Fuzzy Scheduling
Priyadharshini M; B. Vanathi
Volume 22, Issue 6 , November and December 2025, Pages 1-20
Abstract
Security in share markets is essential to affirm the integrity, stability, and trust of financial transactions, protecting investors from fraud and cyber threats. Current methods face challenges in high-volume attacks due to their scalability and static rule-based mechanisms. To resolve these issues, ...
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Security in share markets is essential to affirm the integrity, stability, and trust of financial transactions, protecting investors from fraud and cyber threats. Current methods face challenges in high-volume attacks due to their scalability and static rule-based mechanisms. To resolve these issues, this paper develops a Hybrid Isolated Fuzzy Logic to detect anomalies in transactions. The Fuzzy Logic System with the Zebra Optimization Algorithm is utilized to enhance the attack detection accuracy. The isolation forest algorithm computes the threshold abnormal score to distinguish the normal from the malicious transactions. The Reinforcement Learning-based Proximal Policy Optimization algorithm dynamically updates network policies. The Network Function Virtualization for Distributed Denial-of-Service Scrubbing is combined to improve scalability and deliver cost-effective mitigation. The experimental analysis utilizing stock anomaly detection datasets is conducted. The experimental outcomes affirm that the proposed model attains an accuracy of 98.90%, a false positive rate of 2.9%, and improved mitigation efficiency than existing methods.
Fuzzy modelling
Rambabu Vana; Perumandla Karunakar
Volume 22, Issue 6 , November and December 2025, Pages 21-39
Abstract
This article outlines the significance of the coupled Boussinesq-Burgers Equations (cBBEs) for the prediction of fluid flow in complicated systems, with special emphasis on shallow water waves (SWW) close to the coasts and lakes. The manuscript also defines the Fuzzy Elzaki Adomian Decomposition Method ...
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This article outlines the significance of the coupled Boussinesq-Burgers Equations (cBBEs) for the prediction of fluid flow in complicated systems, with special emphasis on shallow water waves (SWW) close to the coasts and lakes. The manuscript also defines the Fuzzy Elzaki Adomian Decomposition Method (FEADM) as a new technique suitable for solving the cBBE’s in crisp and fuzzy manners. Due to uncertainties in initial conditions caused by the fluid velocity or wave height, one parameter is taken to be a triangular fuzzy number (TFNs). Numerical implementations illustrate the performance of the FEADM against Homotopy Perturbation Method (HPM), Optimal Homotopy Asymptotic Method (OHAM) demonstrate that in terms of accuracy and efficiency, the method is superior. The proof of theorems also shows how the FEADM approaches the solution of nonlinear systems under uncertainties. These results of the research work show how useful the FEADM can be for the modeling of fluid dynamics in deterministic and fuzzy environments.
Fuzzy modelling
Salar Askari; Shaho Zarei
Volume 22, Issue 6 , November and December 2025, Pages 41-65
Abstract
Traditional TSK system employs standard membership functions with adjustable crisp parameters in the IF part and linear combinations of input variables in the THEN part, which leads to loss of data fuzziness. Moreover, linear THEN part of this system restricts the number of learning parameters. On the ...
