Machine Learning for Intelligent Decision Science

by Jitendra Kumar Rout (Editor), Minakhi Rout (Editor), Himansu Das (Editor)

Overview

The book discusses machine learning-based decision-making models, and presents intelligent, hybrid and adaptive methods and tools for solving complex learning and decision-making problems under conditions of uncertainty. Featuring contributions from data scientists, practitioners and educators, the book covers a range of topics relating to intelligent systems for decision science, and examines recent innovations, trends, and practical challenges in the field. The book is a valuable resource for academics, students, researchers and professionals wanting to gain insights into decision-making.

Product Details

ISBN-13:	9789811536915
Publisher:	Springer Nature Singapore
Publication date:	04/03/2020
Series:	Algorithms for Intelligent Systems
Edition description:	1st ed. 2020
Pages:	209
Product dimensions:	6.10(w) x 9.25(h) x (d)

About the Author

Jitendra Kumar Rout is an Assistant Professor at the School of Computer Engineering, KIIT Deemed to be University, Bhubaneswar, India. He completed his Masters and PhD at the National Institute of Technology, Rourkela, India, in 2013 and 2017 respectively, and was a lecturer at various engineering colleges, such as GITA and TITE Bhubaneswar. He is a life member of Odisha IT Society (OITS) and has been actively involved in conferences like ICIT (one of the oldest conferences in Odisha). He is also a life member of IEI, and a member of IEEE, ACM, IAENG, and UACEE. His main research interests include data analytics, machine learning, NLP, privacy in social networks and big data, and he has published his work with IEEE and Springer.

Minakhi Rout is currently an Assistant Professor at the School of Computer Engineering, KIIT Deemed to be University. She received her M.tech and Ph.D. degrees in Computer Science & Engineering from Siksha ‘O’ Anusandhan University, Odisha, India, in 2009 and 2015, respectively. She has more than 13 years of teaching and research experience at various respected institutes, and her interests include computational finance, data mining and machine learning. She has published more than 25 research papers in various respected journals and at international conferences. She is editor for the Turkish Journal of Forecasting.

Himansu Das is an Assistant Professor at the School of Computer Engineering, Kalinga Institute of Industrial Technology (KIIT) Deemed to be University, Bhubaneswar, India. He holds a B. Tech degree from the Institute of Technical Education and Research, India and an M. Tech degree in Computer Science and Engineering from the National Institute of Science and Technology, India. He has published several research papers in various international journals and at conferences. He has also edited several books for leading international publishers like IGI Global, Springer and Elsevier. He serves as a member of the editorial, review or advisory boards of various journals and conferences. Further, he has served as organizing chair, publicity chair and member of the technical program committees of several national and international conferences. He is also associated with various educational and research societies like IET, IACSIT, ISTE, UACEE, CSI, IAENG, and ISCA. He has more than 10 years of teaching and research experience, and his interests include data mining, soft computing and machine learning.

Table of Contents

Development of Different Machine Learning Ensemble Classifier for Gully Erosion Susceptibility in Gandheswari Watershed of West Bengal, India.- Classification of ECG Heartbeat using Deep Convolutional Neural Network.- Breast Cancer Identification and Diagnosis Techniques.- Energy Efficient Resource Allocation in Data Centers using a Hybrid Evolutionary Algorithm.- Root Cause Analysis using Ensemble Model for Intelligent Decision-Making.- Spider Monkey Optimization Algorithm in Data Science: A Quantifiable Objective Study.- Multi-Agent Based Systems In Machine Learning and Its Practical Case Studies.- Computer Vision and Machine Learning Approach for Malaria Diagnosis in Thin Blood Smears from Microscopic Blood Images.