Kartik G Alur, Shivabharat S Kopparad, Kumbhar Trupti Ravikumar
{"title":"ANN Based Heart Disease Prognosis","authors":"Kartik G Alur, Shivabharat S Kopparad, Kumbhar Trupti Ravikumar","doi":"10.48001/joaii.2023.111-4","DOIUrl":"https://doi.org/10.48001/joaii.2023.111-4","url":null,"abstract":"Heart disease is a major health concern which is responsible for cause of death worldwide. The different factors that cause heart disease includes inadequate blood flow due to fatty plaques in the arteries, stress, family history etc. Early and accurate identification of heart disease can help people take appropriate preventative action, by which the death rate can be reduced. Artificial neural networks (ANNs), have shown good results in different medical applications as they are able to learn complex patterns from the large datasets. An ANN-based models have the capacity to learn intricate patterns from tremendous datasets, thus it is possible to early detection and medication. In this paper, we introduce a machine learning method that makes use of ANNs to predict heart disease utilizing a variety of risk indicators, including age, sex, blood pressure, cholesterol levels, and other important clinical features. The model is trained on this dataset.","PeriodicalId":201326,"journal":{"name":"Journal of Artificial Intelligence and Imaging","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114905516","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Yoga Pose Trainer and Analyser: Machine Learning (TensorFlow and Open CV)","authors":"Shubham Bandgar, Mihir Vanave, Kaivalya Bulbule","doi":"10.48001/joaii.2023.1122-24","DOIUrl":"https://doi.org/10.48001/joaii.2023.1122-24","url":null,"abstract":"The \"Yoga Pose Trainer and Analyzer\" is a sophisticated software system designed to enhance the yoga experience for practitioners of all levels. This project combines computer vision, machine learning, and user-friendly interfaces to provide valuable insights and guidance during yoga sessions. Users can interact with the system, receiving real-time feedback on their yoga poses and personalized recommendations to improve their practice. The core functionalities of the system include pose recognition and classification. Using advanced computer vision techniques, the system identifies key points on the user's body and classifies their pose, accurately determining which yoga asana (pose) they are performing. Additionally, users can specify their goals or desired poses, and the system tailors their practice accordingly. The software provides a comprehensive library of yoga poses, offering detailed instructions and highlighting the benefits of each pose. Users can track their progress over time, set goals, and receive guidance on how to achieve their objectives. Safety and privacy considerations are paramount, with the system promoting proper form to prevent injury and ensuring user data security.","PeriodicalId":201326,"journal":{"name":"Journal of Artificial Intelligence and Imaging","volume":"27 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139369228","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}