Название: Applied Intelligent Decision Making in Machine LearningАвтор: Himansu Das, Jitendra Kumar Rout, Suresh Chandra Moharana, Nilanjan Dey
Издательство: CRC Press
Год: 2021
Страниц: 263
Язык: английский
Формат: pdf (true)
Размер: 35.1 MB
Decision making is the process of selection of best choice made logically from the accessible options. Decision making analyzes the large volume of data in a particular field and is a very difficult task to do manually. A technique must be used to find out the possible outcome of each option available. It also determines the option that is best at a particular moment for an effective decision-making process. Machine Learning (ML) uses a set of mathematical models and algorithms for the construction of a decision-making system that gradually improves its performance on specific tasks. It is also based on a model that develops intelligent, adaptive, and hybrid tools and methods for solving complex decision-making problems. Artificial intelligence has already been recognized as a standard tool for the decision-making process. Despite several such developments, many findings still need to be addressed due to the advancement of technologies such as artificial intelligence, machine learning, and information technology.
The computer system should analyze dynamically each option as well as alternatives to make an appropriate intelligent decision. This book will also specifically focus on applied intelligent decision-making fields that perform various tasks effectively for predictive analysis, classification, clustering, feature selection, feature reduction, recommendations, information retrieval, and pattern recognition in a very limited amount of time with high performance.
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.
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