and 87.92% (without pass verification). © 2024 IEEE.

关键词： Random forests

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Multi-Scale Time Series Segmentation Network Based on Eddy Current Testing for Detecting Surface Metal Defects

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IEEE/CAA Journal of Automatica Sinica 2025年第3期12卷 528-538页

作者： Xiaorui Li Xiaojuan Ban Haoran Qiao Zhaolin Yuan Hong-Ning Dai Chao Yao Yu Guo Mohammad S.Obaidat George Q.Huang the School of Intelligence Science and Technology University of Science and Technology Beijing the Beijing Advanced Innovation Center for Materials Genome Engineering the Key Laboratory of Intelligent Bionic Unmanned Systems and the Institute of Materials Intelligent Technology Liaoning Academy of Materials IEEE the Department of Computer Science Hong Kong Baptist University the School of Computer and Communication Engineering Key Laboratory of Advanced Materials and Devices for Post-Moore Chips Ministry of Education University of Science and Technology Beijing the Beijing Advanced Innovation Center for Materials Genome Engineering University of Science and Technology Beijing the School of Computer and Communication Engineering University of Science and Technology Beijing the King Abdullah Ⅱ School of Information Technology The University of Jordan the Department of Computational Intelligence the School of Computing SRM University the School of Engineering The Amity University The Hong Kong Polytechnic University

In high-risk industrial environments like nuclear power plants, precise defect identification and localization are essential for maintaining production stability and safety. However, the complexity of such a harsh environment leads to significant variations in the shape and size of the defects. To address this challenge, we propose the multivariate time series segmentation network(MSSN), which adopts a multiscale convolutional network with multi-stage and depth-separable convolutions for efficient feature extraction through variable-length templates. To tackle the classification difficulty caused by structural signal variance, MSSN employs logarithmic normalization to adjust instance distributions. Furthermore, it integrates classification with smoothing loss functions to accurately identify defect segments amid similar structural and defect signal subsequences. Our algorithm evaluated on both the Mackey-Glass dataset and industrial dataset achieves over 95% localization and demonstrates the capture capability on the synthetic dataset. In a nuclear plant's heat transfer tube dataset, it captures 90% of defect instances with75% middle localization F1 score.

关键词： Eddy current testing nondestructive testing semantic segmentation time series analysis

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Real Time Road Lane Detection and Vehicle Detection on YOLOv8 with Interactive Deployment 16

Real Time Road Lane Detection and Vehicle Detection on YOLOv...

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16th IEEE International Conference on Computational Intelligence and Communication Networks, CICN 2024

作者： Segu, Girish Sai Pavan Kumar Sivannarayana, Annam Devi Satya Naga Ramesh, S. School of Computing College of Engineering and Technology Srm Institute of Science and Technology Department of Computing Technologies India

ISBN: (纸本)9798331505264

Modern strides in autonomous vehicles and embedded advanced driver assistant part systems(ADAS) have forced the need of an efficient in addition to accurate system intended for clear road lane as in addition to vehicle detection. For road lane detection, this study introduces a new proposal through the combination of YOLOv8 (You Only Look Once), and YOLO for vehicle detection, creating a complete package for road understanding and navigation. To achieve accurate road lane detection, YOLOv8, which is the fastest and most accurate model to date, is used to ensure that it can keep track of these lanes in real-time with consistent accuracy whenever the camera is placed under various scenarios with insufficient lighting and occlusions as well. At the same time it uses YOLO for vehicle detection, which improves the recognition and interpretation of the presence and motion of vehicles in real time. The system uses Streamlit, which is an open-source app framework for Machine Learning and Data science projects to provide an intuitive user experience. Finally, we build an interface to provide results of lane and vehicle detection in realtime manner to allow ease of monitoring and evaluation on the overall system. The combination of YOLOv8 and YOLO with Streamlit provides a powerful, scalable, and deployable solution for practical computer vision applications. Combining well established object detection algorithms with a simplified deployment platform, the proposed system tackles some of the hardest and time consuming areas of autonomous driving. The goal is to make significant contributions to both the safety and efficiency of autonomous vehicles and pave the way for further inevitable advances in ADAS. © 2024 IEEE.

