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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.

Sample covers of Uncertainty, Computational Techniques and Decision Intelligence

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


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.


Image of Tofigh Allahviranloo


Tofigh Allahviranloo, P.h.D


Faculty of Engineering and Natural Sciences, Istinye University

Read more about Tofigh Allahviranloo, P.h.D
Image of Narsis Kani


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
Image of Witold Pedrycz


Witold Pedrycz, P.h.D

Professor & Canada Research Chair (CRC)

University of Alberta

Read more about Witold Pedrycz, P.h.D