Fuzzy MABAC Deep Learning for Diagnosis of Alzheimers Disease: Analysis of Complex Propositional Linear Diophantine Fuzzy Power Aggregation Insights
محورهای موضوعی : Transactions on Fuzzy Sets and Systems
1 - Department of Information Management, National Yunlin University of Science and Technology, Yunlin, Taiwan (R.O.C.).
کلید واژه: Alzheimers Disease, Complex Propositional linear Diophantine fuzzy sets, MABAC deep learning methods, Power aggregation operators.,
چکیده مقاله :
Alzheimers disease is an unpredictable and progressive neurodegenerative disorder that initially affects memory thinking and behavior. Some key features of Alzheimers disease are memory loss, cognitive decline, behavioral changes, disorientation, and physical symptoms. In this article, we design the procedure of a multiattributive border approximation area comparison deep learning algorithm for the diagnosis of Alzheimers Disease. For this, first, we goal to design the model of complex propositional linear Diophantine fuzzy information with their basic operational laws. In addition, we analyze the model of complex propositional linear Diophantine fuzzy power average operator, complex propositional linear Diophantine fuzzy weighted power average operator, complex propositional linear Diophantine fuzzy power geometric operator, complex propositional linear Diophantine fuzzy weighted power geometric operator, and also initiate their major properties. Additionally, the key role of this paper is to arrange relevant from different sources for diagnosing Alzheimers disease under the consideration of the designed technique. Finally, we compare both (proposed and existing) ranking information to address the supremacy and strength of the designed models.
Alzheimers disease is an unpredictable and progressive neurodegenerative disorder that initially affects memory thinking and behavior. Some key features of Alzheimers disease are memory loss, cognitive decline, behavioral changes, disorientation, and physical symptoms. In this article, we design the procedure of a multiattributive border approximation area comparison deep learning algorithm for the diagnosis of Alzheimers Disease. For this, first, we goal to design the model of complex propositional linear Diophantine fuzzy information with their basic operational laws. In addition, we analyze the model of complex propositional linear Diophantine fuzzy power average operator, complex propositional linear Diophantine fuzzy weighted power average operator, complex propositional linear Diophantine fuzzy power geometric operator, complex propositional linear Diophantine fuzzy weighted power geometric operator, and also initiate their major properties. Additionally, the key role of this paper is to arrange relevant from different sources for diagnosing Alzheimers disease under the consideration of the designed technique. Finally, we compare both (proposed and existing) ranking information to address the supremacy and strength of the designed models.
[1] Knopman DS, Amieva H, Petersen RC, Chtelat G, Holtzman DM, Hyman BT, ... , Jones DT. Alzheimer disease. Nature reviews Disease primers. 2021; 7(1), 33. DOI: http://doi.org/10.1038/s41572-021-00269-y
[2] Castellani RJ, Rolston RK, Smith M A. Alzheimer disease. Disease-a-month: DM. 2010; 56(9), 484. DOI: http://doi.org/10.1016/j.disamonth.2010.06.001
[3] Cummings JL, Cole G. Alzheimer disease. Jama. 2002; 287(18), 2335-2338. DOI: http://doi.org/10.1001/jama.287.18.2335
[4] Elbanna S. Strategic decisionmaking: Process perspectives. international Journal of Management reviews. 2006; 8(1), 1-20. DOI: http://doi.org/10.1111/j.1468-2370.2006.00118.x
