An intuitionistic fuzzy data-driven product ranking model using sentiment analysis and multi-criteria decision-making

2021 ◽  
Vol 173 ◽  
pp. 121158
Author(s):  
Jalil Heidary Dahooie ◽  
Romina Raafat ◽  
Ali Reza Qorbani ◽  
Tugrul Daim
Keyword(s):  
Decision Making ◽  
Sentiment Analysis ◽  
Data Driven ◽  
Fuzzy Data ◽  
Multi Criteria Decision Making ◽  
Intuitionistic Fuzzy ◽  
Ranking Model ◽  
Intuitionistic Fuzzy Data ◽  
Product Ranking

MAGDM-Miner

Fuzzy Systems ◽  
2017 ◽  
pp. 1708-1738
Author(s):  
John P. Robinson ◽  
Henry E.C. Amirtharaj
Keyword(s):  
Decision Making ◽  
Fuzzy Data ◽  
Rule Mining ◽  
Numerical Illustration ◽  
Aggregated Data ◽  
Intuitionistic Fuzzy ◽  
Fuzzy Correlation ◽  
Decision Making System ◽  
Correlation Rules ◽  
Intuitionistic Fuzzy Data

In this article, the authors propose a new framework called the MAGDM-Miner, for mining correlation rules from trapezoidal intuitionistic fuzzy data efficiently. In the MAGDM-Miner, the raw data from a Multiple Attribute Group Decision Making (MAGDM) problem with trapezoidal intuitionistic fuzzy data are first pre-processed using some arithmetic aggregation operators. The aggregated data in turn are processed for efficient data selection through fuzzy correlation rule mining where the unwanted or less important decision variables are pruned from the decision making system. Using this MAGDM-Miner, a decision-maker can overcome the drawbacks in the conventional methods of Decision Support Systems (DSS) especially when dealing with large data-set. The algorithm is also presented, in which the technique of Fuzzy Correlation Rule Mining (FCRM) is fused into the MAGDM problem, in order to enhance the efficiency and accuracy in decision making environment. A numerical illustration is presented to show the effectiveness and accuracy of the newly developed MAGDM-Miner algorithm.

TOPSIS in Generalized Intuitionistic Fuzzy Environment

2016 ◽  
pp. 630-642 ◽  
Author(s):  
Amal Kumar Adak ◽  
Debashree Manna ◽  
Monoranjan Bhowmik ◽  
Madhumangal Pal
Keyword(s):  
Decision Making ◽  
Multiple Attribute Decision Making ◽  
Fuzzy Data ◽  
Ideal Solution ◽  
Intuitionistic Fuzzy Numbers ◽  
Intuitionistic Fuzzy ◽  
Fuzzy Environment ◽  
Multiple Attribute ◽  
Multi Attribute Decision Making ◽  
Intuitionistic Fuzzy Data

The aim of this chapter is to investigate the multiple attribute decision making problems to a selected project with generalized intuitionistic fuzzy information in which the information about weights is completely known and the attributes values are taken from the generalized intuitionistic fuzzy environment. Here, we extend the technique for order performance by similarity to ideal solution (TOPSIS) for the generalized intuitionistic fuzzy data. In addition, obtained the concept of possibility degree of generalized intuitionistic fuzzy numbers and used to solve ranking alternative in multi-attribute decision making problems.

MAGDM-Miner

2014 ◽  
Vol 6 (1) ◽  
pp. 34-59 ◽  
Author(s):  
John P. Robinson ◽  
Henry Amirtharaj
Keyword(s):  
Decision Making ◽  
Large Data ◽  
Fuzzy Data ◽  
Rule Mining ◽  
Numerical Illustration ◽  
Data Set ◽  
Intuitionistic Fuzzy ◽  
Fuzzy Correlation ◽  
Multiple Attribute ◽  
Intuitionistic Fuzzy Data

In this article, the authors propose a new framework called the MAGDM-Miner, for mining correlation rules from trapezoidal intuitionistic fuzzy data efficiently. In the MAGDM-Miner, the raw data from a Multiple Attribute Group Decision Making (MAGDM) problem with trapezoidal intuitionistic fuzzy data are first pre-processed using some arithmetic aggregation operators. The aggregated data in turn are processed for efficient data selection through fuzzy correlation rule mining where the unwanted or less important decision variables are pruned from the decision making system. Using this MAGDM-Miner, a decision-maker can overcome the drawbacks in the conventional methods of Decision Support Systems (DSS) especially when dealing with large data-set. The algorithm is also presented, in which the technique of Fuzzy Correlation Rule Mining (FCRM) is fused into the MAGDM problem, in order to enhance the efficiency and accuracy in decision making environment. A numerical illustration is presented to show the effectiveness and accuracy of the newly developed MAGDM-Miner algorithm.

scholarly journals Fire Risk Assessment in Healthcare Settings: Application of FMEA Combined with Multi‐Criteria Decision Making Methods

2020 ◽  
Vol 2020 ◽  
pp. 1-12
Author(s):  
Fateme Omidvari ◽  
Mehdi Jahangiri ◽  
Reza Mehryar ◽  
Moslem Alimohammadlou ◽  
Mojtaba Kamalinia
Keyword(s):  
Risk Assessment ◽  
Decision Making ◽  
Fire Risk ◽  
Fire Detection ◽  
Healthcare Setting ◽  
Multi Criteria Decision Making ◽  
Intuitionistic Fuzzy ◽  
Healthcare Settings ◽  
Fire Risk Assessment ◽  
Risk Parameters

Fire is one of the most dangerous phenomena causing major casualties and financial losses in hospitals and healthcare settings. In order to prevent and control the fire sources, first risk assessment should be conducted. Failure Mode and Effect Analysis (FMEA) is one of the techniques widely used for risk assessment. However, Risk Priority Number (RPN) in this technique does not take into account the weight of the risk parameters. In addition, indirect relationships between risk parameters and expert opinions are not considered in decision making in this method. The aim is to conduct fire risk assessment of healthcare setting using the application of FMEA combined with Multi‐Criteria Decision Making (MCDM) methods. First, a review of previous studies on fire risk assessment was conducted and existing rules were identified. Then, the factors influencing fire risk were classified according to FMEA criteria. In the next step, weights of fire risk criteria and subcriteria were determined using Intuitionistic Fuzzy Multiplicative Best-Worst Method (IFMBWM) and different wards of the hospital were ranked using Interval-Valued Intuitionistic Fuzzy Combinative Distance-based Assessment (IVIFCODAS) method. Finally, a case study was performed in one of the hospitals of Shiraz University of Medical Sciences. In this study, fire alarm system (0.4995), electrical equipment and installations (0.277), and flammable materials (0.1065) had the highest weight, respectively. The hospital powerhouse also had the highest fire risk, due to the lack of fire extinguishers, alarms and fire detection, facilities located in the basement floor, boilers and explosive sensitivity, insufficient access, and housekeeping. The use of MCDM methods in combination with the FMEA method assesses the risk of fire in hospitals and health centers with great accuracy.

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