Fuzzy MABAC Deep Learning for Diagnosis of Alzheimers Disease: Analysis of Complex Propositional Linear Diophantine Fuzzy Power Aggregation Insights
Zeeshan Ali
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.
چکیده انگلیسی :
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.
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