Fuzzy Model Research Articles

Simulation methods realized by virtual reality modeling language for 3D animation considering fuzzy model recognition

The creation of 3D animation increasingly prioritizes the enhancement of character effects, narrative depth, and audience engagement to address the growing demands for visual stimulation, cultural enrichment, and interactive experiences. The advancement of virtual reality (VR) animation is anticipated to require sustained collaboration among researchers, animation experts, and hardware developers over an extended period to achieve full maturity. This article explores the use of Virtual Reality Modeling Language (VRML) in generating 3D stereoscopic forms and environments, applying texture mapping, optimizing lighting effects, and establishing interactive user responses, thereby enriching the 3D animation experience. VRML’s functionality is further expanded through the integration of script programs in languages such as Java, JavaScript, and VRML Script via the Script node. The implementation of fuzzy model recognition within 3D animation simulations enhances the identification of textual, musical, and linguistic elements, resulting in improved frame rates. This study also analyzes the real-time correlation between the number of polygons and frame rates in a virtual museum animation scene. The findings demonstrate that the frame rate of the 3D animation within this virtual setting consistently exceeds 40 frames per second, thereby ensuring robust real-time performance, preserving the quality of 3D models, and optimizing rendering speed and visual effects without affecting the system’s responsiveness to additional functions.

A medical disease assisted diagnosis method based on lightweight fuzzy SZGWO-ELM neural network model

A medical disease assisted diagnosis method based on lightweight fuzzy SZGWO-ELM neural network model

Advancing aquaculture: fuzzy logic-based water quality monitoring and maintenance system for precision aquaculture

AbstractAquaculture plays a vital role in global food production, and maintaining optimal water quality is essential for the health and growth of aquatic species. This research addresses the need for an efficient, adaptive solution by introducing an innovative water quality monitoring and maintenance system for aquaculture ponds. Unlike conventional systems, our approach uniquely integrates fuzzy logic with IoT technologies to optimise the precision and adaptability of pond management. The system stands out with its continuous monitoring of critical parameters such as temperature, pH, dissolved oxygen (DO), weather conditions and salinity and its ability to autonomously adjust operational controls, such as aerators and water pumps, based on dynamic environmental changes. This ensures ideal water conditions without manual intervention, providing a reliable and effective solution for aquaculture pond management. This work’s novelty lies in applying fuzzy Logic to handle the complexity and variability of aquaculture environments, allowing for nuanced control decisions that improve water quality management. The system’s efficiency was demonstrated through a 72-h operational test, where it maintained optimal DO and salinity levels, showcasing its reliability and effectiveness in real-world conditions. The fuzzy logic model has demonstrated a commendable accuracy rate of 98%. These results validate the system’s performance and underscore its practical benefits, meeting the demands of aquaculture production and significantly enhancing operational efficiency by enabling remote monitoring and rapid issue identification. This research contributes a robust technological solution for aquafarmers, offering a promising advancement in aquaculture management by improving productivity and ensuring the health and growth of aquatic species.

A new EEG-based schizophrenia diagnosis method through a fuzzy DL model

A new EEG-based schizophrenia diagnosis method through a fuzzy DL model

Investigation and Assessment the Level of SMES Leanness Using Fuzzy Dematel/Fuzzy AHP/Fuzzy Topsis Integrated Model

Abstract Small and Medium Enterprises (SMEs) have a crucial role in driving economic growth and innovation. In the modern business landscape characterized by uncertainty, the adoption of lean philosophy has gained prominence as a means to continuously improve SMEs’ operational efficiency and competitiveness. A fuzzy assessment model was proposed as comprehensive framework and guide for SMEs during their lean journey that utilizes the Fuzzy Analytic Hierarchy Process (Fuzzy AHP), Fuzzy Decision-Making Trial and Evaluation Laboratory (Fuzzy DEMATEL), and Fuzzy Technique for Order of Preference by Similarity to Ideal Solution (Fuzzy TOPSIS) to serve many objectives; identifying the most important and influence lean dimensions on SMEs leanness; assessing the level of adoption of lean philosophy in SMEs for five essential lean dimensions, namely management, process, supplier, customer, and employee; identifying the interrelationships among them; and identifying the driving dimensions for the improvement process to focus SMEs limited resources on it in improvement process due to its direct influence on the other lean dimensions. The proposed fuzzy model was applied in Iraqi medium-sized enterprise producing healthy water, juice, and soft drinks to identify the most important and influence lean dimension on enterprise leanness, identify the driving dimension of the improvement process, and assess the level of leanness of these five lean dimensions. The results showed that the management dimension has the highest level of importance and influence on SMEs’ leanness, equal to 41.31%, and is distinguished as a driving dimension that has a high influence on improving the driven dimensions of process and customer. Although the level of importance and influence of the process dimension is 19.96%, it is an effect dimension and is considered a driven dimension. Plotting the interrelationship among the five lean dimensions in the diagram shows management has an influence on all lean dimensions and, at the same time, has a mutual relationship with employees and customers. An employee has a mutual relationship with the process dimension. In addition, the results showed that enterprise has deficiency in adopting a lean philosophy, where its level of leanness equal 0.39.

