1. Introduction
Complex issues in various fields such as medicine, economics, environmental sciences, engineering, and social sciences occur because of the use of classical mathematical modeling and of numerous kinds of ambiguities. As conventional mathematical techniques cannot succeed to resolve such types of complex issues, we employ certain mathematical modeling methods such as the notion of the fuzzy set (FS) [
1], interval mathematics [
2], rough set (RS) [
3], and probability, which are famous and effective techniques for dealing with ambiguity and vagueness. However, these notions have their specific fundamental limitations and boundaries; one of their key drawbacks being the lack of parametrization tools. To overcome this, Molodtsov [
4] invented the idea of the soft set (SS) which is an impressive mathematical tool to interpret existing ambiguities and vagueness. Recently, various researchers have employed the notion of SS. A new perspective regarding SSs was invented by Maji et al. [
5]. Serval new operations for SSs were invented by Ali et al. [
6]. Babitha et al. [
7] devised SS relations and function. The SS theory (SST) and uni–int decision-making (DM) were explored by Cagman and Enginoglu [
8]. A note on SST was given by Yang [
9]. Herwan and Deris [
10] interpreted a SS for association rules mining. Georgious and Megaritis [
11] presented SST and topology. Yang and Guo [
12] explored kernels and closures of SS relation and mapping. The similarity in SST was invented by Min [
13]. Ma et al. [
14] presented a survey of DM methods based on two classes of hybrid SS models. The SS applied to ideals in d-algebras was invented by Jun et al. [
15]. Mahmood [
16] conceived a new idea of bipolar SS (BSS).
FS was invented by Zadeh [
1] in 1965, which is a handy technique to interpret circumstances in which the information is vague or ambiguous. FSs cope with such circumstances by providing a truth grade (TG) to which a particular element belongs to a fixed set. After the appearance of the idea of FS, a lot of scholars paid attention to improved FSs. Zimmermann [
17] explored the applications of FS to mathematical programming. Adlassnig [
18] utilized FS in medical diagnosis. The concept of fuzzy SS (FSS) was invented by Maji et al. [
19]. Roy and Maji [
20] employed FSS in DM problems. A novel algorithm of FSS based on DM was explored by Alcantud [
21]. Jun et al. [
22] applied the FSS to BCK/BCI-algebras. FS is a kind of vital mathematical form to signify a group of items whose boundary is imprecise. There are numerous generalizations of FS, such as intuitionistic FS [
23], picture FS [
24], spherical FS [
25], etc. Bipolar FS (BFS) [
26] is another generalization of FS that carries a positive TG (PTG), which lies in
, and negative TG (NTG) which lies in
. Abdullah et al. [
27] invented the concept of bipolar FSS (BFSS). Riaz and Tehrim [
28] presented bipolar fuzzy (BF) soft mapping. The concept of fuzzy BSS was developed by Naz and Shabir [
29]. Riaz and Tehrim [
30] invented BF soft topology with DM.
The idea of complex FS (CFS), conceived by Ramot et al. [
31], is the modification of FS theory whose range is expended from
to the unit disk in a complex plane. The TG of CFS carries an amplitude term and a phase term to cope with ambiguities and vagueness in two-dimensional planes. Later, Tamir et al. [
32] invented a new interpretation of CFS. Ma et al. [
33] invented a method for multiple periodic factor prediction problems employing CFS. The concept of complex FSS (CFSS) was interpreted by Thirunavukarasu et al. [
34]. Selvachandran and Singh [
35] invented interval-valued CFSS. Akram et al. [
36] gave the idea of complex spherical FSSs. The concept of complex hesitant FS (CHFS) was interpreted by Mahmood et al. [
37]. Chinram et al. [
38] invented cosine similarity measures (SM) for CHFSs. The notion of complex dual hesitant FS (CDHFS) was established by ur Rehman et al. [
39]. The complex fuzzy N-soft set (CFN-SS) was interpreted by Mahmood et al. [
40]. When the decision analyst delivers the data in two dimensions along with positive and negative aspects, then the above prevailing concepts are not able to solve such kind of data. To overcome this, Mahmood and Ur Rehman [
41] invented the concept of bipolar CFS (BCFS). BCFS is signified by PTG, which belongs to
, and NTG which belongs to
.
The concept of the bipolar complex fuzzy soft set (BCFSS) was interpreted by Alqaraleh et al. [
42] by modifying the theory of BFSS. For this modification, they considered the BCFS described by Alkouri et al. [
43]. However, Mahmood and ur Rehman [
41] argued that this idea by Alkouri et al. [
43] contradicts the basic definition of the polar form of the complex number as Alkouri et al. [
43] took the value of amplitude term of the NTG from a negative interval, i.e.,
which is not possible, and propounded a novel definition of BCFS which is in the cartesian form instead of polar form and more accurate. Then here, a question arises: how can the utilization of such an idea, which contradicts the basic definition of a complex number, be correct? That is why, in this article, we interpret a novel concept named BCFSS, which is based on the correct notion of BCFS presented in [
41]. We are also motivated by the need for BCFSS to cope with complicated and vagueness information which involved the positive and negative aspects, fuzzy information, and the parameters in a single set. The concept is inspired by the bipolarity of parameters and then the complex fuzziness of the information comes into play. We combine three notions, i.e., parameterization, complex fuzziness, and bipolarity, which simplify the modeling of a problem where these three factors are included. The BCFSS is the fusion of BCFS and SS and modified prevailing theories such as BCFS, BFSS, BFS, FS, SS, etc. The invented notion is a substantial technique to handle clumsy and problematic real-life matters and plays a significant role in the DM process, which we will demonstrate in this study with the assistance of two genuine life applications.
The rest of the article is constructed as follows: In
Section 2, we review some prevailing notions including SS, BFS, CFS, and BCFS with their few properties.
Section 3 contains three subsections; in
Section 3.1, we introduce the idea of BCFSS, null BCFSS, absolute BCFSS; in
Section 3.2, we establish a few elementary operations for BCFSSs such as complement, union, γ intersection, extended intersection, and related properties; and in
Section 3.3, we describe OR and AND operations for BCFSSs. In
Section 4, we present a DM procedure for solving information in the setting of BCFSSs and apply it to real-life DM problems. In
Section 5, we offer a comparative study and evaluate the advantages of the invented concepts to display the benefits and accomplishments of the established BCFSSs. In
Section 6 of the article, the conclusion of this study is provided.
6. Conclusions
In this article, we invented the concept of BCFSS which is the combination of BCFS and SS. The concept of BCFS modifies many prevailing notions such as SS, FS, FSS, BFS, CFS, CFSS, and BCFS which we discussed in the comparative section. We also interpreted the tabular form of the invented BCFSS. Additionally, in this study, we invented the elementary operations for BCFSS, such as a complement, union, intersection, extended intersection, and related properties, and illustrated them with the assistance of examples. The OR and AND operations for the bipolar complex fuzzy soft set are presented in this study, along with examples. Furthermore, we interpreted the decision-making algorithm and real-life examples (scenario 1 and scenario 2) for BCFSS to display the success and useability of the invented BCFSS. Finally, a comparative study of invented notions with some prevailing ideas is also discussed in this article to demonstrate the efficiency and powerfulness of the invented work.
In the future, we will employ the invented work in various domains such as CHFS [
44] and picture fuzzy N-SS [
45].