MODELING OF COGNITIVE STRUCTURE OF UNCERTAIN SCIENTIFIC CONCEPTS USING FUZZY-ROUGH SETS AND INTUITIONISTIC FUZZY SETS: EXAMPLE OF THE LIFE CONCEPT

2009 ◽  
Vol 17 (05) ◽  
pp. 747-769 ◽  
Author(s):  
NURETTIN YOREK ◽  
SERKAN NARLI
Keyword(s):  
Rough Set ◽  
Fuzzy Set ◽  
Structural Model ◽  
Intuitionistic Fuzzy Set ◽  
Cognitive Structure ◽  
Human Motion ◽  
Scientific Uncertainty ◽  
Structured Interviews ◽  
Fuzzy Rough Set ◽  
Intuitionistic Fuzzy

In this study, using fuzzy-rough set and intuitionistic fuzzy set approaches, we propose a cognitive structural model for the concept of life for which a certain definition can not be made because of scientific uncertainty as well as moral, legal, and theological aspects. Total 191 first-year students from seven different high schools in a large western city in Turkey participated in the study. An open-ended conceptual understanding (CULC) test, developed by the researcher, was used for data collection. Semi-structured interviews were carried out with 14 students and their biology teachers to clarify ambiguous points in students' responses to the CULC test. The results of analyses indicated that students constructed the concept of life by associating it predominantly with 'human'. Motion appeared as the most frequently associated term with the concept of life. The results suggest that the life concept has been constructed using animistic-anthropocentric cognitive schemes. In the next step, we evaluated the data obtained from the CULC test using the fuzzy-rough set and intuitionistic fuzzy set theories. Consequently, we propose an 'animistic-anthropocentric structural model' about cognitive construction of the concept of life.

A Hybrid Scheme for Breast Cancer Detection Using Intuitionistic Fuzzy Rough Set Technique

Biometrics ◽  
2017 ◽  
pp. 1195-1219 ◽  
Author(s):  
Chiranji Lal Chowdhary ◽  
D. P. Acharjya
Keyword(s):  
Feature Extraction ◽  
Rough Set ◽  
Fuzzy Set ◽  
Intuitionistic Fuzzy Set ◽  
Hybrid Scheme ◽  
Fuzzy Rough Set ◽  
Intuitionistic Fuzzy ◽  
Prime Concern ◽  
Diagnosis Of Cancer ◽  
Occurrence Matrix

Diagnosis of cancer is of prime concern in recent years. Medical imaging is used to analyze these diseases. But, these images contain uncertainties due to various factors and thus intelligent techniques are essential to process these uncertainties. This paper hybridizes intuitionistic fuzzy set and rough set in combination with statistical feature extraction techniques. The hybrid scheme starts with image segmentation using intuitionistic fuzzy set to extract the zone of interest and then to enhance the edges surrounding it. Further feature extraction using gray-level co-occurrence matrix is presented. Additionally, rough set is used to engender all minimal reducts and rules. These rules then fed into a classifier to identify different zones of interest and to check whether these points contain decision class value as either cancer or not. The experimental analysis shows the overall accuracy of 98.3% and it is higher than the accuracy achieved by hybridizing fuzzy rough set model.

A Hybrid Scheme for Breast Cancer Detection using Intuitionistic Fuzzy Rough Set Technique

2016 ◽  
Vol 11 (2) ◽  
pp. 38-61 ◽  
Author(s):  
Chiranji Lal Chowdhary ◽  
D. P. Acharjya
Keyword(s):  
Feature Extraction ◽  
Rough Set ◽  
Fuzzy Set ◽  
Intuitionistic Fuzzy Set ◽  
Hybrid Scheme ◽  
Fuzzy Rough Set ◽  
Intuitionistic Fuzzy ◽  
Prime Concern ◽  
Diagnosis Of Cancer ◽  
Occurrence Matrix

Diagnosis of cancer is of prime concern in recent years. Medical imaging is used to analyze these diseases. But, these images contain uncertainties due to various factors and thus intelligent techniques are essential to process these uncertainties. This paper hybridizes intuitionistic fuzzy set and rough set in combination with statistical feature extraction techniques. The hybrid scheme starts with image segmentation using intuitionistic fuzzy set to extract the zone of interest and then to enhance the edges surrounding it. Further feature extraction using gray-level co-occurrence matrix is presented. Additionally, rough set is used to engender all minimal reducts and rules. These rules then fed into a classifier to identify different zones of interest and to check whether these points contain decision class value as either cancer or not. The experimental analysis shows the overall accuracy of 98.3% and it is higher than the accuracy achieved by hybridizing fuzzy rough set model.

The prediction of online time series with concept drift based on dynamic intuitionistic fuzzy cognitive map

2021 ◽  
Vol 25 (4) ◽  
pp. 949-972
Author(s):  
Nannan Zhang ◽  
Xixi Yao ◽  
Chao Luo
Keyword(s):  
Fuzzy Set ◽  
Sequence Data ◽  
Concept Drift ◽  
Intuitionistic Fuzzy Set ◽  
Cognitive Map ◽  
Fuzzy Cognitive Maps ◽  
Fuzzy Cognitive Map ◽  
Online Data ◽  
Intuitionistic Fuzzy ◽  
Public Data

Fuzzy cognitive maps (FCMs) have widely been applied for knowledge representation and reasoning. However, in real life, reasoning is always accompanied with hesitation, which is deriving from the uncertainty and fuzziness. Especially, when processing the online data, since the internal and external interference, the distribution and characteristics of sequence data would be considerably changed along with the passage of time, which further increase the difficulty of modeling. In this article, based on intuitionistic fuzzy set theory, a new dynamic intuitionistic fuzzy cognitive map (DIFCM) scheme is proposed for online data prediction. Combined with a novel detection algorithm of concept drift, the structure of DIFCM can be adaptively updated with the online learning scheme, which can effectively improve the representation of online information by capturing the real-time changes of sequence data. Moreover, in order to tackle with the possible hesitancy in the process of modeling, intuitionistic fuzzy set is applied in the construction of dynamic FCM, where hesitation degree as a quantitative index explicitly expresses the hesitancy. Finally, a series of experiments using public data sets verify the effectiveness of the proposed method.

