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4th IEEE Asian Conference on Innovation in technology, ASIANCON 2024

作者： Guduri, Naga Venkata ramakrishna Charaan, r.M. dilip Kalaiyarasi, r. Samuthira Pandi, V. reddy, d.Laxma Selvaprabhu, T. University of Technology and Applied Sciences Faculty of Engineering Al Musannah Oman Vel Tech Rangarajan Dr.Sagunthala R&d Institute of Science and Technology Department of Computer Science and Engineering Tamil Nadu Chennai India Rajalakshmi Institute of Technology Department of Artificial Intelligence and Data Science Tamil Nadu Chennai India Chennai Institute of Technology Centre for Advanced Wireless Integrated Technology Tamil Nadu Chennai India Mlr Institute of Technology Department of Ece Hyderabad India Dhanalakshmi College of Engineering Department of Cse Tamil Nadu Chennai India

ISBN: (纸本)9798350354218

In the era of Industry 4.0, edge devices in industrial settings generate vast amounts of data crucial for process optimization and Predictive Maintenance (PM). Traditional cloud-based approaches for handling this data are becoming untenable due to cost, latency, and privacy issues. Federated learning emerges as a viable solution, offering localized and efficient data processing while mitigating these challenges. This paper presents a Federated Convolutional Neural Network with Temporal Attention Mechanism (FedCNN-TAM) for PM tasks in Internet of Things (IoT) networks. This model leverages temporal features in sensor data to enhance predictive accuracy. Evaluated on the CMAPSS dataset, FedCNN-TAM outperforms representative models with a significant performance gain. This model exhibits the potential for broader applications in industrial IoT, setting the stage for future research on its scalability and adaptability to multi-modal data types. © 2024 IEEE.

关键词： Spatio-temporal data

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Advancements and Applications in Object detection and Pattern recognition 5

Advancements and Applications in Object Detection and Patter...

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5th International Conference on Sustainable Communication Networks and Application, ICSCNA 2024

作者： Govinda rajulu, G. Savija, J. Shereen Priscila, S. Anish, T.P. Nalini, M. Siva Subramanian, r. Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology India Artificial Intelligence and Data Science Velammal Engineering College Chennai India Easwari Engineering College Department of IT Chennai India R.M.K. College of Engineering and Technology Dept of CSE Tiruvallur India S A Engineering College Dept of CSE India R.M.K College of Engg and Tech Dept of CSE India

ISBN: (纸本)9798331530013

Object detection and pattern detection are fundamental problems in computer vision, and have real-world uses such as in autonomous vehicles, healthcare, and security. Over the recent years, improvements made in the deep learning framework have democratized these fields and made it easier for researchers to build accurate models. CNN has become very popular in object detection and is currently implemented through features architectures like Faster rCNN and the Single Shot MultiBox detector (SSd). When it comes to pattern recognition, especially in sequential data, recurrent Neural Networks (rNN), especially Long Short- Term Memory (LSTM) network outperforms the rest. However, some of the important issues that remain unresolved include issues of robustness to varying environmental conditions and the question of computation cost. From the results and analysis of the research, it can be concluded that the following directions for future studies can be distinguished, enhance the model's robustness with methods such as domain adaptation, create new, less complex architectures for real-time applications. Also, multi-modal fusion and unsupervised learning are the two areas with a brighter future in the field of automating machine learning. In sum, more advanced object detection and pattern recognition will continuously push the development and usability of these technologies for safer, more efficient, and intelligent systems at different domains. © 2024 IEEE.

关键词： Intelligent systems

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Cloud Based remote File Access from PC to Mobile Using File directory 1

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Proceedings of the 6th IFIP TC 12 International Conference on Computational Intelligence in data Science, ICCIdS 2023

作者： Kanimozhi, J. Bharathi, Sv. Shri Kathiroli, Panimalar Sharmila, P. Ayshwariya, S. Abi Department of CSE Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai India Department of Data Science and Business Systems SRM Institute of Science and Technology Chennai India Department of Electronics and communication Engineering Vel Tech Multi Tech Dr. Rangarajan Dr. Sagunthala Engineering College Chennai India

ISBN: (数字)9783031382963

ISBN: (纸本)9783031382956

People can share their digital data or information with others through networks with the aid of file sharing techniques. Peer-to-peer, cloud storage, and removable storage are the three ways that file sharing can be accomplished, according to the examination of file sharing methods. The most popular option for remote file sharing today is using a cloud storage services. Therefore, by utilizing cloud storage services, a file access method is established, in which the user can access a copy of a file that is located on their PC device using a mobile phone with additional security services offered. It is possible because JSON is utilized to render the file structure in the mobile application and NodeJS is used to index the local machine’s available files. The user can choose the necessary file from the rendered file system using their mobile device, and the UrL will download the file to their device. Security is provided in this file access method by a key issued by the NodeJS application, which uses the JSON Web Token (JWT) technique for confirmation of the user. result of the developed work is compared with file sharing services provided by Google drive, Gmail and others, and the proposed work is performing better in terms of all metrics. © 2023, IFIP International Federation for Information Processing.

