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Efficient IOT based COVID patient healthcare control system using arduino with mobile app

Measurement: Sensors 2024年 33卷

作者： sudha, K. Mercy Theresa, M. Vinayagam, P. Suganthi, R. Sripriya, T. uthayakumar, G.S. Computer Science and Business System R. M. D Engineering College India Department of Data Science and Business Systems SRM Institute of Science and Technology Kattankulathur India Department of ECE Saveetha Engineering College India Department of ECE Panimalar Engineering College India Department of Electronics and Communication Engineering Jeppiaar Institute of Technology Tamilnadu Chennai India Department of ECE St.Joseph's Institute of Technology India

Healthcare monitoring frameworks emerged as one of the most essential frameworks and innovations established over the last decade. As a result of failing to provide adequate clinical attention to patients at the appropriate time, many people are facing the possibility of an untimely death. Ultimately, the goal was to develop an IoT-based integrated healthcare monitoring framework that could be relied upon by healthcare professionals to screen their patients, whether they were in the hospital or at home, to ensure that they were being well-cared for. Sensors, an information acquisition unit, a microcontroller (e.g., Arduino), and a product modification were used to construct a mobile phone-based remote healthcare monitoring framework that may provide continuous web-based data about a patient's physiological states (i.e., JAVA). The framework checks, displays, and stores the patient's temperature, heart rate, and EEG data before sending it out on the specialist's portable device with the application. In this way, an IoT-based patient monitoring framework may effectively monitor a patient's health state and save their life on time. © 2024

关键词： Data acquisition

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Microgrid Recent Innovations & Uses

Microgrid Recent Innovations & Uses

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International Conference on Computing and Networking Technology (ICCNT)

作者： Neeraj Kaushik Shubhashis Goswami Keerti Sachan V.J. Vijayalakshmi P. Gajendran V.A. Mishra Department of ECE TMU School of Engineering and Computer Dev Bhoomi Uttarakhand University Uttarakhand School of computer science and application IIMT University meerut Department of Electrical and Electronics Engineering Karpagam Academy of Higher Education Coimbatore Department of Electrical and Electronics Engineering Karpagam College of Engineering Coimbatore Computer Engg Vishwakarma Institute of Information Technology Pune

ISBN: (数字)9798350370249

ISBN: (纸本)9798350370270

Concerns about global warming brought on by increasing carbon dioxide levels in the environment have sped up technological development in the sustainable energy sector over the past few years. The advancement in science while the shift to less polluting materials sources possesses increased the appeal for DG, subsequent generations distributed generation, using renewable supplies. Nevertheless, it has become widely recognized that increasing DG entry can negatively affect the matrix’s layout and operations. A suggestion made to reduce the effect about DG while improve the suitability of traditional grids over massive DG installation involves the Located concept. encompassing a variety of electrical devices’, the elements and Micro-grids and related power gadgets fields are now very well-liked research areas. This essay examines the history and theory behind microgrids, as well as the present situation regarding the writing, active research initiatives, and pertinent requirements. It additionally provides a more thorough analysis of the numerous Located pilot initiatives which have been implemented surrounding the globe and addresses possible directions for future study.

关键词： Technological innovation Layout Hydroelectric power generation Microgrids Carbon dioxide Production Writing Inverters Research initiatives Standards

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Energetic Content based Image Retrieval Scheme using Improved Deep Learning Strategy with Image Analysis Technique 9

Energetic Content based Image Retrieval Scheme using Improve...

