Conditional Aggregation Operator Defined by the Power Information Concerning Type-2 Fuzzy Deep Learning Algorithm for Financial Investment Data Decision-Making

Abstract

Financial investment data decision-making is part of corporate financial management. Deep Learning Algorithms (DLAs) have represented significant and meaningful promise in many financial investment applications, leveraging their capability and verity to automatically learn hierarchical interpretations from large and complicated datasets. The major influence of this application is to derive the most preferable and the most dominant kind of deep learning architecture, used in financial investment data analysis. In this study, we concentrate on evaluating some new conditional aggregation operators based on Aczel-Alsina and power operators under the presence of Type-2 Fuzzy (T2F) DLAs. For this, we derive the Aczel-Alsina operational laws for T2F values and also simplify their related results with the help of some suitable examples. Thus, we evaluate the novel theory of conditional aggregation operators, such as the T2F Aczel-Alsina Weighted Power Averaging (T2FAAWPA) operator and the T2F Aczel-Alsina Weighted Power Geometric (T2FAAWPG) operator. Some properties are the above two proposed operators are also initiated. Additionally, we analyze the problems of DLAs for financial investment data analysis under the presence of the proposed operators. For this, we discuss the Multi-Attribute Decision-Making (MADM) procedure based on initiated operators for T2F uncertainty. Finally, we illustrate or demonstrate some numerical examples for evaluating the comparison between the proposed theory with some existing techniques to show the supremacy and validity of the initiated techniques for financial investment data decision-making.