Reduce Latency and Transmission Overhead for Massive Big Data using Convolutional Neural Networks

Abstract

In the field of big data analytics, deep learning has garnered a lot of interest due to the fact that it is capable of doing feature extraction and classification. The categorization of big data has traditionally been carried out by academics via the use of Machine Learning methods; nonetheless, the process of feature extraction has traditionally been carried out manually. Deep learning is a method developed by researchers that allows for the extraction of features via the use of various algorithms. This research made use of an algorithm known as "Convolutional Neural Network," or CNN for short, in order to get a deeper comprehension of the dependability of big data analytics. Primary research has been conducted in order to get an understanding of how hidden nodes and layers influence the accuracy of this neural network. The "Recurrent Neural Network" (also known as RNN) and the "Artificial Neural Network" (also known as ANN) are the two other neural network algorithms that have been selected for comparison with CNN in order to evaluate whether or not CNN is more effective than the other algorithms. In the first research, the reliability of CNN as well as other methods was analyzed in relation to the number of nodes, the number of hidden layers, the amount of training time, and the amount of validating time. Control variables were employed in both correlation and regression investigations. These variables included Hidden Nodes, Training time, Valid time, and Secret Layers. CNN, ANN, and RNN were significant variables. According to the findings, CNN is more dependable than other neural networks for analyzing huge amounts of data, scoring 92%, whereas other neural networks scored less than 90%. The inclusion of hidden nodes results in a significant increase in CNN's reliability.