A Survey of the Computational Intelligence Techniques for Big Data

Abstract

The term "Big Data" refers to the unprecedented growth in volume, diversity, and velocity of today's data. Despite the complexity and dynamic nature of modern data, handling massive amounts of data from various sources efficiently is a difficult undertaking. The present data will not be handled by traditional ways. Computational intelligence is used because a methodical strategy to handling current data must adapt to and learn from the changing environment. It is an area of artificial intelligence (AI) has applications in the development of intelligent computer systems. Issues and challenges across a variety of AI applications, including Natural Language Processing, Image and Video Processing, Robotics and Automation, Decision Support Systems, Bioinformatics, and Medicine, can be addressed with the help of CI approaches and techniques. Predictive analytics, real-time processing, pattern recognition, and scalability are benefits of CI. The most common technologies are Evolutionary Algorithms, Neural Networks, Fuzzy Systems, Swarm Intelligence, Expert Systems, Machine Learning, Probabilistic Reasoning and Hybrid Systems. The main methods of computational intelligence are explored in this study, along with the real-time Big Data Mining uses for them.