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5th IEEE Global Conference for Advancement in Technology, GCAT 2024

作者： Banumathi, S. Saravanan, K. Nadanasabai, R. Vijayakumar, Radhamani Sumalatha, A. Ahamed, B. Shamreen Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Department of Computer Science and Engineering India R.M.K Engineering College Department of Information Technology Tamil Nadu Kavaraipettai India St. Josephs College of Engineering Department of Mba Chennai India Rajalakshmi Engineering College Department of Ece Thandalam Chennai India Kristu Jayanti College Autonomous Department of Computer Science Bengaluru India Sathyabama Institute of Science and Technology Department of Computer Science and Engineering Jeppiaar Nagar Chennai India

ISBN: (纸本)9798350376685

A new age has begun in the financial sector with the arrival of blockchain technology, and cryptocurrencies have emerged as a paradigm-shifting invention. This study investigates the potential for financial innovation and safety by exploring the interplay between blockchain and cryptocurrency. The study demonstrates how decentralization, transparency, and immutability - the three pillars of blockchain - allow for the development of safe and effective cryptocurrency systems. Cryptocurrencies have the ability to revolutionize the financial industry by providing better privacy, lower transaction costs, and cross-border transactions, among other benefits. Additional topics covered include legislative barriers, market volatility, and security issues that are linked to cryptocurrency. Smart contracts, decentralized finance (deFi), and asset tokenization are just a few examples of how this study uses a literature review, case studies, and empirical analysis to pinpoint important areas where blockchain and cryptocurrencies may work together to propel financial innovation. The study highlights the significance of a well-rounded regulatory system that encourages innovation while reducing potential dangers. New investment opportunities, payment systems, and financial inclusion opportunities may arise as a result of the combination of blockchain technology and cryptocurrencies, according to the results. To overcome obstacles and capitalize on this revolutionary synergy, however, business stakeholders, regulators, and lawmakers must work together to realize this promise. © 2024 IEEE.

关键词： decentralized finance

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IoT-Enabled Predictive Healthcare Monitoring Using Machine learning Models 3rd

IoT-Enabled Predictive Healthcare Monitoring Using Machine L...

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3rd International Conference on Universal Threats in Expert Applications and Solutions, UNI-TEAS 2024

作者： Patil, Nitin Jagannath Sharma, Avinash Prasad, K.d.V. Yogi, Kottala Sri dankan Gowda, V. Department of Instrumentation Engineering D. N. Patel College of Engineering Shahada Maharashtra Untavad India Professor Department of Computer Science and Engineering Chandigarh Engineering College Chandigarh Group of Colleges Punjab Mohali140307 India Pune India Pune India Department of Electronics and Communication Engineering BMS Institute of Technology and Management Karnataka Bengaluru India

ISBN: (纸本)9789819751457

This study explores the capabilities of integrating the Internet of Things (IoT) with machine learning to monitor health in real time. Using IoT-powered devices like heart rate monitors, glucose meters, and blood pressure devices, health metrics were consistently gathered and sent to a cloud-based system. After refining the data for better quality, machine learning techniques were used to create health prediction models. The findings showed significant precision, especially when using neural networks, in predicting health-related events. This combined system can identify and notify healthcare experts about potential health concerns immediately. However, the promise of ongoing health tracking and early detection comes with concerns regarding data protection, data handling, and model reliability. © The Author(s), under exclusive license to Springer Nature Singapore Pte ltd. 2024.

关键词： Electronic health record

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Telemedical Robot Using IoT with live Supervision and Emergency Alert 3

Telemedical Robot Using IoT with Live Supervision and Emerge...

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3rd International Conference on Pervasive Computing and Social Networking, ICPCSN 2023

作者： Vimala, S. Karthika, G. Geetha, C. Reddy, R. Vijaya Kumar Ganesh, C. S. Sundar Ganesh, R. Karthik Panimalar Engineering College Department of Electrical and Electronics Engineering Tamil Nadu Chennai600123 India R.M.D. Engineering College Department of Electronics and Communication Engineering Tamil Nadu Kavaraipettai601206 India R. M. K. Engineering College Department of Computer Science and Engineering Tamil Nadu Kavaraipettai601206 India Koneru Lakshmaiah Education Foundation Department of Computer Science and Engineering Andhra Pradesh Vaddeswaram522502 India Karpagam College of Engineering Department of Electrical and Electronics Engineering Tamil Nadu Coimbatore641032 India Scad College of Engineering and Technology Departrtent of Computer Science and Engineering Tamil Nadu Tirunelveli627414 India

