Abstract
Many industries are developing robust models, capable of analyzing huge and complex data by using machine learning (ML) while delivering faster and more accurate results on vast scales. ML is a subfield of artificial intelligence, which is broadly defined as the capability of a machine to imitate intelligent human behavior. ML tools enable organizations to swiftly identify profitable opportunities and potential risks. Besides these uses, ML also has a wide range of applications in our daily lives. So, the development in ML is most important in this age of digital system to solve more complex problems. In order to further develop ML and diminish the uncertainties to improve accuracy, an innovative concept of complex bipolar intuitionistic fuzzy sets (CBIFSs) is introduced in this article. Further, the Cartesian product of two CBIFSs is defined. Moreover, the complex bipolar intuitionistic fuzzy relations (CBIFRs) and their types with suitable examples are defined. In addition, some important results and properties are also presented. The proposed modeling techniques are used to study different ML factors and their interrelationship, so that the functionality of ML might be enhanced. Furthermore, the advantages and benefits of proposed methods are described by their side to side comparison with preexisting frameworks in the literature.
Highlights
Uncertainty is the main thing found in each decision of humans
Atanassov [13] introduced a new concept of intuitionistic fuzzy sets (IFSs) which deals with membership grades as well as nonmembership grades
The omnipotence of the newly proposed framework of complex bipolar intuitionistic fuzzy relations (CBIFRs) is verified through a comparison of CBIFRs with preexisting structures such as complex fuzzy relations (CFRs), complex intuitionistic fuzzy relations (CIFRs), and CBFRs
Summary
Uncertainty is the main thing found in each decision of humans. An increasing sense of uncertainty reflects a changing environment that will impact the choices we make. The innovative structure of CBIFSs is superior to all preexisting structure, i.e., FS, CFS, IFS, CIFS, BFS, and BIFS The benefit of this newly introduced structure is that it explains the membership and nonmembership with the properties of possibility and impossibility. It covers all the predefined structures in a way that if nonmembership is equal to zero, it converted into CFBRs. If the phase term and nonmembership are simultaneously removed, it changes into BFRs. If only the phase term is removed, we get a structure with only amplitude terms, i.e., BIFRs. This article proposes an application of effective working of ML, which is an important part of a digital system.
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