Research Expansion Alliance (REA) on behalf of Ayandegan Institute of Higher EducationJournal of Fuzzy Extension and Applications2783-14422220210601New characterization theorems of the mp-quantales10611912890610.22105/jfea.2021.279892.1090ENGeorge GeorgescuDepartment of Computer Science, Faculty of Mathematics and Computer Science, University of Bucharest, Bucharest, Romania.Journal Article20210307The mp-quantales were introduced in a previous paper as an abstraction of the lattices of ideals in mp-rings and the lattices of ideals in conormal lattices. Several properties of m-rings and conormal lattices were generalized to mp-quantales. In this paper we shall prove new characterization theorems for mp-quantales and for semiprime mp-quantales (these last structures coincide with the P F-quantales). Some proofs reflect the way in which the reticulation functor (from coherent quantales to bounded distributive lattices) allows us to export some properties from conormal lattices to mp-quantales.Research Expansion Alliance (REA) on behalf of Ayandegan Institute of Higher EducationJournal of Fuzzy Extension and Applications2783-14422220210601Regular T_0 ‐type Separations in Fuzzy Topological Spaces in the Sense of Quasi‐coincidence12012913015910.22105/jfea.2021.275958.1085ENGurusamy SaravanakumarDepartment of Mathematics
M.Kumarasamy College of Engineering
Karur, India0000-0003-1309-7609S. TamilselvanMathematics Section, Faculty of Engineering and Technology, Annamalai University, Annamalainagar, India-608002.A. VadivelDepartment of Mathematics, Government Arts College (Autonomous), Karur, India-639007.Journal Article20210202In this paper, we introduce and study three notions of property in fuzzy topological spaces using quasi-coincidence sense, and we relate to other such notions. Then, we show that all these notions satisfy good extension property. These concepts also satisfy hereditary, productive and projective properties. We note that all these concepts are preserved under one-one, onto, fuzzy regular open and fuzzy regular continuous mappings. Finally, we discuss initial and final fuzzy topological spaces on our concepts.Research Expansion Alliance (REA) on behalf of Ayandegan Institute of Higher EducationJournal of Fuzzy Extension and Applications2783-14422220210601New view of fuzzy aggregations. part I: general information structure for decision-making models13014312789410.22105/jfea.2021.275084.1080ENGia SirbiladzeDepartment of Computer Sciences, Javakhishvili Tbilisi State University, Tbilisi.Journal Article20210225The Ordered Weighted Averaging (OWA) operator was introduced by Yager [57] to provide a method for aggregating inputs that lie between the max and min operators. In this article two variants of probabilistic extensions the OWA operator-POWA and FPOWA (introduced by Merigo [26] and [27]) are considered as a basis of our generalizations in the environment of fuzzy uncertainty (parts II and III of this work), where different monotone measures (fuzzy measure) are used as uncertainty measures instead of the probability measure. For the identification of “classic” OWA and new operators (presented in parts II and III) of aggregations, the Information Structure is introduced where the incomplete available information in the general decision-making system is presented as a condensation of uncertainty measure, imprecision variable and objective function of weights.Research Expansion Alliance (REA) on behalf of Ayandegan Institute of Higher EducationJournal of Fuzzy Extension and Applications2783-14422220210601Well drilling fuzzy risk assessment using fuzzy FMEA and fuzzy TOPSIS14415512789510.22105/jfea.2021.275955.1086ENMazdak Khodadadi-KarimvandDepartment of Industrial Engineering, Najafabad Branch, Islamic Azad University, Iran.0000-0003-2765-3784Hadi ShirouyehzadDepartment of Industrial Engineering, Najafabad Branch, Islamic Azad University, Iran.Journal Article20210203One of the most important issues that organizations have to deal with is the timely identification and detection of risk factors aimed at preventing incidents. Managers’ and engineers’ tendency towards minimizing risk factors in a service, process or design system has obliged them to analyze the reliability of such systems in order to minimize the risks and identify the probable errors. Concerning what was just mentioned, a more accurate Failure Mode and Effects Analysis (FMEA) is adopted based on fuzzy logic and fuzzy numbers. Fuzzy TOPSIS is also used to identify, rank, and prioritize error and risk factors. This paper uses FMEA as a risk identification tool. Then, Fuzzy Risk Priority Number (FRPN) is calculated and triangular fuzzy numbers are prioritized through Fuzzy TOPSIS. In order to have a better understanding toward the mentioned concepts, a case study is presented.Research Expansion Alliance (REA) on behalf of Ayandegan Institute of Higher EducationJournal of Fuzzy Extension and Applications2783-14422220210601Application of fuzzy algebraic model to statistical analysis of neuro-psychopathology data15616212957110.22105/jfea.2021.279105.1089ENSemiu AyinlaAlayandeDepartment of Mathematical Sciences, College of Natural Sciences, Redeemer’s University Ede, Osun state
