A new approach to investigate the effects of artificial neural networks based on bipolar complex spherical fuzzy information

Abstract

Artificial neural network is revolutionizing business and everyday life, bringing us to the next level in artificial intelligence. It has a unique ability to extract meaning from complex data to find patterns and detect trends that are too convoluted for the human brain. This paper analyzes the artificial neural network impact on different computational organizations by using the innovative structure of bipolar complex spherical fuzzy relation which is any subset of the Cartesian product of two bipolar complex spherical fuzzy sets. This notion has a comprehensive structure that consists of membership grade, abstinence grade, and non-membership grade. Furthermore, various kinds of bipolar complex spherical fuzzy relation with suitable examples are given and some authentic results also have been proved. These newly defined structures are used to investigate the impact of artificial neural network work on a variety of organizations. The innovative framework is also compared with the existing structure in the field of fuzzy set theory to prove its superiority.

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“…It is noted that the invented theory can't overcome with information that is in the model of BCF linguistic set [45], BCF soft set [46], picture FS [47], complex picture FS [48], complex bipolar FS [49], bipolar complex spherical FS [50] and other generalization of BCFS. Because of this limitation and the benefits of logarithmic AOs, there is a deliberate plan to use them in a wider range of frameworks, such as BCF linguistic set, BCF soft se, picture FS, bipolar complex spherical FS, etc.…”

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“…We can also extend these notions to complex picture fuzzy soft set theory [ 47 ]. Moreover, these notions can be extended to complex fuzzy soft set theory [ 48 ] and bipolar complex intuitionistic fuzzy soft set theory [ 49 ].…”