Engineering
Alireza Pourmoayed; Mohammad Ali Ranjbar; Mohammad Ali Heidarpour
Volume 22, Issue 5 , September and October 2025, Pages 1-19
Abstract
Quadrotors are increasingly employed in a wide range of applications, including surveying, agriculture, military operations, and rescue missions, due to their superior maneuverability. However, achieving accurate and stable control continues to pose a significant challenge. This study aims to enhance ...
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Quadrotors are increasingly employed in a wide range of applications, including surveying, agriculture, military operations, and rescue missions, due to their superior maneuverability. However, achieving accurate and stable control continues to pose a significant challenge. This study aims to enhance the performance of an adaptive critic-based neurofuzzy controller for quadrotors by replacing the conventional proportional-derivative (PD) critics with fuzzy critics. The control performance was quantitatively evaluated using the root mean square error (RMSE) criterion. Simulation results demonstrate that incorporating fuzzy critics improves performance by more than 39% compared to the traditional PD-based approach. To evaluate the trajectory tracking performance of the adaptive fuzzy-critic-based neurofuzzy controller under disturbance, external disturbances were introduced into the quadrotor model. The controller employing fuzzy critics exhibited strong disturbance rejection, maintaining accurate trajectory tracking despite external interferences. These findings highlight the effectiveness of fuzzy-critic-based neurofuzzy controllers in improving quadrotor trajectory tracking performance under dynamic and uncertain real-world conditions.
Fuzzy Math
Mina Shahidi; Estevão Esmi; Tofigh Allahviranloo; Svetlin Georgiev
Volume 22, Issue 5 , September and October 2025, Pages 21-35
Abstract
In this study, we introduce a concept of fuzzy fractional differentiability and integrability on time scales for S-correlated fuzzy functions and propose their fundamental properties. Using these concepts, we study fuzzy fractional dynamic equations on time scales for S-correlated fuzzy functions, establishing ...
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In this study, we introduce a concept of fuzzy fractional differentiability and integrability on time scales for S-correlated fuzzy functions and propose their fundamental properties. Using these concepts, we study fuzzy fractional dynamic equations on time scales for S-correlated fuzzy functions, establishing existence and uniqueness results for their fuzzy solutions. Additionally, we study non-homogeneous first-order linear fuzzy fractional dynamic equations with both real and fuzzy coefficients and establish their general solution forms on time scales. Furthermore, we provide illustrative examples to illustrate our results.
Fuzzy Math
Erdal Bayram; Mikail Et; Abdullah Aydın
Volume 22, Issue 5 , September and October 2025, Pages 37-47
Abstract
This research paper introduces and investigates two new concepts, namely pointwise deferred $f$-statistical convergence of order $\alpha$ and strong pointwise deferred $f$-summability of order $\alpha$, within the context of sequences of fuzzy mappings. The study explores the relationships between these ...
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This research paper introduces and investigates two new concepts, namely pointwise deferred $f$-statistical convergence of order $\alpha$ and strong pointwise deferred $f$-summability of order $\alpha$, within the context of sequences of fuzzy mappings. The study explores the relationships between these newly proposed concepts and establishes several inclusion theorems. These findings contribute valuable insights into the properties of the introduced concepts, shedding light on their characteristics and applications in the realm of fuzzy mappings.
Fuzzy modelling
Hieu Huynh-Van; Tai Vovan
Volume 22, Issue 5 , September and October 2025, Pages 49-68
Abstract
This article develops a novel classification algorithm that integrates the improved Bayesian approach with Principal Component Analysis (PCA). First, PCA significantly reduces the dimensionality of the data while enhancing inter-class separability, thereby improving classification performance. Next, ...
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This article develops a novel classification algorithm that integrates the improved Bayesian approach with Principal Component Analysis (PCA). First, PCA significantly reduces the dimensionality of the data while enhancing inter-class separability, thereby improving classification performance. Next, the prior probabilities are refined based on newly derived variables established for both the training and test sets using a fuzzy clustering analysis technique. Finally, the Bayesian classification rule is constructed based on the probability density functions of the classes, using the transformed variables obtained from PCA and the estimated prior probabilities. The algorithm is presented in detail, including procedural steps, illustrative example, and implementation on both numerical and image data through a the establised RStudio procedure. A key contribution of this study lies in the theoretical enhancement of Bayes error analysis and the proof of convergence of the proposed algorithm. Empirical applications demonstrate that the proposed algorithm yields stable results and outperforms several existing approaches when applied to certain numerical and image datasets using statistical parameters.
Fuzzy modelling
Hamed Rafiei; Ali Salehi; Fahimeh Baghbani; Mohammad-R. Akbarzadeh-T.
Volume 22, Issue 5 , September and October 2025, Pages 69-96
Abstract
Vaccination and isolation are crucial strategies for controlling pandemics, but healthcare infrastructure constraints, such as limited medical resources and workforce shortages, often limit their effectiveness. This research explores the impact of these constraints on vaccination and isolation policies ...
