Fuzzy Optimization and Modeling Journal (FOMJ)
type: channel
description: Fuzzy Optimization and Modeling Journal (FOMJ), a peer-reviewed research and open-access journal, has four annual issues. It provides a global forum for exchanging research findings and case studies that bridge the latest fuzzy technology and various optimization techniques. It promotes how fuzzy technology improves decision techniques and how the development of optimization methods affects fuzzy technology. This journal is peer-reviewed and publishes high-quality theoretical and practical papers on a broad range of fuzzy optimization-related topics. Journal DOI: 10.57647/fomj Journal Abbreviation: Fuzzy Optim. Model. J. Journal Title Acronym: FOMJ Journal Owner: Islamic Azad University Publisher: Islamic Azad University, Co-publishing, OICC Press, UK Keywords: Fuzzy Sets, Fuzzy Systems, Fuzzy Optimization , Fuzzy Modeling , Soft Computing , Artificial Intelligence Editor-in-Chief: Professor Ali Ebrahimnejad, PhD ( 0000-0001-6003-6601)
publisher: OICC Press
language: en
publication name: Fuzzy Optimization and Modeling Journal (FOMJ)
issn: 2676-7007
items: An ABS-GA Algorithm for Solving Fuzzy Optimization Problems
items: Efficiency Analysis of Insurance Companies under Fuzzy Environment
items: Contour-Based Video Inpainting using Neutrosophic Sets
items: DFT-Based fuzzy topological indices integrated with machine learning for accurate prediction of topical drug properties
items: Vague Graph Structures: Some New Concepts and Applications
items: A Framework for Identifying and Analyzing Drivers Affecting the Futures of Cryptocurrency FinTechs in Iran with Fuzzy Delphi and Fuzzy Dematel
items: Fixed Point Theorem in Fuzzy Metric Space
items: Anti-Fuzzy Bi-Ideals in Semirings Under S-norms
items: Choosing the Best Company for Investment according to the Financial Factors in the Neutrosophic Environment (Case Study: Automotive Industry)
items: Coping Uncertainty in the Supplier Selection Problem Using a Scenario- Based Approach and Distance Measure on Type-2 Intuitionistic Fuzzy Sets
items: Fuzzy-enhanced hybrid recommender system using autoencoder and GRU with fuzzy ACO weighted fusion
items: A hybrid fuzzy cognitive–system dynamics framework for modeling environmental risk evolution in construction megaprojects
items: Two novel distance functions for interval-valued trapezoidal intuitionistic fuzzy numbers and their applications in decision-making
items: A fuzzy-based planning method and meta-heuristic optimization for traffic signal control
items: A new weighted goal programming technique to solve fully triangular intuitionistic fuzzy multi-objective fixed charge solid transportation problem
items: A General Approach to Construct Intuitionistic Fuzzy Regression Model Based on a New Least Absolute Discrepancy
items: Enhancing Data Cleaning through the Extraction and Expansion of Relaxed Functional Dependencies
items: Simultaneous Minimization of Cost, Cycle Time, and Workstations in Intuitionistic Fuzzy Assembly Line Systems
items: Intelligent Image-Based Recognition of Rice Cultivars Using PSO-Optimized ANFIS
items: Developing a Hybrid SWOT-MCDM Framework for Formulating Smart Technology Adoption Strategies in Urban Construction Under Conditions of Uncertainty: A Case Study of Qom Municipality
items: A new approach to managing maintenance and repairs of offshore oil and gas platforms using fuzzy-DEMATEL
items: Predicting Efficiency Scores for Digital Transformation Management in the Banking System under Uncertainty Using Stochastic Data Envelopment Analysis and Machine Learning
items: Identifying and prioritizing obstacles to the implementation of building information modeling in sustainable construction projects in Iran using hybrid Fuzzy Delphi-BWM approach
items: A robust model for the Best-Worst Method (BWM) based on goal programming
items: Fuzzy logic approach in the evaluation of radiation shielding behaviour of borided Co-Cr-Mo alloy
items: Development of a series-parallel data envelopment analysis model within an intuitionistic fuzzy environment
items: A hybrid fuzzy framework for clustering and ranking listed companies via the K-means algorithm
items: A Fuzzy Logic-Based Framework for Multi-Objective Controller Placement in Software Defined Networks
items: Optimizing bipolar fuzzy fractional programmingproblems using the variable neighborhood searchalgorithm
items: A hybrid method for medical image denoising and segmentation using optimized fuzzy clustering and autoencoder

An ABS-GA Algorithm for Solving Fuzzy Optimization Problems
type: item
description: This paper presents a fuzzy programming model with LR fuzzy coefficients. To solve it efficiently, we propose a novel hybrid ABS-GA algorithm that synergistically combines the ABS algorithm for dimensionality reduction with a Genetic Algorithm (GA). First, ABS projects the original n-dimensional problem into a reduced (n−m)-dimensional subspace using the linear constraints Ax = b, ensuring feasibility and shrinking the search space. Then, a tailored GA optimizes within this reduced space, employing Ghanbari et al. [1] O(1) comparison formula for direct and efficient fuzzy number evaluation, and a novel tangent cone-based mutation operator for enhanced local exploration. Numerical experiments demonstrate that ABS-GA significantly outperforms existing methods in both solution quality and computational efficiency, validating the effectiveness of the integrated approach.
subject: Original Articles
  topic: section
subject: Triangular intuitionistic fuzzy regression model (IFRM)
  topic: keywords
subject: Full IFRM
  topic: keywords
subject: Triangular intuitionistic fuzzy numbers (TIFNs)
  topic: keywords
subject: Intuitionistic fuzzy least absolute of discrepancies (IFLAD)
  topic: keywords
subject: Homogeneity principleFuzzy optimization
  topic: keywords
subject: Hybrid ABS-GA algorithm
  topic: keywords
subject: Fuzzy comparison
  topic: keywords
subject: LR fuzzy numbers
  topic: keywords
subject: Genetic algorithm
  topic: keywords
creator: Ali Mehrabian
creator: Reza Ghanbari
creator: Khatere Ghorbani-Moghadam
rights: Copyright (c) 2026 Ali Mehrabian, Reza Ghanbari, Khatere Ghorbani-Moghadam (Author) https://creativecommons.org/licenses/by/4.0
date: 2026-03-30
publication date: 2026-03-30
doi: 10.57647/fomj.2026.0701.04

Efficiency Analysis of Insurance Companies under Fuzzy Environment
type: item
description: Performance evaluation is crucial for the sustainable growth and effective resource allocation of insurance companies. However, the uncertainty and imprecision inherent in operational and financial data limit the applicability of traditional methods. This study proposes a novel fuzzy two-stage data envelopment analysis (DEA) model to assess the efficiency of insurance companies under such conditions. The model employs trapezoidal fuzzy numbers to represent inputs, intermediate measures, and outputs, thereby providing a more robust and realistic efficiency measurement than conventional deterministic DEA. Applied to a dataset of 40 insurance companies, the model yields individual stage efficiencies, overall efficiency scores, and optimal improvement targets for each firm. The key findings reveal that this approach not only identifies significant inefficiencies but also offers actionable strategic insights. Specifically, it enables managers to enhance cost control, optimize policy issuance volumes, accelerate claims settlement, and improve customer satisfaction. These improvements are essential for achieving a sustainable competitive advantage in the highly dynamic insurance market. The study concludes that the fuzzy two-stage DEA model is a valuable tool for both managers and policymakers in making informed decisions for superior resource allocation and performance management in the insurance sector.
subject: Original Articles
  topic: section
creator: Javad Gerami
rights: Copyright (c) 2026 Javad Gerami (Author) https://creativecommons.org/licenses/by/4.0
date: 2026-03-30
publication date: 2026-03-30
doi: 10.57647/fomj.2026.0701.05

