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Towards Optimized Physical Activity: A Fusion of Graph Convolutional Networks and XGBoost for Exercise Analysis

Towards Optimized Physical Activity: A Fusion of Graph Convo...

Smart Systems for Electrical, Electronics, Communication and computer Engineering (ICSSEECC), International Conference on

作者： Kishan V Rajesh M Dhanush B Rajiv Vincent School of Computer Science and Engineering Student Vellore Institute of Technology Chennai Chennai India School of Computer Science and Engineering Vellore Institute of Technology Chennai Chennai India School of Computer Science and Engineering Student Vellore Institute of Technology Chennai Chennai India

ISBN: (数字)9798350378177

ISBN: (纸本)9798350378184

This research explores the implementation of the GCN-XGBoost with attention mechanism within the detection and correction of exercises through computer vision, utilizing the 2-dimensional pose coordinates from the MMFit [ 1 ] dataset. The objective is to enhance the precision and reliability of squat performance analysis, leveraging the robust, scalable machine learning capabilities of XGBoost. By processing and analyzing two-dimensional skeletal data, the study aims to identify key postural deviations and provide corrective feedback in realtime, potentially reducing the risk of injury and improving overall exercise effectiveness. Initial results demonstrate the model's capability to discern between correct and incorrect exercise forms with high accuracy, indicating the feasibility of applying advanced machine learning techniques to personal fitness and rehabilitation contexts. This paper details the methodology, from data preprocessing to model training and evaluation, highlighting the contributions to the fields of sports science and machine learning in physical activity monitoring. We have used MMFit dataset for the model that we have trained. We use the 2D coordinates of 18 of the 33 joints in the human body as the main features to categorize various exercises and forecast the accuracy of the poses identified by the Google's AI framework MediaPipe [2]. In this paper, we first classify an individual's exercises (squats, lunges, bicep curls, situps, pushups, triceps extensions, dumbbell rows, jumping jacks, dumbbell shoulder press, lateral shoulder raises) and check whether the pose associated with an exercise is correct. The final model trained has a 98% final validation accuracy.

关键词： Training Presses Accuracy Lung Internet Performance analysis Reliability

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COVID-19 vaccine sentiment analysis using public opinions on Twitter

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Materials Today: Proceedings 2022年 64卷 448-451页

作者： Chinnasamy, P. Suresh, V. Ramprathap, K. Jebamani, B. Jency A. Srinivas Rao, K. Shiva Kranthi, M. Department of Computer Science and Engineering MLR Institute of Technology Hyderabad India Department of Computer Science and Engineering Dr.N.G.P. Institute of Technology Coimbatore India Department of Management Studies M.Kumarasamy College of Engineering Karur India Department of Computer Science and Engineering KPR Institute of Engineering and Technology Coimbatore India UG Student Department of Computer Science and Engineering MLR Institute of Technology Hyderabad India

Twitter, as is well known, is one of the most active social media platforms, with millions of tweets posted every day, in which different people express their opinions on topics such as travel, economic concerns, political decisions, and so on. As a result, it is a useful source of knowledge. We offer Sentiment Analysis using Twitter Data for the research. Initially, our technology retrieves currently accessible tweets and hashtags about various types of covid vaccinations posted on Twitter through using Twitter's API. Following that, the imported Tweets are automatically configured to generate a collection of untrained rules and random variables. To create our model, we're utilizing, Tweepy, which is a wrapper for Twitter's API. Following that, as part of the sentiment analysis of new Messages, the software produces donut graphs. © 2022

关键词： Vaccines

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Machine learning based cardiovascular disease prediction

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Materials Today: Proceedings 2022年 64卷 459-463页

作者： Chinnasamy, P. Arun Kumar, S. Navya, V. Lakshmi Priya, K. Sruthi Boddu, Siva Department of Computer Science and Engineering MLR Institute of Technology Hyderabad India Department of Computer Science and Engineering Bethesda Institute of Technology and Science Gwalior India Department of Electronics and Communication Engineering East Point College of Engineering & Technology Bangalore India Department of Electronics and Communication Engineering PERI Institute of Technology Chennai India UG Student Department of Computer Science and Engineering MLR Institute of Technology Hyderabad India

