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检索条件"机构=M.Tech Computer Science and Engineering"

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Robust Heart Rate monitoring System: Contactless Approach using Fast Fourier Transform 15

Robust Heart Rate Monitoring System: Contactless Approach us...

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15th International Conference on Advances in Computing, Control, and Telecommunication technologies, ACT 2024

作者： Sujatha, E. Divya, m. Nandhini, I. Raju, D. Naveen Chennai India Chennai India Department of Electronics and Communication Engineering School of Electrical and Communication Vel Tech Rangarajan R&D Institute of Science and Technology Avadi Chennai India Department of Computer Science and Engineering R.M.K. Engineering College Kavaraipettai Chennai India

ISBN: (纸本)9798331300579

Today, to be alive in this world we need to have all our body parts working in good condition and since technology is improving faster day by day everything has become automatic and AI enabled. measuring the heart-rate (HR) of people has multiple applications in telemedicine, Internet-of-Things (IoT), sports, security, etc. However, sometimes it becomes difficult to use a classic method for measuring HR and it does not support scalability. This project is about giving a solution that works live and gives the HR of a person using web camera. Face detection is combined with object tracking is used to produce a set of frames, which are sampled in the later stages of the pipeline for color variations. The average of the color, in a region of interest (ROI) is chosen on the face, representing a signal which corresponds to the heart rate. Signal processing techniques are used to extract the frequency from the signals obtained. The algorithm used is FFT (Fast Fourier Transform) to make image transformation between frequency and special domain. This method is precise and efficient. And using this system another method is also enabled in which the video of a person is uploaded and the heart rate is found through video. By utilizing object tracking combined with face detection, this method reduces the processing power needed and allows better scaling. The accuracy is 98% compared to the real time existing systems. © Grenze Scientific Society, 2024.

关键词： Fast Fourier transforms

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Efficient Weather Detection Using Lightweight Convolutional Neural Networks suitable for Implementing and Deploying on Edge Devices

Efficient Weather Detection Using Lightweight Convolutional ...

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2024 International Conference on Integration of Emerging technologies for the Digital World, ICIETDW 2024

作者： Arumuga Arun, R. Saravanan, K. manju, J. Suresh, A. Priyanka, N. Islabudeen, m. Vellore Institute of Technology School of Computer Science and Engineering Vellore India R.M.D. Engineering College Department of Information Technology Chennai India Vel Tech Multi Tech Dr. Rangarajan Dr. Sakunthala Engineering College Department of Information Technology Chennai India

ISBN: (纸本)9798331541170

Weather detection is vital in many fields, including disaster recovery, farming, transportation, and climate studies. making educated decisions and reducing potential dangers requires the quick and precise observation of weather patterns. As CNN-based deep learning models automatically train and extract significant features from raw image data, they often achieve higher accuracy in a variety of image classification tasks and exhibit good data generalization. In addition to the accurate detection, it should be lightweight to deploy to edge locations. To address this, we have developed a Smart lightweight deep-learning model to detect the weather accurately and it can be conveniently deployable in resource-constrained devices. This study uses several deep lightweight learning models, including mobileNetV2, mobileNetV3_Small, and NasNetmobile, where mobileNetV3_Small is the most efficient regarding weather detection and computational complexity reduction. The proposed approach utilized a multi-class Weather Dataset for Image Classification for building Smart lightweight deep-learning models. Besides the weather classification, the proposed approach also gives the confidence score for the predicted climate, which provides a numerical indicator to evaluate how likely the predicted climate is to occur. © 2024 IEEE.

关键词： Convolutional neural networks

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Revolutionizing Cardiovascular Attack Prediction: A Comprehensive machine Learning Approach for Accurate and Timely Detection 4

Revolutionizing Cardiovascular Attack Prediction: A Comprehe...

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4th International Conference on Expert Clouds and Applications, ICOECA 2024

作者： Durai, S. Jaganathan, D. Prabhu, V. Harini, R. Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology Avadi Dept. of CSE Chennai India Madanapalle Institute of Technology & Science Department of Artificial Intelligence Andhra Pradesh India R.M.K. Engineering College Department of Computer Science and Engineering Thiruvallur Kavaraipettai India vel Tech Multi Tech Dr.Rangarajan Dr. Sakunthala Engineering College Artificial Intelligence & Data Science Chennai India

