Applied Intelligent Decision Making in Machine Learning

The objective of this edited book is to share the outcomes from various research domains to develop efficient, adaptive, and intelligent models to handle the challenges related to decision making. It incorporates the advances in machine intelligent techniques such as data streaming, classification, clustering, pattern matching, feature selection, and deep learning in the decision-making process for several diversified applications such as agriculture, character recognition, landslide susceptibility, recommendation systems, forecasting air quality, healthcare, exchange rate prediction, and image dehazing. It also provides a premier interdisciplinary platform for scientists, researchers, practitioners, and educators to share their thoughts in the context of recent innovations, trends, developments, practical challenges, and advancements in the field of data mining, machine learning, soft computing, and decision science. It also focuses on the usefulness of applied intelligent techniques in the decision-making process in several aspects. To address these objectives, this edited book includes a dozen chapters contributed by authors from around the globe. The authors attempt to solve these complex problems using several intelligent machine-learning techniques. This allows researchers to understand the mechanism needed to harness the decision-making process using machine-learning techniques for their own respective endeavors.

1. Data Stream Mining for Big DataChandresh Kumar Maurya2. Decoding Common Machine Learning Methods —Agricultural Application Case Studies using Open Source SoftwareSrinivasagan N. Subhashree, S. Sunoj, Oveis Hassanijalilian, C. Igathinathane*3. A multi-stage hybrid model for Odia compound character recognitionDibyasundar Das, Deepak Ranjan Nayak*, Ratnakar Dash, Banshidhar Majhi4. Development of Hybrid Computational Approaches for Landslide Susceptibility Mapping using Remotely Sensed Data in East Sikkim, IndiaIndrajit Chowdhuri, Paramita Roy, Rabin Chakrabortty, Subodh Chandra Pal*, Biswajit Das, Sadhan Malik 5. Domain-Specific Journal Recommendation using Feed Forward Neural NetworkNickolas S, Shobha K6. Forecasting Air Quality in India through Ensemble Clustering TechniqueJ Anuradha, S Vandhana and Sasya I Reddi7. Intelligence based health biomarker identification system using microarray analysisBibhuprasad Sahu1, Amrutanshu Panigrahi2, Chinmayee Rout3, J. Chandrakant Badjena* 8. Medical Entities Extraction using Matrix based Pattern Matching MethodRuchi Patel, Sanjay Tanwani9. Supporting Environmental Decision-making: Application of Machine learning techniques to Australia’s emissionsAlex O. Acheampong, Emmanuel B. Boateng 10. Comparative Study of Classification Algorithms on Imbalanced DatasetsChhaya Patidar, Ruchi Patel, Rohit Patel11. Prediction Analysis of Exchange Rate Forecasting using Deep Learning based Neural Network ModelsDhiraj Bhattarai, Ajay Kumar Jena and Minakhi Rout*12. Optimal Selection of Features based on Teaching Learning based Optimization Algorithm for ClassificationHimansu Das, Soham Chakraborty, Biswaranjan Acharya, Abhaya Kumar Sahoo 13. Enhanced Image Dehazing Procedure using CLAHE and Guided FilterBadal Soni, Ujwala Baruah

Himansu Das is working as an Assistant Professor in the School of Computer Engineering, Kalinga Institute of Industrial Technology (KIIT) Deemed to be University, Bhubaneswar, India. Jitendra Kumar Rout is an Assistant Professor in School of Computer Engineering, KIIT Deemed to be University, Bhubaneswar, India. Suresh Chandra Moharana is an Assistant Professor in School of Computer Engineering at KIIT Deemed to be University. Nilanjan Dey is an Assistant Professor in Department of Information Technology at Techno India College of Technology (under Techno India Group), Kolkata, India.