Fuzzy Rule-based System Research Articles

A review of Fuzzy Logic Analysis in COVID-19 Pandemic and a new technique through extended hexagonal intuitionstic fuzzy number in Analysis of COVID -19

Several intuitionistic fuzzy logic approaches have been used for the diagnosis of COVID-19 patients. We have developed a fuzzy rule base system for the detection of COVID-19 patients. In this study, we have considered six major parameters based symmetric/asymmetric, linear/non-linear hexagonal intuitionistic fuzzy numbers (HIFN) for the input-output factors of the problem. In real-life diagnosis problems, such as assessing COVID-19 symptoms, applying symmetric and asymmetric, linear and non-linear hexagonal intuitionistic fuzzy numbers allows for a more accurate representation of patient conditions. Centre of area method is used for the defuzzied value of the hexagonal intuitionistic fuzzy parameters. HIFN are used because they provide a detailed representation of uncertainty, incorporating both membership and non-membership degrees through six parameters. This flexibility allows for nuanced modelling of real-world scenarios, such as medical diagnoses, where data often includes ambiguity. Then the HIFN approach is used for obtaining the compromising and superlative solution in the diagnostic process of COVID-19 patients. To figure out the adaptability of the proposed HIFN based technique, a comparative study is also introduced. The originality, limitations, future aspects and advantages of using this HIFN based technique is also discussed in this article.

Dynamic Performance Evaluation of a Five-Phases Induction Motor System controlled using Fuzzy Logic Director

The five-phase induction motor drive offers distinct benefits that enhance the motor's torque production capabilities. The proposed fuzzy logic controller is well suited for high-performance five-phase induction motor drives. This paper is discussed about a speed control strategy for a five-phase induction motor drive system using a Fuzzy Logic Controller (FLC). This controller adjusts system parameters through a rule-based fuzzy logic system, mimicking human reasoning for process control. The speed control algorithm employs indirect vector control.

Multi-Layer Perceptron and Radial Basis Function Networks in Predictive Modeling of Churn for Mobile Telecommunications Based on Usage Patterns

Customer retention is a key priority for mobile telecommunications companies, as acquiring new customers is significantly more costly than retaining existing ones. A major challenge in this field is predicting customer churn—users discontinuing services. Traditional predictive models such as rule-based systems often struggle with the complex, non-linear nature of customer behavior. To address this, we propose the use of deep learning techniques, specifically multi-layer perceptron (MLP) and radial basis function (RBF) networks, to improve the accuracy of churn predictions. However, while neural networks excel in predictive performance, they are often criticized for being “black-box” models, lacking interpretability. A real-world data set is considered, which originally contained information about 15,000 randomly selected clients. Various network structures and configurations are analyzed. The obtained results are compared with results generated using fuzzy rule-based and rough-set rule-based systems. The MLP model achieved an almost perfect accuracy of 0.999 with an F-measure of 0.989, outperforming traditional methods such as fuzzy rule-based and rough-set systems. Although the RBF model slightly lagged in accuracy, it demonstrated a superior recall of 0.993, indicating better identification of potential churners. These results demonstrate that neural network models significantly enhance predictive performance in churn modeling. The interpretability of the model is also discussed since it bears significance in real applications. Our contribution lies in showing that deep learning methods significantly enhance churn prediction accuracy, though the challenge of model interpretability remains a critical area for future work.

Fuzzy-based task offloading in Internet of Vehicles (IoV) edge computing for latency-sensitive applications

As vehicular applications continue to evolve, the computational capabilities of individual vehicles alone are no longer sufficient to meet the increasing demands. This has led to the integration of edge computing in the Internet of Vehicles (IoV) as an essential solution. Due to the limited resources within vehicles, there is often a need to offload tasks to edge nodes. However, task offloading in IoV environments presents several challenges, including high mobility, dynamic network topology, and varying node density. Traditional offloading methods fail to effectively address these challenges. Moreover, tasks differ in importance, necessitating a mechanism for edge nodes to prioritize tasks based on their urgency. To overcome these challenges, we propose a Vehicle-to-Vehicle (V2V) fuzzy-based task offloading scheme. In this scheme, fuzzy logic plays a critical role by enabling dynamic prioritization of tasks based on their urgency and the available computational resources at edge nodes, ensuring intelligent, context-aware decision-making. The user vehicle selects an appropriate edge node using an edge selection mechanism, which guarantees prolonged connection time and sufficient computational resources. Tasks at the edge are then organized based on their latency requirements and evaluated using a fuzzy rule-based inference system. Our simulation results demonstrate improved task execution rates, reduced overall system delay, and minimized queuing delays.