Assessing Flood Risks in Coastal Plain Cities of Zhejiang Province, Southeastern China

Constructing a precise and effective evaluation index system is crucial to flood disaster prevention and management in coastal areas. This study takes Lucheng District, Wenzhou City, Zhejiang Province, southeastern China, as a case study and constructs an evaluation index system comprising three criterion levels: disaster-causing factors, disaster-gestation environments, and disaster-bearing bodies. The weights of each evaluation index are determined by combining the Analytic Hierarchy Process (AHP) and the entropy method. The fuzzy matter-element model is utilized to assess the flood disaster risk in Lucheng District quantitatively. By calculating the correlation degree of each evaluation index, the comprehensive index of flood disaster risk for each street area is obtained, and the flood disaster risk of each street area is classified according to the risk level classification criteria. Furthermore, the distribution of flood disaster risks in Lucheng District under different daily precipitation conditions is analyzed. The results indicate that: (1) the study area falls into the medium-risk category, with relatively low flood risks; (2) varying precipitation conditions will affect the flood resilience of each street in Lucheng District, Wenzhou City. The flood disaster evaluation index system and calculation framework constructed in this study provide significant guidance for flood risk assessment in coastal plain cities.

Enhanced Fuzzy Score-Based Decision Support System for Early Stroke Prediction

According to the Global Health Observatory (GHO), stroke ranks second worldwide in causing dementia, right after Alzheimer's disease. The mortality rate linked to dementia resulting from stroke is high because symptoms are often recognized late, and stroke can be misinterpreted as other brain disorders. Early detection and diagnosis of stroke is crucial. Therefore, increasing awareness of stroke symptoms and implementing preventive measures becomes imperative. Prompt intervention by healthcare professionals can improve outcomes and reduce long-term complications of stroke. The research introduces an innovative approach for early stroke prediction using a fuzzy scoring-based Decision Support System. This approach encompasses three main modules: Mind-map based Data Modeling, Fuzzy scoring computing, and ML-based Decision System. By incorporating fuzzy logic, the approach extracts valuable knowledge from imprecise and uncertain data. Combining the fuzzy stroke risk model with a machine learning-based decision support system aims to enhance stroke prediction accuracy and improve preventive measures and patient outcomes. The approach's effectiveness was validated using real clinical data and tested with various machine learning classifiers, including K-Nearest Neighbour (KNN), Logistic Regression (LR), Decision Tree (DT), Artificial Neural Network (ANN), and Support Vector Machine (SVM). The results showed a strong correlation between stroke cases and computed risk-scoring values. In comparison to predictions without fuzzy scoring and other related works, the stroke risk prediction using the proposed approach demonstrated higher accuracy, making it a promising method for early stroke detection and prevention.