ISBN: (纸本)9798350385793

Cardiac attacks are a leading cause of mortality worldwide, underscoring the critical need for timely and accurate healthcare detection. Predicting cardiovascular attacks is a formidable challenge in clinical data analysis. machine Learning (mL) proves to be a valuable tool for analyzing vast healthcare data. This innovative approach focuses on employing mL techniques to identify significant features, thereby improving the accuracy of cardiovascular attack prediction. A variety of characteristics and well-known classifiers, including Naïve Bayes (NB), K-Nearest Neighbor (KNN), Support Vector machine (SVm), Random Forest (RF), Decision Tree (DT), and Logistic Regression (LR), are used to create the final prediction model. Through collaborative efforts, these algorithms pave the way for binary classification, effectively distinguishing between true and false outcomes. By analyzing their accuracies, this approach enables the possibilities for successful predictions. © 2024 IEEE.

关键词： Adversarial machine learning

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KNN-Powered Online Voting System to Improve Accuracy and Transparency of Election Process

KNN-Powered Online Voting System to Improve Accuracy and Tra...

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2025 International Conference on multi-Agent Systems for Collaborative Intelligence, ICmSCI 2025

作者： Jaswanth, Ullamparthy muthukumar, D. Surendiran Abitha, R. Ganesh, Pamu Naveen, Nandyala Kavitha, G. Kalasalingam Academy of Research and Education Department Computer Science and Engineering Tamil Nadu Virudhunagar India Vel Tech Multi Tech Dr Rangarajan Dr Sakunthala Engineering College Department of Artificial Intelligence and Data science Tamil Nadu Chennai India Dr.M.G.R Educational andResarch Institute Department of Electronics and Communication Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798331509828

The proposed system aims to create a safe online voting system. Nowadays voting remains a serious safety and security issue so this system provides high performance and security. The System seeks to develop a face recognition-based voting system that authenticates voters using machine learning, especially K-Nearest Neighbors (KNN). Entering the voter's unique username and Password is the first step in the online voting system. If the voter's Username and password are valid, the system proceeds to the next step, requesting permission to turn on the webcam and take a picture of them. Once the photo is taken, it verifies that the voter has only taken it once. The website allows the voter to cast his vote only once. Voter access is denied if the same voter picture is entered again. By logging into the website voter can cast their vote from any place. Every minute the website updates the results of the election. So, voters do not need to wait in long queues to cast their votes. To give voters real-time information on their voting progress. The system also features dynamic voice feedback to guide users through voting, ensuring clarity and engagement. This initiative uses facial recognition technology to enhance the security and efficiency of voting systems. © 2025 IEEE.

关键词： Face recognition

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A Novel Framework for Classifying Remote Sensing Images using Convolutional Neural Networks

A Novel Framework for Classifying Remote Sensing Images usin...

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2024 International Conference on Knowledge engineering and Communication Systems, ICKECS 2024

作者： manoharan, Thiyagarajan Basha, Shaik Hedayath murugan, J.Senthil Suja, G.P. Rajkumar, R. Srimathi, S. Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology India R.M.K. College of Engineering and Technology Department of Ece India Vel Tech High Tech Dr. Rangarajan Dr.Sakunthala Engineering College Department of Cse Avadi Chennai India Muslim Arts College Thiruvithancode Department of Computer Science Kanyakumari629174 India Rajarajeswari College of Engineering Computer Science and Engineering Mysore Road Bengaluru74 India Simats Saveetha School of Engineering Department of Bio Technology Tamilnadu Chennai India

ISBN: (纸本)9798350359688

The classification of Hyperspectral Remote Sensing Images (HRSIs) using Convolutional Neural Networks (CNNs) has gained significant. Due to excessive dimensional data and a lack of sample training examples, the Hughes phenomenon is frequently seen in hyperspectral remote sensing photos. In the realm of high-dimensional information processing, it is worth noting that there is a notable consumption of time and computing power. Additionally, there is a possibility that the features extracted may not accurately represent the data, leading to suboptimal classification efficiency and accuracy. In this research, a novel framework called GCNN is proposed, which combines the concepts of global context spatial attention (GCSA) and three-dimensional convolutional neural network (3DCNN). The GCNN framework aims to enhance the performance of spatial attention and convolutional neural networks by leveraging global context information. By integrating GCSA and 3DCNN, GCNN offers a promising approach for various applications in computer vision and image analysis. The mix-up operation is a commonly employed technique in the field of remote sensing image analysis. Its primary purpose is the spatially heterogeneous data available in these photos, and improve it. This is achieved by transforming the discrete sample space into a continuous one, thereby promoting improved flatness in the local vicinity of the data space. The integration of GCSA into the network allows for the encoding of contextual information from remote sensing scene images into the local features. The findings of this study indicate that the utilization of CNN for remote sensing image recognition yields significantly higher accuracy compared to traditional image recognition models. The recognition accuracy achieved by CNN in this context is reported to be as high as 97%. © 2024 IEEE.

