Research Expansion Alliance (REA) on behalf of Ayandegan Institute of Higher EducationJournal of Fuzzy Extension and Applications2783-14423420221001Fuzzy simple linear regression using Gaussian membership functions minimization problem27928915510710.22105/jfea.2022.345298.1222ENBesma BelhadjLaREQuaD, FSEGT, University of ElManar, Tunisia.0000-0001-7464-6208Journal Article20220602Under the additional assumption that the errors are normally distributed, the Ordinary Least Squares (OLS) method is the maximum likelihood estimator. In this paper, we propose, for a simple regression, an estimation method alternative to the OLS method based on a so-called Gaussian membership function, one that checks the validity of the verbal explanation suggested by the observer. The fuzzy estimation approach demonstrated here is based on a suitable framework for a natural behavior observed in nature. An application based on a group of MENA countries in 2015 is presented to estimate the employment poverty relationship.https://www.journal-fea.com/article_155107_24583b21a95541723ceb8e7bc158641e.pdfResearch Expansion Alliance (REA) on behalf of Ayandegan Institute of Higher EducationJournal of Fuzzy Extension and Applications2783-14423420221001Fuzzy cognitive study on post pandemic impact on occupational shift in rural areas29030115442110.22105/jfea.2022.352027.1225ENNivetha MartinDepartment of Mathematics, Arul Anandar College (Autonomous), Karumathur, India.A.Velankanni AnanthDepartment of Mathematics, Arul Anandar College (Autonomous), Karumathur, India.P.K. SharmilaDepartment of Mathematics, Arul Anandar College (Autonomous), Karumathur, India.T. PriyaDepartment of Mathematics, Arul Anandar College (Autonomous), Karumathur, India.Journal Article20220416The pandemic has created a wide range of impacts on the livelihood of the people especially in their occupation and income generation. The horrific pandemic impacts have caused the populace to switch their occupations for the sake of their livelihood sustainability. This research works aims in determining the impacts of the occupational shifts especially in case of rural populace. The decision-making method of Fuzzy Cognitive Maps (FCM) is used in combinations with the statistical data collection methods of survey methodology, participatory approach and multi stage purposive sampling. It is observed that a significant percentage of people have shifted from their occupation and the occupational shifts have impacts on the personal, economic, social and health dimensions of the rural populace. The factors under each dimension and their inter associational impacts are also determined using the method of FCM and FCM Expert software. Based on the findings of the research work, it is very evident that the occupational shifts have created a lot of impacts on the livelihood of the rural populace and also each of the person has experienced the impacts more personally. The societal contribution of the research lies in communicating the results and inferences to the concerned administrators so as to facilitate the affected rural populace in getting back to their primary occupation.https://www.journal-fea.com/article_154421_12dbee6310d996b73a0f1be709174d0b.pdfResearch Expansion Alliance (REA) on behalf of Ayandegan Institute of Higher EducationJournal of Fuzzy Extension and Applications2783-14423420221001Detection of counterfeit banknotes using genetic fuzzy system30231215462210.22105/jfea.2022.345344.1223ENMahmut DirikDepartment of Computer Engineering, Sirnak University, Turkey.0000-0003-1718-5075Journal Article20220602Due to developments in printing technology, the number of counterfeit banknotes is increasing every year. Finding an effective method to detect counterfeit banknotes is an important task in business. Finding a reliable method to detect counterfeit banknotes is a crucial challenge in the world of economic transactions. Due to technological development, counterfeit banknotes may pass through the counterfeit banknote detection system based on physical and chemical properties undetected. In this study, an intelligent counterfeit banknote detection system based on a Genetic Fuzzy System (GFS) is proposed to detect counterfeit banknotes efficiently. GFS is a hybrid system that uses a network architecture to fine-tune the membership functions of a fuzzy inference system. The learning algorithms Fuzzy Classification, Genetic Fuzzy Classification, ANFIS Classification, and Genetic ANFIS Classification were applied to the dataset in the UCI machine learning repository to detect the authenticity of banknotes. The developed model was evaluated based on Accuracy (ACC), Mean Absolute Error (MAE), Root Mean Square Error (RMSE), Error Mean, Error STD, and confusion matrix. The experimental results and statistical analysis showed that the classification performance of the proposed model was evaluated as follows: Fuzzy = 97.64%, GA_Fuzzy = 98.60%, ANFIS = 80.83%, GA_ANFIS = 97.72% accuracy (ACC). This shows the significant potential of the proposed GFS models for fraud detection.https://www.journal-fea.com/article_154622_1194de5a86b581c9416be7aaf0bcab25.pdfResearch Expansion Alliance (REA) on behalf of Ayandegan Institute of Higher EducationJournal of Fuzzy Extension and Applications2783-14423420221001Soft set product extended to hypersoft set and indetermsoft set cartesian product extended to indetermhypersoft set31331615798210.22105/jfea.2022.363269.1232ENFlorentin SmarandacheUniversity of New Mexico, Gallup Campus, NM 87301, USA.0000-0002-5560-5926Journal