Overview
- Discusses major issues pertaining to big data analysis using computational intelligence techniques
- Focuses on women in research and education
- Provides a way to create an intensive educational experience
Part of the book series: Lecture Notes on Data Engineering and Communications Technologies (LNDECT, volume 16)
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About this book
This book presents conjectural advances in big data analysis, machine learning and computational intelligence, as well as their potential applications in scientific computing. It discusses major issues pertaining to big data analysis using computational intelligence techniques, and the conjectural elements are supported by simulation and modelling applications to help address real-world problems. An extensive bibliography is provided at the end of each chapter. Further, the main content is supplemented by a wealth of figures, graphs, and tables, offering a valuable guide for researchers in the field of big data analytics and computational intelligence.
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Keywords
Table of contents (35 papers)
Editors and Affiliations
About the editors
Dr. Durgesh Kumar Mishra  is a Professor (CSE) and Director of the Microsoft Innovation Centre at Sri Aurobindo Institute of Technology, Indore, India and visiting faculty at IIT-Indore. He has 24 years of teaching and 12 years of research experience. He has published more than 90 papers in refereed international/national journals and conferences including IEEE, ACM conferences and organized many conferences as General Chair and Editor. He is a Senior Member of the IEEE, CSI, ACM, Chairman IEEE MP Subsection, IEEE Computer Society Bombay Chapter. At present he is Chairman of CSI Division IV Communication at the National Level and ACM Chapter Rajasthan and MP State.
Prof.  Xin-She Yang is an Associate Professor of Simulation Modelling at Middlesex University, London. Prof. Yang’s main interests are applied mathematics, algorithm development, computational intelligence, engineering optimisation, mathematical modelling, optimisation and swarm intelligence. His research projects have been supported by the National Measurement Office, BIS, Southwest Development Agency (UK), Euro Met, EPSRC, NPL, and the National Science Foundation of China. He is EEE CIS Task Force Chair of the BIKM, Technical Committee of Computational Finance and Economics of IEEE Computational Intelligence Society; Advisor to the International Journal of Bio-Inspired Computation; Editorial Board Member of Elsevier’s Journal of Computational Science; and Editor-in-Chief of the International Journal of Mathematical Modelling and Numerical Optimisation.
Dr. Aynur Unal is a Strategic Adviser & Visiting Full Professor at the IIT Guwahati, India. She has created a product-focused engineering program using the cloud-based infrastructure. Her maininterests include Ecologically and socially responsible engineering, Zero waste Initiative and Sustainable Green Engineering. Her research focuses on both rural and urban sustainable development, renewable energy, solar towers and pumps. She has taught at Stanford University, and worked in Silicon Valley to develop products for data mining from big data (Triada’s Athena I & II), Collaborative Design and Manufacturing, secure and private communication, and collaboration software platforms (Amteus, listed in LSE AIM)
Bibliographic Information
Book Title: Data Science and Big Data Analytics
Book Subtitle: ACM-WIR 2018
Editors: Durgesh Kumar Mishra, Xin-She Yang, Aynur Unal
Series Title: Lecture Notes on Data Engineering and Communications Technologies
DOI: https://doi.org/10.1007/978-981-10-7641-1
Publisher: Springer Singapore
eBook Packages: Intelligent Technologies and Robotics, Intelligent Technologies and Robotics (R0)
Copyright Information: Springer Nature Singapore Pte Ltd. 2019
Softcover ISBN: 978-981-10-7640-4Published: 02 August 2018
eBook ISBN: 978-981-10-7641-1Published: 01 August 2018
Series ISSN: 2367-4512
Series E-ISSN: 2367-4520
Edition Number: 1
Number of Pages: XXIV, 406
Number of Illustrations: 57 b/w illustrations, 95 illustrations in colour
Topics: Computational Intelligence, Big Data, Data Mining and Knowledge Discovery, Systems and Data Security