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3rd International Conference on Emerging electronics and Automation, E2A 2023

作者： Kushagra, C. Kirtimaan, S. Rushit, P. Jane Joshita, K. Divya Navamani, J. Lavanya, A. Department of Computer Science Engineering SRM Institute of Science and Technology Tamil Nadu Kattankulathur India Department of Electronics and Instrumentation Engineering SRM Institute of Science and Technology Tamil Nadu Kattankulathur India Department of Electrical and Electronics Engineering SRM Institute of Science and Technology Tamil Nadu Kattankulathur India

ISBN: (纸本)9789819730896

Currently, IoT rules many unmanned applications to improve supervision and productivity. The proposed work concentrates on the need for a cooling system for solar Photovoltaic (PV) panels to enhance its efficiency. An increase in temperature will reduce the efficiency of the solar panel. The cooling system should be monitored, automated, and controlled without human intervention. This is achieved by developing a web App by which the cooling system can be controlled automatically. DHT11–Temperature and Humidity Sensor is used to monitor the temperature of the panel. If the module temperature increases more than 35 °C to 60 °C, the sensor sends the signal to the actuator to cool the PV panel, which will increase the output power. This technique is proposed for solar rooftop home applications where the grey water can be treated and used for cooling the solar panel rather than using fresh, clean water. Finally, a web-interface is done to cool the solar panel with water pumping through the tubes below the panel. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Cooling IoT Solar Temperature Web interface

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Innovative Approach for Early Heart Disease Prediction: Gaussian Mixture Model and K-Nearest Neighbour 3

Innovative Approach for Early Heart Disease Prediction: Gaus...

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3rd International Conference on Intelligent Data Communication Technologies and Internet of Things, IDCIoT 2025

作者： Surendran, R. Anandaraj, B. Deepa, P. Mageshwari, M. Balamurugan, K.S. Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai India Department of Computer Science and Engineering Madnapalle Institute of Technology & Science Andhra Pradesh Madanapalle India Department of Computer Science and Engineering Panimalar Engineering College Chennai India Department of Computer science and engineering Karpaga Vinayaga College of Engineering and Technology Tamil Nadu Chengalpattu India Department of Electronics and communication engineering Karpaga Vinayaga College of Engineering and Technology Tamil Nadu Chengalpattu India

ISBN: (数字)9798331527549

ISBN: (纸本)9798331527549

An Electrocardiogram (ECG) contributes significantly to early diagnosis and classification of heart diseases, arrhythmia which means irregular heart rate. Regrettably, the process became difficult due to asymmetric and minimal amplitude from recordings of ECG and cannot do the extraction from multiple signals. By using machine learning technology, it can classify and extract multiple samples, but cannot extract the data features from complex data structures. To overcome the limitations, previously introduced metaheuristic optimization (MHO) to improve the classification and extraction. Proposed a method which supports and K-Nearest Neighbour (KNN) Gaussian Mixture Model (GMM) algorithms for determine the abnormalities, predicting cardiac events, and improve diagnostic accuracy. As a result, this can help us to get results more quickly and accurately with sensitivity 99.7% and accuracy 99.2%, for helping healthcare professionals in diagnosing arrythmias, myocardial infarction and any other heart conditions. © 2025 IEEE.

关键词： Adversarial machine learning

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CardioNet: An Integrative AI-Machine Learning Framework for Enhanced Prediction and Management of Cardiovascular Diseases Using Deep Data Analytics and Clinical Insights 4

CardioNet: An Integrative AI-Machine Learning Framework for ...

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4th International Conference on Power, Energy, Control and Transmission Systems, ICPECTS 2024

作者： Senthil, G.A. Prabha, R. Thamarai, I. Roopa, D. Rajesh Kanna, R. Srimathi, C. Agni College Of Technology Department of Information Technology Chennai India Sri Sairam Institute of Technology Department of Electronics and Communication Engineering Chennai India Panimalar Engineering College Department of Computer Science and Engineering Chennai City Campus India Sri Sairam Institute of Technology Department of Computer Science and Engineering Chennai India Srm Institute of Science and Technology Department of Computer Science and Engineering Ramapuram Campus Chennai India

ISBN: (纸本)9798331508845

Cardio Vascular Disease (CVD) causes dysfunction in our heart and blood arteries, which frequently results in death or paralysis. Therefore, many lives can be prevented by the early and automatic recognition of CVD. Although this goal has been the subject of numerous studies, performance and dependability can yet be enhanced. This work represents another advancement in that direction. Given that cardiovascular diseases (CVDs) remain the primary cause of death worldwide, effective strategies for early detection and management are necessary. To help with CVD prediction and management, we introduce CardioNet, an integrated machine learning framework with strong data analytics and clinical insights. To accurately predict the risk and progression of CVD, CardioNet integrates state-of-the-art machine learning algorithms with a variety of medical data sources, including genetic profiles, imaging data, and electronic health records. CardioNet integrates clinical information and feedback mechanisms to offer customized risk assessment and therapy suggestions. This, in turn, improves patient outcomes and lowers the cost of healthcare. The potential of CardioNet as a comprehensive strategy to counter the widespread threat of CVDs is highlighted. © 2024 IEEE.

