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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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Implementing Data science and Big Data Analytics to Their Full Potential: Protocols for Gaining Useful Understanding and Promoting Data-Driven Innovation 5

Implementing Data Science and Big Data Analytics to Their Fu...

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

作者： Arthy, m. Nivethitha, S.B. Karuppiah, S.P. Vijayanandh, T. Sumalatha, A. Ramesh, T. School of Computing College of Engineering and Technology Department of Data Science and Business Systems Tamil Nadu Kattankulathur India Rajalakshmi Engineering College Mechatronics Engineering Tamil Nadu Chennai India St.Joseph's College of Engineering Department of Mba India Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology School of Computing Department of Computer Science & Engineering Tamil Nadu Chennai India Kristu Jayanti College Autonomous Department of Computer Science Bengaluru India R.M.K Engineering College Department of Computer Science & Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350376685

The proper implementation of data science and big data analytics has become an essential component for gaining valuable insight and driving innovation across a variety of industries in this era, which is characterized by the constant presence of data. The strategies and protocols that are necessary to fully exploit the promise of these transformational technologies are investigated in depth by this research. A complete framework for enterprises to harness their data assets is the goal of the study, which will be accomplished by analyzing the fundamental principles of data science as well as the difficulties connected with big data. A number of advanced analytics methods, including machine learning, predictive modeling, and natural language processing, are being investigated as potential integrations into the decision-making process as part of this research. The relevance of data governance, quality, and the ethical considerations that surround data usage are also discussed in this document. It is demonstrated in this study that data-driven insights may lead to strategic advantages, operational efficiencies, and the development of creative goods and services. This is accomplished through a number of case studies and practical examples. In order for businesses to make the most of the potential that are given by data science and big data analytics, it is vital for them to have a culture of data literacy as well as a robust infrastructure, according to the results made. At the end of the study, a collection of best practices and recommendations for businesses and governments to implement in order to cultivate a data-driven environment that encourages innovation and usage of data in a responsible manner are presented. In light of the findings of this study, it is clear that data science and big data analytics require a more comprehensive approach, one that strikes a balance between technological capabilities, organizational readiness, and the influence on society. © 2024

关键词： Big Data Analytics Data Governance Data Infrastructure Data Literacy Data Quality Data science Data-Driven Decision making Data-Driven Innovation Ethical Considerations machine Learning Natural Language Processing Predictive modeling Strategic Advantage

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Unifying Sky and Chain: A Perspective on Blockchain's Role in UAV Swarms 2

Unifying Sky and Chain: A Perspective on Blockchain's Role i...

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2nd International Conference on Artificial Intelligence, Blockchain, and Internet of Things, AIBThings 2024

作者： Phadke, Abhishek Sekharan, Chandra N Vasudeva, Karthik Kumar Kar, Dulal Christopher Newport University Department of Physics Computer Science and Engineering Newport NewsVA United States Texas A&m University-Corpus Christi Department of Computer Science Corpus ChristiTX United States Texas Tech University Department of Mathematics and Statistics LubbockTX United States

ISBN: (纸本)9798331529529

UAV (Unoccupied Aerial Vehicle) swarms are coordinated agents working towards accomplishing a common goal in a dynamic environment. The nature of the environment is such that these swarms can always benefit from upgrades to their operation, behavior, and security. Incorporating blockchain technology into UAV swarms presents a transformative approach to enhancing their security, operations, and coordination. Blockchain is a decentralized ledger technology that offers a robust solution to scalability, data integrity, and trust challenges in many modern world systems. It can also be a potentially excellent method to enhance UAV swarm systems. By harnessing its security, transparency, and immutability characteristics, blockchain can offer a range of advantages to current deployments. This study categorizes the various areas of UAV swarm systems that benefit from this integration, briefly reviews current methods and architecture, and discusses three prominent case studies for integration. Thorough an examination of current trends, it is concluded that blockchain technology can significantly boost its target systems' capabilities, paving the way for advanced and reliable aerial swarm operations. © 2024 IEEE.