Contour-Based Video Inpainting using Neutrosophic Sets
type: item
description: Image and video inpainting represent critical challenges within the domain of visual information processing, serving pivotal roles in tasks such as restoration, reconstruction, and occlusion removal. The fundamental objective in these applications is to seamlessly restore missing or corrupted regions without introducing perceptible artifacts or structural inconsistencies. While image inpainting operates within a spatially static framework, video inpainting must additionally contend with temporal continuity, necessitating rigorous preservation of motion coherence and inter-frame consistency. This study presents an advanced inpainting methodology grounded in Neutrosophic logic, a mathematical framework adept at modeling uncertainty and indeterminacy within both spatial and intensity domains. By leveraging Neutrosophic-based segmentation, the proposed approach effectively isolates uncertain regions and guides the subsequent restoration process with enhanced precision. The primary objective of this research is to propose a novel approach for constructing an optimal contour database, which serves as a foundation for achieving superior foreground completion. This method aims to enhance the accuracy and visual coherence of the inpainting process by leveraging precise contour information, ultimately improving the overall quality of video restoration. Comprehensive experimental evaluations validate the efficacy of the proposed method, demonstrating notable improvements in perceptual quality and structural fidelity when benchmarked against state-of-the-art alternatives. Nonetheless, certain limitations persist, particularly in processing scenes characterized by high semantic complexity and rapidly changing backgrounds, which constitute promising directions for future investigation.
subject: Original Articles
  topic: section
subject: Video inpainting
  topic: keywords
subject: Video Segmentation
  topic: keywords
subject: Hole Filling
  topic: keywords
subject: Neutrosophic Sets
  topic: keywords
subject: Indeterminacy
  topic: keywords
creator: Amanna Ghanbari Talouki
creator: Abbas Koochari
creator: S. Ahmad Edalatpanah
rights: Copyright (c) 2026 Amanna Ghanbari Talouki, Abbas Koochari, S. Ahmad Edalatpanah (Author) https://creativecommons.org/licenses/by/4.0
date: 2026-03-30
publication date: 2026-03-30
doi: 10.57647/fomj.2026.0701.03

DFT-Based fuzzy topological indices integrated with machine learning for accurate prediction of topical drug properties
type: item
description: In this study, we developed a novel framework to predict the physicochemical properties of topical drugs by integrating fuzzy topological indices with machine learning (ML) models. The chemical structures of the selected drugs were optimized using Gaussian software, and both bond lengths (edges) and vertex properties, corresponding to the atomic masses of each molecule, were fuzzified through Density Functional Theory (DFT). Fuzzy topological indices (FTIs) were then calculated to capture the relationships between molecular geometry, atomic composition, and topological features. Two machine learning algorithms, Linear Regression (LR) and optimized Support Vector Regression (SVR-Tuned), were employed for property prediction. The models were trained on the main dataset and validated on additional test drugs excluded from training, enabling a rigorous assessment of generalization, predictive accuracy, and the absence of overfitting. The results showed that the proposed fuzzy QSPR framework, combined with optimized, achieves high predictive performance, robustness, and strong generalization. This methodology provides an efficient computational tool for estimating molecular properties and can support the rational design of next-generation topical pharmaceutical agents.
subject: Original Articles
  topic: section
subject: Machine Learning (ML)
  topic: keywords
subject: Fuzzy QSPR
  topic: keywords
subject: Topical drugs
  topic: keywords
subject: Fuzzy topological indices (FTIs)
  topic: keywords
creator: Negar Kheirkhahan
creator: Masoud Ghods
rights: Copyright (c) 2025 Negar Kheirkhahan, Masoud Ghods (Author) https://creativecommons.org/licenses/by/4.0
date: 2025-12-30
publication date: 2025-12-30
doi: 10.57647/j.fomj.2025.0604.24

Vague Graph Structures: Some New Concepts and Applications
type: item
description: A graph structure is a useful tool in solving the combinatorial problems in different areas of computer science and computational intelligence systems. In this paper, we apply the concept of vague sets to graph structures. We introduce certain notions, including vague graph structure (VGS), strong vague graph structure, vague B<!-- [if gte mso 9]><xml> <o:OLEObject Type="Embed" ProgID="Equation.DSMT4" ShapeID="_x0000_i1025" DrawAspect="Content" ObjectID="_1827661754"> </o:OLEObject> </xml><![endif]-->-cycle, and illustrate these notions by several examples. We study P<!-- [if gte mso 9]><xml> <o:OLEObject Type="Embed" ProgID="Equation.DSMT4" ShapeID="_x0000_i1026" DrawAspect="Content" ObjectID="_1827661755"> </o:OLEObject> </xml><![endif]-->-complement, self-complement, strong self- complement, totally strong self-complement in vague graph structures, and investigate some of their properties. Finally, an application of vague influence graph structure is given.
subject: Original Articles
  topic: section
creator: Reza Shahverdi
rights: Copyright (c) 2025 https://creativecommons.org/licenses/by/4.0
date: 2022-12-30
publication date: 2022-12-30
starting page: 51
ending page: 66
doi: 10.30495/fomj.2023.1987580.1095

A Framework for Identifying and Analyzing Drivers Affecting the Futures of Cryptocurrency FinTechs in Iran with Fuzzy Delphi and Fuzzy Dematel
type: item
description: The present research aimed to identify and analyze effective drivers in the future of crypto fintechs using a fuzzy approach. The research was applied in terms of purpose and had a quantitative methodology. The theoretical population consisted of financial experts in fintechs and block chain technology. Judgmental sampling was performed based on the expert's expertise. A sample size of 10 was studied. Two quantitative techniques, fuzzy Delphi and DEMATEL, were utilized in the study, and the expert and effect assessment questionnaires, which had desired validity and reliability, were used to collect data. The research had three stages; first, 22 drivers were obtained using the literature review and interviews with experts. Second, the drivers were screened by distributing expert assessment questionnaires and the fuzzy Delphi method. Third, 10 drivers were selected for the final analysis due to the suitable deffuzification number. Such drivers were evaluated by distributing effect assessment questionnaires and the fuzzy DEMATEL technique. The drivers of national regulatory policies, the development of national RegTechs, and the development of smart contracts in the financial industry respectively had the highest net effects. Practical suggestions were extracted based on prior drivers and interviews with focus groups.
subject: Original Articles
  topic: section
creator: Mohammad Hasan Maleki
creator: Mohammad Javad Zare Bahnamiri
creator: Iman Dadashi
rights: Copyright (c) 2022 https://creativecommons.org/licenses/by/4.0
date: 2022-12-30
publication date: 2022-12-30
starting page: 37
ending page: 50
doi: 10.30495/fomj.2023.1987397.1084

