A New Approach to Model Machine Learning by Using Complex Bipolar Intuitionistic Fuzzy Information

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.

Bipolar complex fuzzy subalgebras and ideals of BCK/BCI-algebras

“…In the future, we would like to employ the investigated work to some of the prevailing works like BCF soft sets (Mahmood et al, 2022), bipolar complex intuitionistic FSs (Al-Husban, 2022Jan, 2022).…”

Analyzing the effect of different types of pollution with bipolar complex fuzzy power Bonferroni mean operators

“…In the future, our goal would be to review various notions like the bipolar complex fuzzy soft set (SS) (Mahmood et al, 2022c), Pythagorean FS (Li et al, 2022), picture FS (Ullah, 2021), picture fuzzy SSs (Khan et al, 2019), T-spherical FS (Javed et al, 2022), complex bipolar intuitionistic FS (Jan et al, 2022), enhancing digital innovation for the sustainable transformation of manufacturing industry (Yin et al, 2022) and try to employ the introduced work in these notions.…”

Prioritization of Strategies of Digital Transformation of Supply Chain Employing Bipolar Complex Fuzzy Linguistic Aggregation Operators

“…In the future, we are thinking to expand this work to various notions where the proposed work is not applicable such as bipolar complex fuzzy uncertain linguistic set, bipolar complex fuzzy 2-tuple linguistic set, BCFSS [34], complex bipolar intuitionistic FS [39], complex bipolar picture FS [40] complex hesitant FS [41], [42], picture FS [43], and picture fuzzy soft set [44].…”