Sport is the most important topic in education, which individually shows motor skills in health activities. Athletic training should focus graduates on sports, low-involvement task activities, and physical fitness or exercise. Teaching concepts and methodologies are innovative, coaching methods and procedures, evaluation of coaching sessions in sports-all this is accompanied by development in the sports hall and successful improvement of sports performance. Here, he provides extraordinary assistance to students by predicting early school leaving and improving the potential application of wireless platforms in sports programs and changing the nature of sports, including visualization and repetition, and incorporating it into sports training. A sports network learning platform based on the discrete similarity approach is proposed for design and implementation in this paper. First, data is collected from a real-time data set. After that, the data submitted for preprocessing removes noise and imperfect records, which can be removed using a global constant or a most likely learning method using an Adaptive Savitsky-Golay Filtering (ASGF) method. The preprocessing method is fed into the feature extraction utilizing Multi‐ Hypothesis Fuzzy‐Matching Radon Transform (MHFMRT). MHFMRT extract statistical features such us kurtosis, variance, energy, mean and standard deviation. Then, the Similarity-Based Convolutional Neural Network (SBCNN) method is optimized by the Chaotic Satin Bower Bird Optimization (CSBBO) algorithm, which effectively classifies the visualization and a satisfactory training platform of the sports network. The proposed system is executed in the MATrix LABoratory (MATLAB) platform and the performance of the proposed methods attains 28.5%, 27.9% and 29.5% higher accuracy, 28%, 21% and 26.5% higher precision, 21.2%, 28.75% and 21.3% higher recall compared with the existing methods like Recurrent Neural model to analyze the effect of Physical Training and Treatment in Relation to Sports Injuries (PTT-RSI-RNN), recognizing Sports Activities from Video Frames using Deformable Convolution and Adaptive Multiscale Features (SAVF-DCN) and Sports Match Prediction model for training and Exercise using attention-based LSTM network (SMPE-LSTM) respectively.

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