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Traditional TSK system employs standard membership functions with adjustable crisp parameters in the IF part and linear combinations of input variables in the THEN part, which leads to loss of data fuzziness. Moreover, linear THEN part of this system restricts the number of learning parameters. On the other hand, parameters of the system are tuned to meet a predefined error threshold, but the system does not necessarily display actual structure of data, which impairs its interpretability. This work proposes modifications to the TSK system to tackle these issues and improve its performance. Extended TSK System (ETSK) is presented by replacing crisp parameters of membership functions with fuzzy numbers and adopting arbitrary functions in THEN part of the system. In addition, a Clustering-based TSK system (CTSK) is proposed, which is established from fuzzy clustering algorithms and represents structures and patterns of the data. CTSK has no IF part parameters to tune, which makes the system much faster, but its THEN part is the same as ETSK. Finally, CETSK system is presented as a combination of CTSK and ETSK to study the possibility of further improvements of the systems. The IF parts of TSK, ETSK, and CETSK are tuned by genetic algorithms and their THEN parts are adjusted by the least-squares estimate method. Performance of the systems are studied using 22 datasets in terms of accuracy and runtime. On average, CTSK is 10.82% and 4.84% more accurate than TSK and CETSK; and 91.58% and 92.21% faster than these systems, respectively. CETSK is 6.27% more accurate than TSK, but 6.91% slower because fuzzy clustering and genetic algorithms are jointly employed to tune its IF part whereas only genetic algorithms is used in TSK for this purpose. Keywords: Fuzzy systems, fuzzy clustering, genetic algorithms, gradient descent algorithm, particle swarm optimization, least-squares estimate method, TSK system, fuzzy numbers.
Fuzzy Math
Yongwei Jia
Volume 22, Issue 6 , November and December 2025, Pages 67-81
Abstract
In this paper, we consider a class of fuzzy delay evolution equations under generalized differentiability. By using the operator semigroup theory, the upper and lower solutions and the monotoneiterative technique, the existence and uniqueness of mild solutions for the initial value problem of fuzzydelay ...
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In this paper, we consider a class of fuzzy delay evolution equations under generalized differentiability. By using the operator semigroup theory, the upper and lower solutions and the monotoneiterative technique, the existence and uniqueness of mild solutions for the initial value problem of fuzzydelay evolution equations are obtained. The continuous dependence of two kinds of mild solutions onthe initial value is also proved. In particular, the first innovation of this paper is to introduce the delayterm into the fuzzy evolution equation, and the second innovation is that when discussing the continuousdependence of the solution on the initial value, it completely eliminates the restriction of the nonlinearfunction on the Lipschitz condition coefficients and optimizes the conditions for the existence of the solutions, which is relatively rare. Corresponding examples are added at the end of the article to make theconclusion better applied to practice.
Fuzzy Math
Muhammad Naveed; Madad Khan
Volume 22, Issue 6 , November and December 2025, Pages 83-101
Abstract
Over the years, traditional fuzzy graph models such as Intuitionistic Fuzzy Graphs (IFGs), Pythagorean Fuzzy Graphs (PFGs), and q-Rung Orthopair Fuzzy Graphs (q-ROFGs) have emerged as powerful tools for modeling uncertainty in complex networked structures. Despite their advancements, these models exhibit ...
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Over the years, traditional fuzzy graph models such as Intuitionistic Fuzzy Graphs (IFGs), Pythagorean Fuzzy Graphs (PFGs), and q-Rung Orthopair Fuzzy Graphs (q-ROFGs) have emerged as powerful tools for modeling uncertainty in complex networked structures. Despite their advancements, these models exhibit critical limitations, particularly in scenarios where the sum of membership and non-membership degrees exceeds one. For example, graph vertex data of the form {(v₁,(1,0.5)),(v₂,(1,1)),(v₃,(0.3,1))} or edge data of the form {(e₁,(1,1)),(e₂,(1,0.6)),(e₃,(0.3,1))} violate the constraint 0≤μ+ν≤1. Moreover, existing models often struggle to eliminate the inherent dependency between the membership and non-membership grades.To overcome these limitations, we propose a novel framework called the q-Fractional Fuzzy Graph (q-FFG), which extends fuzzy graph theory, allows greater flexibility and independence between membership and non-membership grades. Unlike previous models, q-fractional fuzzy graphs can handle extreme degrees of uncertainty by letting both membership and non-membership values approach 1 independently, without violating consistency. We formally define the structure of q-fractional fuzzy graphs and some operations on these graphs. Additionally, we discuss degree and adjacency matrices of these graphs. These fundamental matrices serve as essential tools for understanding the structure and behavior of these graphs. Through illustrative examples, we highlight their practical applications and significance. The study presents an extensive review of the Laplacian matrices and their associated energies, uncovering essential information about the graph's spectral properties. These mathematical formulations are necessary for analyzing the stability and connectivity of q-fractional fuzzy graphs. Additionally, an algorithm based on Principal Component Analysis (PCA) technique is proposed which effectively reduces the dimensionality of data while preserving the essential features.