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9th IEEE International Conference on Smart Structures and Systems, ICSSS 2023

作者： Karkuzhali, S. Malathi, P. Thenmozhi, A. Aiyyasamy, Velu Keerthika, A. Uthayakumar, G.S. R.M.K. Engineering College Department of General Engineering Tamil Nadu Chennai India Simats Saveetha College of Liberal Arts and Sciences Department of Data Analytics India Kings Engineering College Department of Electronics and Communication Engineering Tamil Nadu Chennai India Meenakshi Sundararajan Engineering College Department of Electronics and Communication Engineering Tamil Nadu Chennai India Prince Shri Venkateshwara Padmavathy Engineering College Department of Computer Science and Engineering Tamil Nadu Chennai India St. Joseph's Institute of Technology Department of Ece Tamil Nadu Chennai India

ISBN: (纸本)9798350384208

One popular approach to retrieving images from huge, unprocessed datasets is content-based image retrieval, or CBIR. The conventional ways of data retrieval, however, are not meeting the needs of consumers. The number of consumer-accessible photographs also keeps growing, and there are an abundance of digital platforms for development and dissemination. Consequently, digital image processing is both ubiquitous and permanent in many fields. Consequently, effective strategies are required for both the speedy access to these massive picture databases and the recovery of comparable photos from this massive number of images based on a specific picture (query). The computation of depiction of features and resemblance is crucial to the efficiency of a CBIR system. We introduce LAIRS, a cutting-edge deep learning method that integrates the extraction of features and aims to do this with classification to facilitate rapid picture retrieval. For a transparent evaluation of the proposed scheme's efficiency, the suggested logic is cross-validated using Convolutional Neural Networks (CNNs), a classic deep learning paradigm. Several in-depth observational experiments for various CBIR tasks employing an image database have shown encouraging results, teaching us important lessons for making CBIR system more efficient. With the use of CBIR structures, a different collection can find similar pictures to the query image. There is little doubt that Google's search per photo tool is perhaps the most widely used CBIR approach. © 2023 IEEE.

关键词： Image analysis

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Protecting Data from DDOS Attack in a Cloud Based Intrusion Detection System Security through Enhanced RSA Algorithm 2

Protecting Data from DDOS Attack in a Cloud Based Intrusion ...

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

作者： Naik, Pramod Kumar Divya, S. Mohan, M. Velusamy, Jayaraj Radhakrishnan, P. Rajasekaran, Arun Sekar Dayananda Sagar University Bengaluru Department of Cse India Biomedical Engineering Dr. N.G.P. Institute of Technology India Kpr Institute of Engineering and Technology Department of Computer Science and Engineering Coimbatore India Nehru Institute of Engineering and Technology Department of Electronics and Communication Engineering Tamilnadu India School of Cs and Ai Sr University Telangana Warangal India Sr University Department of Ece Telangana Warangal India

ISBN: (纸本)9798350369960

Since cloud computing platforms have become an essential component in a range of different industries, it is still challenging to ensure that user data and system resources will remain secure inside these settings. This paper proposes a methodology to identify and mitigate unwanted traffic and packets, specifically duplicate packets, in cloud computing envi- ronments. The procedure requires the creation of an IntrusionFinder and Detector (IF-AD) system that allots secondary storage and keeps user information in a secure manner. This technique does a comparison between the sizes of downloaded files and the sizes of the original files, searching for any variations between the two that would suggest a probable distributed denial of service assault (DDoS). The RSA cryptography procedure is employed in order to further strengthen the degree of security that is provided by any future file transfers that may take place. The problem that has been identified and a potential solution for has been proposed with the intention of enhancing the safety of cloud-based systems by identifying and preventing unauthorised access to files as well as modifications to them. © 2023 IEEE.

关键词： Cloud computing decryption duplicate packets encryption Intrusion-Finder and Detector RSA

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Deep Learning-Based GYM Monitoring System using YOLOv5 and Pose Estimation Algorithm 7

Deep Learning-Based GYM Monitoring System using YOLOv5 and P...

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7th International Conference on electronics, Communication and Aerospace Technology, ICECA 2023

作者： Renugadevi, R. Arul, P. Subashree, V. Sathi, G. Dharmateja, M. Indhumathi, G. Vignan Foundation for Science Technology and Research Department of CSE Andhra Pradesh Guntur India RMD Engineering College Department of ECE Tamil Nadu Chennai India Saveetha Engineering College Department of Electronics and Communication Engineering Tamil Nadu Chennai India Prince Shri Venkateshwara Padmavathy Engineering College Department of Mechanical Engineering Tamil Nadu Chennai India Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology Department of Computer Science and Engineering Tamil Nadu Chennai India Rajalakshmi Engineering College Department of Electronics and Communication Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350340600