关键词： Blockchain

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A Critical Study on Hybrid Computing and Its Current Influences and Future Challenges 3

A Critical Study on Hybrid Computing and Its Current Influen...

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3rd International Conference on Intelligent and Innovative technologies in Computing, Electrical and Electronics, IITCEE 2025

作者： Praveen, K.V. Joe Prathap, P.m. Beevi, L. Sherin Dani, W. Vinil St. Peter's College of Engg. and Tech. Department of Information Technology Chennai India R.M.D. Engineering College Department of Computer Science and Engineering Kavaraipettai India St. Peter's College of Engg. and Tech. Department of Eee Chennai India

ISBN: (纸本)9798331515911

This research focuses on the evolving field of hybrid computing, which combines traditional computing architectures with advanced computational paradigms like quantum computing, neuromorphic computing, or cloud-edge computing. Hybrid computing involves integrating multiple computing models or systems to leverage their complementary strengths. For example, combining classical computing with quantum or neuromorphic approaches to solve complex problems more efficiently. The goal is to enhance computational power, increase efficiency, and tackle problems that are difficult for traditional computing models alone. Emerging technologies, including quantum computing, machine learning, and AI, are driving the need for hybrid systems. These technologies enable the handling of more complex computations that classical systems struggle with, such as optimization, simulation, and cryptography. The integration of cloud and edge computing is also central to hybrid computing, enabling decentralized computation and processing closer to data sources. Combining different computing paradigms requires seamless communication and integration between systems, which is a significant technical challenge. main challenging areas are scalability, software and hardware compatibility, energy efficiency and security. There is a need for standard protocols and frameworks to enable smoother integration of various computing systems. New algorithms that can take advantage of hybrid architectures need to be developed. Depend on applications significant advancements in hardware, including quantum processors, neuromorphic chips, and integrated hybrid processors required. The rise of hybrid computing will also bring ethical concerns, such as the impact on jobs, privacy, and inequality in access to advanced technologies. The study concludes by emphasizing the transformative potential of hybrid computing while acknowledging the technical, economic, and societal hurdles that need to be overcome. It highlights

关键词： Quantum computers

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Solar Power Generation technique and its Challenges - A Comprehensive Review 15

Solar Power Generation Technique and its Challenges - A Comp...

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

作者： Kitey, Himanshu Chandankhde, Pankaj Jajulwar, Kapil Shaikh, mohammad Shahnawaz Fatinge, Pragati m. M. Tech Communication Engineering Department of Electronics and Telecommunication Engineering G H Raisoni College of Engineering Nagpur Nagpur India Department of Electronics and Telecommunication Engineering G H Raisoni College of Engineering Nagpur India Department of Computer Science and Engineering G H Raisoni Institute of Engineering and Technology Nagpur India

ISBN: (纸本)9798331300579

In the contemporary world, energy stands as an essential driving force behind socioeconomic development. However, with the alarming levels of environmental pollution, there has been a growing focus on renewable energy sources. These sustainable alternatives are gaining increasing attention, particularly as the depletion of fossil fuels becomes more ap-parent. Natural components such as sunlight, wind, and rainfall are used to generate renewable energy. Among these non-traditional renewable sources, solar energy emerges as a prominent contender for power generation. Solar power generation has gained recognition as a promising and environmentally sustain-able renewable energy source to meet growing global energy demands while minimizing cli-mate change consequences. This paper extensively examines solar power generation techniques, encompassing Photovoltaic (PV) Systems and Solar Thermal technologies. The paper explores the present state of solar power generation technology, outlines its advantages, and researches the various challenges obstructing its widespread adoption. Furthermore, the study offers insights into potential solutions and outlines future directions for advancing solar power generation techniques. © Grenze Scientific Society, 2024.

关键词： Photovoltaic (PV) System Renewable Energy Socio-economic Solar Power Solar Thermal technologies

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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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An Innovative method for mobile Network Payment Security System Based on hybrid LSTm-Ensemble Learning-Based model 3

An Innovative Method for Mobile Network Payment Security Sys...

