Abstract
In Gong and Hua [1], two bipolar picture fuzzy graph based multiple attribute decision making algorithms have been proposed, labelled as Algorithm A and Algorithm B respectively. Furthermore, a case study is presented to explain how to implement Algorithm A for a specific multiple attribute decision making problem. This paper is a continue work of [1], in which a case study on medical diagnosis is manifested to show how to use Algorithm B for a specific multiple attribute decision making problem.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Gong, S., Hua, G.: Bipolar Picture Fuzzy Graph Based Multiple Attribute Decision Making Approach-Part I. ML4CS (2022)
Ye, J.: Cosine similarity measures for intutistic fuzzy sets and their applications. Math. Comput. Model. 53, 19–97 (2011)
Amanathulla, S.K., Muhiuddin, G., Al-Kadi, D., Pal, M.: Multiple attribute decision-making problem using picture fuzzy graph. Mathematical Problems in Engineering, 9937828 (2021). https://doi.org/10.1155/2021/9937828
Acknowledgements
This work is supported by 2021 Guangdong Basic and Applied Basic Youth Fund Project (No. 2021A1515110834), Guangdong University of Science and Technology University Major Scientific Research Achievement Cultivation Program Project 2020 (No. GKY-2020CQPY-2), 2022 Special Projects in Key Fields of Higher Education Institutions in Guangdong Province, and Guangdong Provincial Department of Education Project (No. 2020KTSCX166).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Gong, S., Hua, G., Zhang, X. (2023). Bipolar Picture Fuzzy Graph Based Multiple Attribute Decision Making Approach-Part II. In: Xu, Y., Yan, H., Teng, H., Cai, J., Li, J. (eds) Machine Learning for Cyber Security. ML4CS 2022. Lecture Notes in Computer Science, vol 13656. Springer, Cham. https://doi.org/10.1007/978-3-031-20099-1_26
Download citation
DOI: https://doi.org/10.1007/978-3-031-20099-1_26
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-20098-4
Online ISBN: 978-3-031-20099-1
eBook Packages: Computer ScienceComputer Science (R0)