A revolutionary GYM Monitoring System that employs deep learning techniques, YOLOv5 (You Only Look Once) for activity monitoring, and state-of-the-art posture estimation with MediaPipe to potentially improve exercise precision and ensure compliance with social distance norms. In an initial phase of the system, bounding box information and Euclidean distance are used to evaluate user behavior and determine optimal exercise distances. In the second phase, keypoint detection is used to determine joint angles. The software accurately categorizes gym postures according to approximated joint angles, providing immediate feedback and paving the way for modifications to prevent overuse injuries. This cutting-edge machine revolutionizes the fitness industry by combining the benefits of digital technology and physical activity. © 2023 IEEE.

关键词： Deep learning

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The Effective Way of using Machine Learning Classifier Technique to Predict the Heart Muscle Condition 4

The Effective Way of using Machine Learning Classifier Techn...

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4th IEEE International Conference on Advance Computing and Innovative Technologies in engineering, ICACITE 2024

作者： Vimal, V.R. Joany, R.M. Rao, Kolli Himantha Krishnammal, P. Muthu Rashid, Zakhara Abdel Hassan Safi, Hassn Saveetha University Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Computer Science and Engineering Chennai Thandalam India Sathyabama Institute of Science and Technology Department of Ece Chennai India Saveetha Engineering College Institute of Cse Department of Artificial Intelligence and Machine Learning India Vitap University School of Electronics Engineering Andhra Pradesh Amaravati India National University of Science and Technology College of Technical Engineering Dhi Qar64001 Iraq Al-Farahidi University Baghdad Iraq

ISBN: (纸本)9798350360165

Free-text is categorised into predetermined groups in text classification, a crucial field of machine learning. Due to its many uses, such as sentiment analysis, topic labelling, language identification, and spam filtering, it has attracted a great deal of study interest. Ontologies and machine learning give the knowledge of data relations or patterns, which greatly increases the text categorization efficiency. The goal of this study is to diagnose myocardial infraction among patients by machine learning classifiers. The code that goes with the created approach is meant to be flexible and work with any text categorization system. In this research, we present an improved architecture that makes use of ontology to improve the efficacy of text categorization and diagnose myocardial infraction. In particular, we provide a novel ontology-driven text categorization technique that leverages the extensive semantic data from the Disease Ontology (DOID). We examine previous research and argue that ontology-based text categorization strategies are better than traditional techniques in a number of criteria, including as F-measure, Accuracy, Precision, and Recall. © 2024 IEEE.

关键词： Adversarial machine learning

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Precise Identification of Anomalies in Brats MR Brain Images using Metaheuristic Optimization Techniques 7

Precise Identification of Anomalies in Brats MR Brain Images...

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7th International Conference on Inventive Computation Technologies, ICICT 2024

作者： Rosaline, S. Maheswari, K. Dhanasakkaravarthi, B. Vaishali, M. Kannan, Sathish Selvaraju, Nandakumar R.M.K. Engineering College Department of Electronics And Communication Engineering Thiruvallur Tamil Nadu Kavaraipettai601206 India Cmr Technical Campus Department of Computer Science And Engineering Hyderabad Telangana Kandlakoya501401 India Agni College of Technology Department of Mechanical Engineering Chennai Tamil Nadu Thazhambur603103 India Sri Eshwar College of Engineering Department of Computer Science And Business Systems Tamil Nadu Coimbatore641 202 India Saveetha School of Engineering Saveetha Institute of Medical And Techinical Sciences Department of Vlsi Microelectronics Tamil Nadu Chennai602105 India Hindustan Institute of Technology And Science Department of Automobile Engineering Chennai Tamil Nadu Padur603103 India