Fixed Point Theorem in Fuzzy Metric Space
type: item
description: In this present work, we prove fixed point theorem for contractive mapping F: X--X in fuzzy metric spaces that have a nonempty F− invariant complete subspace E, then prove the uniqueness the fixed point in E. Though many theorems in fuzzy metric space in this case, our theorem is a new type of these theorems as it is we proved that a unique fixed point there exists in F− invariant complete subset E in X. Finally, we give an interesting example in complete fuzzy metric space that satisfies in the conditions of our theorem and prove the uniqueness fixed point of F.
subject: Original Articles
  topic: section
subject: Fixed Point
  topic: keywords
subject: Fuzzy Metric Spaces
  topic: keywords
subject: F −invariant
  topic: keywords
creator: Samaneh Ghods
rights: Copyright (c) 2022 https://creativecommons.org/licenses/by/4.0
date: 2025-12-19
publication date: 2025-12-19
starting page: 32
ending page: 36
doi: 10.30495/fomj.2023.1952452.1061

Anti-Fuzzy Bi-Ideals in Semirings Under S-norms
type: item
description: In this paper‎, ‎by using S-norms‎, ‎the notion of anti fuzzy bi-ideals in semirings will be defined and investigated some properties of them‎. ‎Next prime‎, ‎strongly prime‎, ‎semiprime‎, ‎irreducible‎, ‎strongly irreducible of them will be introduced and obtained some results about them‎. ‎Latter‎, ‎they will be investigated under regular and intra-regular semirings‎. ‎Finally‎, ‎they will be characterized under totally ordered by inclusion‎.
subject: Original Articles
  topic: section
creator: Rasul Rasuli
rights: Copyright (c) 2022 https://creativecommons.org/licenses/by/4.0
date: 2022-12-25
publication date: 2022-12-25
starting page: 17
ending page: 31
doi: 10.30495/fomj.2023.1967637.1074

Choosing the Best Company for Investment according to the Financial Factors in the Neutrosophic Environment (Case Study: Automotive Industry)
type: item
description: The analysis of financial ratios is one of the methods of examining the state of companies, which expresses the relationships between the items in the financial statements. The study of financial ratios helps us to get a comprehensive and correct understanding of the performance and financial status of companies and to benefit from them when investing and checking the efficiency of companies. Since many current phenomena in finance and the economy are ambiguous, they are treated as if they are clear, so this article, using Neutrosophic, examines the selection of a company for investment using financial ratios in the Neutrosophic environment. Where verbal judgments (qualitatively) are used for decision-making, the proposed approach will perform better than previous approaches. To rank the options in this research, eight financial ratios widely used in investment analysis are considered indicators of the problem. Then, ranking the alternatives is done by defining the ideal option (with the best performance in all indicators) and applying the known criterion of similarity. A numerical example is operated to understand the proposed approach better, and the results are presented.
subject: Original Articles
  topic: section
creator: Mohsen Imeni
creator: Bardia Pouresmaeil Motlagh
creator: Faezeh Pirouzb
creator: Azadeh Shemshad
rights: Copyright (c) 2022 https://creativecommons.org/licenses/by/4.0
date: 2022-12-30
publication date: 2022-12-30
starting page: 9
ending page: 16
doi: 10.30495/fomj.2023.1971807.1081

Coping Uncertainty in the Supplier Selection Problem Using a Scenario- Based Approach and Distance Measure on Type-2 Intuitionistic Fuzzy Sets
type: item
description: Supplier selection (SS) is a process in which companies identify, evaluate, and select suppliers. The MCDM methods are often used for supplier selection in supply chain management. An unlimited number of MCDM techniques, such as analytic hierarchy process (AHP), analytic network process (ANP), the technique of order preference distance to the ideal solution (TOPSIS), etc., have been deployed to solve the supplier selection problems. Though they can manage problem complexity, MCDM techniques cannot deal with problem uncertainty. Hence, they have been combined with the fuzzy set, intuitionistic fuzzy set, etc., to provide more accurate solutions to supplier selection problems. Nonetheless, the future uncertainty related to the environmental changes is ignored in the SS literature. Therefore, we use future scenarios as criteria to select the best supplier in this study. Moreover, we applied a distance measure to rank Type-2 Intuitionistic Fuzzy Set, which is a suitable approach to deal with the vagueness of verbal judgments. A numerical example explains the 5-step proposed approach.
subject: Original Articles
  topic: section
subject: Uncertainty
  topic: keywords
subject: Supplier Selection Scenario
  topic: keywords
subject: Intuitionistic Fuzzy Sets
  topic: keywords
subject: Distance Measure
  topic: keywords
creator: Ali Sorourkhah
rights: Copyright (c) 2022 https://creativecommons.org/licenses/by/4.0
date: 2022-12-15
publication date: 2022-12-15
starting page: 1
ending page: 8
doi: 10.30495/fomj.2022.1953705.1066

Fuzzy-enhanced hybrid recommender system using autoencoder and GRU with fuzzy ACO weighted fusion
type: item
description: Personalization in large-scale systems is made possible by recommender systems. Collaborative filtering is a traditional approach for recommendation, but it struggles to handle inaccuracy, data sparsity, and prediction error. Our study proposes a hybrid recommendation model that integrates timestamps, user characteristics, and item characteristics to enhance prediction evaluation criteria, thereby addressing these limitations. A new user feature was created by fuzzy c-means clustering algorithm. Using three separate learning mechanisms, AutoEncoder (AE) and Singular Value Decomposition (SVD), the method produces initial rating predictions based on the user features and item features, which are combined with user-item ratings. The Gated Recurrent Unit (GRU) model takes a combination of user-item ratings and their timestamps as input to identify temporal patterns in behaviors and make another initial prediction. The independent predictions are combined by an optimization process, which is then followed by a weighted averaging step. The Fuzzy Ant Colony Optimization (Fuzzy ACO) method is utilized at this point to fine-tune the weights to carry out the ultimate prediction. The efficacy of our hybrid model was evaluated on the MovieLens 1M and MovieLens 100K datasets. In terms of RMSE, MAE, precision, and recall, our model surpasses baseline models in evaluation measures.
subject: Original Articles
  topic: section
subject: Keywords Recommender system, Fuzzy c-means, AutoEncoder, Fuzzy ACO, GRU
  topic: keywords
creator: Mahshid Salehi
creator: Hassan Shakeri
creator: Seyyed Javad Seyyed Mahdavi Chabok
rights: Copyright (c) 2025 Mahshid Salehi, Hassan Shakeri, Seyyed Javad Seyyed Mahdavi Chabok (Author) https://creativecommons.org/licenses/by/4.0
date: 2025-12-30
publication date: 2025-12-30
doi: 10.57647/j.fomj.2025.0604.22