In past few years, one of the really tough situations in medicine is already making predictions cardiac dysfunction. Almost any moment, roughly one patient dies through myocardial infarction with in present era. Due to the difficulty of forecasting cardiac sickness, it is vital to systematize the procedure in place to minimize the risk associated with and to educate the patient well enough in preparation. The risk of cardiovascular disease in the World is tremendous. The accurate and thorough assessment of a physician's cardiovascular risk is critical for reducing the incidence and severity attack and stroke and improving cardiovascular protection. In order to solve this issue, we are proposing method to discover these heart abnormalities as soon as reasonably practicable to avert fatal consequences. The next step is to determine whether or not the user is at risk of developing cardiovascular disease. It also answers difficult problems concerning heart detection of diseases, allowing clinicians to make better clinical decisions. Medical specialists have accumulated a large number of medical data that can be analysed and important fact retrieved. Machine learning approaches can improve in the prognosis and early identification of heart illness after hypothesis testing. © 2022

关键词： Machine learning

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Performance Framework for Virtual Machine Migration in Cloud Computing

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computers, Materials & Continua 2023年第3期74卷 6289-6305页

作者： Tahir Alyas Taher M.Ghazal Badria Sulaiman Alfurhood Munir Ahmad Ossma Ali Thawabeh Khalid Alissa Qaiser Abbas Department of Computer Science Lahore Garrison UniversityLahore54000Pakistan School of Information Technology Skyline University CollegeUniversity City SharjahSharjah1797UAE Center for Cyber Security Faculty of Information Science and TechnologyUniversiti Kebansaan Malaysia(UKM)Bangi43600SelangorMalaysia Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversitySaudi Arabia School of Computer Science National College of Business Administration&EconomicsLahore54000Pakistan Dean of Student Affairs Skyline University CollegeUniversity City SharjahSharjah1797UAE Networks and Communications Department College of Computer Science and Information TechnologyImam Abdulrahman Bin Faisal UniversityDammam31441Saudi Arabia Faculty of Computer and Information Systems Islamic University Madinah Madinah42351Saudi Arabia

In the cloud environment,the transfer of data from one cloud server to another cloud server is called *** can be delivered in various ways,from one data centre to *** research aims to increase the migration performance of the virtual machine(VM)in the cloud *** allow cloud customers to store essential data and ***,server usage has grown dramatically due to the virtualization of computer systems,resulting in higher data centre power consumption,storage needs,and operating *** VMs on one data centre manage share resources like central processing unit(CPU)cache,network bandwidth,memory,and application ***-cloud,VMmigration addresses the performance degradation due to cloud server configuration,unbalanced traffic load,resource load management,and fault situations during data *** speed is influenced by the size of the VM,the dirty rate of the running application,and the latency ofmigration *** a result,evaluating VM migration performance while considering all of these factors becomes a difficult *** effort of this research is to assess migration problems on *** simulation results in Matlab show that if the VMsize grows,themigration time of VMs and the downtime can be impacted by three orders *** dirty page rate decreases,themigration time and the downtime grow,and the latency time decreases as network bandwidth increases during the migration time and post-migration overhead calculation when the VMtransfer is *** the simulated cases of VMs migration were performed in a fuzzy inference system with performance graphs.

关键词： Latency cloud computing dirty page ratio storage migration performance

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Application of Liquid State Machine for Accurate Rainfall Prediction

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Procedia computer Science 2025年 258卷 1554-1564页

作者： Meet K Patel Mohini Darji Bansari Patel Student of Bachelor of Technology Department of Computer Science & Engineering Devang Patel Institute of Advance Technology and Research (DEPSTAR) Faculty of Technology and Engineering (FTE) Charotar University of Science and Technology (CHARUSAT) Changa 388421 Gujarat India

Adequate rainfall prediction is important in many aspects, the most important being planning in agriculture, correct management of water resources, and early warnings in the event of impending natural disasters. Traditional techniques have been unsuccessful in handling nonlinear and dynamic characteristics of atmospheric processes. In this research, we look into the usage of Liquid State Machines, a type of spiking neural network, in rainfall prediction. One of the main reasons behind this choice is that the LSMs are known to handle temporal data with dynamic memory capabilities robustly, so they would turn out to be the right choice in modeling nonlinear and time-dependent patterns inherent in rainfall data. This will include preprocessing the dataset to normalize the input features and create lag features to capture the temporal dependencies. Then, the constructed high-dimensional state of the reservoir has to be mapped back to the desired output—rainfall amount or quantity—which involves building a model for LSM with an input layer, a dynamic reservoir or liquid state, and then the readout layer. The model will be trained using backpropagation across time to minimize the mean squared error between the anticipated and real rainfall quantities. For this purpose, the LSM model is trained and tested on a dataset built from the historical weather and rainfall data of the Gujarat region. Various metrics such as MSE, RMSE, MAE, and R² are used to quantify the accuracy of the predictions. The LSM model accurately predicts rainfall by capturing temporal and nonlinear interactions in the data.

