Advances in Fuzzy Systems
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Acceptance rate14%
Submission to final decision94 days
Acceptance to publication33 days
CiteScore4.200
Journal Citation Indicator0.560
Impact Factor-

An Extended Interval Type-2 Fuzzy VIKOR Technique with Equitable Linguistic Scales and Z-Numbers for Solving Water Security Problems in Malaysia

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 Journal profile

Advances in Fuzzy Systems provides an international forum for original research articles in the theory and applications of fuzzy subsets and systems.

 Editor spotlight

Chief Editor, Professor Melin, is a professor at the Tijuana Institute of Technology. Her research interests include modular neural networks, type-2 fuzzy logic, pattern recognition, fuzzy control, neuro-fuzzy and genetic-fuzzy hybrid approaches.

 Special Issues

We currently have a number of Special Issues open for submission. Special Issues highlight emerging areas of research within a field, or provide a venue for a deeper investigation into an existing research area.

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Research Article

Predictive Model of Humidity in Greenhouses through Fuzzy Inference Systems Applying Optimization Methods

Establishing the indoor and outdoor humidity values in a greenhouse allows us to describe the crop yield during its entire developmental cycle. This study seeks to develop a predictive model of indoor relative humidity values in a greenhouse with high accuracy and interpretability through the use of optimized fuzzy inference systems, in order to offer greenhouse users a clear and simple description of their behaviour. The three-phase methodology applied made use of descriptive statistics techniques, correlation analysis, and prototyping paradigm for the iterative and incremental development of the predictive model, validated through error measurement. The research resulted in six models which define the behaviour of humidity as a result of temperature, CO2, and soil moisture, with percentages of effectiveness above 90%. The implementation of a Mamdani-type fuzzy inference system, optimized by a hybrid method combining genetic and interior point algorithms, allowed to predict the relative humidity in greenhouses with high interpretability and precision, with an effectiveness percentage of 90.97% and MSE (mean square error) of 8.2e − 3.

Research Article

Extension of the TOPSIS Method for Decision Making Problems under Complex Fuzzy Data Based on the Central Point Index

This paper presents the CP-TOPSIS Model in group decision-making using complex Fuzzy Data. Complex numbers were employed in this model, and the central point index was used to define both the negative and positive ideals as well as the distance between each option. In this approach, the options are graded using complex data (due to replacing linguistic variables). One of the advantages of this model in decision-making is the capability that creates a complex fuzzy technique for investigating, grading, and selecting the best option related to complex fuzzy data. The results show that this model effectively rates and grades the complex fuzzy data through an alternative period. Quantum mechanics wave functions could not be analyzed, nor could signals or time series or stock exchange transactions predict factors of a multiperiod alternation, nor could predictions be made about any of these variables. As a result, there are numerical results in rating with high precision.

Research Article

Multiattribute Decision-Making Method Based on Hesitant Triangular Fuzzy Power Average Operator

In the decision-making process, it often happens that decision makers hesitate between several possible preference values, so the multiattribute decision-making (MADM) problem of hesitant triangle fuzzy elements (HTFEs) has been widely studied. In related research works, different operators are used to fuse information, and the weighting model is used to represent the degree of difference between information fusion on various indicators, but the mutual influence between information is often not considered. In this sense, the purpose of this paper is to study the MADM problem of the hesitant triangular fuzzy power average (HTFPA) operator. First, the hesitant triangular fuzzy power-weighted average operator (HTFPWA) and the hesitant triangular fuzzy power-weighted geometric (HTFPWG) operator are given, their properties are analyzed and special cases are discussed. Then, a MADM method based on the HTFPWA operator and the HTFPWG operator is developed, and an example of selecting futures products is used to illustrate the results of applying the proposed method to practical problems. Finally, the effectiveness and feasibility of the HTFPA operator are verified by comparative analysis with existing methods.

Research Article

Mitigation of Corruption by Implementing e-Government Using Soft Computing

Electronic government (e-government) allows citizens to contact government authorities directly through computers, smartphones, and the Internet. In the return reducing face-to-face interaction with government employees decreases their permissive role and the potential for corruption, hence enabling the government to be more effective and trustworthy and provide transparency and accountability. However, e-government is not the only aspect of the larger battle against corruption; it is not the only way to reduce corruption. e-government is successful in the fight against trivial and administrative corruption. In spite of that, broad governmental actions, including both preventative and perhaps disciplinary anticorruption measures are required to combat corruption. This research aims to identify the factors that affect success in reducing the level of corruption in e-government, and then evaluate these factors by developing a model that determines the effective factors that impact the mitigation of corruption. We believe that a soft computing-fuzzy logic algorithm is an appropriate method for evaluating and determining the effective factors, and hence might lead to a feasible way to the success of e-government. The findings revealed that the model is adaptable and may be used in e-government performance applications for government authorities and experts.

Research Article

Generalization of (Q,L)-Fuzzy Soft Subhemirings of a Hemiring

This paper investigates the properties and results of (Q,L)-fuzzy soft subhemirings ((Q,L)-FSSHR) of a hemiring R. The motivation behind this study is to utilize the concept of L-fuzzy soft set of a hemiring and to derive a few specific outcomes on (Q, L)-FSSHR. The concepts of strongest Q-fuzzy soft set relation, Q-isomorphism, pseudo-Q-fuzzy soft coset, and some of their related properties are implemented while analyzing the results. Finally, the properties are verified with a numerical example from the 2000 AMS subject classification: 05C38, 05A15, and 15A18.

Research Article

A Guide to Integrating Expert Opinion and Fuzzy AHP When Generating Weights for Composite Indices

Composite indices are a great tool for researchers and policymakers alike as they provide a simplification of reality of complex phenomena, as well as their enabling ability for cross-country comparisons. A troublesome issue with constructing composite indices is the selection of the weighting system as it can greatly influence the results of the index developed. One of the most reliable weighting systems is the expert weighting system, where experts on the topic being studied are delegated the weight selection process, and the average of their responses are then transformed into weights. The limitation of this method, however, is the high subjectivity, uncertainty, and inconsistency of the expert responses. This paper seeks to address this limitation by providing a guide to researchers on how to improve the expert weights by subjecting them to the fuzzy analytic hierarchy process (FAHP) method for multicriteria decision making (MCDM) to compute the fuzzy weights, a more objective and reliable weights relative to expert weights. That said, and despite the benefits of the FAHP method, it can produce weights that can skew the composite index results. To address this limitation, the study introduces the interval weights, which are calculated by finding the midpoint between the expert weights and the fuzzy weights. The resulting interval weights exhibit the benefits of both principal component analysis (PCA) and the FAHP process, the difference being that PCA cannot be applied for noncompensatory indices.

Advances in Fuzzy Systems
 Journal metrics
See full report
Acceptance rate14%
Submission to final decision94 days
Acceptance to publication33 days
CiteScore4.200
Journal Citation Indicator0.560
Impact Factor-
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Article of the Year Award: Outstanding research contributions of 2021, as selected by our Chief Editors. Read the winning articles.