Application of the Sugeno integral in Fuzzy Rule-Based Classification

Fuzzy Rule-Based Classification System (FRBCS) is a well-known technique to deal with classification problems. Recent studies have considered the usage of the Choquet integral and its generalizations (e.g.: CT-integral, CF-Integral and CC-integral) to enhance the performance of such systems. Such fuzzy integrals were applied to the Fuzzy Reasoning Method (FRM) to aggregate the fired fuzzy rules when classifying new data. However, the Sugeno integral, another well-known aggregation operator, obtained good results in other applications, such as brain–computer interfaces. These facts led to the present study, in which we consider the Sugeno integral in classification problems. That is, the Sugeno integral is applied in the FRM of a widely used FRBCS, and its performance is analyzed over 33 different datasets from the literature, also considering different fuzzy measures. To show the efficiency of this new approach, the results obtained are also compared with previous studies that involved the application of different aggregation functions. Finally, we perform a statistical analysis of the application.

Federated Learning of XAI Models in Healthcare: A Case Study on Parkinson’s Disease

Artificial intelligence (AI) systems are increasingly used in healthcare applications, although some challenges have not been completely overcome to make them fully trustworthy and compliant with modern regulations and societal needs. First of all, sensitive health data, essential to train AI systems, are typically stored and managed in several separate medical centers and cannot be shared due to privacy constraints, thus hindering the use of all available information in learning models. Further, transparency and explainability of such systems are becoming increasingly urgent, especially at a time when “opaque” or “black-box” models are commonly used. Recently, technological and algorithmic solutions to these challenges have been investigated: on the one hand, federated learning (FL) has been proposed as a paradigm for collaborative model training among multiple parties without any disclosure of private raw data; on the other hand, research on eXplainable AI (XAI) aims to enhance the explainability of AI systems, either through interpretable by-design approaches or post-hoc explanation techniques. In this paper, we focus on a healthcare case study, namely predicting the progression of Parkinson’s disease, and assume that raw data originate from different medical centers and data collection for centralized training is precluded due to privacy limitations. We aim to investigate how FL of XAI models can allow achieving a good level of accuracy and trustworthiness. Cognitive and biologically inspired approaches are adopted in our analysis: FL of an interpretable by-design fuzzy rule-based system and FL of a neural network explained using a federated version of the SHAP post-hoc explanation technique. We analyze accuracy, interpretability, and explainability of the two approaches, also varying the degree of heterogeneity across several data distribution scenarios. Although the neural network is generally more accurate, the results show that the fuzzy rule-based system achieves competitive performance in the federated setting and presents desirable properties in terms of interpretability and transparency.

Rule-Ranking-Based Approximate Knowledge Interpolation With Directional Monotonicity.

Fuzzy rule interpolation (FRI) empowers fuzzy rule-based systems (FRBSs) with the ability to infer, even when presented with a sparse rule base where no direct rules are applicable to a given observation. The core principle lies in creating an intermediate fuzzy rule-either interpolated or extrapolated-derived from rules neighboring the observation. Conventionally, the selection of these rules hinges upon distance metrics. While this approach is easy to grasp and has been instrumental in the evolution of various FRI methods, it is burdened by the necessity of extensive distance calculations. This becomes particularly cumbersome when swift responses are imperative or when dealing with large datasets. This article introduces a groundbreaking rule-ranking-based FRI method, termed RT-FRI, which overcomes the constraints of the longstanding distance-centric FRI approach. Instead of relying on distances, RT-FRI harnesses ranking scores for rules and unmatched observation. These scores are produced by amalgamating the antecedent attributes using aggregation functions, thus streamlining the rule selection procedure. Recognizing the rigid monotonicity demands of aggregation functions, a variant-DMRT-FRI-has been introduced to ensure directional monotonicity. Experimental results indicate that RT-FRI emerges as a highly efficient technique, with DMRT-FRI exemplifying a notable balance of accuracy and efficiency.

