Academic Press Cognitive Big Data Intelligence with a Metaheuristic Approach (Cognitive Data Science in Sustainable Computing)

Product Description Cognitive Big Data Intelligence with a Metaheuristic Approach presents an exact and compact organization of content relating to the latest metaheuristics methodologies based on new challenging big data application domains and cognitive computing. The combined model of cognitive big data intelligence with metaheuristics methods can be used to analyze emerging patterns, spot business opportunities, and take care of critical process-centric issues in real-time. Various real-time case studies and implemented works are discussed in this book for better understanding and additional clarity. This book presents an essential platform for the use of cognitive technology in the field of Data Science. It covers metaheuristic methodologies that can be successful in a wide variety of problem settings in big data frameworks. Review Presents the latest metaheuristics methodologies related to new challenging big data application domains From the Back Cover <p><i>Cognitive Big Data Intelligence with Meta-Heuristic Approach</i> presents an exact and compact organization of contents related to the latest meta-heuristics methodologies based on new challenging big data application domains and cognitive computing with proper explanation wherever required. </p> <p>The combined model of cognitive big data intelligence with meta-heuristics methods can be used to analyze emerging patterns, spot business opportunities, and take care of critical process-centric issues in real-time. It can be further used to enhance customer interactions with the help of robotic process automation. It presents other technical aspects and alternatives to traditional tools, algorithms, and methodologies. Various real-time case studies and implemented works will be discussed in this book for better understanding and more clarity to readers.</p> <p>This book presents an essential platform for the use of cognitive technology in the field of Data Science. It covers meta-heuristic methodologies that can be successful in a wide variety of problem settings in big data frameworks.</p> About the Author Dr. Sushruta Mishra is working as an Assistant Professor in the School of Computer Engineering, KIIT University, Bhubaneswar, Odisha, India. He pursued his M.Tech from IIIT, Bhubaneswar in 2012 and has completed his Ph.D in Computer Science from KIIT University, Bhubaneswar, Odisha, India in 2017. Dr. Mishra has more than 7 years of teaching experience in various educational institutions. He has handled many subjects such as Computer networks, Data mining, Software engineering, Machine learning etc during his academic experience. His research interest includes Image processing, Machine Learning, Internet of Things and Cognitive computing. He has published several research articles in reputed scopus indexed International Journals, Edited books and Conferences. Dr. Hrudaya Kumar Tripathy is presently working as Associate Professor in School of Computer Engineering at KIIT University, Bhubaneswar, Odisha, India and Program Head of M.Tech(CSE) courses. He has received M.Tech degree in CSE from IIT Guwahati in 2006, and obtained Ph.D degree in Computer Science from Berhampur University, Berhampur, Odisha, India in Computer Science in 2010. Dr. Tripathy had been a visiting faculty in Asia Pacific University, Kuala Lumpur, Malaysia and University Utara Malaysia, Sintok, Malaysia. He is having 20 years of teaching experience with Post Doctorate from Malaysia. He has handled various subjects like Software engineering, Machine learning, Business intelligence etc. His research interest includes Neural Networks, Pattern Recognition, Software Engineering, Machine learning and Big Data. He has published several research papers in various journals and conferences. Besides, academic experience he had been actively involved in various administrative responsibilities in his previous job positions. Dr. Tripathy is a senior member of IEEE, li