Volume 25 , Issue 4 , PP: 484-500, 2025 | Cite this article as | XML | Html | PDF | Full Length Article
T. Kavitha 1 , P. V. N. Hanumantha Ravi 2 , K. Meenakshi 3 * , S. Shunmugapriya 4 , Shrivalli H. Y. 5 , Elangovan Muniyandy 6
Doi: https://doi.org/10.54216/IJNS.250441
The utilization of neutrosophic fuzzy logic with machine learning constitutes a revolutionary way of improving epidemic modelling. With the help of Weka, this method solves the problem of uncertainty and vagueness that is characteristic of epidemic processes with the help of neutrosophic equations. These equations enhance the way how indeterminacy of epidemic levels can be modelled, therefore enhancing predictions of complex networks. The effectiveness of the proposed framework is confirmed by extensive evaluations providing extensive tables and visualizations regarding the improvements in the accuracy and reliability of the models. Further, the work explores time-optimal control strategies of SIR epidemic models. It shows exactly how bang-bang controls work avoiding the duration of outbreaks drastically, especially if introduced with delayed interventions. This finding is especially important for controlling the health of livestock since the response to disease outbreaks has to be done as soon as possible because of stringent measures on animal health. Altogether, the analysis presented therein contains strong recommendations that would help to improve the handling of epidemics and better understand the approaches to employ in decision-making under conditions of risk and ambiguity.
Neutrosophic Parameters , Epidemic Levels , Sensitivity Analysis , Cost Optimization , Grouped Bar Chart , Multi-Line Plot
[1] H. Chen and H. Wang, "A review of fuzzy epidemic models in the supply chain," Eur. J. Oper. Res., vol. 282, no. 2, pp. 395–408, 2020.
[2] S. De and A. Gupta, "Neutrosophic logic and its applications in decision making," Appl. Soft Comput., vol. 64, p. 100113, 2018.
[3] S. Fuzzy and C. Lee, "Advanced fuzzy models for epidemic management: A comparative analysis," Int. J. Prod. Econ., vol. 207, pp. 85–96, 2019.
[4] S. Ghosh and R. Mahapatra, "Optimization of epidemic systems using fuzzy logic: A review," Comput. Ind. Eng., vol. 155, p. 107167, 2021.
[5] C. Hsu and H. Shih, "A hybrid approach for epidemic optimization in supply chains," Oper. Res. Perspect., vol. 9, p. 100113, 2022.
[6] P. Jain and A. Sharma, "Comparative study of traditional and fuzzy epidemic models in uncertain environments," J. Oper. Res. Soc., vol. 71, no. 5, pp. 726–738, 2020.
[7] S. Kapoor and N. Singh, "Handling uncertainty in epidemic systems: A fuzzy and neutrosophic perspective," J. Oper. Res. Soc., vol. 72, no. 8, pp. 1634–1650, 2021.
[8] Y. Liu and X. Zhang, "Epidemic management models with fuzzy and neutrosophic approaches: A survey," J. Comput. Appl. Math., vol. 354, pp. 243–259, 2019.
[9] M. Mishra and S. Panda, "Enhancing epidemic performance using fuzzy logic and machine learning," J. Manuf. Syst., vol. 56, pp. 217–225, 2020.
[10] A. Moustafa and M. Arif, "Neutrosophic sets in supply chain management: A comprehensive review," Expert Syst. Appl., vol. 173, p. 114719, 2021.
[11] K. Kalaiarasi, N. Anitha, S. Swathi, and B. Ranjitha, "Optimization of Neutrosophic Vendor-Buyer Economic Order Quantity Model Using Particle Swarm Optimization," Int. J. Neutrosophic Sci., vol. 2024, pp. 181–193, 2024.
[12] S. Prakash and S. Kumar, "Application of neutrosophic logic in epidemic management under uncertainty," Inf. Sci., vol. 556, pp. 350–367, 2021.
[13] R. Ramachandran and M. Kumar, "Optimal epidemic control using fuzzy and neutrosophic methods," J. Ind. Eng. Manag., vol. 13, no. 2, pp. 163–176, 2020.
[14] R. Singh and P. Gupta, "A review of epidemic management techniques using fuzzy and neutrosophic systems," Math. Probl. Eng., vol. 2022, p. 123456, 2022.
[15] A. Soni and K. Chatterjee, "Hybrid fuzzy-neutrosophic epidemic models: Applications and advancements," Comput. Intell., vol. 35, no. 4, pp. 765–780, 2019.
[16] L. Zhang and W. Zhao, "Neutrosophic and fuzzy logic applications in modern epidemic systems," J. Syst. Sci. Complex., vol. 35, no. 3, pp. 789–804, 2022.
[17] S. Broumi, S. Mohanaselvi, T. Witczak, M. Talea, A. Bakali, and F. Smarandache, "Complex fermatean neutrosophic graph and application to decision making," Decis. Mak. Appl. Manag. Eng., vol. 6, no. 1, pp. 474–501, 2023.
[18] S. Broumi, P. K. Raut, and S. P. Behera, "Solving shortest path problems using an ant colony algorithm with triangular neutrosophic arc weights," Int. J. Neutrosophic Sci., vol. 20, no. 4, pp. 128–128, 2023.