Global warming communicative actions of publics in Türkiye: Utilizing fuzzy rule based system

The effectiveness of government policies and environmental initiatives to mitigate global warming relies heavily on public support, which is closely tied to public perception and awareness. Despite the scientific evidence communicated, the public remains reluctant to take preventive measures against global warming. The aim of the paper is to investigate the communicative actions of publics proposed as in the situational theory of problem solving to understand publics' communicative actions towards global warming. The paper utilizes a fuzzy rule-based system approach to analyze the communicative actions of publics to reveal non-linear relationships; whereas previous studies mostly used linear statistical analysis. The paper provides a deeper understanding into the interplay between problem recognition, constraint recognition, and involvement in shaping information behavior. The results show that the communicative actions of the publics are at a low-to-moderate level. The paper's interesting finding is the nonlinear effects of constraint recognition on communicative action about global warming. Contrary to the current literature, it was found out that the dominant factor that may convince public to start taking action towards global warming seems to be recognizing being constrained at a moderate level. Based on the results, it is suggested for policy makers and communication strategists to mitigate the negative outcomes of global warming by integrating environmental issues into education at all levels and collaborating with non-governmental organizations for national awareness campaigns which focus on increasing public problem recognition and involvement.

Multi-objective optimization of virtual machine migration among cloud data centers

AbstractWorkload migration among cloud data centers is currently an evolving task that requires substantial advancements. The incorporation of fuzzy systems holds potential for enhancing performance and efficiency within cloud computing. This study addresses a multi-objective problem wherein the goal is to maximize the interpretability and the percentage of renewable energy consumed by a fuzzy meta-scheduler system in cloud scenarios. To accomplish this objective, the present research proposes a novel approach utilizing a multi-objective Knowledge Acquisition with a Swarm Intelligence Approach algorithm. Additionally, it takes advantage of a framework built on CloudSim, which includes virtual machine migration capabilities based on an expert system. Furthermore, a hierarchical fuzzy system is employed to assess rule base interpretability, along with another multi-objective algorithm, named Non-dominated Sorting Genetic Algorithm II. The framework and hierarchical system are employed to perform various simulation results concerning renewable energy and interpretability, while the algorithms aim to enhance the system’s performance and interpretability. Empirical results demonstrate that it is possible to improve the performance of cloud data centers while improving the interpretability of the corresponding fuzzy rule-based system. The proposed multi-objective algorithm shows comparable or superior performance to the genetic algorithm across diverse scenarios. The simulation results indicate that improvements in cloud data center performance can be achieved while enhancing system interpretability. The average improvement in the interpretability index ranges from 0.6 to 6%, with a corresponding increase in renewable energy utilization ranging from 5 to 6%.

First-order rule partitions-based decomposition technique of type-1 and interval type-2 rule-based fuzzy systems for computational and memory efficiency

Rule-based fuzzy systems are widely adopted in various fields for their effectiveness in reducing uncertainties. However, large rule bases present significant computational and memory challenges, especially for real-time applications. To overcome this limitation, this paper introduces a novel first-order rule partitions-based decomposition technique (FORPs-DT) for type-1 (T1) and interval type-2 (IT2) fuzzy logic systems (FLSs). This method involves decomposing the universe of discourse, membership functions (MFs), and fuzzy sets (FSs) by defining conditions for non-zero firing levels (intervals). Significantly, this approach allows the construction of subfuzzy systems with separate knowledge bases while maintaining the input–output relationship. The paper provides detailed explanations and visual representations of FORPs-DT and proposes a case of identical FORPs to illustrate its simplicity and resource optimization benefits. Extensive experiments demonstrate the superiority of this technique, showcasing not only reduced execution time and memory usage but also enhanced performance through identical partitions, which allows an increase in the number of FSs without additional computational or memory overhead.