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3rd International Conference for Advancement in technology, ICONAT 2024

作者： Sujatha, N. Pramod, S. Aravindh, S. Babu, Balagopala Ramesh Devi, S. Rukmani Ananthi, m. B. M. S. College of Engineering Department of Mathematics Karnataka Bengaluru India Nitte Meenakshi Institute of Technology Yelahanka Department of Mathematics Karnataka Bangalore India Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology Department of Computer Science and Engineering Tamilnadu Chennai India SRKR Engineering College Department of Computer Science and Engineering Andhra Pradesh Bhimavaram India SIMATS Deemed to Be University Saveetha College of Liberal Arts and Sciences Department of Computer Science Tamilnadu Chennai India KGiSL Institute of Technology Department of Computer Science and Business Systems Tamilnadu Coimbatore India

ISBN: (纸本)9798350354171

It is common practice in many industries to use cash since it accounts for more than 85% of payments in almost all developing countries. A cell phone is become an everyday item. mobile phones have practically become people's constant companions due to the plethora of other uses they offer beyond just communicating. Since they are both cheap and multipurpose, soon everyone will be utilizing them. In an ideal world, everyone would use their cell phone for everything. Verification, preprocessing, and training the model are the three parts that make up the method. In order to confirm the payment, the customer's biometric template is checked during the verification procedure. The three stages of fingerprint preparation include matching fingerprint templates, extracting fingerprint minutiae, and fingerprint preprocessing. The training of the model was carried out using a Hybrid LSTm-Ensemble Learning. The average accuracy of the suggested method is 92.76%, which is higher than LSTm and Ensemble Learning. © 2024 IEEE.

关键词： Contrastive Learning

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E2FP-Net: Elevated Fusion Feature Pyramid Network for Alzheimer Detection

E2FP-Net: Elevated Fusion Feature Pyramid Network for Alzhei...

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2024 International Conference on Signal Processing, Computation, Electronics, Power and Telecommunication, IConSCEPT 2024

作者： Devi, m.Kiruthiga Ramadass, Parthasarathy Karthikeyan, B. mahendran, Rakesh Kumar Kavitha, E. Sanmugasundaram, R. Dr. M.G.R. Educational and Research Institute Department of Information and Technology Chennai India Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Department of Computer Science & Engineering Chennai India Panimalar Engineering college Information Technology India Rajalakshmi Engineering College Department of Computer Science and Engineering Chennai India Dr.M.G.R Educational and Research Institute Department of Physics Chennai India Rajalakshmi Institute of Technology Electronics and Communication Engineering Chennai India

ISBN: (纸本)9798331540685

Alzheimer's Disease (AD), a neurogenerative disorder which is a progressive disease and it affects the cognitive function of the brain and aids in behavioral changes and loss of memory. AD a challenging and a complicated condition in which enhanced diagnosing method, treating method is investigated by the researchers. Treating the symptoms of AD can improve the quality of life to the individuals. Diagnosing methods of AD may include Neuro imaging, CSF analysis, Cognitive testing, Genetic testing etc.., By integrating the mRI images and genetic images we propose an Elevated feature pyramid network based on Adaptive hypergraph neural network. Furthermore, the GNN involves in the process of feature extraction of mRI images and Genomic data. Our proposed framework has the following process for detecting AD: A) Twin data pre-processing B) E2FP based Feature extraction C) Node mingle fusion. The feature extraction process has 3 subprocess includes a) Region based feature extraction for mRI images using Adaptive HGNN b) mid-Level Feature Extraction for Genomic data using Adaptive HGNN c) Amalgamation of Global Feature. Our model performs well in terms of accuracy, sensitivity and specificity. This model has the achieved a great interpretability in genomic data and by enhancing the contrast the mRI data shows a best accuracy. © 2024 IEEE.

关键词： Deep learning

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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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