A Solution of Combined Disjoint Block Fuzzy Cognitive Maps Under the Decision Mathematical Approach

Authors

  • Appasamy Saraswathi Department of Mathematics, SRM Institute of Science and Technology, Kattankulathur – 603 203, Tamilnadu, India‎.
  • Seyyd Ahmad Edalatpanah Department of Mathematics, Ayandegan Institute of Higher Education, Tonekabon, Iran. https://orcid.org/0000-0001-9349-5695
  • Sanaz Hami Hassan Kiyadeh Department of Mathematics, The University of Alabama, Alabama, USA.

DOI:

https://doi.org/10.22105/masi.v1i2.53

Keywords:

FCMs‎, Combined disjoint block FCMs‎, Fixed point‎, Hidden pattern‎, Unsupervised trans-genders‎, Decision making and optimization‎

Abstract

Fuzzy optimization is a branch of mathematical optimization that utilizes fuzzy set theory to tackle uncertainty, imprecision, and vagueness in decision-making processes. Unlike traditional optimization, which relies on precise data, fuzzy optimization accommodates the ambiguous data typical in complex real-world scenarios, such as in engineering and finance. Through fuzzy sets, decision variables and constraints are represented by degrees of membership instead of fixed values, allowing a broader range of feasible solutions. This approach supports linear and nonlinear programming, making it a versatile tool in fields where incomplete data and fluctuating conditions prevail. Consequently, fuzzy optimization has become essential for solving complex, real-world problems characterized by uncertainty, offering robust, adaptable methodologies for both theoretical and applied optimization. Separately, a fuzzy mathematical approach has been applied to analyze transgender issues in Tamil Nadu using a combined disjoint block fuzzy cognitive map. This method, based on the fuzzy cognitive map concept by Kandasamy and Smarandache [1], organizes and analyzes social problems by grouping concepts in large numbers. The study is structured into four sections: solutions to transgender issues, background on the combined disjoint block FCM, analysis of hidden patterns in transgender issues, and conclusions and recommendations based on the findings.

References

‎[1] ‎ Kandasamy, W. B. V., & Smarandache, F. (2003). Fuzzy cognitive maps and neutrosophic cognitive maps. ‎Infinite Study.‎

‎[2] ‎ Zadeh, L. A. (1965). Fuzzy sets. Information and control, 8(3), 338–353.‎

‎[3] ‎ Mamdani. (1977). Application of fuzzy logic to approximate reasoning using linguistic synthesis. IEEE ‎transactions on computers, 26(12), 1182–1191. DOI:10.1109/TC.1977.1674779‎

‎[4] ‎ Kosko, B. (1986). Fuzzy cognitive maps. International journal of man-machine studies, 24(1), 65–75. ‎https://www.sciencedirect.com/science/article/pii/S0020737386800402‎

‎[5] ‎ Axelrod, R. (2015). Structure of decision: the cognitive maps of political elites. Princeton university press.‎

‎[6] ‎ Malhotra, N., Bhardwaj, M., & Kaur, R. (2012). Estimating the effects of gold plating using fuzzy ‎cognitive maps. International journal of computer science and information technologies, 3(4), 4806–4808. ‎https://citeseerx.ist.psu.edu/document?repid=rep1&type=pdf&doi=34294299f99c137ba519d10690ef5d28baa0cb23‎

‎[7] ‎ Murungweni, C., Van Wijk, M. T., Andersson, J. A., Smaling, E. M. A., & Giller, K. E. (2011). Application ‎of fuzzy cognitive mapping in livelihood vulnerability analysis. Ecology and society, 16(4), 1–16. ‎https://www.jstor.org/stable/26268961‎

‎[8] ‎ Yang, B., & Peng, Z. (2009). Fuzzy cognitive map and a mining methodology based on multi-relational ‎data resources. Fuzzy information and engineering, 1(4), 357–366. ‎https://www.tandfonline.com/doi/abs/10.1007/s12543-009-0028-7‎