Energy Consumption Prediction in Residential Buildings—An Accurate and Interpretable Machine Learning Approach Combining Fuzzy Systems with Evolutionary Optimization

This paper addresses the problem of accurate and interpretable prediction of energy consumption in residential buildings. The solution that we propose in this work employs the knowledge discovery machine learning approach combining fuzzy systems with evolutionary optimization. The contribution of this work is twofold, including both methodology and experimental investigations. As far as methodological contribution is concerned, in this paper, we present an original designing procedure of fuzzy rule-based prediction systems (FRBPSs) for accurate and transparent energy consumption prediction in residential buildings. The proposed FRBPSs are characterized by a genetically optimized accuracy–interpretability trade-off. The trade-off optimization is carried out by means of multi-objective evolutionary optimization algorithms—in particular, by our generalization of the well-known strength Pareto evolutionary algorithm 2 (SPEA2). The proposed FRBPSs’ designing procedure is our original extension and generalization (for regression problems operating on continuous outputs) of an approach to designing fuzzy rule-based classifiers (FRBCs) we developed earlier and published in 2020 in this journal. FRBCs operate on discrete outputs, i.e., class labels. The experimental contribution of this work includes designing the collection of FRBPSs for residential building energy consumption prediction using the data set published in 2024 and available from Kaggle Database Repository. Moreover, the comparison with 20 available alternative approaches is carried out, demonstrating that our approach significantly outperforms alternative methods in terms of interpretability and transparency of the energy consumption predictions made while remaining comparable or slightly superior in terms of the accuracy of those predictions.

Fuzzy Rule Based System of Optimal Pavement Maintenance and Rehabilitation Strategies for Yangon-Mandalay Expressway

Purpose: Systematic pavement management plays a vital role in the better Road Asset Management system. The lifespan of pavement can be preserved and prolonged by adequate maintenance measures in proper time. By early detection and repair of defects at initial stages with predictive maintenance reduces the rapid deterioration of the pavement. Moreover, inaccuracy in pavement deterioration prediction leads to an improper maintenance with wastage of time, cost and labor strength. To overcome all of these obstacles, this paper attempts an approach on optimal pavement maintenance strategy by means of fuzzy rule-based system. Materials and Methods: In this research, four pavement indicators of PCI, IRI, PSR and PSI have been taken into account as antecedents and five different types of pavement maintenance as consequents. In this research, ASTM D-6433, Road Lab Pro software, AASHTO guidelines and Fuzzy Logic (Mamdani) model have been used to evaluate the current pavement condition and optimal pavement maintenance strategies. Findings: According to research area, 130 total pavement sample units were undertaken to study for the mean pavement condition of PCI value 54.93 (poor), IRI 7.01 (fair), PSR 3.33 (fair) and PSI 3.35 (fair). Due to 13 years-old life span of traffic volume, temperature and other external factors, this expressway is suffering from potholes, bleeding, longitudinal and transverse cracking, weathering, joint reflection cracking for Asphalt Concrete (AC) overlay and linear cracking, joint seal failure, scaling, faulting, depression, lane/shoulder drop off for existing concrete pavement. According to the fuzzy logic model results, 42 numbers of fuzzy If-Then maintenance rules were verified as the most relevant pavement maintenance and rehabilitation strategies for Yangon-Mandalay Expressway. Implications to Theory, Practice and Policy: This research provide implications to the study and contributions to theory, practice and policy. This research paper insights into effective infrastructure maintenance and investment, emphasizing the role of advanced computational techniques in transportation economics. The key advantage of this research paper is to be effective, supportive, easy and accessible decision-making tool for transportation and pavement engineers, expressway planners and highways department authorities especially from developing countries.

Expert system-based application for fatal ventricular arrhythmia risk level estimation based on QT-Interval prolongation

There is an overwhelming number of medications that can affect the morphology of the beating heart by delaying the ventricular depolarization and repolarization processes, resulting in an elongated QT interval. This circumstance, in turn, can lead to ventricular arrhythmias, which can result in the patient’s death, as was overwhelmingly the case at the beginning of the coronavirus pandemic. In this regard, this document presents an innovative expert system-based application devoted to estimating the level of risk of fatal ventricular arrhythmia as a consequence of QT prolonging treatments. For this purpose, the use of a fuzzy rule-based system has been considered for the decision-making process, establishing three different risk levels regarding the QT interval obtained from an electrocardiogram, its evolution from a basal electrocardiogram, and other significant clinical information of the patient such as age, sex, among others. This novel expert system-based application has been designed by considering the criteria of several specialist physicians from four different Spanish private and public hospitals. The proposed application can be especially important in primary health care centers, where the absence of specialists is remarkable because it provides clinical staff with a powerful tool for estimating the level of risk of fatal ventricular arrhythmia with 96.5% accuracy when comparing its decisions to those of specialist physicians, which could imply a significant reduction in the risk of death due to fatal ventricular arrhythmias in patients with QT prolonging treatment. In this respect, the developed application is currently used in some primary health care centers in Spain.

