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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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MentalHealthAI: Utilizing deep learning for Accurate Stress level Prediction 3

MentalHealthAI: Utilizing Deep Learning for Accurate Stress ...

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3rd International Conference on Automation, Computing and Renewable Systems, ICACRS 2024

作者： Bharathi, S.T. Raj, J. Relin Francis Krishnan, R. Santhana Rajalakshmi, V. Kumar, V. Vinoth Anchitaalagammai, J.V. Department of Information Technology PSNA College of Engineering and Technology Dindigul India Department of Electronics and Communication Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai India Department of Electronics and Communication Engineering SCAD College of Engineering and Technology Cheranmahadevi India Department of Management Studies SCAD College of Engineering and Technology Cheranmahadevi India Department of Electronics and Communication Engineering Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology Avadi Chennai India Department of Computer Science and Engineering Velammal College of Engineering and Technology Madurai India

ISBN: (纸本)9798331532420

MentalHealthAI is an advanced deep learning system designed to accurately predict stress levels by integrating various data sources, including user surveys, heart rate metrics, sentiment scores from social media interactions, and demographic information. This innovative approach employs a Fully Connected Neural Network (FCNN) architecture, enabling the model to uncover complex relationships among multiple stress-related factors, such as PHQ-9 and GAd-7 questionnaire scores, heart rate variability, sleep patterns, and calculated sentiment scores. By utilizing Natural language Processing (NlP) techniques, specifically the VAdER model, the system analyzes social media text data to derive sentiment scores, providing insights into users' emotional states. This holistic assessment empowers individuals to understand the interplay between their mental health and daily activities, fostering proactive stress management. The advantages of MentalHealthAI include its capacity to deliver timely and personalized stress assessments, allowing users to make informed decisions about their mental well-being. By combining objective data from wearables with subjective sentiment analysis, the system offers a comprehensive perspective on stress dynamics. Ultimately, MentalHealthAI aims to enhance mental health support, providing valuable insights that encourage healthier lifestyles and improved emotional resilience. © 2024 IEEE.

关键词： Electronic health record

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design of Cost-Effective Automated CPAP with labView for Sleep Apnea Patient 2

Design of Cost-Effective Automated CPAP with LabView for Sle...

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2nd IEEE International Conference on Recent Advances in Information Technology for Sustainable development, ICRAIS 2024

作者： Prasanth, A. Ragavi, B. Kuntavai, T. Sudha, M. devi Sowndarya, K.K. Vel Tech Rangarajan Dr. Sagunthala R and D Institute of Science and Technology Department of Computer Science and Engineering Chennai India Dr.N.G.P Institute of Technology Department of Biomedical Engineering Tamil Nadu India AdhiparaSakthi Engineering College Department of Electrical and Electronics Engineering Tamil Nadu Chengalpattu India Adhi College of Engineering and Technology Department of Electronics and Communication Engineering Tamil Nadu India DMI College of Engineering Department of Electronics and Communication Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350354461

Obstructive sleep apnea (OSA) has become more common in recent days due to overweight, obesity, stress, and depression. It is a serious disorder where the patient's breathing repeatedly stops and starts. It is linked to social, economic, and health consequences. due to the complicated design of the existing work, a new design of a simplified Continuous Positive Airway Pressure (CPAP) device with labVIEW software has been proposed. In lab view software, the different virtual components like the blower, relay, and respiratory sensor will be placed. First, the virtual patient is positioned in virtual labVIEW to check whether the patient is affected by OSA or a normal level patient. The blower is mainly used to level the flow of air (i.e., inhale and exhale). Further, the relay is employed to provide a time delay function, and it is utilized to control high-voltage and current circuits. In labVIEW, the breathing level and threshold level frequencies are fixed with the aid of a respiratory sensor. The key aim of this proposed work is to control OSA patients. If the patient suffers from OSA, the respiratory sensor can supply the air pressure to the OSA patients based on different respiratory rate ranges. © 2024 IEEE.

关键词： Blowers

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A Machine learning Framework for Integrating Multi-Omics data for Early leukemia detection 8

A Machine Learning Framework for Integrating Multi-Omics Dat...