‎[9] ‎ Kannan, V., Appasamy, S., & Kandasamy, G. (2022). Comparative study of fuzzy floyd warshall ‎algorithm and the fuzzy rectangular algorithm to find the shortest path. AIP conference proceedings (Vol. ‎‎2516). AIP Publishing. https://pubs.aip.org/aip/acp/article-abstract/2516/1/200029/2828755‎

‎[10] ‎ Broumi, S. (2024). An efficient approach for solving time-dependent shortest path problem under ‎fermatean neutrosophic environment. Neutrosophic sets and systems, 63(1), 6. ‎https://digitalrepository.unm.edu/cgi/viewcontent.cgi?article=2562&context=nss_journal

‎[11] ‎ Vidhya, K., & Saraswathi, A. (2023). A novel method for finding the shortest path with two objectives ‎under trapezoidal intuitionistic fuzzy arc costs. International journal of analysis and applications, 21, 121. ‎https://etamaths.com/index.php/ijaa/article/view/2993‎

‎[12] ‎ Prakash, Y., & Appasamy, S. (2023). Optimal solution for fully spherical fuzzy linear programming ‎problem. Mathematical modelling of engineering problems, 10(5). ‎https://search.ebscohost.com/login.aspx?direct=true&profile=ehost&scope

‎[13] ‎ Saraswathi, A. (2019). A fuzzy-trapezoidal dematel approach method for solving decision making ‎problems under uncertainty. AIP conference proceedings (Vol. 2112). AIP Publishing. ‎https://www.researchgate.net/profile/A-Saraswathi

‎[14] ‎ Dharmaraj, B., & Appasamy, S. (2023). Application of a modified gauss elimination technique for ‎separable fuzzy nonlinear programming problems. Mathematical modelling of engineering problems, 10(4). ‎https://search.ebscohost.com/login.aspx?‎

‎[15] ‎ Kannan, V., & Appasamy, S. (2023). Employing the bellman-ford algorithm with score functions to ‎address the linear diophantine fuzzy shortest path problem in network analysis. Mathematical modelling of ‎engineering problems, 10(5). https://search.ebscohost.com/login.aspx?‎

‎[16] ‎ Saraswathi, A., & Nedumaran, P. (2024). Comparative study to find the critical path using triangular ‎fuzzy number. Journal of computational analysis and applications (JOCAAA), 33(05), 345–354. ‎http://www.eudoxuspress.com/index.php/pub/article/view/518‎

‎[17] ‎ Saraswathi, A. (2024). A study on triangular fuzzy clustering model‎ under uncertainty. Uncertainty ‎discourse and applications, 1(1), 20–28. https://www.uda.reapress.com/journal/article/view/19‎

‎[18] ‎ Prakash, Y., & Appasamy, S. (2024). A novel approach for multi-objective linear programming model ‎under spherical fuzzy environment and its application. Journal of intelligent & fuzzy systems, (Preprint), ‎‎1–22. https://content.iospress.com/articles/journal-of-intelligent-and-fuzzy-systems/ifs233441‎

‎[19] ‎ Karthick, S., Saraswathi, A., & Baranidharan, B. (2024). Neutrosophic linear fractional programming ‎problem using denominator objective restriction method. Dynamics of continuous, discrete and impulsive ‎systems series b: applications and algorithms, 31(2), 89–101. ‎https://www.researchgate.net/profile/Saraswathi/publication/379544653_‎

‎[20] ‎ Nedumaran, P., Peter, M., Stephy, J. J., & Sheeba, J. J. (2024). Optimum route finding in tourism ‎transportation in tamil nadu using weighted fuzzy graph. Journal of computational analysis and ‎applications (JOCAAA), 33(7), 487–495. http://eudoxuspress.com/index.php/pub/article/view/1085‎

‎[21] ‎ Dickerson, J. A., & Kosko, B. (1996). Virtual worlds as fuzzy dynamical systems. Technology For ‎Multimedia.‎

Published

2024-09-12

How to Cite

A Solution of Combined Disjoint Block Fuzzy Cognitive Maps Under the Decision Mathematical Approach. (2024). Management Analytics and Social Insights, 1(2), 246-259. https://doi.org/10.22105/masi.v1i2.53

Similar Articles

You may also start an advanced similarity search for this article.