ISBN: (纸本)9798350322842

A highly qualitative service is required for monitoring and assisting the remote patients as the telemedicine industry soars to new heights. When patients are far, doctors find it challenging to interact with them and diagnose their health conditions. In addition, individuals find it challenging to administer injections on their own in an emergency. This project provides a design for a medical assistance, real-time monitoring, and injection robot as well as human-robot interaction (HRI) in the field of healthcare for a robot known as a Telemedicine robot that runs on an IOT platform. Based on a live video conference and saved in an electronic medical record, it can be utilized for remote diagnostics and injecting patients. The Arduino platform is used for IOT functionality wherein the doctor can command the robot through mobile for locomotion. When necessary, the doctor in the range can deploy a set of sensors to keep an eye on the patient's wellbeing and store the information of the patient in the database and control the robot to put injection. The project emphasizes low cost and modular design with customizable sensor modules. The IOT capabilities of the Arduino platform allow the doctor to control the robots movement using a mobile device. When necessary, the doctor from a distance can deploy a collection of sensors to keep an eye on the patients wellbeing, save the patients data in a database, and command a robot to administer an injection. The project places a strong emphasis on modularity, cheap cost, and adaptable sensor modules. © 2023 IEEE.

关键词： Telemedicine

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Resolution-Preserving Multi-Scale Network for 5G-lTE Spectrogram-Based Spectrum Sensing

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IEEE Wireless Communications letters 2025年第6期14卷 1673-1677页

作者： Nguyen, Huu-Tai dang, Hai-Trang Phuoc Pham, Quoc-Viet Huynh-The, Thien Ho Chi Minh City University of Technology and Education Department of Computer and Communications Engineering Ho Chi Minh City71307 Viet Nam Trinity College Dublin School of Computer Science and Statistics Dublin 2 D02 PN40 Ireland

This paper introduces PRMNet, an advanced deep model with a convolutional architecture for segmenting wireless signals in time-frequency occupancy spectrograms, designed to enhance spectrum sensing accuracy. PRMNet features a resolution-preserving architecture combined with a multi-scale feature extraction module to capture features across multiple resolution levels while preserving the original resolution of input spectrograms. This design facilitates an effective extraction of both local and global spectral features, thus robustly analyzing wideband spectrograms under diverse signal and challenging channel conditions. Experimental results reveal that PRMNet outperforms state-of-the-art models, such as FCN, U-Net, U-Net++, deeplabV3+, and SegFormer, with a mean accuracy of 92.44%, a mean IoU of 87.6%, a mean F1 score of 93.35%, and a mean precision of 94.65%. despite its compact architecture with only 14.5M parameters, PRMNet delivers remarkable performance, thus offering a promising solution for intelligent spectrum sensing in next-generation wireless communication systems. © 2025 IEEE. All rights reserved.

关键词： 5G mobile communication systems

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AI-Powered Predictive Analysis for Pest and disease Forecasting in Crops 2

AI-Powered Predictive Analysis for Pest and Disease Forecast...

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2nd IEEE International Conference on Communication, Security and Artificial Intelligence, ICCSAI 2023

作者： Palani, Hari Kumar Ilangovan, S. Senthilvel, P. Gururama Thirupurasundari, d.R. Rajesh Kumar, K. School of Engineering Ajeenkya D y Patil University India K.L.N. College of Engineering Department of Information Technology Sivagangai Tamilnadu Pottapalayam630612 India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Computer Science and Engineering India Bharath Institute of Higher Education and Research Department of Computer Science and Engineering Tamil Nadu Chennai India Madha Engineering College Kundrathur Department of Electronics and Communication Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350369960

Artificial Intelligence(AI) has transformed agriculture by enhancing resilience, sustainability, and yield. A new AI-driven crop disease and pest outbreak prediction model is presented in this research. The algorithm uses satellite imagery, meteorological data, historical pest and disease incidence records, and field IoT sensor feeds to dynamically anticipate hazards. Recurrent neural networks (RNNs) for time-series data analysis, convolutional neural networks (CNNs) for high-resolution satellite imagery analysis, and other machine learning techniques to detect diseases and pests are major elements of the system. The model's accuracy has been carefully evaluated against historical epidemics, showing significantly better predictions than conventional methods. This method allows farmers to obtain early warnings and prevent biotic dangers from causing harm. Predictive insights help agronomists and policymakers maximize pest and disease control, promoting sustainable agriculture and food security. To conclude, AI-driven predictive analysis could revolutionize agricultural pest and disease control. This study shows how AI revolutionizes crop harvests using several data sources and cutting-edge algorithms. Future study will improve the model's precision, expandability, and adaptability to different crops and regions. © 2023 IEEE.