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8th International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud), I-SMAC 2024

作者： Nikshya, J. Ebens Karthikeyan, M. Saravana Prasad, Shalini Krishnan, R. Santhana Balamurugan, S. Raj, J. Relin Francis Scad College of Engineering and Technology Department of Electronics and Communication Engineering Cheranmahadevi India Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Department of Computer Science and Engineering Chennai Avadi India City Engineering College Department of Electronics and Communication Engineering Doddakalasandra India Velammal College of Engineering and Technology Department of Mathematics Madurai India Saveetha Institute of Medical and Technical Sciences Saveetha School of Engineering Department of Electronics and Communication Engineering Chennai India

ISBN: (纸本)9798350376425

The early detection of leukemia remains a critical challenge in medical diagnostics due to the complexity and variability of omics data. This research introduces a novel machine learning framework that integrates multi-omics data for the early detection of leukemia, utilizing the proposed CCA-RFE Selector (CCARS) algorithm. The CCARS algorithm combines Canonical Correlation Analysis (CCA) with Recursive Feature Elimination (RFE) to enhance feature selection and classification accuracy. The effectiveness of CCARS is evaluated through extensive simulation analyses, comparing its performance with existing algorithms, including Principal Component Analysis (PCA), lasso Regression (lR), and Canonical Correlation Analysis (CCA). The evaluation metrics used include accuracy, F1-score, and computational time. Results demonstrate that the CCARS algorithm significantly improves accuracy and F1-score while reducing computational time compared to the existing methods. The CCARS algorithm achieves notable performance enhancements, validating its potential for integrating multi-omics data in leukemia detection. © 2024 IEEE.

关键词： Contrastive learning

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Exploring the Synergy between Blockchain Technology and Cryptocurrencies: Implications for Financial Innovation and Security 5

Exploring the Synergy between Blockchain Technology and Cryp...

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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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Cognitive defense Cyber Attack Prediction and Security design in Machine learning Model 6

Cognitive Defense Cyber Attack Prediction and Security Desig...

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6th International Conference on Contemporary Computing and Informatics, IC3I 2023

作者： devadarshini, P.T. Chandrashekar, B. Pundir, Sumit Tiwari, Mohit Madala, Ravikiran Indhuma, E. Easwari Engineering College Department of Artificial Intelligence and Data Science TamilNadu Chennai India St. Martin's Engineering College Department of Computer Science and Engineering Kompalli Telangana Secunderabad India Graphic Era Deemed to Be University Department of Computer Science and Engineering Uttrakhand Dehradun India Bharati Vidyapeeth's College of Engineering Department of Computer Science and Engineering Delhi India Network and Data Center Engineer Ph.D Information Technology University of Cumberlands DurhamNC United States St.Martln's Engineering College Department of Computer Science and Engineering Telangana Secunderabad India

ISBN: (纸本)9798350304480

Companies of all sizes in today's linked digital world are susceptible to cyber dangers. In this study, we provide a novel approach to cyber defence that integrates machine learning with cognitive defensive techniques to identify and prevent cyber attacks in their tracks. We built three Ml models that use cognitive defensive strategies and other cutting-edge security measures like encryption and intrusion detection. A 95% accuracy rate was reached by our models, which is far greater than where things stand right now models. Our work has the potential to have a huge effect since the models we've developed may one day help businesses take a more preventative stance against cyber threats. Our findings may potentially be used to fields outside than cyber security. Overall, our study shows that machine learning and cognitive defense are useful for foreseeing and stopping cyber threats, and points the way toward fruitful future study in this area. © 2023 IEEE.

关键词： Accuracy Advanced security features Cognitive defense Cyber security Cyber-attacks Encryption Intrusion detection Machine learning Prediction Prevention Proactive approach Threat landscape

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deep learning Models for Understanding Audience Sentiments in Movie Tweets 5

Deep Learning Models for Understanding Audience Sentiments i...