关键词： Advanced driver assistance systems

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A Survey on Deep Learning Techniques for Secure and Accurate Mobile Sink Position Prediction in Vehicular Pattern WSN 8

A Survey on Deep Learning Techniques for Secure and Accurate...

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8th International Conference on Inventive Systems and Control, ICISC 202

作者： Shareef, D.K. Jyothsna, V. School of Computing Department of Computer Science and Engineering Andhra Pradesh Tirupati India School of Computing Department of Data Science Andhra Pradesh Tirupati India

ISBN: (纸本)9798350386578

This research study presents a comprehensive survey of deep learning methods applied in order to improve the security along with accuracy of the mobile sink position prediction in Vehicular Pattern Wireless Sensor Networks (V-WSNs). V-WSNs are pivotal in modern wireless communication, especially in dynamic environments like urban traffic systems. The survey begins with an overview of V-WSNs, highlighting their unique characteristics, applications, and challenges, with a particular focus on mobile sink position prediction. It then delves into the nuances of routing protocols in V-WSNs, discussing the advantages and limitations of existing methods, and underscores the necessity for deep learning-based solutions. The paper further explores the integration of deep learning in WSNs, examining its applications, benefits, and inherent challenges. The significance of accurate and secure routing is analyzed, followed by an in-depth discussion on the integration of deep learning for enhancing these aspects in V-WSN routing. The paper also addresses the current challenges and open issues in mobile sink prediction and secure, accurate routing, paving the way for future research directions. A comparative analysis of various deep learning approaches and their practical applications in real-world scenarios is presented, offering insights into the effectiveness of these techniques. The survey concludes by summarizing the key findings and reaffirming the importance of integrating deep learning for secure and accurate mobile sink position prediction in V-WSNs. © 2024 IEEE.

关键词： Wireless sensor networks

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Optimizing DDoS Attack Detection Using Machine Learning 2

Optimizing DDoS Attack Detection Using Machine Learning

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2nd International Conference on Computational Intelligence, Communication Technology and Networking, CICTN 2025

作者： Aggarwal, Saransh Behera, Bhagrajyoti Singh, Murari Kumar Sharma, Ajeet Kumar School of Computing Science and Engineering Sharda University Greater Noida India School of Computer Science and Engineering Sharda University Greater Noida India

ISBN: (纸本)9798331530389

The increase in the Distributed Denial of Service attack (DDoS) leads to a significant threat to the network security. Inability to timely and accurately detect DDoS attacks disrupts services offered by companies and the government causing financial losses. Additionally, prolonged DDoS attacks can spoil user experience and degrade company reputation. The recent literature reveals various techniques for detecting DDoS attacks, including pre-trained machine learning and deep learning algorithms. This research paper addresses this challenge by tweaking random forest algorithms for maximum accuracy using CICDDoS2019 dataset. The methodology includes collecting of dataset, data processing, feature scaling and training multiple machine learning algorithms to identify maximum accuracy for detecting DDoS attacks. The result shows improvement of accuracy and precision of machine learning models used. Future work may include training the model for other types of attacks, real time implementation of the model developed, developing a hybrid approach for detection and protection from DDoS attack. © 2025 IEEE.

关键词： Network security

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Blockchain-based Decentralized Digital Forensics Case Management System using IPFS 3

Blockchain-based Decentralized Digital Forensics Case Manage...

