Enhanced Prediction of Swimmer Fitness Using Modified Resilient PSO Algorithm

Sports develop both physical and mental growth of an individual. In order to enhance the physical and mental health effectively and effortlessly, swimming is considered to be one of the healthier activities, in developing the metabolism via flexibility, weight loss, reducing asthma and enhancing the fitness levels of the body. In most of the cases, existing studies provide the current status of the swimmer by delivering details like time taken by the swimmers to finish the lap or location of the swimmer, however they lack in identifying the calorie level reduction of the swimmer during the swimming, as, the detection of calories burnt by the swimmer helps in overcoming issues like loss of weight and other health risk rates. Therefore, proposed study intensifies in achieving the prediction of the fitness of a swimmer as the primitive activity in view of reducing the calories by using PSO algorithm using resilient based techniques for enhanced exploration ability and using modified resilient PSO in order to tune the hyper parameter of the random forest for better optimization outcome. Modified resilient PSO helps in preventing the over-fitting of the model and delivering enhanced global solutions. Besides, hyper parameter are tuned in the Random Forest (RF), Decision Tree (DT), AdaBoost and Support Vector Machine (SVM) regression model for an error free outcome and with satisfactory performance analytical values. Finally, the performance of the proposed model is assessed using performance metrics such as R-square, RMSE, MSE and MAE, unveiling the effectiveness of the projected approach MPSO-XG-Boost. The projected model has achieved effectual outcomes encompassing less error rate based outcomes in ranges of 0.037, 0.191, 0.059, 0.9. Further, the proposed is compared with the existing models in order to determine the efficiency of the proposed framework.