A hybrid fuzzy cognitive–system dynamics framework for modeling environmental risk evolution in construction megaprojects
type: item
description: Emphasizing the critical role of sustainability, this research comprehensively assesses environmental risks in industrial-construction megaprojects and shows that identifying and managing these risks can help reduce negative environmental consequences and improve sustainable decision-making. The present study aims to analyze the environmental risk management system in a megaproject as a case. Therefore, the innovation of this research lies in the use of a system dynamics approach and fuzzy cognitive mapping to identify and assess environmental risks in large-scale industrial and civil engineering projects. Based on this analysis, each aspect of a project's environmental risk management is ranked using the Analytic Network Process (ANP), and Decision Making Trial and Evaluation Laboratory (DEMATEL) and causal relationships are examined. Also, using the Fuzzy Cognitive Map (FCM) approach, the in degree, out degree, and centrality of each risk have been identified. Then, the behavioral model is determined based on the system dynamics. The risk of completing the project timely and the issues related to the increase in time due to environmental laws were ranked first, an over predicted increase in the project time due to environmental laws was ranked second, and the factor of environmental issues and problems was ranked third. Environmental issues and problems have the highest impact and the risk of completing the project timely and the issues related to the increase in time due to environmental laws were the most affected factor. Experts believe that the involvement of several institutions and decision-making from the perspective of environmental climate issues is one of the most significant environmental risks affecting the completion of the project. This issue is because the risk of completing the project timely from an environmental perspective and environmental issues and problems is reduced by controlling and reducing this risk. These two risks are increasing with …
subject: Original Articles
  topic: section
subject: Megaproject
  topic: keywords
subject: Environment
  topic: keywords
subject: Risk
  topic: keywords
subject: System dynamics
  topic: keywords
creator: Seyed Amin Seyedi
creator: Alireza Mirjalili
creator: Mahoudreza Maheri
creator: Abolfazl Sadeghian
rights: Copyright (c) 2025 Seyed Amin Seyedi, Alireza Mirjalili, Mahoudreza Maheri, Abolfazl Sadeghian (Author) https://creativecommons.org/licenses/by/4.0
date: 2025-12-30
publication date: 2025-12-30
doi: 10.57647/j.fomj.2025.0604.19

Two novel distance functions for interval-valued trapezoidal intuitionistic fuzzy numbers and their applications in decision-making
type: item
description: Despite the extensive application of interval-valued intuitionistic fuzzy numbers (IVIFNs) in modeling uncertainty within complex decision-making problems, distance measurement for their important subclass, interval-valued trapezoidal intuitionistic fuzzy numbers (IVTraIFNs), suffers from conceptual and computational limitations. Existing distance measures often rely on discrete features or neglect the continuous geometry and surface structure of these numbers, leading to information loss and inadequate discrimination between alternatives. This paper addresses this gap by introducing a novel surface-area-based distance measurement framework designed explicitly for IVTraIFNs. We propose two new distance functions, a Euclidean and a Manhattan measure, derived from the integration of areas under the left and right curves of the continuous membership and non-membership functions. A conceptual parametric framework for representing continuous IVTraIFNs is first established, and the mathematical properties of the proposed measures, including compliance with distance metric axioms and the triangle inequality, are formally proven. Explicit, practical formulas for these measures for IVTraIFNs are then developed. To demonstrate practical efficacy, the new measures are systematically integrated into two established multi-criteria group decision-making (MCGDM) methods: TOPSIS and CODAS. A comprehensive numerical example in the context of investment project selection, supported by sensitivity analysis on the weight parameter and standard validity tests, confirms the effectiveness, robustness, and advantages of the proposed approach compared to some existing methods. The results indicate that the surface-based distance measures provide more appropriate discrimination between alternatives while offering flexibility through a parameter that weights the membership and non-membership components, remaining consistent with existing decision-making schemes.
subject: Original Articles
  topic: section
subject: Interval-valued intuitionistic fuzzy set, Interval-valued trapezoidal intuitionistic fuzzy number, Distance measure, Multi-criteria group decision-making.
  topic: keywords
creator: Madineh Farnam
creator: Majid Darehmiraki
rights: Copyright (c) 2025 Madineh Farnam, Majid Darehmiraki (Author) https://creativecommons.org/licenses/by/4.0
date: 2025-12-30
publication date: 2025-12-30
doi: 10.57647/j.fomj.2025.0604.20

A fuzzy-based planning method and meta-heuristic optimization for traffic signal control
type: item
description: To regulate traffic volume and average vehicle delay, traffic signal control systems aim to develop a computational model to examine vehicle lines on urban roadways. Based on the optimization of vehicle speed on each street and traffic light timing, a hybrid traffic signal control system based on fuzzy logic and wild horse optimization algorithm (HFWHO-TSC) is introduced in this study. The first phase is optimized using the wild horse meta-heuristic algorithm (WHO) technique, and the timing of the traffic lights is optimized using fuzzy logic depending on the traffic intensity of the nearby streets in various directions of the intersection. Additionally, an iterative loop is used in this work to find the ideal fuzzy time for traffic light lighting. The proposed hybrid system is compared with two other controller systems: a fixed-time signal controller and a signal controller that combines particle swarm optimization algorithms and fuzzy logic in a hybrid manner (HFPSO-TSC). With a performance gain of almost 37% over the fixed-time traffic controller and 20% over HFPSO-TSO, respectively, the HFWHO-TSC solution outperformed the others. The proposed hybrid system's performance effectively reduces both traffic congestion and driver annoyance brought on by signals. Additionally, it lessens fuel consumption and pollution.
subject: Original Articles
  topic: section
subject: Traffic Signal Control (TSC) system; Fuzzy logic system; Wild Horse Optimization (WHO) algorithm; Particle Swarm Optimization (PSO) algorithm; Matlab software; Fuzzy time.
  topic: keywords
creator: Seyed Mehdi Seifivand
creator: Parvaneh Asghari
creator: Hamid Haj Seyyed Javadi
creator: Hamzehali Nourmohammadi
rights: Copyright (c) 2025 Seyed Mehdi Seifivand, Parvaneh Asghari, Hamid Haj Seyyed Javadi, Hamzehali Nourmohammadi (Author) https://creativecommons.org/licenses/by/4.0
date: 2025-12-30
publication date: 2025-12-30
doi: 10.57647/j.fomj.2025.0604.23

A new weighted goal programming technique to solve fully triangular intuitionistic fuzzy multi-objective fixed charge solid transportation problem
type: item
description: Modern society depends substantially on transport because it allows for mobility and the movement of goods. However, it also has numerous negative effects on the environment and makes a major contribution to carbon emissions. Different vehicles use a variety of fuels to transmit energy in transportation systems. For energy communication, vehicles emit greenhouse gases that pollute the environment. Also, the parameters in transportation problems are not always precisely define rather are imprecise in nature in most cases. Recalling this fact, this paper explores the study of a multi-objective fully intuitionistic fuzzy fixed charge solid transportation problem considering carbon emission as an objective function. A new solution algorithm is developed to obtain the compromised optimal solution of the proposed model. Solved the proposed transportation problem models by using LINGO.19.0 optimization software and obtained the crisp as well as fuzzy objective values. A real-life-based numerical problem is presented and solved to validate the concept and the results are compared with other existing methods.
subject: Original Articles
  topic: section
subject: Fully fuzzy transportation problem
  topic: keywords
subject: Multi-objective
  topic: keywords
subject: Fixed charge
  topic: keywords
subject: Intuitionistic fuzzy;
  topic: keywords
subject: Carbon emission
  topic: keywords
subject: Weighted goal programming
  topic: keywords
creator: Awdhesh Kumar Bind
creator: Deepika Rani
creator: Kapil Kumar Goyal
rights: Copyright (c) 2025 Awdhesh Kumar Bind, Deepika Rani, Kapil Kumar Goyal (Author) https://creativecommons.org/licenses/by/4.0
date: 2025-09-30
publication date: 2025-09-30
doi: 10.30495/fomj.2023.1991385.1103

