An efficient recommendation system for athletic performance optimization by enriched grey wolf optimization

Abstract

Recent research works have shown the robustness towards the recommendation system for athletics using an AI automated system that enhances the longevity of the system. With this model, the automated recommendation helps to improve the quality of athletes during the process of training or other processes. Moreover, domain experts only can understand the rationale of the recommender system where the analyzed data is stored in the cloud system. This research proposes a machine learning–based solution for an athletic dataset that automatically predicts the state of the individual with features like age, gender, calories, temperature, pressure, heart rate, pulse rate, sugar level, respiratory conditions, and state of the body. This research concentrates on modeling a framework for implementing the machine learning approaches with an optimization problem. Here, a novel extreme multi-gradient evolutionary computation (EMGEC) with improved grey wolf optimization (IGWO) is proposed to achieve exploration and exploitation during the selection of features. The dataset collected from the athletes during the marathon (running) is collected from online resources and the feature subsets are extracted from the dataset. The features of these data are analyzed and encoded before placing it over the cloud environment. The performance of the proposed machine learning approach is compared with other approaches and provides better prediction accuracy, precision, recall, and F-measure respectively. The accuracy of the anticipated model is 83.13%, precision is 91.1%, and recall is 91.3% which is substantially higher than of other approaches. The proposed model shows a better trade-off in contrast to prevailing approaches like SVM, RF, k-NN, and logistic regression.