关键词： Convolutional neural networks

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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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AI-Driven Zero Trust Architecture: Enhancing Cyber-Security Resilience

AI-Driven Zero Trust Architecture: Enhancing Cyber-Security ...

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International Conference on Computation System and Information technology for Sustainable Solutions (CSITSS)

作者： K. Chokkanathan S.m. Karpagavalli G. Priyanka K. Vanitha K. Anitha P. Shenbagavalli Department of Artificial Intelligence Madanapalle Institute of Technology & Science Department of M.Tech Computer Science and Engineering Erode Sengunthar Engineering College Erode India Department of Computer Science and Engineering Karpagam Academy of Higher Education Coimbatore India Department of Information Technology CSI College of Engineering Coimbatore India Department of Artificial Intelligence and Data Science Rathinam Technical Campus Coimbatore

ISBN: (数字)9798331505462

ISBN: (纸本)9798331505479

In the ever-changing field of cybersecurity, conventional defenses that rely on boundaries are no longer adequate to safeguard against sophisticated and persistent assaults. The Zero Trust Architecture (ZTA) paradigm, powered by AI, provides a strong answer by removing the concept of inherent trust within a network and demanding ongoing verification for each access request. This article examines the combination of artificial intelligence (AI) with Zero Trust principles to improve the resilience of cyber-security. This study demonstrates the enhanced accuracy, sensitivity, and specificity of AI-driven techniques in comparison to conventional security models. The findings highlight the significance of utilizing AI-driven Zero Trust Architecture (ZTA) to protect contemporary digital infrastructures. It provides robust protection against emerging cyber threats as well as addressing challenges related to data privacy and system integration. The paper also explores potential areas for future study and the practical consequences of implementing AI-driven Zero Trust Architecture (ZTA) in different organizational settings.

关键词： Data privacy Accuracy Training data computer architecture Transforms Zero Trust Time factors computer security Artificial intelligence Resilience

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Home Security Voice based Alert System using IoT 5

Home Security Voice based Alert System using IoT

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5th International Conference on Smart Electronics and Communication, ICOSEC 2024

作者： Pandiaraja, P. Venkatramani, R. Arunraj, R. Rajesh Kanna, P. mahendran, G. Suresh, T. Vel Tech Rangarajan Dr. Sagunthala R & D Institute of Science and Technology School of Computing Department of Computer Science and Engineering Chennai600062 India M. Kumarasamy College of Engineering Department of Computer Science and Engineering Karur Tamilnadu Thalavapalayam639113 India K. Ramakrishnan college of Engineering Department of Information Technology Tamil Nadu Samayapuram621112 India Bannari Amman Institute of Technology Department of Computer Science and Engineering Tamil Nadu Erode638401 India K. Ramakrishnan college of Engineering Department of Aiml Tamilnadu Samayapuram621112 India

ISBN: (纸本)9798331504403

Every home has an issue, such as the inability to determine if a gas leak is occurring or not. Similarly, when a water storage tank overflows, we are unable to determine the level of the condition until it causes a spill. An existing system is employed to address this issue;however, it has a low level of attentiveness, making it difficult for users to correctly identify the problem. To solve all of these issues in every home, we have developed a voice-based Internet of Things device. This instrument's voice-based output enables the user to readily understand the situation. When a gas leak occurs, a gas detector that can identify LPG gas sends a signal to a microcontroller, which then sends the signal to a voice bank to generate a speech-based warning message. Additionally, it comes into touch with water when the water is expected to flow out, sends a signal to the Arduino, and produces a voice-based output. This will cut down significantly on accidents and product waste. © 2024 IEEE.

关键词： microcontrollers

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HiAt-Net: A Novel Three-Layered Encoder-Decoder with Hierarchical Attention for Classifying Cervical Cancer with Cytology Images

HiAt-Net: A Novel Three-Layered Encoder-Decoder with Hierarc...