A General Approach to Construct Intuitionistic Fuzzy Regression Model Based on a New Least Absolute Discrepancy
type: item
description: The data sufferance from uncertainty and vague events impose statistical regression modeling to be treated in fuzzy environment for more accuracy upon causal relationship between independent and dependent variables. For more informative fuzzy, among limited studies on intuitionistic fuzzy regression models (IFRMs), this study proposed the most general approach to construct a full IFRM (the model’s parameters and output as well as input variable(s) are represented as symmetric and/or asymmetric positive and/or negative and/or mixed of neither negative nor positive triangular intuitionistic fuzzy numbers (TIFNs)). The estimated parameters of the proposed IFRM determined on solving linear programming problems based on intuitionistic fuzzy least absolute of discrepancies (IFLAD). The proposed approach respects homogeneity principle in modeling such that the constructed IFRM for fitting symmetric TIFNs is symmetric either (the intercept and slope(s) are symmetric in this case). Furthermore, two illustrative examples are used to show the soundness and robustness of the proposed model and compared with existing IFRM.
subject: Original Articles
  topic: section
subject: Triangular intuitionistic fuzzy regression model (IFRM)
  topic: keywords
subject: Full IFRM
  topic: keywords
subject: Triangular intuitionistic fuzzy numbers (TIFNs)
  topic: keywords
subject: Intuitionistic fuzzy least absolute of discrepancies (IFLAD)
  topic: keywords
subject: Homogeneity principle
  topic: keywords
creator: Abdullah Al-Qudaimi
creator: Walid Yousef
rights: Copyright (c) 2026 Abdullah Al-Qudaimi, Walid Yousef (Author) https://creativecommons.org/licenses/by/4.0
date: 2026-03-30
publication date: 2026-03-30
doi: 10.57647/fomj.2026.0701.02

Enhancing Data Cleaning through the Extraction and Expansion of Relaxed Functional Dependencies
type: item
description: Data cleaning is pivotal for ensuring high-quality datasets in machine learning and data analytics, yet traditional methods often rely on manual thresholding, which is subjective and inefficient. This paper proposes RFD-Sugeno-KMeans-Clean, a data cleaning framework that integrates a Sugeno fuzzy inference system for automatic thresholding to extract Relaxed Functional Dependencies (RFDs). Then, by combining K-means++ clustering, it extracts the most effective pattern functional dependencies for data cleaning from complex datasets. In the second phase, a two-step SQL method is employed to detect and clean the data. The method also utilizes Principal Component Analysis (PCA), Identifying and Repairing Multiple Violations (Spline), Median, and Adaptive Neuro-Fuzzy Inference System (ANFIS) to enhance outlier detection across diverse datasets. The effectiveness of the proposed method has been validated using a dataset from the UCI database. The results demonstrate that the proposed method achieves a significant reduction in the number of data points and tuples, with a decrease of approximately 99.5% in most cases, while ensuring stability and reliability in responses. The proposed method’s ability to extract optimal patterns in data cleaning demonstrates higher stability and reliability in responses compared to earlier methods, making it a viable solution for addressing data cleaning challenges in various domains.
subject: Original Articles
  topic: section
subject:
  topic: keywords
  value: Data Cleaning; Relaxed Functional Dependencies; K-means Clustering Algorithm; Sugeno Fuzzy System; Automatic Thresholding; Two-Step SQL Method; Unit Violations; Multiple Violations; Outlier Detection; ANFIS
creator: Mona Kardehi Moghadam
creator: Seyyed Javad Seyyed Mahdavi Chabok
creator: Reza Sheybani
creator: Reza Ghaemi
rights: Copyright (c) 2026 Mona Kardehi Moghadam, Seyyed Javad Seyyed Mahdavi Chabok, Reza Sheybani, Reza Ghaemi (Author) https://creativecommons.org/licenses/by/4.0
date: 2026-03-30
publication date: 2026-03-30
doi: 10.57647/fomj.2026.0701.06

Simultaneous Minimization of Cost, Cycle Time, and Workstations in Intuitionistic Fuzzy Assembly Line Systems
type: item
description: Assembly line design is done to coordinate a set of sequential activity stations that are used to maximize the utilization of workstations and manpower. The stations are deployed in such a way that materials flow through them continuously, continuously and at a constant rate. In this paper, a multi-objective model in intuitionistic fuzzy conditions is proposed for the assembly line balance problem. In order to solve the proposed model, a two-stage method is presented. In the first stage, the multi-objective problem of assembly line balance in intuitionistic fuzzy conditions is transformed into a deterministic multi-objective problem, and in the second stage, an efficiency solution for the multi-objective problem is obtained using the fuzzy programming method. In order to demonstrate the efficiency of the proposed method, a numerical example is presented.
subject: Original Articles
  topic: section
subject: Multi-objective assembly line balancing problem; Trapezoidal intuitionistic fuzzy numbers; Fuzzy mathematical programming; Pareto solution.
  topic: keywords
creator: Ali Mahmoodirad
rights: Copyright (c) 2025 Ali Mahmoodirad (Author) https://creativecommons.org/licenses/by/4.0
date: 2025-12-30
publication date: 2025-12-30
doi: 10.57647/j.fomj.2025.0604.21

Intelligent Image-Based Recognition of Rice Cultivars Using PSO-Optimized ANFIS
type: item
description: Rice is considered to be one of the most significant staple foods on a global scale, particularly within developing countries such as Iran. In these countries, rice cultivars frequently exhibit considerable variation in terms of quality, price, and characteristics. Accurate identification of rice cultivars is imperative for maintaining market transparency, ensuring quality control, and supporting agricultural decision-making. This study proposes a novel methodology for the classification of rice cultivars, which integrates advanced image processing techniques with an Adaptive Neuro-Fuzzy Inference System (ANFIS) that has been optimised by the Particle Swarm Optimisation (PSO) algorithm. A total of three rice types, which are commonly cultivated in northern Iran, were selected for the study: Native Tarom, Hashemi Tarom, and Pakistani rice. The images from these three rice types were processed in standard conditions using Canny edge detection algorithms. The extraction of morphological features was then utilised for the training and testing of the ANFIS-PSO model. The experimental results obtained achieved a classification accuracy of 99.47%, thereby demonstrating the superiority of the proposed method in comparison to traditional techniques with regard to precision and applicability. Moreover, the method employs a cost-effective and readily accessible approach that lends itself to practical applications, including the development of smartphone-based software for real-time rice identification. This study proposes a non-invasive, efficient, and scalable solution to rice authentication and classification challenges, with potential application in agricultural quality assurance and market regulation.
subject: Original Articles
  topic: section
subject: Rice cultivar identification
  topic: keywords
subject: Image processing
  topic: keywords
subject: Canny edge detection
  topic: keywords
subject: Adaptive Neuro-Fuzzy inference system
  topic: keywords
subject: Particle swarm optimizatio
  topic: keywords
creator: Sayedeh Fatemeh Sakhaei
creator: Ahmad J. Afshari
creator: Alireza Bosaghzade
creator: Meghdad H.M. Jahromi
rights: Copyright (c) 2025 Sayedeh Fatemeh Sakhaei, Ahmad J. Afshari, Alireza Bosaghzade, Meghdad H.M. Jahromi (Author) https://creativecommons.org/licenses/by/4.0
date: 2025-09-30
publication date: 2025-09-30
doi: 10.57647/j.fomj.2025.0603.17

