Volume 24 , Issue 3 , PP: 179-189, 2024 | Cite this article as | XML | Html | PDF | Full Length Article
Manjula G. J. 1 , Shaik Khaja Mohiddin 2 * , A. P. Pushpalatha 3 , Vadali Srinivas 4 , M. Premalatha 5 , Sakthi R. 6
Doi: https://doi.org/10.54216/IJNS.240316
The created SDE-Framework combines neutronosophic logic and fuzzy mathematics in a novel method, aiming at facilitating more informed decision outcomes in computational systems and information technology management. This method hopes to aid in determining strategic solutions by controlling the expected sophistication and ambiguity in these two technologically dynamic industries. Neutronosophic logic divides data into three components: truth, indeterminacy, and falsity, build an exhaustive technique for addressing contradiction and indeterminacy. This significantly increases the method by enabling a more complete exploration of potential options with ambiguous and inadequate data. Second, the fuzzy mathematics gives a valuable contribution. It offers a refined method for managing the levels of probability and certainty through membership features, resulting in more exact and flexible evaluations. By the usage of such compared sophisticated mathematics concepts, SDE-Framework addresses potential decision-making scenarios by letting the computer formulates do the judgements for the determinable and in determinable explicit data. The subsequent crucial parameters are adopted to tolerance values: validity and responsibility, falseness foreach, indeterminacy magnitude to each, and truth value. This guarantees its combination of complexity supportive rand reading of actual surroundings.
Adaptive Systems , Decision-Making , Fuzzy Mathematics , Management , Neutrosophic Logic , Uncertainty Handling
[1] Zadeh, L. A. (1965). Fuzzy sets. Information and Control, 8(3), 338-353. https://doi.org/10.1016/S0019-9958(65)90241-X
[2] Atanassov, K. (1986). Intuitionistic Fuzzy Sets. Fuzzy Sets and Systems, 20(1), 87-96. https://doi.org/10.1016/S0165-0114(86)80034-3
[3] Smarandache, F. (1999). A Unifying Field in Logics. Neutrosophy: Neutrosophic Probability, Set, and Logic. American Research Press.
[4] Wang, H., Smarandache, F., Zhang, Y., & Sunderraman, R. (2010). Single-valued neutrosophic sets. Multispace and Multistructure, 4, 410-413.
[5] Vlachos, I. K., & Sergiadis, G. D. (2007). Intuitionistic fuzzy information - Applications to pattern recognition. Pattern Recognition Letters, 28(2), 197-206. https://doi.org/10.1016/j.patrec.2006.08.004
[6] Broumi, S., & Smarandache, F. (2013). Neutrosophic Sets and Systems. Procedia Engineering, 5, 217-222.
[7] Chaira, T. (2011). A novel intuitionistic fuzzy C means clustering algorithm and its application to medical images. Applied Soft Computing, 11(2), 1711-1717. https://doi.org/10.1016/j.asoc.2010.04.024
[8] Ye, J. (2014). Multicriteria fuzzy decision-making method using entropy weights-based correlation coefficients under single-valued neutrosophic environment. Mathematical Problems in Engineering, 2014. https://doi.org/10.1155/2014/645953.
[9] Sindhu, M. S., Siddique, I., Ahsan, M., Jarad, F., & Altunok, T. (2022). An approach of decision-making under the framework of Fermatean fuzzy sets. Mathematical Problems in Engineering, 2022. https://doi.org/10.1155/2022/8442123
[10] Li, X., Zhang, L., Li, D., & Guo, D. (2022). Construction and simulation of a strategic HR decision model based on recurrent neural network. Journal of Mathematics, 2022.
[11] Wang, H., et al. (2023). Monte Carlo simulations in environmental policy decision-making: A case study on air quality management. Environmental Management Journal, 2023.
[12] Alojail, M.; Alturki, M.; Bhatia Khan, S. An Informed Decision Support Framework from a Strategic Perspective in the Health Sector. Information 2023, 14, 363. https://doi.org/10.3390/info14070363.
[13] Broumi, S., Mohanaselvi, S., Witczak, T., Talea, M., Bakali, A., & Smarandache, F. (2023). Complex fermatean neutrosophic graph and application to decision making. Decision Making: Applications in Management and Engineering, 6(1), 474-501.
[14] Broumi, S., Raut, P. K., & Behera, S. P. (2023). Solving shortest path problems using an ant colony algorithm with triangular neutrosophic arc weights. International Journal of Neutrosophic Science, 20(4), 128-28.