Fuzzy rule based classifier model for evidence based clinical decision support systems

Clinicians benefit from the use of artificial intelligence and machine learning techniques applied to health data within health records, which identify commonalities between them. It enables them to get evidence-based support in recommending shared treatment paths for undiagnosed health records. The collective inference from these patterns, drawn from an array of health records, further enhances the capacity to mine essential features, supporting public health experts in their management of population health conditions. This paper presents a novel mapping tool model designed to analyze electronic health records and provide healthcare providers with evidence-based decision support. The work focuses on the analysis of health records from hospital databases, encompassing parameters extracted from routine health checkups. By scrutinizing patterns within examined health records, healthcare providers can seamlessly align with newer health records for diagnosis and treatment recommendations. Core to this approach is the integration of a fuzzy rule-based classifier system within the proposed system. This incorporation facilitates the processing of health records, extracting pertinent features to augment decision-making with the support of knowledge bases. The model architecture provides flexibility and customizability, enabling easy configuration of the system to accurately map new health records to the examined dataset. Additionally, the model utilizes a specially developed distance-measure technique tailored for the proposed fuzzy-based system. Results showcase satisfying performance and robust discriminant capability for accurate recommendations. The alignment of outcomes with expert evaluations underscores the model's efficacy and attainment of benchmarks.

Fuzzy Contrast Enhancement Techniques Using Rule-Based System and Intensification Operator

Contrast enhancement is a crucial step in digital image processing to ensure the production of high-quality images. This paper presents two distinct fuzzy techniques designed to address the nuanced concept of contrast. that utilize rule-based system and intensification operator, respectively, to handle the inherent vagueness associated with the concept of contrast. The fuzzy rule-based system calculates the intensity value of the output image based on the contrast measure (standard deviation) of the input image, thereby enabling effective contrast enhancement for low contrast images. Meanwhile, the fuzzy intensification operator aims to accentuate the differences in intensity levels within the image, thereby enhancing the contrast between various features and details. The experimental results show better image contrast is obtained using both methods. However, fuzzy rule-based approach shows more images with better quality compared to the intensification operator approach. The performance of these techniques is evaluated both visually and quantitatively.

Enhancing AI interpretation and decision-making: Integrating cognitive computational models with deep learning for advanced uncertain reasoning systems

Advancements in uncertain reasoning systems within healthcare are crucial for navigating the complexities of patient data, requiring innovative methodologies that integrate AI interpretation capabilities and robust handling of inherent ambiguity. Healthcare systems face the challenge of handling uncertainty inherent in patient data, necessitating sophisticated decision-making tools like Uncertain Reasoning Systems (URS) for effective ambiguity navigation. Recognizing the complexity of healthcare scenarios, advancements in AI interpretation within URS are crucial beyond traditional methods. Conventional techniques like statistical approaches and rule-based systems often prove inadequate due to their rigid frameworks and limited ability to manage inherent ambiguity. This paper proposes an innovative methodology that integrates Min-Max normalization and robust missing data handling techniques with Hybrid Fuzzy Rule-Based Systems and Neural Networks, supplemented by Game Theory for model refinement. Through the integration of Game Theory, it can dynamically adjust its strategies to healthcare data uncertainties, thereby enhancing its resilience and efficacy. Implemented using Python tools, the proposed system achieves an exceptional 99.4 % accuracy, surpassing baseline methods such as FNN (88.1 %) and Naïve Bayes (90 %), highlighting its superior performance in healthcare decision-making. These findings represent significant strides in AI interpretation and decision-making within Uncertain Reasoning Systems, underscoring the practical relevance of the proposed approach.