关键词： Cloud storage

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reducing Traffic Wastage in Video Streaming via Bandwidth-Efficient Bitrate Adaptation

arXiv

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arXiv 2024年

作者： Su, Hairong Wang, Shibo Yang, Shusen Huang, Tianchi ren, Xuebin National Engineering Laboratory for Big Data Analytics Xi'an Jiaotong University China Ministry of Education Key Laboratory for Intelligent Networks and Network Security Xi'an Jiaotong University China R&D department Sony

Bitrate adaptation (also known as ABr) is a crucial technique to improve the quality of experience (QoE) for video streaming applications. However, existing ABr algorithms suffer from severe traffic wastage, which refers to the traffic cost of downloading the video segments that users do not finally consume, for example, due to early departure or video skipping. In this paper, we carefully formulate the dynamics of buffered data volume (BdV), a strongly correlated indicator of traffic wastage, which, to the best of our knowledge, is the first time to rigorously clarify the effect of downloading plans on potential wastage. To reduce wastage while keeping a high QoE, we present a bandwidth-efficient bitrate adaptation algorithm (named BEABr), achieving consistently low BdV without distinct QoE losses. Specifically, we design a precise, time-aware transmission delay prediction model over the Transformer architecture, and develop a fine-grained buffer control scheme. Through extensive experiments conducted on emulated and real network environments including WiFi, 4G, and 5G, we demonstrate that BE-ABr performs well in both QoE and bandwidth savings, enabling a 60.87% wastage reduction and a comparable, or even better, QoE, compared to the state-of-the-art methods. Copyright © 2024, The Authors. All rights reserved.

关键词： Bandwidth

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Blockchain and Machine Learning Integration for Safe data Sharing in Finance and Hr

Blockchain and Machine Learning Integration for Safe Data Sh...

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2024 International Conference on Intelligent Algorithms for Computational Intelligence Systems, IACIS 2024

作者： Sivakumar, T.B. Prathiba, E. Sharma, Shweta Prasad, rashmi Saket ramaratnam, M.S. Krishnamoorthy, V. School of Computing Vel Tech Rangarajan Dr.Sagunthala R&d Institute of Science and Technology Department of Cse Chennai India Mailam Engineering College Department of Artificial Intelligence and Data Science Mailam India Shambhunath Institute of Engineering & Technology Department of Business Administration Prayagraj India D Y Patil Pgdm Institute Akurdi Pune India Sri Chandrasekharendra Saraswathi Viswa Mahavidyalaya Department of Management Studies Kanchipuram India Kongu Engineering College Department of Management Studies Perundurai Erode India

ISBN: (纸本)9798350360660

Modern finance and Hr businesses have challenges in exchanging data due to centralized systems that are subject to hacking and lack transparency. The study proposes employing blockchain technology and machine intelligence (ML) to address these issues holistically. The proposed system enhances integrity and resilience to assaults by utilizing a decentralized ledger for secure data exchange. ML algorithms enable predictive analytics, risk management, and personalized insights. The proposed system outperforms the existing system in terms of accuracy, security, and efficiency. The accuracy of fraud detection increases from 0.75 to 0.90, as does the estimate of staff turnover from 0.80 to 0.95. The proposed system achieves an overall accuracy of 95%, surpassing the existing system's 85%. Blockchain and ML integration can alter data management methods in finance and Hr industries, with improved security measures resulting in a risk score of 2 compared to 7 in the existing system. © 2024 IEEE.

关键词： decentralized finance

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Exploring Meibomian Gland dysfunction Grading: A Comparison of Machine Learning and XAI Approaches 2

Exploring Meibomian Gland Dysfunction Grading: A Comparison ...

