Description
Title: SET UP MACHINE LEARNING METHODS TO PROJECT AYURVEDIC CONSTITUENT BALANCING IN THE HUMAN BODY
Abstract: Here, we show how various machine-learning techniques, including support vector machines (SVM), naive Bayes (NB), decision trees (DT), k-nearest neighbor (KNN), artificial neural networks (ANN), and AdaBoost algorithms, perform in predicting the composition of the human body. Although ayurveda-dosha studies have been used for a while, these diagnostic techniques still lack a quantitative reliability measurement. A thorough and pertinent analysis produces a treatment that is successful in predicting the composition of the human body. It can be seen from the results that the AdaBoost algorithm with hyperparameter tuning offers improved precision and F-score (0.96), recall (0.97), and RSME values (0.64). The experimental results show that the enhanced model, which is based on ensemble-learning techniques, performs noticeably better than conventional approaches. The results suggest that improvements in the suggested algorithms could pave the way for a bright future for machine learning.
Keywords: machine learning, artificial neural networks, diagnose, Ayurveda constituent, support vector machine
Paper Quality: SCOPUS / Web of Science Level Research Paper
Paper type: Analysis Based Research Paper
Subject: Computer Science
Writer Experience: 20+ Years
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