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3rd IEEE International Conference on Blockchain and Distributed Systems Security, ICBDS 2024

作者： Viswanathan, Jayaraj Kumar, S. Udhaya Amrita School of Computing Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350354348

Blockchain technology has gained significant attention for its ability to provide a decentralized and immutable platform for various applications. In this paper, we propose a Blockchain based Decentralized Case Management System using the InterPlanetary File System (IPFS) protocol. The decentralized nature of blockchain ensures data integrity and transparency, while IPFS facilitates efficient and decentralized storage of case files. Our system allows for secure and fine-grained access control to case data, ensuring that only authorized users can view and manage cases. We present the architecture of the system, which includes smart contracts deployed on the blockchain to manage case data and access control rules. Additionally, we have discussed the implementation details and use case scenarios to demonstrate the functionality of the proposed system. Performance testing revealed that our system significantly enhances data integrity and transparency. Comparative analysis demonstrated that IPFS, despite having slightly higher latencies than traditional FTP, provides superior security and redundancy. Through our research, we aim to provide a secure, transparent, and efficient solution for managing cases in various domains, including legal, healthcare, and finance. © 2024 IEEE.

关键词： Data integrity

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A Game Theoretic Link Scheduler for Wireless Networks with Heterogeneous Energy Sources

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China Communications 2024年第12期21卷 108-125页

作者： He Tengjiao Kwanwu Chin Wang Yishun Sieteng Soh College of Information Science and Technology Jinan UniversityGuangzhouChina School of Electrical Computer and Telecommunications EngineeringUniversity of WollongongNSWAustralia School of Electrical Engineering Computing and Mathematical SciencesCurtin UniversityWAAustralia

This paper considers link scheduling in a wireless network comprising of two types of nodes:(i)hybrid access points(HAPs)that harvest solar en-ergy,and(ii)devices that harvest radio frequency(RF)energy whenever HAPs *** aim is to de-rive the shortest possible link schedule that determines the transmission time of inter-HAPs links,and uplinks from devices to *** first outline a mixed in-teger linear program(MILP),which can be run by a central node to determine the optimal schedule and transmit power of HAPs and *** then out-line a game theory based protocol called Distributed Schedule Minimization Protocol(DSMP)that is run by HAPs and ***,it does not require causal energy arrivals and channel gains *** results show that DSMP produces schedule lengths that are at most 1.99x longer than the schedule computed by MILP.

关键词： channel access finite battery mathemati-cal program renewable energy wireless power trans-fer

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Classification of Aerial Images and Applications: A Deep Learning Perspective 16

Classification of Aerial Images and Applications: A Deep Lea...

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16th IEEE International Conference on Computational Intelligence and Communication Networks, CICN 2024

作者： Monika, Pralayakaveri Reddy, Chinta Midhilesh Venkata Sai Ramesh, S. College of Engineering and Technology Srm Institute of Science and Technology Department of Computing Technologies School of Computing India

ISBN: (纸本)9798331505264

Aerial image detection is a crucial technology for a variety of applications, including but not limited to urban planning, environmental monitoring, and disaster management. In this project, we implement deep learning algorithms, in particular the AlexNet model to classify aerial images taken from different landscapes. The dataset includes cityscape, road, and nature images, which are resized and normalized to make sure these image inputs conform to the input size for the AlexNet model. Fine-tuning means changing the top layer of AlexNet to have the corresponding number of classes as that of the dataset. Using pre-trained weights helps speed up the time it will take to learn the model set up in pytorch. We use momentum Stochastic Gradient Descent (SGD) to optimize the model, and a learning rate scheduler to boost the performance. The data is then divided into training and testing sets, and the accuracy of the model is assessed using the test set. The classification results indicate that fine-tuned alexnet model work well in different aerial image classification. We present the full potential of deep learning for fast aerial image classifications, which will serve as an accurate tool for aerial image classification with applications in disaster response, urban development and environmental monitoring. This model can be further improved for different datasets and for real time image processing problems. © 2024 IEEE.