Computer Science
Priti Chakurkar; Deepali Vora
Volume 22, Issue 6 , November and December 2025, Pages 103-123
Abstract
Cracks on pedestrian sidewalks and walkways pose a significant safety hazard, increasing the risk of trips, falls, and injuries, particularly for vulnerable groups such as the elderly and children. Traditional crack detection and severity assessment methods are usually manual, time-consuming, and subjective, ...
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Cracks on pedestrian sidewalks and walkways pose a significant safety hazard, increasing the risk of trips, falls, and injuries, particularly for vulnerable groups such as the elderly and children. Traditional crack detection and severity assessment methods are usually manual, time-consuming, and subjective, especially in the Indian context, where road andsidewalk inspections are still conducted mainly through visual surveys due to cost and infrastructure constraints. This paper proposes an integrated framework of deep learning and fuzzy logic to analyze sidewalk crack severity and predict pedestrian fall risk automatically. A novel crack quantification method using edge detection and adaptive segmentation is proposed to measure crack width accurately. A fine-tuned deep learning model is employed for automating crackseverity prediction, which achieved 95% accuracy and demonstrated robustness to noise, blur, and lighting variations. To estimate fall risk, a fuzzy inference system is developed considering four inputs: crack severity, road condition, weather, and pedestrian age, and a set of expert-defined fuzzy rules is applied to estimate risk levels. The outcomes show the effectiveness of the proposed FIS scheme, which achieved 95% accuracy and outperformed non-fuzzy baseline approaches.
Optimization
Reza Etesami; Mohsen Madadi; Farshid Keynia
Volume 22, Issue 6 , November and December 2025, Pages 125-145
Abstract
In recent years, swarm intelligence metaheuristic algorithms have emerged as powerful tools for solving real-world engineering optimization problems. However, their performance often degrades when applied to complex, high-dimensional problems. To address this limitation, we propose an Adaptive Fuzzy ...
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In recent years, swarm intelligence metaheuristic algorithms have emerged as powerful tools for solving real-world engineering optimization problems. However, their performance often degrades when applied to complex, high-dimensional problems. To address this limitation, we propose an Adaptive Fuzzy Swarm-based Search Algorithm (AFSSA), which incorporates a Fuzzy Dynamic Control Mechanism to dynamically adjust the optimization coefficients of swarm intelligence algorithms. AFSSA employs a Mamdani fuzzy inference system to enable smooth phase transitions during optimization, ensuring adaptability to the problem's unique characteristics. In this study, AFSSA is applied to enhance the acceleration coefficients of Particle Swarm Optimization (PSO) and Golden Search Optimization (GSO), resulting in AFSSA-PSO and AFSSA-GSO. The performance of these modified algorithms is evaluated on 23 standard benchmark functions (with dimensions of 30, 100, and 500) and the CEC2019 test suite, showing competitive results compared to other well-known optimization methods. Additionally, AFSSA is tested on data clustering problems, further demonstrating its versatility in handling complex real-world applications.
Optimization
Mehdi Allahdadi; Samaneh Soradi-Zeid; Hamed Torabi; Elnaz Hosseini
Volume 22, Issue 6 , November and December 2025, Pages 147-165
Abstract
In this paper, as a novel control strategy that is implemented online, we use the model predictive control. In natural phenomena, some parameters may have uncertainty in which their exact amount is unknown, but we know that they are in a certain range. In this case, the predictive control formulation ...
