Intuitionistic Fuzzy Modulus Similarity Measure

Gora, Pawan; Tomar, Vijay Prakash

doi:10.4018/ijdsst.315757

Cited by 3 publications

(3 citation statements)

References 49 publications

(54 reference statements)

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“…It is noted that for aggregation, the results of many participants based on TWD is a very difficult problem. Researchers used the classical way to calculate the alternatives for TWD [37][38][39][40]. In the existing TWD model [25,37], to determine the equivalence classes, an external concept is required.…”

Section: Motivation For Proposed Workmentioning

confidence: 99%

A Novel Interval-Valued Decision Theoretic Rough Set Model with Intuitionistic Fuzzy Numbers Based on Power Aggregation Operators and Their Application in Medical Diagnosis

Ali,

Shaheen,

Haq

et al. 2023

Mathematics

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Intuitionistic fuzzy information is a potent tool for medical diagnosis applications as it can represent imprecise and uncertain data. However, making decisions based on this information can be challenging due to its inherent ambiguity. To overcome this, power aggregation operators can effectively combine various sources of information, including expert opinions and patient data, to arrive at a more accurate diagnosis. The timely and accurate diagnosis of medical conditions is crucial for determining the appropriate treatment plans and improving patient outcomes. In this paper, we developed a novel approach for the three-way decision model by utilizing decision-theoretic rough sets and power aggregation operators. The decision-theoretic rough set approach is essential in medical diagnosis as it can manage vague and uncertain data. The redesign of the model using interval-valued classes for intuitionistic fuzzy information further improved the accuracy of the diagnoses. The intuitionistic fuzzy power weighted average (IFPWA) and intuitionistic fuzzy power weighted geometric (IFPWG) aggregation operators are used to aggregate the attribute values of the information system. The established operators are used to combine information within the intuitionistic fuzzy information system. The outcomes of various alternatives are then transformed into interval-valued classes through discretization. Bayesian decision rules, incorporating expected loss factors, are subsequently generated based on this foundation. This approach helps in effectively combining various sources of information to arrive at more accurate diagnoses. The proposed approach is validated through a medical case study where the participants are classified into three different regions based on their symptoms. In conclusion, the decision-theoretic rough set approach, along with power aggregation operators, can effectively manage vague and uncertain information in medical diagnosis applications. The proposed approach can lead to timely and accurate diagnoses, thereby improving patient outcomes.

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Section: Motivation For Proposed Workmentioning

confidence: 99%

A Novel Interval-Valued Decision Theoretic Rough Set Model with Intuitionistic Fuzzy Numbers Based on Power Aggregation Operators and Their Application in Medical Diagnosis

Ali,

Shaheen,

Haq

et al. 2023

Mathematics

View full text Add to dashboard Cite

show abstract

“…It should be highlighted that aggregating the outcomes of numerous participants through TWD poses a significant challenge. Researchers have traditionally employed conventional methods to compute alternatives for TWD, as evidenced by references [37][38][39][40]. In the existing TWD model, as outlined in references [25,37], an external concept becomes necessary for determining equivalence classes.…”

Section: Introductionmentioning

confidence: 99%

An Innovative Approach on Yao’s Three-Way Decision Model Using Intuitionistic Fuzzy Sets for Medical Diagnosis

Ali,

Shaheen,

Haq

et al. 2024

Neutrosophic syst. appl.

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In the realm of medical diagnosis, intuitionistic fuzzy data serves as a valuable tool for representing information that is uncertain and imprecise. Nevertheless, decision-making based on this kind of knowledge can be quite challenging due to the inherent vagueness of the data. To address this issue, we employ power aggregation operators, which prove effective in combining several sources of data, such as expert thoughts and patient information. This allows for a more correct diagnosis; a particularly crucial aspect of medical practice where precise and timely diagnoses can significantly impact medication policy and patient results. In our research, we introduce a novel methodology to the three-way decision idea. Initially, we revamp the three-way decision model using rough set theory and incorporate interval-valued classes to handle intuitionistic fuzzy data. Secondly, we explore the use of intuitionistic fuzzy power weighted and intuitionistic fuzzy power weighted geometric aggregation operators to consolidate attribute values within the data system. Furthermore, we present a case study in the medical field to exhibit the validity and efficiency of our offered technique. This innovative method enables us to classify participants into three distinct zones based on their symptoms. The manuscript concludes with a summary of key points provided by the authors.

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“…In their work, they considered the training of data set and the normalization of data is also done for better automated predictions. The author in [5] proposed different modulus similarity measures based on Intuitionistic Fuzzy sets, which will be very supportive in the field of MAGDM problems. Comparisons were made with many existing similarity measures in the literature.…”

Section: Introductionmentioning

confidence: 99%

A Novel Learning Rule in Intuitionistic Fuzzy Artificial Neural Network and its Applications to MAGDM Support System

Robinson

2023

Preprint

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In this work, Artificial Neural Network (ANN)-based Multiple Attribute Group Decision Making (MAGDM) models have been provided. These models take into consideration pairs of justifications referred to as weighted geometric pairs, where one component is employed to induce an ordering over the second component, which are intuitionistic fuzzy values, and then aggregated. The recommended strategy of a novel Delta Learning rule named as Robin-Delta Learning rule-based ANN is proposed in this article, where the inputs take the form of intuitionistic fuzzy matrices and are resolved and comparisons are made with the traditional Delta Learning Rule with a numerical illustration. Because it removes the unimportant decision alternatives from the system, the Robin-Delta ANN, which is a groundbreaking contribution in the field of intuitionistic fuzzy ANN, proves to be more effective than the previous methods of MAGDM problems and hence provide a new decision support system in the field of decision making.

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Intuitionistic Fuzzy Modulus Similarity Measure

Cited by 3 publications

References 49 publications

A Novel Interval-Valued Decision Theoretic Rough Set Model with Intuitionistic Fuzzy Numbers Based on Power Aggregation Operators and Their Application in Medical Diagnosis

A Novel Interval-Valued Decision Theoretic Rough Set Model with Intuitionistic Fuzzy Numbers Based on Power Aggregation Operators and Their Application in Medical Diagnosis

An Innovative Approach on Yao’s Three-Way Decision Model Using Intuitionistic Fuzzy Sets for Medical Diagnosis

A Novel Learning Rule in Intuitionistic Fuzzy Artificial Neural Network and its Applications to MAGDM Support System

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