关键词： Adaptability Artificial Intelligence convolutional neural networks IoT sensor Pest and disease outbreaks Predictive model Satellite imagery

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Adaptive Fault detection Scheme Using an Optimized Self-healing Ensemble Machine learning Algorithm

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CSEE Journal of Power and Energy Systems 2022年第4期8卷 1145-1156页

作者： levent Yavuz Ahmet Soran AhmetÖnen Xiangjun li S.M.Muyeen Electrical and Computer Engineering Department in Abdullah Gül University Kayseri 38080Turkey Department of Electrical and Computer Engineering College of EngineeringSultan Qaboos UniversityAl-KhoudMuscat123Oman Department of Electrical Engineering Qatar UniversityDoha 2713Qatar State Key Laboratory of Control and Operation of Renewable Energy and Storage Systems China Electric Power Rescarch InstituteBeijing 100192China

This paper proposes a new cost-efficient,adaptive,and self-healing algorithm in real time that detects faults in a short period with high accuracy,even in the situations when it is difficult to *** than using traditional machine learning(Ml)algorithms or hybrid signal processing techniques,a new framework based on an optimization enabled weighted ensemble method is developed that combines essential Ml *** the proposed method,the system will select and compound appropriate Ml algorithms based on Particle Swarm Optimization(PSO)*** this purpose,power system failures are simulated by using the PSCA d-Python *** of the salient features of this study is that the proposed solution works on real-time raw data without using any pre-computational techniques or pre-stored ***,the proposed technique will be able to work on different systems,topologies,or data *** proposed fault detection technique is validated by using PSCAd-Python co-simulation on a modified and standard IEEE-14 and standard IEEE-39 bus considering network faults which are difficult to detect.

关键词： decision tree(dT) ensemble machine learning algorithm fault detection islanding operation k-Nearest Neighbor(kNN) linear discriminant analysis(ldA) logistic regression(lR) Naive Bayes(NB) self-healing algorithm

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Blockchain-based access control and interoperability framework for electronic health records (ANCIlE)

Blockchain-based access control and interoperability framewo...

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International Conference on Artificial Intelligence, Blockchain, Computing and Security, ICABCS 2023

作者： Senthilkumar, G. Srinivasan, Aravindan Venkatesh, J. Kuchipudi, Ramu Vinoth, K. Ramamoorthy, A. Department of Computer Science and Engineering Panimalar Engineering College Chennai India Department of Computer Science Engineering Koneru Lakshmaiah Education Foundation Andhra Pradesh Vaddeswaram India Department of Computer Science and Engineering Malla Reddy Institute of Engineering and Technology Telangana Hyderabad India Department of Information Technology Chaitanya Bharathi Institute of Technology Telangana Hyderabad India Department of Electrical and Electronics Engineering Vel Tech Rangarajan Dr. Sagunthala R and D Institute of Science and Technology Tamil Nadu Chennai India Department of Mathematics Velammal Engineering College Tamil Nadu Chennai India

ISBN: (纸本)9781032669663

Even if big cities are working globally to build the infrastructure for smart cities and there is a greater emphasis on the security of electronic health records, patient privacy is frequently compromised. Previous attempts to combat this have left patients with largely unavailable data. Currently used record-management systems struggle to strike a balance between data privacy and patient and provider access. Blockchain, a new technology, the ability to share data in a decentralized and transactional manner. To balance the accessibility and privacy of electronic health records, blockchain technology may be applied in the healthcare industry. The blockchain-based architecture we present in this work enables patients, healthcare professionals, and third parties to access medical information in a secure, efficient, and straightforward manner while maintaining the privacy of sensitive patient data. Modern cryptographic techniques are used in our architecture, Ancile, to increase security. The usage of smart contracts developed on the Ethereum blockchain improves access control and data obscuration. This article will look at how Ancile works with the varied needs of patients, providers, and third parties in order to understand how the framework might address reoccurring privacy and security concerns in the healthcare industry. © 2024 The Author(s).

关键词： Smart contract

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OpenCV Based Real-Time Traffic Analyzer 1

OpenCV Based Real-Time Traffic Analyzer

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1st IEEE International Conference on Computing and data Science, ICCdS 2024

作者： Pandi, C. Ponnuviji, N.P. Srinivasan, R. Parthasarathy, C. Karthick, P. Rohith Kumar, V. Vel Tech Multi Tech Dr.Rangarajan Dr.Sakunthala Engineering College Avadi India R.M.K College of Engineering and Technology Puduvoyal India Vel Tech Rangarajan Dr Sagunthala R&D Institute of Science and Technology Avadi India Rajalakshmi Engineering College Department of Computer Science and Engineering Thandalam India