关键词： Environmental monitoring

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Multi-Task Learning for UAV Trajectory and Caching With Federated Cloud-Assisted Knowledge Distillation

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IEEE Transactions on Network and Service Management 2025年第3期22卷 2516-2533页

作者： Tietaa Maale, Gerald Arden Elorm Kuadey, Noble Arafat, Yeasin Thomas, Kwantwi Sun, Guolin Liu, Guisong Research Institute of Intelligent Computing University of Electronic Science and Technology of China School of Computer Science and Engineering National Key Laboratory of Intelligent Collaborative Computing Chengdu611731 China McCoy University College of Education Department of Mathematics and ICT Nadowli Ghana Research Institute of Intelligent Computing at the University of Electronic Science and Technology of China School of Computer Science and Engineering National Key Laboratory of Intelligent Collaborative Computing Chengdu611731 China Ho Technical University Department of Computer Science Ho Ghana University of Mines and Technology Department of Computer Science and Engineering Tarkwa Ghana Southwestern University of Finance and Economics Complex Laboratory of New Finance and Economics School of Computing and Artificial Intelligence Chengdu610074 China Ministry of Education Engineering Research Center of Intelligent Finance Chengdu610030 China

The proliferation of Internet of Things (IoT) technologies and ubiquitous connectivity has led to uncrewed aerial vehicles (UAVs) playing key role as edge servers, revolutionizing the wireless communications landscape by facilitating computing and caching resources closer to ground users (GUs). This advancement significantly alleviates core network loads, reduces latency, and guarantees content availability even in congested or remote areas. However, jointly optimizing UAV caching strategies and trajectories gives rise to a multi-task optimization (MTO) problem. This paper introduces a novel multi-task geo-temporal caching (MT-GTC) framework that addresses the interplay between UAV caching mechanisms and trajectory optimization in a cohesive manner. Leveraging a proposed multi-task learning (MTL) model for joint optimization of UAV caching and trajectory design, we develop a federated learning cloud-assisted knowledge distillation (FL-CAKD) scheme to preserve data privacy and adapt to data heterogeneity. FL-CAKD transfers knowledge from a cloud model orchestrator (CMO), which houses a large and sophisticated teacher model, to a lightweight on-device MTL student models using soft target distributions instead of large model parameters, significantly reducing communication costs. MT-GTC optimizes caching and trajectories to maximize cache hits and minimize latency. Evaluations on real-world mobility datasets demonstrate up to 95% cache hit rates and 21% lower delays compared to baselines. © 2004-2012 IEEE.

关键词： Multi-task learning

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Artificial intelligence algorithms for object detection and recognition in video and images

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Multimedia Tools and Applications 2025年 1-18页

作者： Dakshinamoorthy, Prabakar Rajaram, Gnanajeyaraman garg, Shruti Murugan, Prabhu Manimaran, A. Sundar, Ramesh Department of Data Science and Business System School of Computing SRM Institute of Science and Technology SRM Nagar Kattankulathur Chennai India Department of Computer Science Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Tamilnadu Chennai India Birla Institute of Technology Mesra Ranchi India Department of ECE Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai602105 India Department of Computer Science Engineering Saveetha School Of EngineeringSaveetha Institute of Medical and Technical Sciences Chennai6021055 India Department of Netwoking and Communication School of Computing SRM Institute of Science and Technology SRM Nagar Kattankulathur Chennai India

The usage of machine learning and deep learning algorithms have necessitated Artificial Intelligence'. AI is aimed at automating things by limiting human interference. It is widely used in IT, healthcare, finance, and agriculture. It is achieved through several deep learning algorithms that reflect the human brain's intelligence. These AI algorithms can be manipulated according to changing needs and improved efficiency. This paper tries to utilize the developments made in AI technology to classify the images and recognize the objects present in them. One widely used AI algorithm is CNN (Convolutional Neural Networks). The CNN is a deep learning-based algorithm that consists of various layers that extract and filters the parameters present in the images. Some additional layers of ResNet50 and the CNN algorithm are used to extract the parameters to improve image recognition accuracy. The image dataset taken for training and testing the proposed model is imageNet. The images are initially processed before sending them to the proposed model. The proposed model is trained, validated, and tested through the images obtained after the initial processing. The same process is repeated several times until getting the maximum accuracy. The accuracy of the proposed model in terms of image recognition is recorded. The obtained results are compared with other image classification algorithms like VGG16 and VGG19. It is concluded that the proposed model outperforms other traditional methods in terms of accuracy. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2025.

关键词： Convolutional neural networks

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