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 Hoodie 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 30" Warming Drawer (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.