Research on Information Processing System of Sports Combination Training Model Based on Machine Learning and Neural Network

Information processing systems in sports and training applications are backboned by artificial intelligence for non-human intervening and accurate analysis. The fitness, performance, etc. outcomes are delivered by the system through learning implications over the different inputs. However, the recommendation/ prediction outcomes are down-surged in analyzing similar information due to learning complexity and non-adaptable outcome. Therefore, the problem is resolved by fragmenting and processing the information using a similarity measure. Therefore, this method is named as Sliced-Information Processing with Analogous Learning (SIP-AL). In this method, a neural network is used for deciding the processing feature for better accuracy. In the contrary case of down-surges, the information slicing based on an analogous point is performed. This prevents the continuity between redundant and continuous data preventing errors.