Description
Analytical tools and algorithms are essential in business data and information systems. Efficient economic and financial forecasting in machine learning techniques increases gains while reducing risks. Providing research on predictive models with high accuracy, stability, and ease of interpretation is important in improving data preparation, analysis, and implementation processes in business organizations.
Machine Learning Techniques for Improved Business Analytics is a collection of innovative research on the methods and applications of artificial intelligence in strategic business decisions and management. Featuring coverage on a broad range of topics such as data mining, portfolio optimization, and social network analysis, this book is ideally designed for business managers and practitioners, upper-level business students, and researchers seeking current research on large-scale information control and evaluation technologies that exceed the functionality of conventional data processing techniques.
Author's/Editor's Biography
Dileep G. (Ed.)
Dileep Kumar G was born in 1982, in India. He has got B.Tech in Computer Science & Engineering and M.Tech in Software Engineering degrees from Jawaharlal Nehru Technology University, Hyderabad, India, in 2000 and in 2009. He is pursuing a PhD degree in Computer Science from Jawaharlal Nehru Technology University, Hyderabad, India. Currently he is an Assistant Professor in the Department of Computing, Adama Science and Technology University, Adama, Ethiopia. He has authored more than 20 publications in international journals, books chapters, and refereed international conference proceedings. He has also authored the book
Data structures Through C++(ISBN: 978-81-8487-488-4, Narosa Publications,2015). His research interests include Network Security, Data Mining, Mobile Adhoc Networks, Grid Computing and Big Data. He is a Member of ACM and Life Member of ISTE and Senior Member of IEEE.