Ubaid Ur Rehman

The challenges in decision-making in the selection of combined heat and power (CHP) system are usually typified by conflicting criteria, inaccurate information, and the presence of both positive and negative assessments. The current decision-making models that rely on fuzzy sets (FSs), bipolar fuzzy sets (BFSs), fuzzy rough sets (FRSs) and their variants can only address some of these aspects separately, but they cannot jointly represent bipolarity and rough boundary uncertainty in a single framework. Fruther, up till now, no Schweizer-Sklar aggregation operators (AOs) have been formulated in the framework of bipolar fuzzy rough sets (BFRSs), and no multi-criteria decision-making (MCDM) method has been built using Schweizer-Sklar operators in a BFRSs. These critical research challenges are addressed in this manuscript by developing an MCDM model based on Schweizer-Sklar operators in the framework of a BFRS. The primary contribution of this research is the construction of Schweizer-Sklar operators in bipolar fuzzy rough (BFR) information to combine the data and then apply these operators in the construction of the MCDM approach. The proposed methodology can systematically incorporate Schweizer-Sklar operators, which gives it unprecedented power to address the intricate uncertainties of CHP system selection criteria. The importance of this research can be explained by the detailed examination of CHP system selection for industrial energy requirements, which is a field that is highly uncertain and has rather vague decision criteria. The proposed approach provides not only a more accurate mathematical solution but also creates a new paradigm for solving the multi-criteria problem in various disciplines. The research also shows that it can alter decision-making approaches by a great extent when compared with certain prevailing theories as it offers a more detailed and mathematically rigorous method of handling uncertainty and complexity.

The selection of enterprise resource planning (ERP) systems is one of the most strategic and profound decisions for organizations since it impacts their performance and competitive position. Due to the complexity and variety of the ERP systems that have evolved over the years, the decision-making (DM) process has become more difficult. These complexities have raised several major research gaps in the literature and the selection methodologies of ERP systems. One significant weakness is that the current strategies pay attention only to the positive aspects of the attributes of ERP systems and ignore the negative aspects of the attributes of ERP systems. As a result, the decision maker may end up with an ERP evaluation that is either unbalanced or skewed, and therefore not entirely accurate. Besides, most of the existing methods do not consider extra fuzzy information concerning the attributes of ERP, which leads to the loss of some important information needed for making a wise decision. Thus, in this manuscript, we propose a new MADM method, namely bipolar complex fuzzy MADM (BCF-MADM), which not only covers positive and negative aspects of ERP attributes but also includes additional fuzzy information and reduces the influence of the extreme values of the decision-makers’ opinions. To overcome the problem of outlier assessments, the power average (PA) operator is used, and the Muirhead mean (MM) operator is used to consider the interrelation between attribute arguments, which provides more flexibility in the aggregation process, and new power MM operators and power dual MM operators are proposed for bipolar complex fuzzy set (BCFS). These operators are used in the aggregation process of the developed MADM problem. After that, we analyze a case study related to the selection of an ERP system and then compare the anticipated theory with certain existing ones to demonstrate the supremacy of the anticipated theory.

The selection of an AI-based radar system to detect drones is a multi-criteria decision-making (MCDM) problem with many conflicting criteria. Exchanges between positive and negative aspects of each radar system in uncertain and incomplete information should be assessed by decision-makers. The traditional MCDM models are usually not effective in dealing with such complexities, especially when both positive and negative aspects are involved, and comparative reasoning is needed (dominance). In order to address these shortcomings, this article suggests an advanced model using the bipolar fuzzy dominance rough set (BFDRS) approach. The suggested method combines fuzzy logic to deal with uncertainty, dominance-based rough sets to model preferences, and bipolar fuzzy sets to manage dual-natured assessments. In order to operationalize the framework, we propose two new aggregation operators, namely bipolar fuzzy dominance rough dombi averaging (BFDRDA) and bipolar fuzzy dominance rough dombi geometric (BFDRDG), to combine expert opinions in the context of multiple criteria successfully. After that, we develop an MCDM methodology, which is the WASPAS method, within the framework of BFDRS, to prioritize AI radar alternatives in the presence of uncertainty. An extensive case study proves the relevance of the suggested model, and a comparative analysis with the currently existing ones proves its strength and higher decision-support abilities in complex and contradictory environments.

As cyberattacks become more advanced, advanced AI-based big data visualization is now needed for effective threat detection. Yet, choosing the best visualization tools is some multicriteria decision-making (MCDM) task that involves considering many criteria containing both positive and negative aspects. WhileMCDMmethods that address the selection and classification of AI-driven big data visualization tools focus on the positive aspects of the evaluation criteria and ignore the negative aspects of the criteria, resulting in incomplete evaluations. Further, although Einstein operators have shown strong results in uncertain and imprecise situations and MCDM approaches, they have not yet been used in bipolar fuzzy frameworks, which leaves a major gap in decision-making methods. To overcome these problems, this article interprets a bipolar fuzzy MCDM methodology based on Einstein prioritized operators to systematically evaluate and classify AI-driven big data visualization tools for cybersecurity threat detection. For this method, Einstein prioritized operators within a bipolar fuzzy framework devised in this article, which can aggregate both positive and negative aspects of the criteria.Acomprehensive case study is shown to assess and classify the prominent AI-driven big data visualization tools for cybersecurity threat detection, considering critical criteria with dual aspects. The proposed methodology is meticulously compared with the prevailing MCDM methods to validate its dominance in handling uncertainty and the bipolarity of the criteria. This article helps security professionals choose the right AI-powered visualization tools which, in turn improve the cybersecurity of their organizations and make it easier to detect threats.

The integration of AI simulation models within smart electrical prosthetic systems represents a significant advancement in disability disease diagnosis. However, the selection and evaluation of these AI models interpret some multi-criteria decision-making dilemmas because of the presence of uncertainty and bipolarity (positive and negative aspects) of the selection criteria. Current literature lacks the selection and evaluation of AI simulation models that consider both bipolarity and uncertainty of the criteria, while prevailing Choquet integral aggregation operators despite their strong capabilities for handling information relationships, fail to efficiently process bipolar fuzzy information. The existence of this limitation makes it challenging to identify element interactions and non-linear relationships in uncertain environments containing both positive and negative aspects. To overcome these gaps, first, we develop two operators that are the bipolar fuzzy Choquet integral averaging and bipolar fuzzy Choquet integral geometric operators that uniquely integrate dual aspects (bipolarity) with criterion interaction modeling capabilities, fundamentally differing from traditional fuzzy approaches that cannot simultaneously process dual aspects of criterion. Secondly, we design a new multi-criteria decision-making approach using these operators to assess AI simulation models for prosthetic systems, since the criteria involved such as diagnostic accuracy, computational efficiency, and system reliability, have both positive and negative aspects that need to be considered together. Our method was applied in detail to select AI simulation models for smart electrical prosthetic systems and compared with some prevailing methods and standard Choquet integral approaches. This showed that our method is more precise and produces better evaluation results. It introduces a new theoretical basis for bipolar fuzzy Choquet integral aggregation and offers medical professionals a reliable way to pick the best AI simulation models for important prosthetic applications that influence patient outcomes and the functioning of prosthetics.