ISBN: (纸本)9798350359299

A condition known as an anomaly occurs when some brain features behave improperly and cause either minor or significant harm to brain cells. Brain tumors, vascular malformations, and inflammatory lesions are among the most well-known abnormalities. A variety of Machine Learning (ML) and Deep Learning (DL) methods are employed in the analysis and detection of brain irregularities. The use of metaheuristic optimization is one such method. Three optimization processes have been chosen for this investigation. The study examines and documents anomaly detection efficiency. A public dataset comprising brain scan images from disparate institutions is employed in the research to analyze the effectiveness of the detection procedure. The three algorithms used are Sequential Minimal Optimization (SMO), Krill Herd Optimization (KHO), and Particle Swarm Optimization (PSO). The three algorithms are fed the preprocessed images for training and testing. From the simulation result, it is established that the KHO algorithm is the best method for identifying brain abnormalities. The KHO achieved an accuracy of 0.96, whereas the competitive model like PSO scored an accuracy value of 0.93 and SMO attained 0.95. The KHO method is quite accurate in detecting the presence of any kind of abnormality. © 2024 IEEE.

关键词： Anomaly detection

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Adaptive Vehicle Control System for Improved Safety With Image Processing 5

Adaptive Vehicle Control System for Improved Safety With Ima...

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5th IEEE International Conference on Electro-Computing Technologies for Humanity, NIGERCON 2024

作者： Vijayalakshmi, N. Priya, L. Ramaraj, S. Nanjundan, Menakadevi Palanisamy, Satheesh Kumar Olalekan Salau, Ayodeji Braide, Sepiribo Lucky Alliance University Alliance School of Advanced Compuitng Department of Computer Science and Engineering Bangalore India Department of Ece Kinathukadavu Tamilnadu Coimbatore641202 India Karpagam College of Engineering Department of Computer Science and Engineering India Karpagam Institute of Technology Department of Ece Coimbatore India Bms Institute of Technology and Management Department of Ece Karnataka Bengaluru India Afe Babalola University Department of Electrical/Electronics and Computer Engineering Ado-Ekiti Nigeria Saveetha Institute of Medical and Technical Sciences Saveetha School of Engineering Chennai India Rivers State University Department of Electrical and Electronics Engineering Port Harcourt Nigeria

ISBN: (纸本)9798331542559

We propose the development of an Adaptive Vehicle Control (AVC) system using readily available components such as a Raspberry Pi microcontroller, a motor, a motor driver, and a Raspberry Pi camera. This system aims to enhance safety and convenience by automatically adjusting vehicle speed based on surrounding traffic conditions and recognizing relevant road signs like school zones, speed limits, and hospital zones. The process begins with real-time image capture using the Raspberry Pi camera module. These images are then analyzed using Python-based computer vision algorithms, primarily leveraging OpenCV. The image processing pipeline employs techniques such as object detection and template matching to identify specific road signs within the captured frames. Upon sign detection, the system interprets their meanings and determines the appropriate speed limits associated with them. Using the information from the detected signs and their corresponding speed limits, the system regulates the motor connected to the vehicle's throttle through GPIO pins on the Raspberry Pi. By adjusting the motor's speed, the AVC system ensures compliance with the specified speed limits across various road conditions. Safety measures are paramount, with fail-safe mechanisms implemented to prevent accidents in case of system errors or malfunctions. This AVC system offers a cost-effective and adaptable solution for integrating intelligent cruise control capabilities into vehicles. Leveraging the computational power of the Raspberry Pi and the versatility of image processing techniques, the system can adapt to diverse driving environments, thereby enhancing overall driving safety. This project contributes to the progression of smart transportation systems and lays the foundation for further advancements in autonomous driving technologies. © 2024 IEEE.

关键词： Adaptive cruise control

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Disaster Management Redefined: Integrating SVM-AE Techniques with Remote Sensing and Meteorological Data

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Remote Sensing in Earth Systems Sciences 2024年第4期7卷 294-308页

