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This book highlights major issues related to big data analysis using computational intelligence techniques, mostly interdisciplinary in nature. It comprises chapters on computational intelligence technologies, such as neural networks and learning algorithms, evolutionary computation, fuzzy systems and other emerging techniques in data science and big data, ranging from methodologies, theory and algorithms for handling big data, to their applications in bioinformatics and related disciplines. The book describes the latest solutions, scientific results and methods in solving intriguing problems in the fields of big data analytics, intelligent agents and computational intelligence. It reflects the state of the art research in the field and novel applications of new processing techniques in computer science. This book is useful to both doctoral students and researchers from computer science and engineering fields and bioinformatics related domains.

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Nanoinformatics: Predicting toxicity using Computational Modelling.- Stock market Prediction Based on Machine Learning Approaches.- Performance Analysis of Denoising of ECG Signals in Time and Frequency domain.- Design and Implementation of Modified Sparse K-means Clustering Method for Gene Selection.- Identifying driver potential in passenger genes using chemical properties of mutated and surrounding amino acids.- Diagnosis of Chest Diseases Using Artificial Neural networks.- Development of Microminiaturized Intramuscular EMG Writer`s Cramp Signals Micro Electrode Recording System.- FGANN: A Hybrid Approach for Medical Diagnosing.- Data Mining Efficiency and Scalability for Smarter Internet of Things.- Multiple DG Placement and Sizing in Radial Distribution System using Genetic Algorithm and Particle Swarm Optimization.- Neighborhood Algorithm For Product Recommendation.- A Quantitative Analysis of Histogram Equalization based methods on Fundus Images for Diabetic Retinopathy Detection.- A novel level based DNA security algorithm using DNA codons.- A Computational approach to Predict Diabetic Retinopathy through Data Analytics.

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Dr. Ch. Satyanarayana is currently working as Professor and Director Academic and Planning in the Department of Computer Science and Engineering, Jawaharlal Nehru Technological University Kakinada, AP, INDIA. He received his PhD in 2007 from JNTU. He has 18 years of teaching experience. He has published 100+ research papers in reputed International Journals. Under his supervision 18 doctoral students awarded PhD. He is a Senior Member of IEEE, and his research interests include Image Processing, Speech Recognition, Pattern Recognition, Network Security and Big data analytics and Computational Intelligence.

Dr. Kunjam Nageswara Rao is currently an Associate Professor at the Department of Computer Science & Systems Engineering at Andhra University College of Engineering, Andhra University, Visakhapatnam, AP, India, and he obtained his Ph.D. in Computer Science & Systems Engineering from the same university. His research interests include cloud computing, wireless networks, sensor networks, Internet of Things (IoT), medical image processing, network security, data mining, bio-informatics, algorithms & data analytics.
Dr. Richard Bush is the Dean, College of Information Technology at Baker College of Michigan. He was the Executive Director, eLearning Services at Lawrence Technological University and Director, Learning Resources and Technology at Eastern Michigan University. He has over 35 years´ experience in the information technology industry including in government, academic, military, manufacturing, and retail positions. Dr. Bush earned his Ph.D. in Applied Management and Decision Sciences specializing in Information Systems Management from Walden University. His research interests are focused on the acceptance and use of emerging technologies by end users, and the trends and impact of the connected world on individuals and groups.

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