[5] Zadeh LA. Fuzzy sets. Information and control. 1965; 8(3), 338-353. DOI: https://doi.org/10.2307/2272014
[6] Atanassov K. Intuitionistic fuzzy sets. In VII ITKRs Session; Deposed in Central Sci.Techn. Library of Bulg. Acad. of Sci., 1697/84; Sofia, Bulgaria. June 1983. DOI: http://doi.org/10.1016/S0165- 0114(86)80034-3
[7] Atanassov K. Intuitionistic fuzzy sets. Fuzzy Sets and Systems. 1986; 20(1), 87-96. DOI: http://doi.org/10.1007/978-3-7908-1870-3 1
[8] Yager RR. Pythagorean fuzzy subsets. IFSA world congress and NAFIPS annual meeting (IFSA/NAFIPS) (pp. 57-61), 2013. DOI: http://doi.org/10.1109/IFSA-NAFIPS.2013.6608375
[9] Yager RR. Generalized orthopair fuzzy sets. IEEE Transactions on Fuzzy Systems. 2016; 25(5), 1222- 1230. DOI: http://doi.org/10.1109/TFUZZ.2016.2604005
[10] Riaz M, Hashmi MR. Linear Diophantine fuzzy set and its applications towards multi-attribute decision-making problems. Journal of Intelligent & Fuzzy Systems. 2019; 37(4), 5417-5439. DOI: http://doi.org/10.3233/JIFS-190550
[11] Ramot D, Milo R, Friedman M, Kandel A. Complex fuzzy sets. IEEE Transactions on Fuzzy Systems. 2002; 10(2), 171-186. DOI: http://doi.org/10.1109/91.995119
[12] Alkouri AMDJS, Salleh AR. Complex intuitionistic fuzzy sets. AIP conference proceedings. American Institute of Physics. 2012; 1482(1), 464-470. DOI: http://doi.org/10.1063/1.4757515
[13] Ullah K, Mahmood T, Ali Z, Jan N. (2020). On some distance measures of complex Pythagorean fuzzy sets and their applications in pattern recognition. Complex & Intelligent Systems. 2020; 6, 15-27. DOI: http://doi.org/10.1007/s40747-019-0103-6
[14] Liu P, Mahmood T, Ali Z. Complex q-rung orthopair fuzzy aggregation operators and their applications in multi-attribute group decision making. Information. 2019; 11(1), 5. DOI: http://doi.org/10.3390/info11010005
[15] Ali Z, Mahmood T. Maclaurin symmetric mean operators and their applications in the environment of complex q-rung orthopair fuzzy sets. Computational and Applied Mathematics. 2020; 39(3), 161. DOI: http://doi.org/10.1007/s40314-020-01145-3
[16] Kamac H. Complex linear Diophantine fuzzy sets and their cosine similarity measures with applications. Complex & Intelligent Systems. 2022; 8(2), 1281-1305. DOI: http://doi.org/10.1007/s40747-021-00573-w
[17] Gottwald S. Fuzzy propositional logic. Fuzzy Sets and Systems. 1980; 3(2), 181-192. DOI: http://doi.org/10.1016/0165-0114(80)90053-6
[18] Atanassov K. Two variants of intuitionistic fuzzy propositional calculus. Mathematical Foundations of Artificial Intelligence Seminar, Sofia, 1988, Preprint IM-MFAIS-5-88. Reprinted: Int J Bioautomation, 2016, 20(S1), S17-S26.
[19] Wang X, Xu Z, Gou X. A novel plausible reasoning based on intuitionistic fuzzy propositional logic and its application in decision making. Fuzzy Optimization and Decision Making. 2020; 19, 251-274. DOI: http://doi.org/10.1007/s10700-020-09319-8
[20] Kahraman C. Proportional picture fuzzy sets and their AHP extension: Application to waste disposal site selection. Expert Systems with Applications. 2024; 238, 122354. DOI: http://doi.org/10.1016/j.eswa.2023.122354
[21] Pamuar D, irovi G. The selection of transport and handling resources in logistics centers using MultiAttributive Border Approximation Area Comparison (MABAC). Expert systems with applications. 2015; 42(6), 3016-3028. DOI: http://doi.org/10.1016/j.eswa.2014.11.057
[22] Yager RR. The power average operator. IEEE Transactions on Systems, Man, and Cybernetics-Part A: Systems and Humans. 2001; 31(6), 724-731. DOI: http://doi.org/10.1109/3468.983429