关键词： Electronic health record

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Enhancing Data Security and Privacy in Cloud Computing: A Survey of Modern Techniques 3

Enhancing Data Security and Privacy in Cloud Computing: A Su...

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3rd International Conference on Intelligent Data Communication Technologies and Internet of Things, IDCIoT 2025

作者： Suganya, N. Gouthami, P. Sathiya, R. Sivaranjani, S. Murugesan, M. Department of Computer Science and Business Systems Dr. N.G.P. Institute of Technology Coimbatore India Department of Information Technology Thangavelu Engineering College Chennai India Departement of Electrical and Electronics Engineering Sri Krishna College of Engineering and Technology Coimbatore India Departement of Electrical and Electronics Engineering Karpagam Institute of Technology Coimbatore India

ISBN: (纸本)9798331527549

A computing system that is based on the internet and offers users all resources as on-demand services. Servers, storage, databases, software, and networking are among these on-demand services. Usually, the user must pay for the use of the resources;they are not their property. The number of people using this technology is increasing dramatically as a result of these features. Although it has benefits, cloud computing poses certain security and privacy risks. The authorized user’s data can be stolen easily. This current research thoroughly investigates modern strategies to these problems, highlighting identity management protocols, effective encryption and blockchain-based solutions. The article identifies research gaps, focuses attention to the inadequacies of current approaches, and recommends the possible ways to improve the efficiency, scalability and trustworthiness. © 2025 IEEE.

关键词： Cloud security

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Secured intelligent transportation with privacy retention through blockchain framework

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Journal of Intelligent and Fuzzy Systems 2024年第4期46卷 10507-10521页

作者： Kaur, Gaganpreet Shobana, M. Kavin, F. Sellakumar, S. Meenakshi, D. Bharathiraja, N. Chitkara University Institute of Engineering and Technology Chitkara University Punjab India Department of Networking and Communications Faculty of Engineering and Technology SRM Institute of Science and Technology KTR Chengalput Tamil Nadu Chennai India Department of Electronics and Instrumentation M.S. Ramaiah Institute of Technology Karnataka India Department of Mechanical Engineering Saveetha Engineering College Tamil Nadu Chennai India Department of Computer Science and Engineering Tagore Engineering College Tamil Nadu India

The Secured Independent Intelligent Transport System (SIITS) is poised to revolutionize traditional transport management systems, leveraging autonomous vehicles (AVs) connected through an open-channel Internet to link Traffic Command Centers (TCCs), Road Side Units (RSUs), and AVs within the SIITS framework. However, this reliance on the Internet exposes users to various security risks, safety vulnerabilities, and other challenges that impede the progress of SIITS applications. In this method, ensuring robust security management and trustworthiness is paramount for the long-term adoption of this innovative trend. While previous efforts have focused on integrating security methods from multiple disciplines into a unified reference design, this article presents a reference architecture primarily centered around ITS safety. Additionally, the article introduces a proposed framework for enhancing ITS safety, addressing the confidence issues. To further address these challenges, the article offers categorization of goods, Big Data methods and services, and validates the utility of ITS business analytics for corporate applications through a groundbreaking multi-tier ITS security architecture. © 2024 - IOS Press. All rights reserved.

关键词： Big data

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Under water motion tracking and monitoring using wireless sensor network and Machine learning

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Materials Today: Proceedings 2023年 80卷 3511-3516页

作者： Gite, Pratik Shrivastava, Anurag Murali Krishna, K. Kusumadevi, G.H. Dilip, R. Manohar Potdar, Ravindra Department of Computer Science and Engineering IPSA-IES Indore India Lakshmi Narain College of Technology and Science Indore Madhya Pradesh India Department of Electronics and Communication Engineering Vignan's Institute of Information Technology Duvvada Andhra Pradesh Visakhapatnam India Department of Electrical and Electronics Engineering Acharya Institute of Technology Bangalore560107 India Department of Mechatronics Engineering Acharya Institute of Technology Karnataka Bengaluru560107 India Department Of Electronics & Telecommunication Engieering Bhilai Institute Of Technology Chhattisgarh Durg India