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2nd IEEE International Conference on Image Processing and Computer Applications, ICIPCA 2024

作者： Liang, Congxiao Wang, Mini Han Liu, Haoyang Chong, Kelvin K.L. Beijing Institute of Technology Center for Sport Science and Physical Education Zhuhai519000 China The Department of Ophthalmology and Visual Sciences Chinese University of Hong Kong 999077 Hong Kong The Faculty of Data Science City University of Macau Macau999078 China The Center of Big Data and Ai Zhuhai Institute of Advanced Technology Chinese Academy of Sciences Zhuhai519000 China The R&d Center of Ai and Digital Medicine Guangdong Digital Medicine Promotion Science and Technology Co. Ltd Guangdong 519000 China Guangdong Medical University Dongguan China Zhuhai Institute of Advanced Technology Chinese Academy of Sciences Zhuhai China Perspective Technology Group Zhuhai China The Faculty of Medicine Chinese University of Hong Kong Hong Kong

ISBN: (纸本)9798350360240

This research investigates the effectiveness of employing a machine learning methodology and an Explainable Artificial Intelligence (XAI) tool for the assessment of Meibomian Gland dysfunction (MGd). Utilizing resNet34, a convolutional neural network (CNN), trained on a diverse dataset comprising images depicting varying degrees of MGd severity, the machine learning approach endeavors to classify MGd severity into four distinct grades (0, 1, 2, 3) by leveraging extracted characteristic features. Findings reveal comparable accuracy between resNet34 and the XAI tool, with resNet34 achieving a slightly lower accuracy of 99.1% in contrast to the XAI tool's 99.4%. However, resNet34 demonstrates marginally higher precision and F1-score at 98.2% and 98.45%, respectively, suggesting a nuanced advantage in precision-recall equilibrium. Conversely, the XAI tool maintains a commendable precision of 97.2% and an Fl-score of 97.94%, alongside a recall of 98.7%, underscoring its effectiveness in discerning positive instances. Noteworthy is that resNet34 operates as a black-box model, providing limited interpretability, while the XAI tool emphasizes transparency by furnishing clinicians with understandable MGd grading elucidations derived from meibomian gland segmentation and atrophy analysis. This transparency enhances trust and comprehension, which are integral for clinical acceptance and decision-making. Furthermore, the utilization of XAI heatmap generation facilitates data quality enhancement, as evidenced by instances of erroneous artifact identification, emphasizing the imperative nature of artifact elimination for the refinement of MGd grading procedures. © 2024 IEEE.

关键词： Convolutional neural networks

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Efficient diabetes detection using Hybrid Machine Learning Model

Efficient Diabetes Detection using Hybrid Machine Learning M...

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2024 International Conference on distributed Systems, Computer Networks and Cybersecurity, ICdSCNC 2024

作者： Gowri, A.S. Jose, P. Karthik, S.M. Shaheema, S Berlin Karuppasway, K.M. Balamurugan, r. School of Computing Srm Institute of Science and Technology Department of Networking and Communications Kattankulathur India Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Department of Computer Science and Engineering Chennai India Department for Applied Research Gandhigram India Stella Mary's College of Engineering Department of Artificial Intelligence and Data Science Azhikal India Bharath Institute of Higher Education and Research Department of Mathematics and Statistics Chennai India Dmi College of Engineering Department of Information Technology Chennai India

ISBN: (纸本)9798350375442

diabetes is a metabolic disease that affects a large number of the global population and is incurable. The primary causes of death symptoms are kidney failure, heart attacks, strokes, and blindness. In this paper Principal component analysis used for pre-processing the data to extract features and The Efficient Hybrid Machine Learning Model Convolution Neural Network-Support Vector Machine is used to categorize the Pima Indian dataset. CNN and non-linear SVM used in a hybrid model to classify the diabetes data from the Pima Indians. To identify positive classes of pima Indians diabetes, the HCNN-SVM extract the features from the original data expression and learns diabetes data through a novel convolution network environment to the SVM classifier with rBF function and achieve the accuracy as 98,04%. © 2024 IEEE.

关键词： Adversarial machine learning

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Advancing Alzheimer's Nodule detection Through a Comprehensive Multi-Scene deep Learning Framework 10

Advancing Alzheimer's Nodule Detection Through a Comprehensi...

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10th International Conference on Advanced Computing and Communication Systems, ICACCS 2024

作者： Premkumar, C. rajeshwari, r. remya, r. Cholaathiraj, M.S. reethika, A. Bharathidasan, S. Bannari Amman Institute of Technology Department of Artificial Intelligence and Data Science Sathyamangalam India Cmr Institute of Technology Department of Computer Science and Engineering Bengaluru India Vel Tech Rangarajan Dr Sagunthala R&d institute of science and Technology Department of Electronics and Communication Engineering Chennai India Bannari Amman Institute of Technology Department of Artificial Intelligence and Machine Learning Sathyamangalam India Sri Ramakrishna Engineering College Department of Electronics and Communication Engineering Coimbatore India Erode Sengunthar Engineering College Department of Electronics and Communication Engineering Erode India

ISBN: (纸本)9798350384369

determining the precise location of Alzheimer's nodules is essential for estimating the risk of brain cancer. Conventional CAd modules, including MrI, PET, and CT, struggle with feature extraction and segmentation due to time limits and complexity. This study proposes an efficient brain nodule detection technique based on the Multi-Scene deep Learning Framework (MSdLF) that makes use of the vesselness filter. A four-channel CNN model is developed using information from two image sequences to enhance radiologists' ability to recognise four-stage nodules. This adaptable model may be used in two different classes. The outcomes demonstrate how well the MSdLF performs while processing substantial volumes of image data for brain nodule recognition, improving with accuracy 98.86% and 98.45% of sensitivity by using decreasing false positives. © 2024 IEEE.