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Vaccination and isolation are crucial strategies for controlling pandemics, but healthcare infrastructure constraints, such as limited medical resources and workforce shortages, often limit their effectiveness. This research explores the impact of these constraints on vaccination and isolation policies through the development of a Vaccination and Isolation Constraint-based Evolutionary Type-2 Fuzzy Controller (VIETFC) intended to assist policymakers. A genetic algorithm optimizes VIETFC's parameters to reduce the number of infected individuals and provide adequate medical care. As a realistic case study, we consider the COVID-19 pandemic and use a stochastic SEIAR (S2EIAR) model to include the pandemic's inherent uncertainties and establish four scenarios with progressively increasing constraints, with the fourth scenario reflecting the most realistic conditions. Results show that VIETFC adapts effectively to resource limitations, maintaining a 15% standard deviation despite a 50% parameter variation. VIETFC surpasses Proportional Integral Derivative (PID) and type-1 fuzzy controllers by offering interpretable solutions through a more practical resource allocation strategy. Future research should focus on enhancing the flexibility of this method for application to additional pandemic models.
Fuzzy Math
Juan de Dios Gonzalez-Hedstrom; Juan-José Miñana; Oscar Valero
Volume 22, Issue 5 , September and October 2025, Pages 97-109
Abstract
In this paper we characterize those functions that induce a fuzzy preorder from a quasi-pseudo-metric even when the considered t-norm is not continuous. On the one hand, we prove that they must be decreasing and fulfill a special property of dominance with respect to the ordinary addition and the t-norm ...
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In this paper we characterize those functions that induce a fuzzy preorder from a quasi-pseudo-metric even when the considered t-norm is not continuous. On the one hand, we prove that they must be decreasing and fulfill a special property of dominance with respect to the ordinary addition and the t-norm T under consideration. On the other hand, we have shown that they must transform asymmetric triangular triplets into asymmetric T-triangular triplets. Moreover, we also study the case in which fuzzy preorders are exactly indistinguishability operators. Concretely, we show that the monotonicity of the function and the previously mentioned dominance are sufficient but not necessary conditions. In addition, we prove that such functions must transform triangular triplets into T-triangular triplets. The developed theory is illustrated by means of appropriate examples. Furthermore, we prove that the well-known technique based on the use of the pseudo-inverses of the additive generators of continuous and Archimedean t-norms is recovered as a particular case of the new one presented here.
Fuzzy modelling
Zou Wei; Xie Jiehua
Volume 22, Issue 5 , September and October 2025, Pages 111-128
Abstract
In this paper, we address the constructions of absolutely continuous copulas characterized by givencurvilinear or opposite-curvilinear sections. For a given function generated by a suitable curve within the unit square, we develop a family of absolutely continuous copulas having this function as a curvilinear ...
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In this paper, we address the constructions of absolutely continuous copulas characterized by givencurvilinear or opposite-curvilinear sections. For a given function generated by a suitable curve within the unit square, we develop a family of absolutely continuous copulas having this function as a curvilinear section, achieved by averaging over a series of copulas sharing the common curvilinear section. Subsequently, we establish theoretical relationships between absolutely continuous copulas with given opposite-curvilinear sections and those with corresponding curvilinear sections. Using these relationships, we propose two approaches for constructing of absolutely continuous copulas with given opposite-curvilinear sections.
Fuzzy Math
Anju C Augustine; Shery Fernandez
Volume 22, Issue 5 , September and October 2025, Pages 129-142
Abstract
I In this work, we attempt to link a fuzzy graph to a fuzzy subgroup.A fuzzy graph, with the vertices representing the fuzzy subgroups elements,is used to represent a fuzzy subgroup. An identity graph is a speci c type ofgraph that can be used to represent a nite group. We de ne fuzzy identitygraph of ...
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I In this work, we attempt to link a fuzzy graph to a fuzzy subgroup.A fuzzy graph, with the vertices representing the fuzzy subgroups elements,is used to represent a fuzzy subgroup. An identity graph is a speci c type ofgraph that can be used to represent a nite group. We de ne fuzzy identitygraph of nite abelian groups by combining the notions of fuzzy graph andidentity graph of a group. For a fuzzy identity graph of a nite abelian group,we determine an upper bound on the number of fuzzy cut vertices and fuzzybridges. The structure of the fuzzy identity graph of the direct product ofabelian groups is also obtained
Engineering
Zehour Ismahane Saci; Mohcene Bechouat; Moussa Sedraoui; Ramazan Ayaz
Volume 22, Issue 5 , September and October 2025, Pages 143-158
Abstract
This paper proposes a novel photovoltaic (PV) array model using an equivalent single-diode electrical circuit with threetime-varying parameters: the diode quality factor, series resistance, and shunt resistance. The goal is to determinean optimal fuzzy inference mechanism to accurately predict the evolution ...