ISBN: (纸本)9798350365337

The report focuses on the development and implementation of a real-time traffic analysis system using OpenCV (Open Source computer Vision library). The project aims to leverage computer vision techniques to analyze and monitor traffic patterns, providing valuable insights for traffic management and urban planning. The system utilizes video streams from surveillance camerasplaced at strategic locations such as traffic intersections or highways. These video streams are processed in real-time usingOpenCV algorithms to extract relevant information about vehicles, traffic flow, and congestion levels. The extracted data is then analyzed and visualized to provide meaningful insights for traffic management authorities and urban planners. The paper involves several key steps, including video input processing, vehicle detection and tracking, traffic flow analysis, and congestion detection. Each step employs specific OpenCV functionalities and techniques to achieve accurate and efficient results. Throughout the report, we will discuss the various components and methodologies used in developing the system. We will explore the algorithms and techniques employed for vehicle detection and tracking, as well as the methods used to analyze traffic flow and detect congestion. Additionally, we willaddress challenges encountered during the project and present potential future enhancements for the system. The findings of this paper demonstrate the feasibility and effectiveness of using OpenCV-based systems for real-time traffic analysis. The insights gained from this research have the potential to contribute to more efficient traffic management strategies and informed decision-making in urban planning. © 2024 IEEE.

关键词： Traffic congestion

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Investigation of different Techniques on digital Actual Frameworks Toward distributed denial of Services Attack 15

Investigation of Different Techniques on Digital Actual Fram...

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15th International Conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Mandale, Prashant Modi, Shabina Jadhav, Makarand Mohan Khawate, Swati Shailesh Borate, Vishal Kisan Mali, Yogesh Kisan Department of Information Technology Pune India Karmaveer Bhaurao Patil College of Engineering Satara India Dept of Electronics & Telecommunication Engg NBN SinhgadTechnical Institutes Campus Pune India Ajeenkya D Y Patil School of Engineering Dept Electronics and Telecommunication Pune India Dr D Y Patil College of Engineering & Innovation Department of Computer Engineering Talegaon Pune India G.H Raisoni College of Engineering and Management Department of Artificial Intelligence Wagholi Pune India

ISBN: (纸本)9798350370249

The arising development of the Web of Things (IoT) offers different adaptability in information move processes, specialized strategies, and capacity. distributed storage is used by limit of the populace to use their information for simple access. Then again, business utilization of distributed storage additionally expanding. The Cloud control is produced through a brought together organization. The arising development gives an approach to outsider access in normally associated networks. digital actual frameworks(CPS) are applied in many computerized networks. The investigation of different difficulties in digital actual frameworks and their effect on information security are examined in different academic articles. The arising development of AI innovation offers solid advantages for digital actual frameworks security. The introduced concentrate on led far reaching research on difficulties in digital actual frameworks and planned an idea for future improvement. © 2024 IEEE.

关键词： Cloud security

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Analyzing Acute lymphoblastic leukemia Across Multiple Classes Using an Enhanced deep Convolutional Neural Network on Blood Smear

Analyzing Acute Lymphoblastic Leukemia Across Multiple Class...

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2024 IEEE International Conference on Information Technology, Electronics and Intelligent Communication Systems, ICITEICS 2024

作者： Pawar, Janardan Bhosle, Amol A. Gupta, Priyanka Mehta, Hemant Borate, Vishal Kisan Mali, Yogesh Kisan Indira College of Commerce & Science Department of Computer Science India Mit Art Design and Technology University Department of Computer Science and Engineering Pune India Dr. D y Patil Institute of Technology Pune Pimpri India School of Engineering & Technology Ambi Pune India Dr D y Patil College of Engineering & Innovation Pune Talegaon India G.H Raisoni Institute of Engineering and Management Pune Wagholi India

ISBN: (纸本)9798350382693

Bone marrow cancer, commonly referred to as Acute lymphoblastic leukemia (All), is marked by the rapid proliferation of immature lymphoid cells. This condition affects both children and adults, standing as the most common type of cancer in children. Recent years have seen considerable advancements in diagnosing and treating All. Precise risk assessment and the formulation of an effective treatment plan hinge on a comprehensive diagnostic approach that incorporates clinical, morphological, cytogenetic, and molecular data. Nevertheless, to improve survival rates and the quality of life for individuals battling this aggressive blood cancer, further research and clinical trials are imperative to overcome challenges related to drug resistance, relapse, and long-term side effects. Accordingly, this research introduces a highly optimized deep learning model, specifically a deep optimized convolutional neural network (CNN), for the early detection and classification of All. The advanced CNN architecture features five convolutional blocks, including 11 convolutional layers and 4 max pooling layers. The optimization of this deep CNN model involved tuning hyper parameters such as epochs, batch size, and the use of specific optimizers, namely Adam and Adamax. Among these, the model achieved superior performance with the Adam optimizer, demonstrating high levels of accuracy and precision, at 0.93 and 0.91 respectively. © 2024 IEEE.

关键词： lung cancer

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