Uncertainty, Computational Techniques and Decision Intelligence
Aim & scope
The Uncertainty, Computational Techniques and Decision Intelligence series focuses on modern advances and innovations in the fields of computational intelligence and decision sciences. The books in this series explain and present innovative theories and methods for decision making, improving, and learning from data in different domains. New methods and applications are presented in an accessible and stimulating style for readers from diverse backgrounds, including but not limited to computer science, mathematics, statistics, engineering, risk analysis, operations research, biology, economics, and management science.
The series targets both readers already established in the fields of computational intelligence and decision sciences, as well as those coming from adjacent fields where application of these techniques is needed. The Series Editors envision that all volumes will build up from introductory chapters and end by addressing the needs of very advanced readers in the field. This series will integrate applications and concepts encountered across computer science, engineering, biology, physics, and decision/social sciences.
Topics include:
Intelligent decision support systems; Uncertainty management in smart decision support systems; Uncertain auto-learning systems; Soft computing in decision making; Dynamical systems under uncertainty; Biological data science and uncertainty; Machine reasoning; Logical rule-based systems; Structural reliability and risk assessment.
Key features:
Introduces novel approaches, solutions, and state-of-the-art tools for decision-making related problems across a wide range of application fields such as engineering, healthcare, biology, environmental science, and management science
Investigates new techniques in the field of decision intelligence for handling, processing, and deciding on the use of multi-type large data sets
Offers researchers from a wide range of fields a unique opportunity to gain insights into the processes of exploiting and steering systems, as well as automating and optimizing their decision-making processes, where uncertainty is inherent
Covers recent breakthroughs in computational intelligence, mathematical foundations and techniques, and integration with learning and pattern recognition methods and their applications
New Volume Proposals:
Volumes can be Edited, Multi-Authored, or Authored Monographs
New volume proposals should:
Include a well-structured Table of Contents
Include a list of confirmed or tentative, geographically distributed, authors (for Edited volumes)
Indexing: All published volumes in this book series are submitted for indexing in:
Scopus
EI Indexing / Compendex
Book Citation Index
Google Scholar
Audience
The primary audience of the series includes upper-division undergraduate and postgraduate students, and researchers in the field of applied mathematics, computer science, and industrial engineering. Decision-makers, R&D managers, and especially those in IT-based companies, can also benefit from this book series.
Publish a book with Elsevier
Editors
TAP
Tofigh Allahviranloo, P.h.D
Professor
Faculty of Engineering and Natural Sciences, Istinye University
Read more about Tofigh Allahviranloo, P.h.D
NAKP
Narsis A. Kiani, P.h.D
Co-leader & Senior Researcher
Algorithmic Dynamics Lab, Department of Oncology-Pathology & Center of Molecular Medicine
Read more about Narsis A. Kiani, P.h.D
WPP
Witold Pedrycz, P.h.D
Professor & Canada Research Chair (CRC)
University of Alberta
Read more about Witold Pedrycz, P.h.D