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Yoga is a years discipline that calls for physical postures, mental focus, and deep breathing. Yoga practice can enhance stamina, power, serenity, flexibility, and well-being. Yoga is currently a well-liked type of exercise worldwide. The foundation of yoga is good posture. Even though yoga offers many health advantages, poor posture can lead to issues including muscle sprains and pains. People have become more interested in working online than in person during the last few years. People who are accustomed to internet life and find it difficult to find the time to visit yoga studios benefit from our strategy. Using the web cameras in our system, the model categorizes the yoga poses, and the image is used as input. However, the media pipe library first skeletonizes that image. Utilizing a variety of deep learning models, the input obtained from the yoga postures is improved to improve the asana. The algorithms like VGG16 (Visual Geometric Group), VGG19, Convo2d, CNN. [ABSTRACT FROM AUTHOR]

Text categorization is the assignment of natural language text to one or more predefined categories based on their content is an important component in many information organization and management task. We compare the effectiveness of four different automatic learning algorithms for text categorization in terms of learning speed and classification accuracy. We also examine training set size and alternative document representation very accurate text classifiers can be learned automatically from training example. Generally we have familiar with KNN is a best classification algorithm but the algorithm spends most time on the classification. In this paper we propose content order on different algorithms. The K- Nearest Neighbors (KNN), Support Vector Machine (SVM), Naive Bayes (NB), Random Tree Forest (RTF) grouping techniques are the strategies which are utilized in the implementation of this paper. Content order has an expanding significance since it permits programmed content association, and we compare the results SVM classification is a straightforward as well as exceptionally exact technique to classify the content and it take less time on the classification. [ABSTRACT FROM AUTHOR]

Rice (Oryza sativa) is a principal cereal crop in the world. It is consumed by greater than half of the world's population as a staple food for energy source. The yield production quantity and quality of the rice grain is affecting by abiotic and biotic factors such as precipitation, soil fertility, temperature, pests, bacteria, virus, etc. For disease management, farmers spending lot of time and resources and they detect the diseases through their penniless naked eye approach which leads to unhealthy farming. The advancement of technical support in agriculture greatly assists for automatic identification of infectious organisms in the rice plants leaves. The convolutional neural network algorithm (CNN) is one of the algorithms in deep learning has been triumphantly invoked for solving computer vision problems like image classification, object segmentation, image analysis, etc. In our work, InceptionResNetV2 is a type of CNN model utilized with transfer learning approach for recognizing diseases in rice leaf images. The parameters of the proposed model is optimized for the classification task and obtained a good accuracy of 95.67%., (Copyright © 2021 Elsevier Inc. All rights reserved.)