Improved Sports Image Classification Using Deep Neural Network And Novel Tuna Swarm Optimization

This article discusses the use of a deep neural network (DNN) and natural topographic self-organizing (NTSO) model for deep learning problems. The DNN utilizes a supervised loss function and meticulously labeled data samples to navigate the hidden layers’ learning process. The NTSO settings include a population size of 50 and a maximum of 100 iterations, with a mutation rate of 0.05 and scaling factors ranging from 0.8 to 1.2.