Current Output Estimation of 12 Volts Solar Power Line Source Using Fuzzy Logic-Based Modelling Technique

This study used the fuzzy logic-based system to estimate the output current of a solar panel system at specified amount of power and percentage of cloud cover in the atmosphere. This established the fuzzy rule-based system which was applied from a research study to analyze the behavior of the current output wherein the available solar panels are three 100 watts and two 50 watts making it difficult to measure the current with power of 175 watts and 350 watts. The amount of the produced current was established through the fuzzy rule-based system stated as the following: if the power is low and the atmosphere is almost cloudy then the current produced is very small; if the power is high and the atmosphere is cloudy then the current produced is small; if the power is medium and the atmosphere is partially cloudy then the current produced is average; if the power is high and the atmosphere is clear then the current produced is large. Through defuzzification the fuzzy system was implemented by computing the output value using the centroid method.

Edge computing-oriented smart agricultural supply chain mechanism with auction and fuzzy neural networks

Powered by data-driven technologies, precision agriculture offers immense productivity and sustainability benefits. However, fragmentation across farmlands necessitates distributed transparent automation. We developed an edge computing framework complemented by auction mechanisms and fuzzy optimizers that connect various supply chain stages. Specifically, edge computing offers powerful capabilities that enable real-time monitoring and data-driven decision-making in smart agriculture. We propose an edge computing framework tailored to agricultural needs to ensure sustainability through a renewable solar energy supply. Although the edge computing framework manages real-time crop monitoring and data collection, market-based mechanisms, such as auctions and fuzzy optimization models, support decision-making for smooth agricultural supply chain operations. We formulated invisible auction mechanisms that hide actual bid values and regulate information flows, combined with machine learning techniques for robust predictive analytics. While rule-based fuzzy systems encode domain expertise in agricultural decision-making, adaptable training algorithms help optimize model parameters from the data. A two-phase hybrid learning approach is formulated. Fuzzy optimization models were formulated using domain expertise for three key supply chain decision problems. Auction markets discover optimal crop demand–supply balancing and pricing signals. Fuzzy systems incorporate domain knowledge into interpretable crop-advisory models. An integrated evaluation of 50 farms over five crop cycles demonstrated the high performance of the proposed edge computing-oriented auction-based fuzzy neural network model compared with benchmarks.

Construction of fuzzy classification systems by fitness sharing based genetic search and boosting based ensemble

This paper concentrates on the development of precise fuzzy rule-based classification systems for high-dimensional and multi-class problems. The approach begins with the extraction on potential fuzzy if-then rules using fitness sharing based genetic algorithms, this ensures effective searching for productive niches, thereby evolving and maintaining a diverse, cooperative population. Subsequently, for the purpose of combining the obtained fuzzy rules and eliminating their conflicts, an adaboost ensemble method is utilized, enhancing the accuracy of the fuzzy classification systems.Experiments have been conducted on 10 UCI datasets and 3 well-known image classification problems. The features for these image tasks were derived from the activation values of the final convolutional layer in pre-trained convolutional neural networks. These datasets, which were chosen to evaluate the effectiveness of the proposed approach, exhibit significant variation in terms of dimensionality and the number of class labels. Comparative analyses are carried out with conventional fuzzy rule-based classification methods, and the results demonstrate that the classification systems can be developed for complex problems, while maintaining a high-level of prediction accuracy.

A belief rule-based classification system using fuzzy unordered rule induction algorithm

A rule-based system is a widely used artificial intelligence system that employs a set of rules to make decisions. The belief rule-based (BRB) classification system is an extension of fuzzy rule-based (FRB) system that handles uncertainty and imprecision in classification tasks by incorporating Dempster-Shafer evidence theory and fuzzy set theory. However, the BRB classification system suffers from the combinatorial explosion and high time complexity problems. To solve these issues, the fuzzy unordered rule induction algorithm (FURIA) is introduced into the BRB classification systems to design a novel BRB classification system in this paper. FURIA is computationally less complex and has superior classification performance. The proposed system outperforms BRB classification systems in terms of classification performance and significantly reduces the number of rules and conditions. Furthermore, the proposed system offers a more effective solution than FURIA. To evaluate the validity and superiority of the proposed system, we conduct two classification experiments, comparing it with five traditional classifiers and seven rule-based systems, respectively, in terms of classification performance and interpretability.