A Non-Probabilistic Strategy to Investigate Imprecisely Defined Nonlinear Eigenvalue for Structural Dynamic Problems

Structural dynamic problems are influenced by many parameters which are uncertain in nature. As such, for better estimation, the problems can be considered with epistemic-type uncertainties. Due to the epistemic type of uncertainties, the modeled system appears in the form of an imprecise nonlinear eigenvalue problem. Therefore, this paper proposes a novel nonprobabilistic approach to analyze nonlinear eigenvalue problems in structural dynamics, particularly when dealing with imprecisely defined parameters. In order to solve the fuzzy nonlinear eigenvalue models, the same is to be transformed into a fuzzy eigenvalue problem. Traditional methods often rely on probabilistic frameworks, which may not adequately capture uncertainties inherent in real-world structural systems. The proposed approach leverages concepts from fuzzy set theory to handle imprecise data, providing a more robust and realistic analysis. Then, the developed method is used to solve a case study of structural dynamic problem. Here, a spring-mass system is demonstrated through the developed technique to quantify the uncertain field variables. Based on the imprecise system parameters, different fuzzy models are discussed in various cases and results are reported here. This framework offers valuable insights for engineers and researchers working on dynamic analysis of complex structures, paving the way for more reliable and informed decision-making in structural engineering applications.

Predicting human reliability for emergency fire pump operational process on tanker ships utilising fuzzy Bayesian Network CREAM modelling

An emergency fire pump is a critical equipment which assists the ship crew in handling extreme emergency situations involving fire on-board tanker ships. In case the main fire pump becomes ineffective during a fire, the emergency fire pump is used to extinguish the fire. In this case, it is necessary for the ship's crew to operate and use the pump without any failure. The aim of this study is to systematically predict human (crew) reliability for emergency fire pump operational processes on tanker ships since human dependability plays a significant role in safer shipment. To achieve this goal, fuzzy and BN (Bayesian Network) CREAM (Cognitive Reliability and Error Analysis Method) modelling is applied. CREAM is used to methodically estimate the probability of human error within a fuzzy set that accounts for uncertainties of CPC (Common Performance Condition), while BN is competent in estimating control modes in CREAM's reliability analysis. The paper's findings show ship crew reliability (9.08E-01) for the emergency fire pump operation process on tanker ships. These findings are expected to provide valuable information to prevent human errors and improve safety on tanker ships during firefighting, thereby reassuring the maritime industry of the potential for increased safety.

Dynamic fuzzy comprehensive evaluation of wet friction components performance degradation based on G1-CRITIC

High power density wet friction components are crucial in heavy equipment transmission systems, directly influencing equipment reliability. This paper proposes a fuzzy comprehensive evaluation method with combined weighting, which dynamically divides the performance degradation of wet friction components into three stages. It constructs a multi-indicator, multi-level evaluation system, in which the performance degradation data is obtained from the wet clutch accelerated life test. Additionally, we utilize the combined weighting method to dynamically allocate weights for evaluation indicators at different stages and introduce the trapezoidal distribution membership function. The accuracy of the fuzzy comprehensive evaluation model is then verified by using two prediction models as a comparison method. The results indicate that the model achieves an average relative error of 14.79% and a correlation coefficient R2 of 0.9580, indicating higher usability and accuracy than the prediction models. Furthermore, the evaluation indicator weights are more sensitive to pressure variations.

Optimal design of neural network-based fuzzy predictive control model for recommending educational resources in the context of information technology

Abstract As the information technology develops, educational resource recommendation system has become an indispensable part of the education field. At the same time, people’s demand for personalised educational resources is also growing; therefore, how to accurately predict user demand and provide personalised recommendations has become an important issue. In this study, a fuzzy predictive control model on the grounds of neural network is proposed to optimise the design of educational resource recommendation in the context of information technology. After experimental testing, the model recommendation fit reached 95.16 and 92.91% on the two test datasets, which are significantly higher than the control model. The average F1 values of the proposed model also reached 95.21 and 88.77%, which are higher than the control model. In other control experiments, the proposed model of this study also has a better performance. The relevant outcomes showcase that the predictive performance and recommendation effect of the model can be further enhanced by improving the structure of the neural network and the parameter optimisation method. Meanwhile, the proposed model has high performance in information overload and personalised demand, which offers a useful reference for the optimal design of educational resource recommendation system.

