Computational Intelligence in Analytics and Information Systems

This new two-volume addresses a number of state-of-the-art developments and unsolved open issues in the field of computational intelligence.

Volume 1 focuses on data science and artificial intelligence. It highlights the use of predictive analytics of data from various application domains to find timely solutions to various problems. The book focuses on the research developments, limitations, and management of real-time problems using computational intelligence by identifying applicable approaches in order to enhance, automate, and develop effective solutions. The volume introduces empirical research, prospects of theoretical research, and applications in data science and artificial intelligence. The chapters present diverse application of computer intelligence and machine learning in security, healthcare and medicine, agriculture, traffic science/smart cities, and language and text conversion.

Volume 2 demonstrates empirical, theoretical, and application perspectives on smart technologies, computational intelligence in network technologies, and computational intelligence in software engineering, identifying the advantages and limitations of each. The chapters on smart technologies address their application in such areas as communication services, healthcare and assistive technology, urban waste management, vehicle pollution and accident detection, and more. The technologies encompass the use of machine learning, blockchain, fog computing, satellite communication technology, ant colony optimization algorithms, etc.

Together, these two volumes offer new research on presented by young and dynamic researchers and experts from industry and academia, sharing a plethora of new and emerging technologies that support their effective operations in computational intelligence in information systems.

These peer-review chapters were selected from the International Conference on Computational Intelligence in Analytics and Information Systems’ (CIAIS- 2021), held in April 2021 at Manav Rachna University, India.

Parneeta Dhaliwal, PhD, has over 16 years of experience in teaching and research. Presently, she is working as Associate Professor in the Department of Computer Science and Technology, Manav Rachna University, India. She is also working as Head of the Research Cluster of Computing (RCC) to facilitate students in their research and innovative projects.

Manpreet Kaur, PhD, is working as an Associate Professor in the Department of Computer Science and Technology, Manav Rachna University, India. She has more than 14 years of teaching and research experience. She is currently working in the domains of machine learning, deep learning, and natural language processing. She is a Senior Member, IEEE (USA).

Hardeo Kumar Thakur, PhD, is working as an Associate Professor in the Department of Computer Science and Technology of Manav Rachna University (MRU), Faridabad, India. He has more than 10 years of teaching and research experience in leading institutions of India. He earned his PhD (Computer Engineering) from the University of Delhi in 2017 in the field of data mining. His current research interests are data mining, dynamic graph mining, machine learning and big data analytics.

Rajeev Kumar Arya, PhD, is currently an Assistant Professor with the Department of Electronics and Communication Engineering at National Institute of Technology, Patna, India. His current research interests are in wireless communication, soft computing techniques, cognitive radio, signal processing, communication systems, and circuits design

Joan Lu, PhD, is a Professor in the Department of Computer Science and the Research Group Leader of Information and System Engineering (ISE) in the Centre of High Intelligent Computing (CHIC) at the University of Huddersfield, United Kingdom, having previously been team leader in the IT Department of the publishing company Charlesworth Group.