关键词： Magnetic resonance imaging

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A Novel Architecture for Efficient Mobile Communication based on Scattered and diverse Combination using Prioritize and Postoperative Soft Modulation 7

A Novel Architecture for Efficient Mobile Communication base...

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7th International Conference on Intelligent Computing and Control Systems, ICICCS 2023

作者： Sumithra, J. Mercy Theresa, M. Geetha Priya, S. Jackson, Beulah Samuthira Pandi, V. Balamurugan, A. R. M. D Engineering College Department of Electronics and Communication Engineering Tamil Nadu India SRM Institute of Science and Technology Department of Data Science and Business Systems Tamil Nadu India R. M. D Engineering College Department of Computer Science and Engineering Tamil Nadu India Vel Tech Rangarajam Dr. Sagunthala R&D Institute of Science and Technology Department of Electronics and Communication Engineering Tamil Nadu India SRM Institute of Science and Technology Department of Electronics and Communication Engineering Tamil Nadu India Chennai Institute of Technology Department of Electrical and Electronics Engineering Tamil Nadu India

ISBN: (纸本)9798350397253

The proposed work suggests a novel approach for the cooperation of geographically scattered individuals and the blending of different points of view. This work ensures that the center on heterogeneous networks in which nodes support both long-range low-rate radio Frequency (rF) interfaces (like cellular) and short-range high-rate rF interfaces (such as IEEE802.11) that communicate over urban rayleigh fading channels. These interfaces are used to connect the devices. The interfaces are what are used to link the various gadgets. This research work proposes and evaluates a wide variety of hierarchically distributed cooperation strategies that can function despite resource restrictions such as limited short-range rF bandwidth. These strategies are contained within the confines of this paradigm, which serves as the framework for proposed work. Post Soft-demodulation Combining, often referred to as PSdC, and Priority Maximum ratio Combining are the two approaches that come highly recommended by our team (PMrC). This work provides evidence that using the offered methods increases throughput, reliability, and signal-to-noise ratio. Moreover, this study found that PMrC and PSdC have the ability to improve throughput performance by more than an order of magnitude across a wide range of communication circumstances. © 2023 IEEE.

关键词： Signal to noise ratio

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Hybrid Blockchain and IoT driven Framework for Supply Chain Optimization by Enhancing Transparency, Efficiency and Cost reduction 3

Hybrid Blockchain and IoT Driven Framework for Supply Chain ...

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3rd International Conference on Electronics and renewable Systems, ICEArS 2025

作者： Pradeep Kumar, S. Basha, C.H. Hussaian Prasath, C. Arun Amirthayogam, G. Lathif, Syed Ismail Abdul Leelavathi, r. Rajalakshmi Engineering College Computer Science Design Department Thandalam Chennai602105 India Sr University Department of Electrical and Electronics Engineering Warangal506371 India Mahendra Engineering College Department of Ece Namakkal637503 India Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology School of Computing Department of Computer Science and Engineering Chennai600062 India Srm Institute of Science and Technology Department of Data Science and Business Systems Chennai603203 India Velalar College of Engineering and Technology Department of Biomedical Engineering Erode638012 India

ISBN: (纸本)9798331509675

Blockchain technology represents a modern supply chain management revolution which fixes operational challenges while providing enhanced transparency at reduced costs. The research develops HB-SCOF (Hybrid Blockchain-Enabled Supply Chain Optimization Framework) which combines blockchain self-verification databases with automatic smart contracts to maintain continuous supply chain observation and evaluate data credibility. The framework integrates tamper-proof transaction tracking mechanisms with unified supply chain network connectivity for securing data transmission as a trust delivery method through unbreakable shared databases. The retail-industry case study demonstrated a 25% decrease in expenses stemming from agency elimination alongside enhanced flow management and better stock distribution. The system demonstrates its effectiveness through assessments of key performance indicators including transaction speed and data accuracy together with business cost efficiency. This part looks at implementation methods through training stakeholders in addition to transforming organizational policies which produce successful outcomes. Blockchain technology labs verify its capability to enhance supply chain management efficiency while showing realistic deployment methods. © 2025 IEEE.

关键词： Smart contract

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