[23] Xu Z, Yager RR. Power-geometric operators and their use in group decision-making. IEEE Transactions on Fuzzy Systems. 2009; 18(1), 94-105. DOI: http://doi.org/10.1109/TFUZZ.2009.2036907
[24] Jiang W, Wei B, Liu X, Li X. Zheng H. Intuitionistic fuzzy power aggregation operator based on entropy and its application in decision-making. International Journal of Intelligent Systems. 2018; 33(1), 49-67. DOI: http://doi.org/10.1002/int.21939
[25] Wei G, Lu M. Pythagorean fuzzy power aggregation operators in multiple attribute decision making. International Journal of Intelligent Systems. 2018; 33(1), 169-186. DOI: http://doi.org/10.1002/int.21946
[26] Garg H, Gwak J, Mahmood T, Ali Z. Power aggregation operators and VIKOR methods for complex q-rung orthopair fuzzy sets and their applications. Mathematics. 2020; 8(4), 538. DOI: http://doi.org/10.3390/math8040538
[27] Liu P, Ali Z, Ding J. Power Dombi Aggregation Operators for Complex Pythagorean Fuzzy Sets and Their Applications in Green Supply Chain Management. International Journal of Fuzzy Systems. 2024; 1-16. DOI: http://doi.org/10.1007/s40815-024-01691-6
[28] Rani D, Garg H. Complex intuitionistic fuzzy power aggregation operators and their applications in multicriteria decisionmaking. Expert Systems. 2018; 35(6), e12325. DOI: http://doi.org/10.1111/exsy.12325
[29] Ali Z. Decision-making techniques based on complex intuitionistic fuzzy power interaction aggregation operators and their applications. Journal of Innovative Research in Mathematical and Computational Sciences,. 2022; 1(1), 107-125. https://jirmcs.agasr.org/index.php/jirmcs/article/view/6
[30] Ali Z, Mahmood T, Ullah K, Pamucar D, Cirovic G. Power aggregation operators based on t-norm and t-conorm under the complex intuitionistic fuzzy soft settings and their application in multi-attribute decision making. Symmetry. 2021: 13(11), 1986. DOI: http://doi.org/10.3390/sym13111986
[31] Moslem S. A novel parsimonious spherical fuzzy analytic hierarchy process for sustainable urban transport solutions. Engineering Applications of Artificial Intelligence. 2024; 128, 107447. DOI: http://doi.org/10.1016/j.engappai.2023.107447
[32] Moslem S, Tezel BT, Kinay AO, Pilla F. A hybrid approach based on a magnitude-based fuzzy analytic hierarchy process for estimating sustainable urban transport solutions. Engineering Applications of Artificial Intelligence. 2024; 137, 109112. DOI: http://doi.org/10.1016/j.engappai.2024.109112
[33] Moslem S, Pilla F. Addressing last-mile delivery challenges by using Euclidean distance-based aggregation within spherical Fuzzy group decision-making. Transportation Engineering. 2023; 14, 100212. DOI: http://doi.org/10.1016/j.treng.2023.100212
[34] Acharya A, Mahata A, Karak M, Sil N, Mukherjee S, Mondal SP, Roy B. Stability Analysis of Diabetes Mellitus Model in Neutrosophic Fuzzy Environment. Franklin Open. 2024; 8, 100144. DOI: http://doi.org/10.1016/j.fraope.2024.100144
[35] Singh P, Gor B, Gazi KH, Mukherjee S, Mahata A, Mondal SP. Analysis and interpretation of the Malaria disease model in a crisp and fuzzy environment. Results in Control and Optimization. 2023; 12, 100257. DOI: http://doi.org/10.1016/j.rico.2023.100257
[36] Momena AF, Mandal S, Gazi KH, Giri BC, Mondal SP. Prediagnosis of disease based on symptoms by generalized dual hesitant hexagonal fuzzy multi-criteria decision-making techniques. Systems. 2023; 11(5), 231. DOI: http://doi.org/10.3390/systems11050231
[37] Acharya A, Mahata A, Mukherjee S, Biswas MA, Das KP, Mondal SP, Roy B. A Neutrosophic differential equation approach for modeling glucose distribution in the bloodstream using neutrosophic sets. Decision Analytics Journal. 2023; 8, 100264. DOI: https://doi.org/10.1016/j.dajour.2023.100264
