Experimental Investigation of Machine Learning and Neural Networks Utilizing Deep Learning

Abstract

The advent of neural network technology may be ascribed to the ongoing advancement of neural network theory and its related ideas and technologies. This sector of intelligent control technology has acquired considerable significance in recent years. An artificial neural network (ANN) is a computer model characterized by nonlinearity and flexibility in information processing. The system has a substantial number of processing units. This project involves the construction of an intelligent system architecture using an adaptive fuzzy neural network (FNN). Furthermore, an activation function is used to include knowledge from the fields of computer science and linguistics. This graphic depicts the neural architecture of the network. The design of the machine learning model was developed using a recursive neural network via deep learning, which is the foundational framework. The implementation of feature vector extraction and normalization algorithms is essential to fulfill the specifications of the neural network model. The clustering method is used to create a varied array of learning styles using feature vectors obtained from different users' learning styles. The functional flow was developed via testing, therefore demonstrating the dependability of the deep learning model. The accurate acquisition of language abilities may stimulate certain areas of the brain, leading to improved deep learning effectiveness and heightened ability to learn more languages.