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2024 IEEE International Conference on Intelligent Systems and Advanced Applications, ICISAA 2024

作者： mahendran, Rakesh Kumar Ramadass, Parthasarathy Devi, m.Kiruthiga Sanmugasundaram, R. Thirunavukkarasu, Usharani Anandaraj, A.Peter Soosai Rajalakshmi Engineering College Department of Computer Science and Engineering Chennai India Vel Tech Rangarajan Dr. Sagunthala RandD Institute of Science and Technology Department of Computer Science and Engineering Chennai India Dr. M.G.R. Educational and Research Institute Department of Information and Technology Chennai India Rajalakshmi Institute of Technology Electronics and Communication Engineering Chennai India Saveetha University Saveetha School of Engineering SIMATS School of Engineering Department of Biomedical Engineering Tamil Nadu Chennai India Vel Tech Rangarajan Dr:Sagunthala RandD Institute of Science and Technology Chennai India

ISBN: (纸本)9798331539948

Cervical cancer (CeCA) is a major worldwide health concern, particularly in low- and middle-income countries where screening and treatment facilities are not widely available. Cervical cells, which connect the lower portion of the uterus to the vagina, are where CeCA, a particular type of cancer, originates. Sexually transmitted infections such as certain strains of the Human Papillomavirus (HPV) are the main cause of cervical cancer. But not every HPV infection results in CeCA;other risk factors include smoking, having several sexual partners, and having a weaker immune system.. Additionally, the classification of CeCA using Deep Learning (DL) can pose challenges related to computational resources, model complexity, and robustness compared to state-of-the-art methodsOur solution, HiAt-Net, tackles these challenges by introducing a 3-layer encoder-decoder approach that incorporates a hierarchical attention mechanism. This method is specifically designed to extract features effectively from complex input data, such as the Herlev dataset and SIPaKmeD dataset. By integrating the attention mechanism at multiple levels, our model can capture hierarchical dependencies and selectively focus on relevant features, resulting in enhanced performance in classification tasks. We conducted a thorough comparison and analysis of our proposed method against existing approaches, evaluating performance metrics such as accuracy, precision, recall, and F1-Score. © 2024 IEEE.

关键词： Diseases

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Assessing Secure Cloud Information Sharing Through Authentication and Encoded Indexing 8

Assessing Secure Cloud Information Sharing Through Authentic...

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

作者： Pandiaraja, P. Karthik, K. Rajesh Kanna, P. Ushapriya, C. mahesh, P.C.Senthil muthumanickam, K. Vel Tech Rangarajan Dr.Sagunthala R&d Institute of Science and Technology School of Computing Department of Computer Science and Engineering Chennai600 062 India M. Kumarasamy College of Engineering Department of Computer Science and Engineering Karur Tamilnadu Thalavapalayam639113 India Bannari Amman Institute of Technology Department of Computer Science and Engineering Tamilnadu Erode638401 India St. Martin's Engineering College Department of Ai & Ml Telangana Secunderabad500100 India Excel Engineering College Department of Computer Science and Engineering Tamilnadu Komarapalayam637303 India Kongunadu College of Engineering and Technology Department of Information Technology Tamil Nadu Trichy621215 India

ISBN: (纸本)9798350376425

The high usage of storage in cloud, ensuring the privacy and security of information stored in the cloud has come up with a difficult concern. One approach to address this issue is the use of techniques which are encrypted, where data is already encrypted being uploaded to the storage of cloud. However, traditional encryption methods hinder the ability to find and retrieve particular information from the secured information, as the information needs to be decrypted first. This limitation hampers the usability and efficiency of storage of cloud systems. To overcome the challenge, this research says a novel approach for keyword search on secured information in storage of cloud using an AES approach. The algorithm of AES is a maximum adopted symmetric encryption method familiar for efficiency and robustness. By leveraging the AES encryption approach, the solution assures the confidentiality of information stored while enabling efficient keyword-based search operations without the need for decryption. The proposed approach utilizes a combination of cryptographic techniques such as AES encryption and index structures to access efficient information findings on the secured information. A secure index is created during encryption process that enables for potent retrieval of encrypted data related to specific keywords. The encrypted data remains confidential, as only authorized users possess the necessary decryption keys to retrieve the plaintext information. During data outsourcing, data owner can set the access permission based on their user identity. Users should have specific identity with decryption key for accessing data from cloud. Here data manager can also provide particular period for accessing information from cloud. The results demonstrate that the keyword findings on secured information in storage of using the AES approach achieve a balance between data privacy and search functionality. The solution offers an effective means to securely store and retrieve data

关键词： Cloud storage

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