Applied Intelligent Decision Making in Machine Learning

Book Description 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.

Content

Chapter 1. Data Stream Mining for Big Data
Chapter 2. Decoding Common Machine Learning Methods: Agricultural Application Case Studies Using Open Source Software
Chapter 3. A Multi-Stage Hybrid Model for Odia Compound Character Recognition
Chapter 4. Development of Hybrid Computational Approaches for Landslide Susceptibility Mapping Using Remotely Sensed Data in East Sikkim, India
Chapter 5. Domain-Specific Journal Recommendation Using a Feed Forward Neural Network
Chapter 6. Forecasting Air Quality in India through an Ensemble Clustering Technique
Chapter 7. An Intelligence-Based Health Biomarker Identification System Using Microarray Analysis 
Chapter 8. Extraction of Medical Entities Using a Matrix-Based Pattern-Matching Method
Chapter 9. Supporting Environmental Decision Making: Application of Machine Learning Techniques to Australia’s Emissions
Chapter 10. Prediction Analysis of Exchange Rate Forecasting Using Deep Learning-Based Neural Network Models
Chapter 11. Optimal Selection of Features Using Teaching-Learning-Based Optimization Algorithm for Classification
Chapter 12. An Enhanced Image Dehazing Procedure Using CLAHE and a Guided Filter