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This paper proposes a novel photovoltaic (PV) array model using an equivalent single-diode electrical circuit with threetime-varying parameters: the diode quality factor, series resistance, and shunt resistance. The goal is to determinean optimal fuzzy inference mechanism to accurately predict the evolution of each parameter under varying climaticconditions, such as solar irradiance, cell temperature, and PV voltage. A dataset of 500 experimental samples, collectedfrom three series-connected monocrystalline PV panels, was used—300 samples for model training and 200 for validation.The proposed design normalizes input data to [0, 1] and feeds it into an initial fuzzy mechanism with predefinedfuzzification rules. The mechanism generates normalized outputs, which are denormalized to compute predicted PVcurrents. These are compared to actual measurements to calculate modeling errors, aggregated as mean squared errors(MSEs). A genetic algorithm (GA) minimizes the MSEs by optimizing the fuzzification rules, retaining only the mosteffective ones. This process iterates until a final fuzzy mechanism with reduced fuzzification rules is achieved, capableof supervising the evolution of each adjustable parameter under varying climatic conditions. Experimental validationconfirms the accuracy and robustness of the proposed adjustable-parameter PV model, outperforming conventionalfixed-parameter models. This approach provides a reliable framework for PV system modeling, significantly improvingpredictive accuracy and adaptability in real-world conditions, with potential applications in advanced MPPT controllersynthesis and renewable energy system optimization.
Computer Science
Leila Yousofvand; Mohammad Bagher Dowlatshahi; Mostafa Pirdadeh Beiranvand
Volume 22, Issue 5 , September and October 2025, Pages 159-179
Abstract
Pneumonia is a life-threatening respiratory disease that requires early and accurate diagnosis for effective treatment and reduced complications. However, conventional diagnostic methods such as PCR are often time-consuming, equipment-dependent, and limited to specialized medical centers. This study ...
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Pneumonia is a life-threatening respiratory disease that requires early and accurate diagnosis for effective treatment and reduced complications. However, conventional diagnostic methods such as PCR are often time-consuming, equipment-dependent, and limited to specialized medical centers. This study introduces a novel anomaly detection framework for pneumonia diagnosis using advanced machine learning techniques applied to chest X-ray images. To enhance classification performance, the framework integrates several feature selection methods, including Correlation-based Feature Selection (CFS) to evaluate feature relevance, Fisher Score to rank features based on discriminative power, Maximum Information Coefficient (MIC) to capture complex dependencies, and Local Learning-based Correlation Feature Selection (LLCFS) to improve accuracy by considering local feature correlations. To further enhance classification performance, this study introduces the first-ever application of Fuzzy VIKOR in pneumonia detection. This fuzzy logic-based ensemble method effectively handles uncertainty in medical imaging data, leading to more balanced decision-making when dealing with conflicting information. The proposed model was trained on a chest X-ray dataset and evaluated using key classification metrics, including accuracy, recall, precision, and F1-score. Experimental results confirm that the model outperforms baseline methods across all metrics, achieving an accuracy of 98.34%. These findings validate the effectiveness of the proposed framework and highlight its high potential for real-world deployment in AI-driven computer-aided diagnosis (CAD) systems, particularly in hospitals and telemedicine applications.
Computer Science
Syed Umar; Nilesh T. Gole; Poorna Chandra Reddy Alla; V S Narayana Tinnaluri
Volume 22, Issue 5 , September and October 2025, Pages 181-202
Abstract
The rapid growth and widespread integration of the Internet of Things (IoT) across various domains have enabled systems to autonomously send, receive, and analyze data. Despite its significant benefits for improving quality of life and service efficiency, IoT still faces considerable challenges related ...
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The rapid growth and widespread integration of the Internet of Things (IoT) across various domains have enabled systems to autonomously send, receive, and analyze data. Despite its significant benefits for improving quality of life and service efficiency, IoT still faces considerable challenges related to security and privacy. An anomaly-based Intrusion Detection System (IDS) can provide security for IoT networks against various cyber-attacks. This study proposes an anomaly-based IDS to prevent cyber-attacks in IoT environments. In this research, we propose a novel optimized ensemble deep learning-based approach for anomaly-based IDS classification. The proposed approach includes several innovative aspects: initially, raw data are pre-processed, and essential features are extracted using CoffeeNet. The approach employs the Binary Bamboo Forest Growth Optimization Algorithm (BBFGOA) to reduce data characteristics and enhance anomaly detection effectiveness. We use the Optimized Fuzzy NFSC approach an ensemble of the Sharpened Cosine Similarity-based Neural Network (SCS-Net) and a rule-based Deep Fuzzy System with Nonlinear Fuzzy Feature Transform (NFFS-DFS)—to categorize network flows as benign or malicious across multiple categories. The Partial Reinforcement Optimization Algorithm (PRO) is used to tune the hyperparameters in the Fuzzy NFSC approach. Additionally, we employ a Conditional Tabular Generative Adversarial Network (CTGAN) to address the class imbalance issue. The proposed anomaly detection approach is evaluated and analyzed using four benchmark datasets to validate its effectiveness. The evaluation's results demonstrate that the approach enhances detection performance, with a 99.43% F1-score, 99.61% accuracy, 99.45% precision, and 99.38% recall. The findings also show that employing an oversampling technique improves minority sample identification rates, which raises overall accuracy. The suggested approach works better than current techniques since it takes less time to train and detect.