关键词： Prediction Rainfall LSM Gujarat Time series

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Comparative Analysis of Traditional and Machine Learning Based Face Recognition Models 2

Comparative Analysis of Traditional and Machine Learning Bas...

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2nd IEEE International Conference on Data, Decision and Systems, ICDDS 2023

作者： Shreya, R. Mulgund, Akanksha P Hiremath, Shravani Sri Harshitha, A. Koundinya, Anjan K PES University Department of Computer Science Bengaluru India PES University Department of Computer Science and Engineering Bengaluru India BMS Institute of Technology and Management Department of Information Science and Engineering IEEE Student Branch Counsellor Bengaluru India

ISBN: (纸本)9798350314168

Face recognition technology uses methodologies to process images, register them, and compare and verify it with a set of faces it has already registered and labelled. Recent development in visual recognition showcases the immense power of utilising machine learning techniques like deep neural networks, convolutional neural networks to achieve face recognition. Based on a comprehensive literature review it was found that some of the algorithms that provide great performance metrics in a variety of conditions are MTCNN, FaceNet model, Haar cascade, LBPH, hence it was chosen to be the subject of comparison under various conditions in the paper. A machine learning model was constructed which included MTCNN for face detection and FaceNet for face *** Performance of KNN and SVM classifiers has been studied. The second model uses a combination of Haar cascade for face detection and LBPH for face recognition, a traditional face recognition technique. The results highlight that MTCNN, FaceNet and KNN perform exceptionally well. The performance of the other models are also discussed in the results and discussion section. The research not only guides researchers and practitioners in choosing suitable face recognition techniques but also offers ground for future improvements in accuracy and reliability in the field of algorithm developments. © 2023 IEEE.

关键词： Face recognition

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Weather Prediction on Different LSTM Techniques for Time Series Data in Chennai, India

Weather Prediction on Different LSTM Techniques for Time Ser...

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2023 International Conference on New Frontiers in Communication, Automation, Management and Security, ICCAMS 2023

作者： Tk, Harikishan Sasikiran, S. Manoj Kumar, G. Joseph, Ferdin Joe John Sathiyanarayanan, Mithileysh Srm Institute of Science and Technology Student of Computer Science and Engineering Ramapuram Campus Chennai India Thai-Nichi Institute of Technology Thai-Nichi International College Bangkok Thailand Research & Innovation Mit Square London United Kingdom

ISBN: (纸本)9798350317060

The prediction of weather has always been an integral factor in our lives as it determines if the day of activity leads to favorable or unfavorable conditions. The existence of different deep learning models has facilitated several major problems which without such techniques would have been a very time-consuming and exhaustive process. In this following paper, we explore various types of LSTM models and analyze which among them provides the best fit for the Time Series data that is present in the Weather data. Upon gathering the datasets, a strategy called Long Short-Term Memory (LSTM) is carried out. LSTM is a deep learning model which is primarily focused on time-series data. By employing such technical improvisations on the same data with multiple models each with their own unique characteristics, we can ultimately determine the best model which has an overall better accuracy. Based on the metrics produced by employing each technique, it is found that our LSTM model which proposed here has the best performance in terms of predicting the data with an overall low error rate and loss during the training phase. © 2023 IEEE.

关键词： Long short-term memory

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Cryptosight: Decrypting Future Trends with LSTM, PyCaret and Jinja 2.0

Cryptosight: Decrypting Future Trends with LSTM, PyCaret and...

