An Advanced Feature Extraction Approach for Classifying and Categorizing Complex Data by Using the MMRDL Framework

Nowadays, Machine learning (ML) plays a significant role in processing multimedia based on user search queries. To develop an efficient classification model that processes the high dimensional data, an ensemble model combined with deep feature extraction and robust classifier. Combining the three algorithms generate the stochastic belief space in this work. The planning of these algorithms based on maximum entropy reinforcement learning (ME-RL), and generative adversary model (GAM). The comparison between various unmodeled adversarial methods is suggested to achieve robustness because the malicious behaviors were applied in the simulation framework to reduce the agent's action predictability. Guided Deep Learning (DL), the reinforcement-based approach, can be employed for multi-model categorization. The text and image data applied on a single neural network method may categorize. The proposed system properly understands the belief space policy using feature extraction on text and image datasets. The proposed model shows the maximum entropy depends on the input dataset.