Developing a Hybrid SWOT-MCDM Framework for Formulating Smart Technology Adoption Strategies in Urban Construction Under Conditions of Uncertainty: A Case Study of Qom Municipality
type: item
description: Smart technology adoption in urban construction is a key driver toward the development of smarter cities. However, the complexities and uncertainties associated with technology acceptance necessitate strategic planning based on structured decision-making frameworks. A hybrid SWOT–MCDM framework was developed under uncertainty for formulating effective strategies for smart technology adoption. In the first phase of the study, a modified six-dimensional SWOT model, including the traditional dimensions (Strengths Opportunitie (SO), Weaknesses Opportunities (WO), Strengths Threats (ST), Weaknesses Threats (WT)) along with two additional dimensions (Weaknesses Strengths (WS), Opportunitie Threats (OT)) was employed to identify and categorize the key factors influencing technology adoption. In the second phase, the Multi-Criteria Decision-Making (MCDM) approach under Uncertainty was utilized to prioritize the resulting strategic alternatives. The proposed framework was implemented in Qom Municipality - Iran. The findings revealed that among the identified factors, “holding training workshops and seminars” with a weight of 0.1152 was the most influential sub-factor in the strengths category. Furthermore, the evaluation of proposed strategies based on the SWOT framework indicated that the SO strategy (leveraging strengths to exploit opportunities) ranked first with a score of 4.826, followed by the SW (4.660), ST (3.809), WO (3.845), WT (3.338), and OT (3.259) strategies, respectively. This prioritization demonstrates that focusing on maximizing strengths and opportunities can facilitate the successful development and implementation of smart technologies. Sensitivity analysis confirmed the robustness and validity of the prioritization results. Hence, the framework can serve as a tool for policy-makers and urban planners in formulating targeted strategies and making informed investment decisions in innovative construction infrastructures.
subject: Original Articles
  topic: section
subject: Multi-Criteria Decision-Making (MCDM)
  topic: keywords
subject: Urban Construction Industry
  topic: keywords
subject: Six-Dimensional SWOT matrix
  topic: keywords
creator: Reza Bakhshipour
creator: Seyed Mohammad Mirhossseini
creator: Ehsanollah Zeighami
creator: Mohammad Ehsanifar
rights: Copyright (c) 2026 Reza Bakhshipour, Seyed Mohammad Mirhossseini, Ehsanollah Zeighami, Mohammad Ehsanifar (Author) https://creativecommons.org/licenses/by/4.0
date: 2026-03-30
publication date: 2026-03-30
doi: 10.57647/fomj.2026.0701.01

A new approach to managing maintenance and repairs of offshore oil and gas platforms using fuzzy-DEMATEL
type: item
description: The maintenance and repair of offshore oil and gas platforms are of critical importance in ensuring operational efficiency, safety, and environmental protection. Conventional maintenance strategies frequently encounter difficulties in addressing the intricacies and uncertainties intrinsic to offshore environments, resulting in elevated downtime and augmented operational expenditures. This study proposes a novel approach that integrates fuzzy logic with the DEMATEL (Decision-Making Trial and Evaluation Laboratory) method, termed fuzzy-DEMATEL, with a view to enhancing maintenance management in offshore settings. The proposed methodology employs fuzzy logic to effectively capture the uncertainties associated with various maintenance factors, while DEMATEL provides a systematic framework for analyzing and visualizing the relationships between these factors. The objective of this research is to develop a comprehensive maintenance management framework that prioritizes maintenance activities based on a holistic assessment of equipment reliability, environmental conditions, and operational constraints. A comprehensive review of the extant literature and a series of case studies were undertaken in order to evaluate the effectiveness of the fuzzy-DEMATEL approach. The results obtained demonstrate the potential of this approach to improve decision-making processes and optimize maintenance strategies in offshore oil and gas platforms. The results obtained in this study indicate that intelligence (C10) has the highest degree of influence, while maintenance and repair management (C1) exhibit a very high level of influence. Furthermore, (C1) exhibits the highest degree of interaction with other criteria. The findings of this study contribute to the advancement of maintenance management practices in the industry, paving the way for more sustainable and efficient operations.
subject: Original Articles
  topic: section
subject: Maintenance and repair management, offshore platforms, oil and gas, fuzzy-DEMATEL approach
  topic: keywords
creator: Reza Dehghani
creator: Abolfazl Sadeghian
creator: Hassan Dehghan Dehnavi
creator: Mozhde Rabbani
creator: Mohammad Taghi Honari Honari
rights: Copyright (c) 2025 Reza Dehghani, Abolfazl Sadeghian, Hassan Dehghan Dehnavi, Mozhde Rabbani, Mohammad Taghi Honari Honari (Author) https://creativecommons.org/licenses/by/4.0
date: 2025-09-30
publication date: 2025-09-30
doi: 10.57647/j.fomj.2025.0603.16

Predicting Efficiency Scores for Digital Transformation Management in the Banking System under Uncertainty Using Stochastic Data Envelopment Analysis and Machine Learning
type: item
description: Digital transformation in the banking system is presented as a comprehensive and multi-dimensional process influenced by various factors, such as customer experience, operational efficiency, innovation adoption, and risk management. These factors are recognized as the main criteria for evaluating and managing digital transformation. The present study analyzes the effectiveness of digital transformation in banking using the network data envelopment analysis method and examines the sensitivity of banks' efficiency scores to data changes. The results show that banks with higher efficiency scores and lower sensitivity are more successful in their digital transformation efforts. Additionally, neural network machine learning techniques have been used to identify hidden patterns and predict data. The presented model has predicted the data with high accuracy and has been able to explain nine key components of digital transformation.
subject: Original Articles
  topic: section
subject: Digital Transformation Management, Efficiency, Data Envelopment Analysis, Machine Learning
  topic: keywords
creator: Mohammad Shirkhoda
creator: Ladan Riazi
creator: Faramarz Fathnejad
rights: Copyright (c) 2025 Mohammad Shirkhoda, Ladan Riazi, Faramarz Fathnejad (Author) https://creativecommons.org/licenses/by/4.0
date: 2025-09-30
publication date: 2025-09-30
doi: 10.57647/j.fomj.2025.0603.18