Wireless Sensor Networks (WSNs) plays an indispensable role in different application and development in new generation technology. In 5G technology, one of the essential features is the connectivity with registered and unregistered networks to promote remote automation. By which the technology can be harnessed at its best and scaled in multi-dimensional applications. These applications are also enabled for diverse motion detection and object tracking applications. In this presented work, an underwater object tracking technique has been proposed, which enable the prediction of the mobile sensor node. The method involves a simulation which demonstrates a 2D-UASN architecture of underwater scenario and in this network using a different number of nodes using random positioning static sensor nodes are deployed. Finally, a mobile sensor node with random mobility is introduced into the network. The approach follows statistical analysis to compute the next possible position of the node. Based on experiments, the accuracy of the predictive methodology is measured in two different scenarios. In the first scenario, the number of nodes is variable, and the simulation area kept fixed size, i.e., 1000X1000. Additionally, in the second scenario, the number of nodes remains fixed, and the simulation area is variable. The results demonstrate that when we deploy sensor nodes randomly in a large area and network node density is less, the detection accuracy significantly affects and demonstrates low accuracy compared to dense area networks. Secondly, the prediction trend from dense to less dense area demonstrates the reducing accuracy. © 2021

关键词： Sensor nodes

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Smart Home Automation with Smart Metering Using ZigBee technology and Deep Belief Networks - Optimization Using MB-ABCOA Algorithm 2

Smart Home Automation with Smart Metering Using ZigBee Techn...

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2nd IEEE International Conference on Contemporary Computing and Communications, InC4 2024

作者： Navaz, K. Mano Raja Paul, M. Yamuna Bee, J. Kavitha, S. Bhuvanesh, A. Mariappan, R. Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Department of Computer Science and Engineering Chennai India Nehru Institute of Engineering and Technology Department of Electrical and Electronics Engineering Coimbatore India PSN College of Engineering and Technology Department of Computer Science and Engineering Tirunelveli India Sri Eshwar College of Engineering Department of English Tamil Nadu Coimbatore India PSN College of Engineering and Technology Department of Electrical and Electronics Engineering Tirunelveli India

ISBN: (纸本)9798350383652

In the domain of smart homes and smart metering, ZigBee technology plays a pivotal role within the energy efficiency of homes, albeit facing challenges such as packet transmission delays and uneven energy consumption among nodes. To address these issues while emphasizing energy efficiency, we propose the Energy-efficient ZigBee Directionally Opportunistic Short-cut Trees Routings (EP-ZDOSTR), which optimizes network lifetimes and throughput by prioritizing energy-efficient packet transmission. Complemented by a hybrid Monarchy Butterfly and Artificial Bees Colonies Optimizations Algorithms (MB-ABCOA) approach, EP-ZDOSTR demonstrates significant improvements in network performances metric, including higher throughputs, increased packet delivery ratios, reduced transmission delays, and minimized energy consumption. Comparisons with existing methods like ZigBee shortcut tree routing (ZSTR), ZigBee opportunistic shortcut tree routing (ZOSTR), and Energy Efficient shortcut tree routing (EPSTR) validate its superiority in enhancing overall network efficiency and reliability. Furthermore, integrating ZigBee with Deep Belief Networks (DBNs) holds promise for further enhancing network intelligence, enabling predictive analysis of energy usage patterns and optimization of network operations, thereby advancing the capabilities of smart home in the context of smart homes and metering systems within the IoT landscape. © 2024 IEEE.

关键词： Zigbee

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Visual Computing Unified Application Using Deep Learning and computer Vision Techniques

International Journal of Interactive Mobile Technologies

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International Journal of Interactive Mobile Technologies 2024年第1期18卷 59-74页

作者： Sowmya, B.J. Meeradevi Seema, S. Dayananda, P. Supreeth, S. Shruthi, G. Rohith, S. Department of Artificial Intelligence and Data Science Ramaiah Institute of Technology Karnataka Bengaluru India Department of Artificial Intelligence and Machine Learning Ramaiah Institute of Technology Karnataka Bengaluru India Department of Computer Science and Engineering M S Ramaiah Institute of Technology Karnataka Bengaluru India Department of Information Technology Manipal Institute of Technology Bengaluru Manipal Academy of Higher Education Karnataka Manipal India School of Computer Science and Engineering REVA University Karnataka Bengaluru India Department of Electronics & Communication Engineering Nagarjuna College of Engineering & Technology Karnataka Bengaluru India