Article20220522In this paper we define the Soft Set Product as a product of many soft sets and afterwards we extend it to the HyperSoft Set. Similarly, the IndetermSoft Product is extended to the IndetermHyperSoft Set. We also present several applications of the Soft Set Product to Fuzzy (and fuzzy-extensions) Soft Set Product and to IndetermSoft Set and IndetermHyperSoft Set.https://www.journal-fea.com/article_157982_59b6e1f8b488bbf42a4e2e10e9821938.pdfResearch Expansion Alliance (REA) on behalf of Ayandegan Institute of Higher EducationJournal of Fuzzy Extension and Applications2783-14423420221001Supply chain management problem modelling in hesitant fuzzy environment31733615527710.22105/jfea.2022.337573.1216ENMadineh FarnamDepartment of Electrical Engineering, Shohadaye Hoveizeh Campus of Technology, Shahid Chamran University of Ahvaz, Dasht-e Azadegan, Khuzestan, Iran.Majid DarehmirakiDepartment of Mathematicsو Behbahan Khatam Alanbia University of Technology, Behbahan, Ahvaz, Iran.Journal Article20220414Complex nature of the current market is often caused by uncertainties, data uncertainties, their manner of use, and differences in managers' viewpoints. To overcome these problems, Hesitant Fuzzy Sets (HFSs) can be useful as the extension of fuzzy set theory, in which the degree of membership of an element can be a set of possible values and provide greater flexibility in design and, thus, model performance. The power of this application becomes clear when different decision-makers tend to independently record their views. In most real-world situations, there are several goals for managers to achieve the desired performance. Therefore, in this study, a description of the solution of the Hesitant Fuzzy Linear Programming (HFLP) problem for solving hesitant fuzzy multi-objective problems is considered. In the following, the multi-objective and three-level supply chain management problem is modeled with the hesitant fuzzy approach. Then, with an example, the flexibility of the model responses is evaluated by the proposed method. The hesitant fuzzy model presented in this study can be extended to other supply chain management problems.https://www.journal-fea.com/article_155277_41d4523955ed6992d560d7d6b609e3ed.pdfResearch Expansion Alliance (REA) on behalf of Ayandegan Institute of Higher EducationJournal of Fuzzy Extension and Applications2783-14423420221001Basic fuzzy arithmetic operations using α–cut for the gaussian membership function33734815324010.22105/jfea.2022.339888.1218ENLeonce LeandryDepartment of Mathematics, Faculty of Commerce, Jordan University, Morogoro, Tanzania.0000-0002-4816-5424Innocent SosomaDepartment of Mathematics and Computer Science, St Augustine University of Tanzania, Mwanza, Tanzan.David KoloseniDepartment of Mathematics, University of Dar es Salaam, Dar es salaam, Tanzan.Journal Article20220428Currently fuzzy set theory has a wide range to model real life problems with incomplete or vague information which perfectly suits the reality and its application is theatrically increasing. This work explored the basic fuzzy operations with the Gaussian Membership using the α-cut method. As it is known that, the Gaussian membership function has a great role in modelling the fuzzy problems this is what impelled to explore its operation which can further be used in analysis of fuzzy problems. Primarily the basic operations which has been discussed here are addition, subtraction, multiplication, division, reciprocal, exponential, logarithmic and nth power.https://www.journal-fea.com/article_153240_a2bc1da93313daeeb25f92936855cc77.pdfResearch Expansion Alliance (REA) on behalf of Ayandegan Institute of Higher EducationJournal of Fuzzy Extension and Applications2783-14423420221001Some picture fuzzy mean operators and their applications in decision-making34936115462110.22105/jfea.2022.341302.1219ENHasan Mohammad KamrulDepartment of Mathematics, Jahangirnagar University, Savar, Bangladesh.Department of Mathematics and Statistics, Bangladesh University of Business and Technology, Dhaka, Bangladesh0009-0003-8270-2590Ali Md. YasinFaculty of Science and Engineering, University of Information Technology & Sciences, Dhaka, Bangladesh.Sultana AbedaDepartment of Mathematics, Jahangirnagar University, Savar, Bangladesh.Mitra Nirmal KantiDepartment of Mathematics and Statistics, Bangladesh University of Business and Technology, Dhaka, Bangladesh.Journal Article20220508Picture fuzzy set is the generalization of fuzzy set and intuitionistic fuzzy set. It is useful for handling uncertainty by considering the positive membership, neutral membership and negative membership degrees independently for each element of a universal set. The main objective of this article is to develop some picture fuzzy mean operators, including Picture Fuzzy Harmonic Mean (PFHM), Picture Fuzzy Weighted Harmonic Mean (PFWHM), Picture Fuzzy Arithmetic Mean (PFAM), Picture Fuzzy Weighted Arithmetic Mean (PFWAM), Picture Fuzzy Geometric Mean (PFGM) and Picture Fuzzy Weighted Geometric Mean (PFWGM), to aggregate the picture fuzzy sets. Moreover, we discuss some relevant properties of these operators. Furthermore, we apply these mean operators to make decisions with practical examples. Finally, to show the efficiency and the validity of the proposed operators, we compare our results with the results of existing methods and concluded from the comparison that our proposed methods are more effective and reliable. https://www.journal-fea.com/article_154621_e4dd1992b5ea2068de509ba506aab5c8.pdf