Identifying and prioritizing obstacles to the implementation of building information modeling in sustainable construction projects in Iran using hybrid Fuzzy Delphi-BWM approach
type: item
description: With the growing global adoption of Building Information Modeling (BIM) technology in the construction industry, this innovative approach has also gained momentum in countries like Iran. However, its practical implementation continues to face numerous challenges. To support broader adoption-particularly within the context of sustainable construction projects, which emphasize environmental, economic, and social considerations throughout the project lifecycle-it is crucial to identify and address these barriers. This study aims to identify and prioritize the key obstacles to BIM implementation in sustainable construction projects in Iran. An initial list of challenges was developed based on a comprehensive literature review and validated through the Fuzzy Delphi Method (FDM), which resulted in the identification of 31 relevant obstacles. Among these, ten were selected as the most critical for further analysis. The Best-Worst Method (BWM) was then applied to rank these barriers. Pairwise comparisons between the best and worst criteria were conducted, and the resulting optimization model was solved using Lingo 18 software. The findings reveal that the most significant challenges are: (1) the absence of clearly defined processes for applying BIM in sustainable building projects (weight = 0.12), (2) the lack of standardized guidelines for BIM integration (weight = 0.107), and (3) the absence of a legal framework for BIM implementation (weight = 0.106). These insights can inform policymakers and stakeholders seeking to improve BIM adoption in the context of sustainable development.
subject: Original Articles
  topic: section
subject: building information modeling
  topic: keywords
subject: obstacles and challenges of building information modeling
  topic: keywords
subject: fuzzy Delphi method, best-worst method
  topic: keywords
subject: sustainable construction projects
  topic: keywords
creator: Sarina Alizadeh Fard
creator: Mohammad Rostami
creator: Mohammad Mahdi Shahbazi
rights: Copyright (c) 2025 Sarina Alizadeh Fard, Mohammad Rostami, Mohammad Mahdi Shahbazi (Author) https://creativecommons.org/licenses/by/4.0
date: 2025-09-30
publication date: 2025-09-30
doi: 10.57647/j.fomj.2025.0603.15

A robust model for the Best-Worst Method (BWM) based on goal programming
type: item
description: The utilization of effective decision-making methodologies has historically captured the attention of decision-makers. The Best-Worst Method (BWM) is a prominent and extensively utilized technique in Multi Attribute Decision Making (MADM), owing to its simplicity, reduced number of pairwise comparisons, and high consistency of results. However, the original BWM operates under the assumption that decision-makers' judgments are precise and certain. In real-world conditions, uncertainty is prevalent. Furthermore, when addressing MADM problems that are not fully consistent and involve more than three criteria, the BWM technique may not result in a unique optimal solution. To address these limitations, this study proposes a Robust BWM approach based on goal programming and formulated as a linear optimization model. This approach incorporates interval preferences to model uncertainty in judgments. Additionally, a methodology is put forward to assess the Consistency Ratio (CR) of input data in the presence of uncertainty, thereby ensuring the reliability of the results obtained. The performance of the model has been examined through six numerical examples, and its validity has been evaluated. The findings suggest that the proposed method exhibits adequate efficiency and performance in addressing MADM problems and managing uncertain data. Consequently, the proposed method can serve as an effective solution for decision-making problems in real-world and uncertainty-based environments.
subject: Original Articles
  topic: section
subject: Best-Worst Method (BWM)
  topic: keywords
subject: Multi Attribute Decision Making (MADM)
  topic: keywords
subject: Robust Optimization
  topic: keywords
subject: Uncertainty
  topic: keywords
subject: Goal Programming
  topic: keywords
subject: Consistency Ratio (CR)
  topic: keywords
creator: Mohammad Reza Dehghani
creator: Ramin Sadeghian
creator: Meisam Jafari Eskandari
rights: Copyright (c) 2025 Mohammad Reza Dehghani, Dr. Ramin Sadeghian, Dr. Meisam Jafari Eskandari (Author) https://creativecommons.org/licenses/by/4.0
date: 2025-08-23
publication date: 2025-08-23
doi: 10.57647/j.fomj.2025.0602.08

Fuzzy logic approach in the evaluation of radiation shielding behaviour of borided Co-Cr-Mo alloy
type: item
description: The present study investigates the radiation shielding behaviour of borided Co-Cr-Mo alloys. The investigation is conducted by utilising a fuzzy logic model, which is employed to analyse the relationship between the boriding temperature and boron content of the boriding powder. In order to achieve this objective, the linear attenuation coefficients of borided alloys were measured as a function of boriding temperature and boron content. Subsequently, a fuzzy logic model was applied to these measured values. The findings demonstrated that the outcomes derived from the measured and fuzzy logic methods exhibited strong concordance. The linear attenuation coefficient is contingent upon the boriding temperature and the boron content. Furthermore, the variation of the linear attenuation coefficient with boriding temperature is more significant than its variation with the boron content. It was concluded that the fuzzy logic model can be used to predict the radiation shielding behaviour of borided Co-Cr-Mo alloys without the need for further experimentation, even under conditions that have not yet been tested. The present study will contribute to the selection and development of radiation shielding materials by demonstrating practical applications of fuzzy logic.
subject: Original Articles
  topic: section
subject: Fuzzy logic model
  topic: keywords
subject: Boriding
  topic: keywords
subject: Radiation shielding
  topic: keywords
subject: Boron powder
  topic: keywords
subject: Co-Cr-Mo alloy
  topic: keywords
creator: Ali Osman Ucar
rights: Copyright (c) 2025 Ali Osman Ucar (Author) https://creativecommons.org/licenses/by/4.0
date: 2025-08-23
publication date: 2025-08-23
doi: 10.57647/j.fomj.2025.0602.11

Development of a series-parallel data envelopment analysis model within an intuitionistic fuzzy environment
type: item
description: Data Envelopment Analysis (DEA) is a widely used tool for evaluating the efficiency of Decision-Making Units. However, traditional DEA models often encounter limitations when applied to complex, multi-stage systems, particularly in the presence of uncertainty and ambiguity in the data. To address these challenges, this study introduces a series–parallel DEA model that integrates both optimistic and pessimistic evaluations within an intuitionistic fuzzy environment. The model is specifically designed to provide a more accurate and nuanced assessment of multi-stage system efficiency, especially when handling diverse data types commonly encountered in real-world settings, including crisp, fuzzy, and intuitionistic fuzzy data. Two numerical examples are presented to validate the proposed model: one based on synthetic data and another adapted from real-world data reported by Kao. These case studies illustrate the model’s effectiveness and practical applicability in evaluating the performance of multi-stage systems with complex and uncertain data. By incorporating both optimistic and pessimistic perspectives, the proposed framework offers a comprehensive and balanced evaluation of DMU performance, making it a valuable tool for decision-makers operating under uncertain and dynamic conditions.
subject: Original Articles
  topic: section
subject: Data Envelopment Analysis
  topic: keywords
subject: Efficiency Evaluation
  topic: keywords
subject: Intuitionistic Fuzzy
  topic: keywords
subject: Multi-Stage Systems
  topic: keywords
creator: Hoda Moradi
creator: Hamid Babaei Meybodi
rights: Copyright (c) 2025 Hoda Moradi, Hamid Babaei Meybodi (Author) https://creativecommons.org/licenses/by/4.0
date: 2025-09-30
publication date: 2025-09-30
doi: 10.57647/j.fomj.2025.0603.14