作者： Priyadharshini, L. Dhanke, Jyoti A. Patil, R.N. Swapna, B. Kalyani, Kapula Syamala, Maganti Deivasigamani, Shanmugavel Saveetha School of Law Saveetha Institute of Medical and Technical Science Chennai India Department of Engineering Science (Mathematics) Bharati Vidyapeeth’s College of Engineering Lavale Pune India Department of Mechanical Engineering Bharati Vidyapeeth’s College of Engineering Lavale Pune India Department of ECE Dr. M.G.R. Educational and Research Institute Chennai India Department of Electronics and Communication Engineering Aditya College of Engineering and Technology Andhra Pradesh Surampalem 533437 India Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation AP Vaddeswaram Guntur India Department of Artificial Intelligence and Data Science Jeppiaar Institute of Technology Anna University Tamilnadu Chennai India

This study presents a novel hybrid model, the Support Vector Autoencoder (SVAE), designed to enhance disaster management through the integration of Support Vector Machines (SVM) and Autoencoders (AE). By leveraging the strengths of both machine learning techniques, the SVAE model offers improved accuracy and reliability in predicting and managing natural disasters. The methodology involves comprehensive data collection from Sentinel-2 satellite imagery and Global Precipitation Measurement (GPM) mission data, supplemented by historical disaster records from the Emergency Events Database (EM-DAT). After rigorous preprocessing, key features such as the Normalized Difference Vegetation Index (NDVI), land surface temperature (LST), soil moisture content, and various meteorological parameters are extracted. These features are then normalized and used to train the SVM for supervised learning and the AE for unsupervised learning. The outputs of these modules are integrated through a fusion layer, which combines classification scores and anomaly detection signals to generate a final risk score. Performance comparison with other models, including Random Forest, k-NN, Decision Tree, and Naive Bayes, demonstrates that the SVAE model achieves superior accuracy, precision, recall, and F1-score. The proposed model’s accuracy reaches 97%, significantly outperforming other techniques in anomaly detection and risk assessment. The results indicate that the SVAE model is a robust tool for enhancing disaster preparedness and mitigation efforts, providing timely and actionable insights to decision-makers. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2024.

关键词： Autoencoder (AE) Disaster management Emergency Events Database (EM-DAT) Global Precipitation Measurement (GPM) Machine learning Normalized Difference Vegetation Index (NDVI) Sentinel-2 Satellite Imagery Support Vector Autoencoder (SVAE) Support Vector Machine (SVM)

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A Novel Approach to Social Sustainability with Machine Learning and the Best Worst Method in Manufacturing 1

A Novel Approach to Social Sustainability with Machine Learn...

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1st International Conference on Advanced Computing and Emerging Technologies, ACET 2024

作者： Yogi, Kottala Sri Franklin Jino, R.E. Dankan Gowda, V. Murthy, C. Ravindra Prasad, Veduri Veera Srinivas, Ved Hyderabad Symbiosis International University Symbiosis Institute of Business Management Department of Operations Pune India V.S.B. Engineering College Department of Information Technology Tamilnadu Karur India Bms Institute of Technology and Management Department of Electronics and Communication Engineering Karnataka Bangalore India Mohan Babu University School of Engineering Department of Ece Andhra Pradesh Tirupati India Aditya College of Engineering and Technology Department of Computer Science & Engineering Andhra Pradesh Surampalem India Thiagarajar School of Management Department of Human Resources Tamil Nadu Madurai India

ISBN: (纸本)9798350367720

This paper is aimed at presenting a novel approach for enhancing the measurement of social sustainability in manufacturing domain with the help of ML with BWM. Social sustainability when dealing with manufacturing is part of the four sustainability pillars that seeks to address aspects such as;welfare of workers, social organization, and practice within the firm. This is why the conventional tools may not sufficiently inform on the right combination of the said aspects. The information from the big data is then processed by the means of ML to disclose the patterns illuminating the stance influences and BWM is integrated to analyze specialists' insight the power of evolving influential aspects of stance. They are useful in minimizing the time as well as the energy required in the formulation of the decisions because the details and correlations needed are already provided. It is illustrated with the help of an example in the manufacturing setting and significant improvements in the values of the social sustainability indices compared to the classical approach. From these conclusions, it is possible to indicate the prospects for the integration of ML and BWM as the authors' contribution to tackling the problem of social sustainability in the manufacturing sector. © 2024 IEEE.

关键词： Smart manufacturing

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