Encyclopedia of Data Science and Machine Learning (3 Volumes)
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Encyclopedia of Data Science and Machine Learning (3 Volumes)

John Wang (Montclair State University, USA)
Projected Release Date: June, 2022|Copyright: © 2022 |Pages: 2500
DOI: 10.4018/978-1-7998-9220-5
ISBN13: 9781799892205|ISBN10: 1799892204|EISBN13: 9781799892212
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Description & Coverage
Description:

Big data and machine learning are driving the Fourth Industrial Revolution. With the age of big data upon us, we risk drowning in a flood of digital data. Big data has now become a critical part of both the business world and daily life, as the synthesis and synergy of machine learning and big data has enormous potential. Big data and machine learning are projected to not only maximize citizen wealth, but also promote societal health. As big data continues to evolve and the demand for professionals in the field increases, access to the most current information about the concepts, issues, trends, and technologies in this interdisciplinary area is needed.

The Encyclopedia of Data Science and Machine Learning examines current, state-of-the-art research in the areas of data science, machine learning, data mining, and more. It provides an international forum for experts within these fields to advance the knowledge and practice in all facets of big data and machine learning, emphasizing emerging theories, principals, models, processes, and applications to inspire and circulate innovative findings into research, business, and communities. Covering topics such as benefit management, recommendation system analysis, and global software development, this expansive reference provides a dynamic resource for data scientists, data analysts, computer scientists, technical managers, corporate executives, students and educators of higher education, government officials, researchers, and academicians.

Coverage:

The many academic areas covered in this publication include, but are not limited to:

  • Agglomerative Clustering
  • Benefit Management
  • Cancer Detection
  • Change Management Science Innovation
  • Global Software Development
  • Industry 4.0
  • Knowledge Representation
  • Machine-First Incident Management
  • Maintenance Prediction
  • Pharmaceutical Manufacturing
  • Recommendation System Analysis
  • Statistical Model Selection
Table of Contents
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Editor Biographies
John Wang is a professor in the Department of Information & Operations Management at Montclair State University, USA. Having received a scholarship award, he came to the USA and completed his PhD in operations research from Temple University. Due to his extraordinary contributions beyond a tenured full professor, Dr. Wang has been honored with a special range adjustment in 2006. He has published over 100 refereed papers and seven books. He has also developed several computer software programs based on his research findings.

He is the Editor-in-Chief of International Journal of Applied Management Science, International Journal of Operations Research and Information Systems, and International Journal of Information Systems and Supply Chain Management. He is the Editor of Data Warehousing and Mining: Concepts, Methodologies, Tools, and Applications (six-volume) and the Editor of the Encyclopedia of Data Warehousing and Mining, 1st (two-volume) and 2nd (four-volume). His long-term research goal is on the synergy of operations research, data mining and cybernetics.
Editorial Review Board
Xueqi Cheng, Chinese Academy of Science, China Verena Kantere, University of Ottawa, Canada Hongming Wang, Harvard University, USA Yanchang Zhao, Commonwealth Scientific and Industrial Research Organisation (CSIRO), Australia
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