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In this paper, as a novel control strategy that is implemented online, we use the model predictive control. In natural phenomena, some parameters may have uncertainty in which their exact amount is unknown, but we know that they are in a certain range. In this case, the predictive control formulation can be changed so that the control system becomes resistant to these parameters. In this article, two methods for solving optimal control problems with uncertainty will be proposed using model predictive control. In first one, we introduce the robust control, in which discretize the continuous time dynamic model and apply the predictive control algorithm of the resilient model to a typical system. In the second one, when the range of parameter has a certain uncertainty, it is appropriate that the solutions are in the form of interval values. So, we introduce the interval model predictive control and solve two sub-models for optimal control problems. According to the simulation results, it can be conclude that even though the dynamic of the system has severe uncertainty and the behavior of the system changes randomly between stable and unstable mode, but the predictive controller of the interval optimal control problems is well able to converge the state variables.
Fuzzy modelling
Mansooreh Pakravan
Volume 22, Issue 6 , November and December 2025, Pages 167-182
Abstract
Fuzzy logic has emerged as a powerful tool for handling uncertainty and imprecision in machine learning, enabling models to make robust predictions in complex, noisy domains. In this work, we integrate fuzzy-set theory with causal attention learning to improve the classification of Cocaine Use Disorder ...
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Fuzzy logic has emerged as a powerful tool for handling uncertainty and imprecision in machine learning, enabling models to make robust predictions in complex, noisy domains. In this work, we integrate fuzzy-set theory with causal attention learning to improve the classification of Cocaine Use Disorder (CUD) from resting-state functiona Magnetic Resonance Imaging (fMRI) brain networks. Building on the Causal Attention Learning (CAL) framework, we introduce FuzzyCAL, which augments node- and edge-level causal masks with fuzzy membership functions that quantify “weak,” “medium,” and “strong” relevance of graph components. This fusion of fuzzy logic and causal Graph Neural Networks (GNNs) not only preserves the interpretability of causal attention but also adapts to the inherent variability of neuroimaging data. Through extensive 5-fold cross-validation on the SUDMEXCONN dataset, FuzzyCAL achieves a mean test accuracy of 86.20% (±7.18%) and F1-score of 85.87% (±7.23%), outperforming both a non-causal GNN baseline and the original CAL model. We further demonstrate how fuzzy-weighted causal masks reveal anatomically meaningful biomarkers in temporal and sensorimotor cortices. Our results suggest that embedding fuzzy reasoning into causal graph models enhances both predictive performance and neuroscientific interpretability, offering a promising direction for precision diagnostics in substance use disorders.
Fuzzy Math
Mahnaz Zarei; Mohammad Sadegh Asgari; Naser Ghafoori Adl
Volume 22, Issue 6 , November and December 2025, Pages 183-203
Abstract
This paper presents a new hybrid prediction framework for evaluating student performance in advanced mathematics, thus overcoming the inherent constraints of classic Adaptive Neuro-Fuzzy Inference Systems (ANFIS). To improve predictive accuracy and model interpretability, our method combines ANFIS with ...
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This paper presents a new hybrid prediction framework for evaluating student performance in advanced mathematics, thus overcoming the inherent constraints of classic Adaptive Neuro-Fuzzy Inference Systems (ANFIS). To improve predictive accuracy and model interpretability, our method combines ANFIS with advanced gradient boosting techniques, namely XGBoost and LightGBM. The proposed framework integrates fuzzy logic for input space partitioning with localized gradient boosting models as rule outcomes, effectively merging the interpretability of fuzzy systems with the strong non-linear modeling capabilities of machine learning. Comprehensive assessment reveals that both the ANFIS-XGBoost and ANFIS-LightGBM models substantially exceed the traditional ANFIS in various performance parameters. Feature selection, informed by SHAP analysis and XGBoost feature importance metrics, pinpointed essential predictors including the quality of previous mathematics education and core course grades. Enhancements in regression measures further highlight the effectiveness of the hybrid methodology. The findings indicate that the suggested framework provides a reliable and effective alternative for educational institutions to forecast student success, guide focused interventions, and enhance learning outcomes in advanced mathematical fields.