Research on Soybean Seedling Stage Recognition Based on Swin Transformer

Accurate identification of the second and third compound leaf periods of soybean seedlings is a prerequisite to ensure that soybeans are chemically weeded after seedling at the optimal application period. Accurate identification of the soybean seedling period is susceptible to natural light and complex field background factors. A transfer learning-based Swin-T (Swin Transformer) network is proposed to recognize different stages of the soybean seedling stage. A drone was used to collect images of soybeans at the true leaf stage, the first compound leaf stage, the second compound leaf stage, and the third compound leaf stage, and data enhancement methods such as image rotation and brightness enhancement were used to expand the dataset, simulate the drone’s collection of images at different shooting angles and weather conditions, and enhance the adaptability of the model. The field environment and shooting equipment directly affect the quality of the captured images, and in order to test the anti-interference ability of different models, the Gaussian blur method was used to blur the images of the test set to different degrees. The Swin-T model was optimized by introducing transfer learning and combining hyperparameter combination experiments and optimizer selection experiments. The performance of the optimized Swin-T model was compared with the MobileNetV2, ResNet50, AlexNet, GoogleNet, and VGG16Net models. The results show that the optimized Swin-T model has an average accuracy of 98.38% in the test set, which is an improvement of 11.25%, 12.62%, 10.75%, 1.00%, and 0.63% compared with the MobileNetV2, ResNet50, AlexNet, GoogleNet, and VGG16Net models, respectively. The optimized Swin-T model is best in terms of recall and F1 score. In the performance degradation test of the motion blur level model, the maximum degradation accuracy, overall degradation index, and average degradation index of the optimized Swin-T model were 87.77%, 6.54%, and 2.18%, respectively. The maximum degradation accuracy was 7.02%, 7.48%, 10.15%, 3.56%, and 2.5% higher than the MobileNetV2, ResNet50, AlexNet, GoogleNet, and VGG16Net models, respectively. In the performance degradation test of the Gaussian fuzzy level models, the maximum degradation accuracy, overall degradation index, and average degradation index of the optimized Swin-T model were 94.3%, 3.85%, and 1.285%, respectively. Compared with the MobileNetV2, ResNet50, AlexNet, GoogleNet, and VGG16Net models, the maximum degradation accuracy was 12.13%, 15.98%, 16.7%, 2.2%, and 1.5% higher, respectively. Taking into account various degradation indicators, the Swin-T model can still maintain high recognition accuracy and demonstrate good anti-interference ability even when inputting blurry images caused by interference in shooting. It can meet the recognition of different growth stages of soybean seedlings in complex environments, providing a basis for post-seedling chemical weed control during the second and third compound leaf stages of soybeans.

Adaptive fuzzy neighborhood decision tree

Decision tree algorithms have gained widespread acceptance in machine learning, with the central challenge lying in devising an optimal splitting strategy for node sample subspaces. In the context of continuous data, conventional approaches typically involve fuzzifying data or adopting a dichotomous scheme akin to the CART tree. Nevertheless, fuzzifying continuous features often entails information loss, whereas the dichotomous approach can generate an excessive number of classification rules, potentially leading to overfitting. To address these limitations, this study introduces an adaptive growth decision tree framework, termed the fuzzy neighborhood decision tree (FNDT). Initially, we establish a fuzzy neighborhood decision model by leveraging the concept of fuzzy inclusion degree. Furthermore, we delve into the topological structure of misclassified samples under the proposed decision model, providing a theoretical foundation for the construction of FNDT. Subsequently, we utilize conditional information entropy to sift through original features, prioritizing those that offer the maximum information gain for decision tree nodes. By leveraging the conditional decision partitions derived from the fuzzy neighborhood decision model, we achieve an adaptive splitting method for optimal features, culminating in an adaptive growth decision tree algorithm that relies solely on the inherent structure of real-valued data. Experimental evaluations reveal that, compared with advanced decision tree algorithms, FNDT exhibits a simple tree structure, stronger generalization capabilities, and superior performance in classifying continuous data.

Understanding the relationship between land use/land cover changes and air quality: A GIS-based fuzzy inference system approach.