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5th International Conference on Electronics and Sustainable Communication Systems, ICESC 2024

作者： Prasath, S. Ram Jeevitha, J.K. Margret, S. Krishnan, R. Santhana Raj, J. Relin Francis Nithila, Ezhil E Scad College of Engineering and Technology Department of Computer Science and Engineering Cheranmahadevi India Psna College of Engineering and Technology Department of Information Technology Dindigul India Scad College of Engineering and Technology Department of Electronics and Communication Engineering Cheranmahadevi India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Electronics and Communication Engineering Chennai India Vel Tech Rangarajan Dr Sagunthala R&d Institute of Science and Technology Department of Electronics and Communication Engineering Chennai Avadi India

ISBN: (纸本)9798350379945

This study explores the application of deep learning models for analyzing audience sentiments in movie tweets, aiming to provide a comprehensive understanding of audience reception. leveraging Twitter data collected using Tweepy and supplemented with web-scraped content via BeautifulSoup, the study employs advanced Natural language Processing (NlP) techniques. Text preprocessing using SpaCy ensures standardized input for subsequent analysis. Sentiment analysis utilizes RoBERTa, an advanced deep learning transformer model known for its robust performance in NlP tasks. RoBERTa effectively classifies tweets into positive, negative, or neutral sentiments, leveraging its contextual understanding to interpret nuanced language expressions prevalent in social media discourse. Simultaneously, RoBERTa is employed for emotion detection, identifying emotional states such as joy, anger, sadness, and more, crucial for gauging audience emotional responses to various aspects of movies. This dual-analysis framework provides detailed insights into audience reactions, facilitating a nuanced evaluation of movie reception across diverse demographic segments. Additionally, the study explores aspect-based sentiment analysis to delve deeper into specific movie attributes like plot, acting, and special effects. Visualizations using Plotly enhance the interpretability of sentiment and emotion distributions over time, supporting informed decision-making in the entertainment industry. © 2024 IEEE.

关键词： Tweets

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Toward land Vehicle Ego-Velocity Estimation Using deep learning and Automotive Radars

IEEE Transactions on Radar Systems

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IEEE Transactions on Radar Systems 2024年 2卷 460-470页

作者： de Araujo, Paulo Ricardo Marques Noureldin, Aboelmagd Givigi, Sidney the Department of Electrical and Computer Engineering Queen’s University KingstonONK7L 3N6 Canada the Department of Electrical and Computer Engineering Royal Military College of Canada KingstonONK7K 7B4 Canada the School of Computing Queen’s University KingstonONK7L 3N6 Canada

This paper presents a deep learning framework for the estimation of land vehicle ego-velocity using Frequency Modulated Continuous Wave (FMCW) automotive radars, addressing the challenges of data sparsity and noise without the need for extrinsic radar calibration. By structuring radar scans into image-based and voxel-based networks, our approach demonstrates robust ego-velocity estimation across multiple sensor configurations and orientations. Experimental results from three distinct datasets—RadarScenes, NavINST, and MSC-RAd4R—validate the framework’s effectiveness, showing superior performance over traditional methods. The models’ adaptability to various sensor specifications and their computational efficiency highlight their potential for real-time applications. We made our implementation open-source at: https://***/paaraujo/deep-ego-velocity. © 2024 IEEE.

关键词： deep learning

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End-To-End Knowledge Graph Construction and Reasoning Using Path Based Metrics 1

End-To-End Knowledge Graph Construction and Reasoning Using ...

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

作者： Shejole, Rohit dofe, Sanket Shejole, Prachi Veermata Jijabai Technological Institute Dept. of Electrical Engineering Mumbai India Veermata Jijabai Technological Institute Dept. of Computer Engineering and Information Technology Mumbai India D.Y. Patil College of Engineering Dept. of Mechanical Engineering Pune India

ISBN: (纸本)9798350356816

The schema-less nature of knowledge graphs has led to their widespread adoption. It makes it possible for knowledge graphs to expand without interruption and to add new entities and relationships as needed. Knowledge graphs are now an effective tool for providing textual data with semantics and representing knowledge as a labelled directed graph. Every item, entity, and user are represented as nodes in a knowledge graph, which is then created by connecting the nodes that communicate with one another through edges. Knowledge graphs can hold a variety of more comprehensive types of information and have a wealth of natural semantics. Its mode of expression is similar to that of natural language. Nevertheless, there is still no accepted definition or format for expressing knowledge graphs. © 2024 IEEE.

关键词： Knowledge graph

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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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