A hybrid fuzzy framework for clustering and ranking listed companies via the K-means algorithm
type: item
description: The extant literature indicates that companies, including those listed on the Tehran Stock Exchange, are typically classified based on their respective industries. The findings of this study indicate that a reclassification of these companies may be warranted if alternative indicators are to be considered in the clustering process. To this end, the present study employs six indicators for the purpose of clustering the companies: price-to-return ratio (P/E), volume, liquidity rating, return on equity (ROE), β coefficient, and return on capital. The values of these indices are extracted for each company, and then the companies are categorized into eight clusters using the K-means method. The significance of these groups varies from user to user, contingent upon the relative importance of the indicators from the user’s perspective. To categorize these clusters, we initially employ the Fuzzy Analytical Hierarchy Process (FAHP) to ascertain the weights of the indexes. Subsequently, we utilize the TOPSIS method to prioritize the clusters. The results indicated that, according to the experts’ assessments, the P/E ratio, beta coefficient, and liquidity rating, with weights of 0.365, 0.221, and 0.212, respectively, are the most significant indicators for the evaluation of the clusters. It is imperative to acknowledge that the decision matrix employed in this study is predicated on the assumption that the centroid of each cluster serves as its representative. The approach delineated in this study has the potential to serve as a foundational element in subsequent research endeavors focused on portfolio construction based on firm similarity.
subject: Original Articles
  topic: section
subject: Clustering
  topic: keywords
subject: K-means method
  topic: keywords
subject: Hierarchical analysis process
  topic: keywords
subject: TOPSIS method
  topic: keywords
subject: Fuzzy sets
  topic: keywords
creator: Hamidreza Rezaei
creator: Abdollah Hadi-Vencheh
creator: Hosein Arman
creator: Mehrdad Nikbakht
rights: Copyright (c) 2025 Hamidreza Rezaei, Abdollah Hadi-Vencheh, Hosein Arman, Mehrdad Nikbakht (Author) https://creativecommons.org/licenses/by/4.0
date: 2025-08-23
publication date: 2025-08-23
doi: 10.57647/j.fomj.2025.0602.12

A Fuzzy Logic-Based Framework for Multi-Objective Controller Placement in Software Defined Networks
type: item
description: Software Defined Networking (SDN) has been hailed as a revolutionary development in network management, with the potential to transform the landscape of the industry. The core principle behind SDN is the decoupling of the control plane from the data plane, a move that has been widely regarded as a significant step towards more centralized control. Nevertheless, determining the optimal placement of controllers, known as the Controller Placement Problem (CPP), remains a significant challenge due to conflicting objectives such as latency, resilience, and load balancing. This paper proposes a novel approach leveraging fuzzy logic to address the CPP. Distinct from heuristic or optimization algorithms, this approach employs fuzzy set theory to manage imprecise and dynamic network parameters while optimising multiple objectives, including latency, inter-controller communication, and load balancing. The proposed method provides a flexible, adaptable framework for CPP across varying network topologies. Experimental results validate the effectiveness of this approach in achieving balanced trade-offs among objectives, ensuring efficient and scalable SDN management, with a 18.7% reduction in maximum node-to-controller latency and a 22% improvement in load imbalance compared to state-of-the-art methods.
subject: Original Articles
  topic: section
subject: Fuzzy Logic
  topic: keywords
subject: Software-Defined Network (SDN)
  topic: keywords
subject: Fuzzy Set Theory
  topic: keywords
subject: Controller Placement Problem Multi-Objective Optimization
  topic: keywords
creator: Ahmad Jalili
rights: Copyright (c) 2025 Ahmad Jalili (Author) https://creativecommons.org/licenses/by/4.0
date: 2025-04-23
publication date: 2025-04-23
doi: 10.57647/j.fomj.2025.16560

Optimizing bipolar fuzzy fractional programmingproblems using the variable neighborhood searchalgorithm
type: item
description: In this paper, we propose a new variable neighbourhood search algorithm for solving a bipolar fuzzy number fractional problem (FBVNS). The proposed algorithm utilises a modified version of Kerre's inequality, a tool employed to compare bipolar fuzzy numbers. The utilisation of the FBVNS algorithm enables the direct resolution of the bipolar fuzzy number fractional problem, obviating the necessity for conversion to a crisp number representation. The FBVNS algorithm is further enhanced by the introduction of a novel local search method, which is informed by ascending directions derived from the resolution of four distinct crisp mathematical programming problems. Finally, the efficacy of the proposed algorithm is demonstrated through its implementation on a range of problems.
subject: Original Articles
  topic: section
subject: Fractional linear optimization
  topic: keywords
subject: Bipolar
  topic: keywords
subject: Fuzzy numbers
  topic: keywords
subject: Modified Kerre’s inequality
  topic: keywords
subject: VNS algorithm
  topic: keywords
creator: Dhurgam Kalel Ibrahim-Alsaad
creator: Aliasghar Foroughi
creator: Khatere Ghorbani-Moghadam
creator: Reza Ghanbari
rights: Copyright (c) 2025 Dhurgam Kalel Ibrahim Alsaad, Aliasghar Foroughi, Khatere Ghorbani-Moghadam, Reza Ghanbari (Author) https://creativecommons.org/licenses/by/4.0
date: 2025-04-23
publication date: 2025-04-23
doi: 10.57647/j.fomj.2025.16559

A hybrid method for medical image denoising and segmentation using optimized fuzzy clustering and autoencoder
type: item
description: Frequently, medical images contain noise, which has the potential to obscure important diagnostic features and complicate accurate segmentation. The proposed framework integrates adaptive autoencoder-based enhancement, optimized fuzzy clustering, and fast bilateral filtering to achieve robust denoising and segmentation. In the preprocessing phase, fast bilateral filtering preserves edge details while reducing noise, thereby creating a reliable basis for subsequent clustering. In order to surmount the limitations of standard fuzzy C-means (FCM) in noisy conditions, the method employs the computation of the absolute difference between the original and filtered images. This process serves to accentuate structural information and suppress residual noise. Subsequently, an adaptive autoencoder is employed to reconstruct sharp, noise-free images by enhancing fine details. The efficacy of the clustering process is augmented by a refined FCM objective function that expedites convergence through a logarithmic accumulation of membership values from prior iterations. Finally, post-processing steps—including sharpening and median filtering-enhance the clarity and accuracy of segmentation results. The experimental findings, derived from the analysis of benchmark MRI and low-dose CT datasets, demonstrate the efficacy of the proposed method in surpassing classical FCM and other contemporary denoising techniques. The employment of this method engenders several key advantages, including enhanced noise reduction, expedited convergence, and refined segmentation accuracy. Furthermore, it guarantees the maintenance of essential diagnostic characteristics, thereby enhancing the reliability and utility of the resulting data.
subject: Original Articles
  topic: section
subject: Adaptive autoencode
  topic: keywords
subject: Fuzzy C-means
  topic: keywords
subject: Medical image denoising
  topic: keywords
subject: Segmentation
  topic: keywords
subject: Fast bilateral filtering
  topic: keywords
subject: Machine learning
  topic: keywords
creator: Omid Darvishi Ghaleh
creator: Vafa Maihami
creator: Keyhan Khamforoosh
rights: Copyright (c) 2025 Omid Darvishi, Vafa Maihami, keyhan Khamfroosh (Author) https://creativecommons.org/licenses/by/4.0
date: 2025-08-23
publication date: 2025-08-23
doi: 10.57647/j.fomj.2025.0602.10