Enhanced multidimensional neo-fuzzy classification system and its learning for the video classification task

This research introduces an innovative hybrid neo-fuzzy system for video classification, integrating multidimensional neo-fuzzy components with adjustable synaptic weights and Gaussian membership functions. By combining extended neo-fuzzy neurons (ENFN) and neo-fuzzy units (NFU) with nonlinear activation functions and incorporating extended nonlinear synapses (ENS), the system leverages the neuro-fuzzy Takagi-Sugeno-Kang inference system to enhance traditional models' approximating capabilities. Video classification is complex due to high data volume and variability, especially with moving objects and varying video quality. Traditional models struggle with real-time processing and maintaining accuracy, necessitating advanced techniques for robust performance. The goal is to develop and optimize a hybrid neo-fuzzy system for real-time video stream classification, maintaining high accuracy. Computational experiments demonstrated its robustness, achieving high precision and recall. The proposed optimization algorithm, using cross-entropy learning with one-hot encoding and adaptive δ-rule adjustments, improved learning speed and accuracy. The novelty lies in developing a hybrid neo-fuzzy system with advanced components and a unique optimization algorithm, ensuring robustness and efficiency in complex video classification tasks.