Vision Studio aims to utilize a diverse range of modern deep learning and computer vision principles and techniques to provide a broad array of functionalities in image and video processing. Deep learning is a distinct class of machine learning algorithms that utilize multiple layers to gradually extract more advanced features from raw input. This is beneficial when using a matrix as input for pixels in a photo or frames in a video. computer vision is a field of artificial intelligence that teaches computers to interpret and comprehend the visual domain. The main functions implemented include deepfake creation, digital ageing (de-ageing), image animation, and deepfake detection. Deepfake creation allows users to utilize deep learning methods, particularly autoencoders, to overlay source images onto a target video. This creates a video of the source person imitating or saying things that the target person does. Digital aging utilizes generative adversarial networks (GANs) to digitally simulate the aging process of an individual. Image animation utilizes first-order motion models to create highly realistic animations from a source image and driving video. Deepfake detection is achieved by using advanced and highly efficient convolutional neural networks (CNNs), primarily employing the EfficientNet family of models. © 2024 by the authors of this article. Published under CC-BY.

关键词： Animation

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Resource Allocation and Energy Efficient Dynamic Particle Swarm Optimisation in Heterogeneous Wireless Communication Networks 4

Resource Allocation and Energy Efficient Dynamic Particle Sw...

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

作者： Pawar, Anuradha Bhalerao Kannan, G. Anuradha, T. Vignesh, V. Kesavan, R. Jose Anand, A. Mit Academy of Engineering Department of Electronics Engineering Alandi Maharastra Pune412105 India Amrita College of Engineering and Technology Department of Electronics and Communication Engineering Tamil Nadu 629 901 India Kcg College of Technology Department of Electrical and Electronics Engineering Tamil Nadu 600097 India Karpagam College of Engineering Department of Computer Science and Engineering Tamil Nadu 641032 India Simts Institute of Computer Science and Engineering Saveetha School of Engineering Tamil Nadu 602105 India Kcg College of Technology Department of Electronics and Communication Engineering Tamil Nadu 600097 India

ISBN: (纸本)9798350354218

This paper investigates the difficult of beam policy in a multi-user peer-to-peer web linkage setting as part of our research. Using a network simulator, the dynamic particle swarm optimization is suggested for this. The challenge is to limit the maximum power consumption per relay, according to minimal Signal to Noise Ratio (SNR) guarantees, after a complete Channel State Information (CSI) is established. It has been established that the non-convex problem satisfies a potent duality. The twofold problem with the highest polynomial complexity was given as Semi Definite Programming (SDP). On the basis of the standards of the optimal dual variables, we looked at the best beam forming vectors for the initial problems in 3 different scenarios. This demonstrated whether the minimum Signal-to-Noise Ratio (SNR) prerequisite for each S-D pair has been met as well as whether a relay's electricity consumption varies among relays. © 2024 IEEE.

关键词： Particle swarm optimization (PSO)

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Fake Review Detection System: A Machine Learning Approach with Linguistic and Semantic Analysis

Fake Review Detection System: A Machine Learning Approach wi...

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2024 International Conference on Artificial Intelligence and Quantum Computation-Based Sensor Applications, ICAIQSA 2024

作者： Janokar, Sagar More, Kiran Raikwar, Rajesh Joshi, Kalpesh Pawar, Prachi Singh, Tanishka Vishwakarma Institute of Technology Electronics and Telecommunication Pune India Vishwakarma Institute of Technology Electrial Engineering Pune India Vishwakarma Institute of Technology Computer Science Engineering Pune India Pune India

ISBN: (纸本)9798331517953

Online platforms heavily rely on user reviews for decision-making, making the identification of fake reviews crucial. This paper presents a novel machine learning-based Fake Review Detection System that incorporates advanced linguistic and semantic analysis. The model's robustness is demonstrated through comprehensive evaluation metrics, showcasing its efficacy in real-world scenarios. Technological innovations in the backend system ensure seamless integration, scalability, and reliability. The outcome contributes to a deeper understanding of linguistic cues, providing a valuable tool for maintaining trust in online platforms. Numerous approaches are employed in detecting fake reviews, predominantly focusing on the linguistic cues of reviewers while overlooking their non-linguistic behaviors. This study identifies various non-linguistic behavioral traits of online reviewers and assesses their significance in detecting fake reviews compared to linguistic cues. Empirical findings from real-world online reviews demonstrate that integrating non-linguistic reviewer characteristics can substantially enhance the efficacy of fake review detection models. Nonverbal features are more important than verbal features in reviews because many reviewers can write fake reviews for promoting a product which is not as good as they portray. © 2024 IEEE.

关键词： Latent semantic analysis

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