Nigeria.0000-0002-0259-8961Ezekiel AkandeDepartment of Mathematical Sciences, College of Natural Sciences, Redeemer’s University Ede, Osun state
Nigeria.Amanze EgereDepartment of Mathematical Sciences, College of Natural Sciences, Redeemer’s University Ede, Osun state
Nigeria.Journal Article20210101The purpose of this paper is to describe and present applications of fuzzy logic in analysis of certain neuro-psychopathological symptoms. These symptoms have been linked to conditions relating to occupational hazards. Our method of data analysis which is based on Hamacher operation on picture fuzzy sets is then applied to analyze such occupational hazards. Our result proves to be effective and applicable in medical decision processes especially in situations where such neuro-psychopathological symptoms are detectable by first-aid diagnostic machines.Research Expansion Alliance (REA) on behalf of Ayandegan Institute of Higher EducationJournal of Fuzzy Extension and Applications2783-14422220210601Fuzzy hypersoft sets and its weightage operator for decision making16317013003810.22105/jfea.2021.275132.1083ENSomen DebnathDepartment of Mathematics, Umakanta Academy, Agartala-799001, Tripura, India.0000-0001-5953-6123Journal Article20210206Hypersoft set is an extension of the soft set where there is more than one set of attributes occur and it is very much helpful in multi-criteria group decision making problem. In a hypersoft set, the function F is a multi-argument function. In this paper, we have used the notion of Fuzzy Hypersoft Set (FHSS), which is a combination of fuzzy set and hypersoft set. In earlier research works the concept of Fuzzy Soft Set (FSS) was introduced and it was applied successfully in various fields. The FHSS theory gives more flexibility as compared to FSS to tackle the parameterized problems of uncertainty. To overcome the issue where FSS failed to explain uncertainty and incompleteness there is a dire need for another environment which is known as FHSS. It works well when there is more complexity involved in the parametric data i.e the data that involves vague concepts. This work includes some basic set-theoretic operations on FHSSs and for the reliability and the authenticity of these operations, we have shown its application with the help of a suitable example. This example shows that how FHSS theory plays its role to solve real decision-making problems.Research Expansion Alliance (REA) on behalf of Ayandegan Institute of Higher EducationJournal of Fuzzy Extension and Applications2783-14422220210601On the prediction of Covid-19 time series: an intuitionistic fuzzy logic approach17119012855110.22105/jfea.2021.263890.1070ENImo EyoDepartment of Computer Science, University of Uyo, Uyo, Akwa Ibom State, Nigeria.0000-0002-6548-7644Jeremiah EyohSchool of Electrical, Electronics and Systems Engineering, AVRRC, Loughborough University, Loughborough, UK.Uduak UmohDepartment of Computer Science, University of Uyo, Uyo, Akwa Ibom State, Nigeria.Journal Article20201227This paper presents a time series analysis of a novel coronavirus, COVID-19, discovered in China in December 2019 using intuitionistic fuzzy logic system with neural network learning capability. Fuzzy logic systems are known to be universal approximation tools that can estimate a nonlinear function as closely as possible to the actual values. The main idea in this study is to use intuitionistic fuzzy logic system that enables hesitation and has membership and non-membership functions that are optimized to predict COVID-19 outbreak cases. Intuitionistic fuzzy logic systems are known to provide good results with improved prediction accuracy and are excellent tools for uncertainty modelling. The hesitation-enabled fuzzy logic system is evaluated using COVID-19 pandemic cases for Nigeria, being part of the COVID-19 data for African countries obtained from Kaggle data repository. The hesitation-enabled fuzzy logic model is compared with the classical fuzzy logic system and artificial neural network and shown to offer improved performance in terms of root mean squared error, mean absolute error and mean absolute percentage error. Intuitionistic fuzzy logic system however incurs a setback in terms of the high computing time compared to the classical fuzzy logic system.