Applied Intelligent Decision Making in Machine Learning Computational Intelligence in Engineering Problem Solving Series

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 Data.

2. Decoding Common Machine Learning Methods: Agricultural Application Case Studies Using Open Source Software.

3. A Multi-Stage Hybrid Model for Odia Compound Character Recognition.

4. Development of Hybrid Computational Approaches for Landslide Susceptibility Mapping Using Remotely Sensed Data in East Sikkim, India.

5. Domain-Specific Journal Recommendation Using Feed Forward Neural Network.

6. Forecasting Air Quality in India through an Ensemble Clustering Technique.

7. Intelligence-Based Health Biomarker Identification System Using Microarray Analysis.

8. Extraction of Medical Entities Using Matrix-Based Pattern-Matching Method.

9. Supporting Environmental Decision Making: Application of Machine Learning Techniques to Australia’s Emissions.

10. Prediction Analysis of Exchange Rate Forecasting Using Deep Learning-Based Neural Network Models.

11. Optimal Selection of Features Using Deep Learning-Based Optimization Algorithm for Classification.

12. An Enhanced Image Dehazing Procedure Using CLAHE and Guided Filter.

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.