Intuitionistic Fuzzy Rough Aczel-Alsina Average Aggregation Operators and Their Applications in Medical Diagnoses

Abstract

Managing ambiguous and asymmetric types of information is a very challenging task under the consideration of classical data. Furthermore, Aczel-Alsina aggregation operators are the new developments in fuzzy sets theory. However, when decision-makers need to use these structures in fuzzy rough structures, these operators fail to deal with such types of values, as fuzzy rough structures use lower and upper approximation spaces. Thus, an encasement of an intuitionistic fuzzy set has a chance of data loss, whereas an intuitionistic fuzzy rough set can resolve the problem of data loss. Motivated by the notion of intuitionistic fuzzy rough sets and new aggregation operators i.e., intuitionistic fuzzy Aczel-Alsina operators, this paper firstly initiates some basic Aczel-Alsina operational rules for intuitionistic fuzzy rough numbers. Secondly, based on these newly defined operational rules, we have developed some new aggregation operators, such as intuitionistic fuzzy rough Aczel-Alsina weighted average (IFRAAWA), intuitionistic fuzzy rough Aczel-Alsina ordered weighted average (IFRAAOWA), and intuitionistic fuzzy rough Aczel-Alsina hybrid average (IFRAAHA) aggregation operators. Moreover, the properties of these aggregation operators have been initiated. These operators can help in evaluating awkward and asymmetric information in real-life problems. The use of aggregation operators in medical diagnosis and MADM is an efficient method that can help in healthcare and decision-making applications. To present an effective use of these developed operators in medical diagnosis and the selection of the best next-generation firewall, we have established an algorithm along with a numerical example to provide authenticity and clarity to the established work. Furthermore, a comparative analysis of the introduced work shows the superiority of the introduced approach.

Selection of Investment Policy Using a Novel Three-Way Group Decision Model under Intuitionistic Hesitant Fuzzy Sets

“…In this section, we applied several existing AOs to the information provided by the decisionmakers in Tables 5-8 to evaluate the validity and feasibility of our proposed methodologies. We compared our approach with IFWA [19], IFWG [18], IvIFAAWA [31], IvIFAAWG [45], HFAAW [25], IFDWA [46], FDWG [46], IFRAAWA [24], IFRAAOW [24], IFRAAHA [24], IHFPWA [14], and IHFPG [14] methods. The results of existing AOs operators are shown in Table 12.…”

“…Additionally, we observed that IFWA [19], IFWG [18], HFAAW [25], IFDWA [46], IFDWG [46], IFRAAWA [24], IFRAAOW [24], and IFRAAHA [24] effectively handle intuitionistic fuzzy and hesitant fuzzy data. However, there are certain situations where these approaches may not be suitable.…”

“…The T.N was introduced by Menger [22], and new procedures were introduced by Aczel and Alsina [23] under the names Aczel-Alsina T.N and Aczel-Alsina T.CN. Ahmmad et al [24] produced Aczel-Alsina aggregation operators for the IFR environment, while Senapati et al [25] explored novel Aczel-Alsina operators under hesitant fuzzy information and applied them in cyclone disaster assessment. Ashraf et al [26] proposed single-valued neutrosophic Aczel-Alsina and utilized them in the decision-making process.…”

An Innovative Decision Model Utilizing Intuitionistic Hesitant Fuzzy Aczel-Alsina Aggregation Operators and Its Application

“…We aim to assess the preferences of our proposed approach in comparison to existing methods. The information system described in Table 1 serves as the basis for this comparative analysis, which includes the approaches referenced in [15,18,[45][46][47][48][49][50].…”

Geometric Aggregation Operators for Solving Multicriteria Group Decision-Making Problems Based on Complex Pythagorean Fuzzy Sets

“…Several researchers studied the prominence of operators related to the IF environment in [5][6][7][8][9][10] and presented many operators with this domain for different challenges. Ahmmad et al [11] introduced the Intuitionistic Fuzzy Rough Aczel-Alsina Aggregation Operators. Rahman et al [12][13][14][15] studied various methods in the PyF environment, such as PFWG operator, PFOWG operator, PFHG operator, PFWA operator, PFOWA operator, and PFHA operator.…”