With rapid evolution and development of the internet-based services and applications, Big Data storage, processing and its analytics is getting huge attention from researchers, industries, and academic communities. Big Data refers to a collection of huge datasets that are very complex in structure and size and cannot be handle with help of traditional data management tools. Nowadays, application of computational intelligence (CI) techniques in big data analytics are very emerging research area among the software industry data engineering researchers and academics community. Recent advancement in Computational Intelligent (CI) techniques by converging the mathematical modelling techniques with data engineering and optimization techniques, motivates the big data engineers and researchers to apply in various engineering domain related to big data. Advanced computational intelligence techniques are being designed and developed in recent years to cope with the various big data challenges to provide fast and efficient analytics which helps in making of the critical decision. Big data processing and Analytics have found various applications in different domain such as healthcare, intelligent transportation system, smart cities, smart grid, smart environment.

Application of the advance Computational Intelligence Techniques such as Evolutionary Algorithms, Swarm Intelligence, Machine Learning, Deep Learning, Reinforcement Learning, and Federated Learning Algorithms in various Big Data problems related to IoT, Image Processing, Information Security, Cyber Security, Data Mining, Health Informatics, Natural Language Processing, Intelligent Transportation Systems, High-performance Computing, and communication networks. Following are the topics of interest but are not limited to:
• Application of Computational Intelligence techniques in big data problem related to image processing and computer vision.
• Application of Computational Intelligence techniques in big data analytics related to cyber security and cyber forensics issues.
• Applying machine and deep learning tools and techniques for malware detection and analysis
* Intelligent analysis of different types of data collected from IoT and Smart Grid and Smart Environment.
• Intelligent analysis of different types of data collected from health-related sensors data, Environmental sensor datasets and different layers of network security solutions
• Generate intelligence and its application in big data
• Automated and intelligent methods for generation of adversary groups profile and smart transportation.