Air pollution is a global issue that demands urgent attention due to its detrimental effects on human health and the environment. Land Use and Land Cover (LULC) change is an essential factor that significantly impacts ambient air quality through alterations in emission sources, vegetation cover, natural processes, and urban design. This study investigates the spatio-temporal variation of key air pollutants resulting from urban LULC changes in the Delhi region. Findings reveal a notable increase in pollutant concentrations, particularly particulate matter, in 2019 (PM10: 318.65 ± 45.80 µg/m3) and 2023 (PM10: 383.70 ± 61.49 µg/m3), compared to 2008 (PM10: 246.76 ± 30.66). LULC change analysis demonstrates a rise in built-up areas 24.59%(2008 to 2019), 33.62% (2008 to 2023) and a decline in vegetation cover 27.49% (2008 to 2019),32.37% (2008 to 2023). Correlation analysis indicates a positive correlation between PM10 and urban indices (+ 0.63) and a negative correlation between PM10 and vegetation indices (- 0.61), highlighting the impact of LULC on air quality deterioration. Subsequently, a fuzzy inference system model integrates LULC information to develop an air quality index (AQI). Incorporating LULC changes in AQI assessment offers a realistic approach to address the complexity arising from combined air pollutant effects, surpassing conventional AQI calculation methods. The findings underscore the significance of understanding the impact of Land Use and Land Cover (LULC) change on ambient air quality in formulating effective air quality management programs and policies. Integrating this knowledge into policymaking is crucial for the successful abatement of air pollution in urbanized areas.

Adaptive Management of Multi-Scenario Projects in Cybersecurity: Models and Algorithms for Decision-Making

In recent years, cybersecurity management has increasingly required advanced methodologies capable of handling complex, evolving threat landscapes. Scenario network-based approaches have emerged as effective strategies for managing uncertainty and adaptability in cybersecurity projects. This article introduces a scenario network-based approach for managing cybersecurity projects, utilizing fuzzy linguistic models and a Takagi–Sugeno–Kanga fuzzy neural network. Drawing upon L. Zadeh’s theory of linguistic variables, the methodology integrates expert analysis, linguistic variables, and a continuous genetic algorithm to predict membership function parameters. Fuzzy production rules are employed for decision-making, while the Mamdani fuzzy inference algorithm enhances interpretability. This approach enables multi-scenario planning and adaptability across multi-stage cybersecurity projects. Preliminary results from a research prototype of an intelligent expert system—designed to analyze project stages and adaptively construct project trajectories—suggest the proposed approach is effective. In computational experiments, the use of fuzzy procedures resulted in an over 25% reduction in errors compared to traditional methods, particularly in adjusting project scenarios from pessimistic to baseline projections. While promising, this approach requires further testing across diverse cybersecurity contexts. Future studies will aim to refine scenario adaptation and optimize system response in high-risk project environments.

Application and Empirical Analysis of Fuzzy Neural Networks in Mining Social Media Users’ Behavioral Characteristics and Formulating Accurate Online Marketing Strategies

In the current digital social environment, social media platforms have become an important position for user behavior insights and precision marketing. User behavioral data on social media contain rich information, but they are often fuzzy, uncertain and highly complex. Fuzzy neural network (FNN), as an advanced model combining fuzzy logic and neural network theory, provides a powerful tool for processing and analyzing social media user behavioral features. This study is dedicated to exploring the application of fuzzy neural networks in social media user behavior analysis and their key role in the design of accurate online marketing strategies. We construct and optimize a fuzzy neural network model by meticulously classifying and quantifying user behavioral features, including behavioral frequency features, content topic features, social interaction features, and time series features, as well as applying fuzzy set theory to deal with fuzzy features such as emotional states. Through empirical analysis, we will show how fuzzy neural networks can reveal the intrinsic laws behind user behaviors, and how these insights can be used to design and implement precise online marketing strategies to improve advertising effectiveness, user engagement, and brand loyalty.

Local fuzzy rough attribute reduction for large-scale mixed data with limited missing labels based on local fuzzy self information