Complex intuitionistic fuzzy Maclaurin symmetric mean operators and its application to emergency program selection

2021 ◽  
pp. 1-22
Author(s):  
Riaz Ali ◽  
Saleem Abdullah ◽  
Shakoor Muhammad ◽  
Muhammad Naeem ◽  
Ronnason Chinram
Keyword(s):  
Decision Making ◽  
Fuzzy Sets ◽  
Fuzzy Set ◽  
Intuitionistic Fuzzy Set ◽  
Score Functions ◽  
Intuitionistic Fuzzy Numbers ◽  
Intuitionistic Fuzzy ◽  
Program Selection ◽  
Maclaurin Symmetric Mean ◽  
Complex Intuitionistic Fuzzy Set

Due to the indeterminacy and uncertainty of the decision-makers (DM) in the complex decision making problems of daily life, evaluation and aggregation of the information usually becomes a complicated task. In literature many theories and fuzzy sets (FS) are presented for the evaluation of these decision tasks, but most of these theories and fuzzy sets have failed to explain the uncertainty and vagueness in the decision making issues. Therefore, we use complex intuitionistic fuzzy set (CIFS) instead of fuzzy set and intuitionistic fuzzy set (IFS). A new type of aggregation operation is also developed by the use of complex intuitionistic fuzzy numbers (CIFNs), their accuracy and the score functions are also discussed in detail. Moreover, we utilized the Maclaurin symmetric mean (MSM) operator, which have the ability to capture the relationship among multi-input arguments, as a result, CIF Maclarurin symmetric mean (CIFMSM) operator and CIF dual Maclaurin symmetric mean (CIFDMSM) operator are presented and their characteristics are discussed in detail. On the basis of these operators, a MAGDM method is presented for the solution of group decision making problems. Finally, the validation of the propounded approach is proved by evaluating a numerical example, and by the comparison with the previously researched results.

ERP software selection using intuitionistic fuzzy and interval grey number-based MACBETH method

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Mustafa Said Yurtyapan ◽  
Erdal Aydemir
Keyword(s):  
Decision Making ◽  
Fuzzy Set ◽  
Selection Process ◽  
Intuitionistic Fuzzy Set ◽  
Decision Makers ◽  
Software Selection ◽  
Content Type ◽  
Intuitionistic Fuzzy ◽  
Expert Opinions ◽  
Erp Software

PurposeEnterprise Resource Planning (ERP) software which is a knowledge-based design on the interconnective communication of business units and information share, ensures that business processes such as finance, production, purchasing, sales, logistics and human resources, are integrated and gathered under one roof. This integrated system allows the company to make fast and accurate decisions and increases its competitiveness. Therefore, for an enterprise, choosing the suitable ERP software is extremely important. The aim of this study is to present new research on the ERP software selection process by clarifying the uncertainties and find suitable software in a computational way.Design/methodology/approachERP selection problem design includes uncertainties on the expert opinions and the criteria values using intuitionistic fuzzy set theory and interval grey-numbers to MACBETH multi criteria decision making method. In this paper, a new interval grey MACBETH method approach is proposed, and the degree of greyness approach is used for clarifying the uncertainties. Using this new approach in which grey numbers are used, it is aimed to observe the changes in the importance of the alternatives. Moreover, the intuitionistic fuzzy set method is applied by considering the importance of expert opinions separately.FindingsThe proposed method is based on quantitative decision making derived from qualitative judgments. The results given under uncertain conditions are compared with the results obtained under crisp conditions of the same methods. With the qualitative levels of experts reflected in the decision process, it is clearly seen that ERP software selection problem area has more effective alternative decision solutions to the uncertain environment, and decision makers should not undervalue the unsteadiness of criteria during ERP software selection process.Originality/valueThis study contributes to the relevant literature by (1) utilizing the MACBETH method in the selection of the ERP software by optimization, and (2) validating the importance of expert opinions with uncertainties on a proper ERP software selection procedure. So, the findings of this study can help the decision-makers to evaluate the ERP selection in uncertain conditions.

Intuitionistic Fuzzy X-Subalgebra of Near-Subtraction Semigroups

2021 ◽  
Vol 7 (3) ◽  
Author(s):  
J. Siva Ranjini ◽  
V. Mahalakshmi
Keyword(s):  
Fuzzy Set ◽  
Intuitionistic Fuzzy Set ◽  
Intuitionistic Fuzzy ◽  
Algebraic Properties

The theory of Intuitionistic fuzzy set is the extension of the fuzzy set that deals with truth and false membership data. We will discuss along with some fundamentals and their algebraic Properties. The results obtained are entirely more beneficial to the researchers. We also expand the Complement of the Set and Homomorphism. The motivation of the present manuscript is to extend the concept of Intuitionistic fuzzy X-subalgebra in near-subtraction semigroups.

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