Comprehensive Analysis of Classification Techniques Based on Artificial Immune System and Artificial Neural Network Algorithms

Abstract

Knowledge mining from large voluminous data, i.e., big data is a difficult and important task nowadays; new techniques are required to be implemented in the field of data sciences, which helps in decision making and implemented in computer science research and development such as artificial intelligence, database, statistics, visualization, and high-performance parallel computing. An artificial immune system includes various algorithms inspired by the biological immune system. These algorithms support machine learning and very useful in solving complex problems such as intrusion detection, anomaly detection, and prevention, data clustering, classification, and exploration. The proposed method provides the comparative study of various supervised learning algorithms of artificial immune recognition system and artificial neural network for classification.