The advent of the era of big data is accompanied by the generation of large-scale data of various types. Extracting the potential value and rules from such data has always been a challenge. Due to various external and internal factors, it is commonplace for large-scale data to exhibit the phenomenon of missing limited labels. In addressing a large-scale mixed information system with limited label missing (LSMDISLML), local neighborhood rough set model (LNRS-model) is typically employed. However, the identical neighborhood radius is often used by such model when confronted with numerical attributes, which could potentially attenuate the classification capability of the data. Local fuzzy rough set model (LFRS-model) can overcomes this point. This paper studies local fuzzy rough attribute reduction for large-scale mixed data with limited missing labels based on LFRS-model via local fuzzy self information and overlap degree function. First, leveraging the statistical distribution of data as a foundation, fuzzy relations on the entire sample set are established, which has the advantage of being able to use different fuzzy similarity radii to calculate similarity, thereby adapting to different data distributions. Subsequently, the samples with missing labels are discarded as they constitute a small proportion of the entire sample set and have little impact on overall performance of dataset. The limited computing resources and storage space are focused on the sample set with complete labels (denoted as target set). Thereafter, based on the target set, local fuzzy λ-upper and lower approximations are defined, and LFRS-model is constructed. This model not only reduces processing time and sources of error in large-scale data but also improves data quality and enhances the reliability of the experimental results. Then, local fuzzy λ-self information is introduced and used to design a local fuzzy rough attribute reduction algorithm in a LSMDISLML. Furthermore, a overlap degree function is introduced to evaluate and reorder the attributes based on their importance, prioritizing the elimination of redundant attributes with high overlap and low importance from the preordered attribute set. This strategy effectively improves the efficiency of obtaining the optimal subset. Finally, a series of experiments are carried out. The experiment results demonstrate that the designed algorithm exhibits excellent performance in classification tasks and outlier detection tasks, surpassing existing four algorithms.

Application of Gradient Boosting in the Design of Fuzzy Rule-Based Regression Models

Application of Gradient Boosting in the Design of Fuzzy Rule-Based Regression Models

Low-Carbon Water–Rail–Road Multimodal Routing Problem with Hard Time Windows for Time-Sensitive Goods Under Uncertainty: A Chance-Constrained Programming Approach

In this study, a low-carbon freight routing problem for time-sensitive goods is investigated in the context of water–rail–road multimodal transportation. To enhance the on-time transportation of time-sensitive goods, hard time windows are employed to regulate both pickup and delivery services at the start and end of their transportation. The uncertainty of both the demand for time-sensitive goods and the capacity of the transportation network are modeled using L-R triangular fuzzy numbers in the routing process to make the advanced routing more feasible in the actual transportation. Based on the carbon tax policy, a fuzzy linear optimization model is established to address the proposed problem, and an equivalent chance-constrained programming formulation is then obtained to make the solution to the problem attainable. A numerical experiment is carried out to verify the feasibility of incorporating the carbon tax policy, uncertainty, and water–rail–road multimodal transportation to optimize the low-carbon freight routing problem for time-sensitive goods. Furthermore, a multi-objective optimization is used to reveal that lowering the transportation costs, reducing the carbon emissions, and avoiding the risk are in conflict with each in the routing. We also analyze the sensitivity of the optimization results concerning the confidence level of the chance constraints and the uncertainty degree of the uncertain demand and capacity. Based on the numerical experiment, we draw several conclusions to help the shipper, receiver, and multimodal transportation operator to organize efficient water–rail–road multimodal transportation for time-sensitive goods.

Spatio-temporal analysis of habitat suitability for the endangered oriental white stork (Ciconia boyciana) in the wetland ecosystem of northeast China

Human-induced habitat transformation often leads to habitat degradation and loss, impacting suitable habitats for endangered and rare species. This study was conducted to assess suitable habitats for the endangered oriental white stork in the wetland ecosystem of northeast China, to facilitate successful species-habitat conservation. The study incorporated field surveys, literature reviews, vegetation index calculations, eDNA for assessing prey abundance and land use and land cover classification for the years 2000, 2011, and 2022. A fuzzy logic-based habitat model was developed to map suitable habitats for the oriental stork. Generalized Linear Models (GLM) and Principal Component Analysis (PCA) were used to assess the influence of various environmental variables on habitat suitability changes. Roosting locations of the oriental stork were used as reference points to evaluate the impact of environmental factors on habitat suitability. The results indicate that 20% of the area is highly suitable for the stork, 73.4% is moderately suitable, and 5.6% is less or not suitable. Habitats near lakes and rivers, with low Normalized Difference Vegetation Index (NDVI) and high Normalized Difference Water Index (NDWI), are more suitable compared to areas close to roads and built-up zones. Prey abundance, particularly fish, is a crucial determinant of oriental stork presence and survival. Marshlands, swamps, and water bodies were identified as the most suitable habitats, as they interface with lotic and lentic ecosystems providing abundant prey for the storks. Our findings emphasize the importance of ongoing efforts to protect wetland ecosystems and habitats for endangered and rare bird species. Priority should be given to habitats identified as critical roosting areas for the oriental stork.