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Smart Power Control and Renewable Energy (ICSPCRE), IEEE International Conference on

作者： Soham Motiram Mhatre Brajesh Kumar Mishra Ishan Kumar Raja Bigit Krishna Goswami Sneha Agrawal Pranjal Upadhyay dept. of Electronics and Communication Engineering Student National Institute of Technology Dimapur Nagaland India dept. of Mechanical Engineering Student National Institute of Technology Dimapur Nagaland India dept. of Electrical & Electronics Engineering Student National Institute of Technology Dimapur Nagaland India dept. of Computer Science and Engineering Student National Institute of Technology Dimapur Nagaland India

ISBN: (数字)9798350377002

ISBN: (纸本)9798350377019

The primary objective of this research is to develop and evaluate combined cryptocurrency price prediction models using Long Short-Term Memory (LSTM) neural networks along with traditional time series models and machine learning techniques like Random Forest and Linear Regression. The LSTM models will capture long-term dependencies in sequential cryptocurrency price data, while the other models provide complementary predictive capabilities. This multi-model approach aims to leverage the strengths of different techniques to improve overall prediction accuracy. Additionally, this research seeks to explore the practical application of these predictions in real-world cryptocurrency trading scenarios. The study will collect historical price data and relevant news for major cryptocurrencies from reputable sources. Data preprocessing, feature engineering, model training, hyper-parameter tuning, and evaluation using metrics like RMSE, MAE, and R-squared will be performed. The predictive performance of the LSTM-based models will be compared against baseline models, with statistical significance testing. Interpretability analysis will provide insights into the factors driving price movements. Furthermore, the research will investigate integrating the predictions into trading algorithms and evaluate their efficacy through back-testing on historical data. Limitations, challenges, and future research directions in cryptocurrency price prediction will also be discussed.

关键词： Measurement Training Renewable energy sources Time series analysis Predictive models Data models Cryptocurrency

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Shared Rainwater Harvesting System Using Google Map API And Edge Detection

Shared Rainwater Harvesting System Using Google Map API And ...

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International Conference on Circuits, Systems, Communication and Information technology Applications (CSCITA)

作者： Briana Rajan Kallis Nadar Tushar Tripathi Kalpita Wagaskar Student of Computer Engineering Don Bosco Institute of Technology Mumbai India Computer Engineering Don Bosco Institute of Technology Mumbai India

In this paper, we propose a GIS-based solution for rainwater harvesting in Metropolitan cities. Currently, the rainwater harvesting systems which have been developed are either for a large region that is not developed at all or a region with a scarcity of water. Our paper focuses on developing a system for areas where the deconstruction of existing buildings is not recommended or appreciated. In most metropolitan regions, another major concern for the implementation of rainwater harvesting tanks is the restriction of space. Our suggested system expresses a shared tank concept, where multiple neighboring buildings could share one water storage tank. This would reduce both-the space and expense related to the construction. An estimation of the cost and volume of the tank that needs to be built would given provided by the system.

关键词： Costs Water storage Satellites Rain Image edge detection Urban areas Pipelines

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Estimating the AGV load and a battery lifetime based on the current measurement and random forest application

Estimating the AGV load and a battery lifetime based on the ...

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2023 IEEE International Conference on Big Data, BigData 2023

作者： Hanzel, Krzysztof Grzechca, Damian Ziebinski, Adam Chruszczyk, Lukas Janus, Artur Silesian University of Technology Faculty of Automatic Control Electronics and Computer Science Department of Telecommunications and Teleinformatics Gliwice Poland Silesian University of Technology Faculty of Automatic Control Electronics and Computer Science Department of Electronics Electrical Engineering and Microelectronics Gliwice Poland Silesian University of Technology Faculty of Automatic Control Electronics and Computer Science Department of Distributed Systems and Informatics Devices Gliwice Poland Silesian University of Technology Student of Electrical EngineeringFaculty of Electrical Engineering Gliwice Poland

ISBN: (纸本)9798350324457

The paper describes the energy consumption from the battery based on the current measurements for various cases, i.e., speed (PWL adjustment) and loads. The main purpose of the research is to have additional and reliable information about power consumption and battery life estimation for autonomous guided vehicles (AGV). The authors propose a two-step algorithm. In the first step, a linear classifier was proposed. Then, the KNN classifier was tested;however, it did not give satisfactory results, so it was finally decided to use the random forest to estimate the load and PWL. The time domain current measurement is evaluated, and the beforementioned algorithms process the selected statistical measures. It has been proven that a two-step algorithm allows for achieving high accuracy. Based on the current observation, the paper is a good starting point for further investigation of the AGV because it is usually implemented in the AGV - so it does not require additional hardware. Moreover, it can lead to better energy management and increase battery lifetime. © 2023 IEEE.

关键词： AGV load estimation machine learning Prediction of energy consumption random forest

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