Overview
- Explains how construct and use rough fuzzy digraphs
- Describes applications to different sets of data and complex problems
- Describes relevant extensions, such as soft rough neutrosophic graphs
Part of the book series: Studies in Fuzziness and Soft Computing (STUDFUZZ, volume 380)
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About this book
This book describes a set of hybrid fuzzy models showing how to use them to deal with incomplete and/or vague information in different kind of decision-making problems. Based on the authors’ research, it offers a concise introduction to important models, ranging from rough fuzzy digraphs and intuitionistic fuzzy rough models to bipolar fuzzy soft graphs and neutrosophic graphs, explaining how to construct them. For each method, applications to different multi-attribute, multi-criteria decision-making problems, are presented and discussed. The book, which addresses computer scientists, mathematicians, and social scientists, is intended as concise yet complete guide to basic tools for constructing hybrid intelligent models for dealing with some interesting real-world problems. It is also expected to stimulate readers’ creativity thus offering a source of inspiration for future research.
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Keywords
- Upper Approximate Fuzzy Digraphs
- Underlying Crisp Digraph
- Isomorphic Rough Fuzzy Digraphs
- Irregular Rough Fuzzy Digraphs
- Regular Fuzzy Soft Graphs
- Irregular Fuzzy Soft Graphs
- Strongest Directed Path
- Connected Fuzzy Subgraphs
- Fuzzy Directed Bridge
- Rough Fuzzy Directed Bridge
- Rough Fuzzy Directed Block
- Neutral Rough Fuzzy Digraphs
- Soft Rough Fuzzy Digraphs
- Bipolar Fuzzy Soft Graphs
- Traffic Densities of the Paths
- Multi-criteria Selection of Objects
- Suitable Career Selection Problem
- Detection of Bipolar Disorder
- Soft Rough Neutrosophic Graphs
- Soft Rough Neutrosophic Influence Graphs
Table of contents (8 chapters)
Authors and Affiliations
About the authors
Dr. Muhammad Akram received MSc degrees in Mathematics and Computer Science, MPhil in Computational Mathematics and PhD in Fuzzy Mathematics. He is currently a Professor in the Department of Mathematics at the University of the Punjab, Lahore, Pakistan, where he has been serving as a PhD supervisor of more than 10 students. Dr. Akram’s research interests include numerical solutions of parabolic PDEs, fuzzy graphs, fuzzy algebras, and fuzzy decision support systems. He has published 7 monographs and 300 research articles in international peer-reviewed journals. He has served as editorial board member of 10 international academic journals and as reviewer of 122 International journals, including Mathematical Reviews and Zentralblatt MATH.
Dr. Fariha Zafar received her PhD degree in Mathematics and MPhil degree in Mathematics from the University of the Punjab, Lahore. She has introduced the notions of Soft Trees and Fuzzy Soft Trees during her MPhil research work. She has published 10 research articles in top-ranked international journals. Her research interests include fuzzy graphs, soft set theory, rough set theory and decision-making.
Dr. Fariha Zafar received her PhD degree in Mathematics and MPhil degree in Mathematics from the University of the Punjab, Lahore. She has introduced the notions of Soft Trees and Fuzzy Soft Trees during her MPhil research work. She has published 10 research articles in top-ranked international journals. Her research interests include fuzzy graphs, soft set theory, rough set theory and decision-making.
Bibliographic Information
Book Title: Hybrid Soft Computing Models Applied to Graph Theory
Authors: Muhammad Akram, Fariha Zafar
Series Title: Studies in Fuzziness and Soft Computing
DOI: https://doi.org/10.1007/978-3-030-16020-3
Publisher: Springer Cham
eBook Packages: Intelligent Technologies and Robotics, Intelligent Technologies and Robotics (R0)
Copyright Information: Springer Nature Switzerland AG 2020
Hardcover ISBN: 978-3-030-16019-7Published: 16 April 2019
eBook ISBN: 978-3-030-16020-3Published: 05 April 2019
Series ISSN: 1434-9922
Series E-ISSN: 1860-0808
Edition Number: 1
Number of Pages: XXV, 434
Number of Illustrations: 278 b/w illustrations, 1 illustrations in colour
Topics: Computational Intelligence, Operations Research, Management Science, Data Mining and Knowledge Discovery, Operations Research/Decision Theory