Fuzzy Logic Model Research Articles

Fuzzy logic modelling of the pollution pattern of potentially toxic elements and naturally occurring radionuclide materials in quarry sites in Ogun State, Nigeria

The accumulation pattern of some inorganic pollutants in quarry sites around Ogun State was modeled using a Fuzzy comprehensive assessment (FCA). Potentially toxic elements (PTEs) and naturally occurring radionuclides materials (NORMs) were assessed from soil samples collected from ten quarry sites in three districts (Odeda, Ajebo, and Ijebu Ode) in Ogun State. Three (3) NORMs (40 K, 238U, and 232Th) were assessed using gamma spectrometer with a NaI detector while ten (10) PTEs (As, Cd, Co, Cr, Cu, Fe, Mn, Ni, Pb, and Zn) were determined by digestion method using Inductively coupled plasma optical emission spectrophotometer. The FCA was used to evaluate soil contamination, and SPSS version 21.0 was used for statistical analysis. Concentration range of PTEs(mg/kg) and NORMs(Bq/kg) in all the site are: As (5.62 ± 0.85 – 15.93 ± 2.40), Cd (BDL—1.26 ± 0.60), Co (5.56 ± 1.34 – 27.25 ± 1.14), Cr (18.68 ± 1.54 -61.43 ± 6.33), Cu (12.40 ± 1.31—82.43 ± 7.94), Fe (15,035.00 ± 81.12 – 36,520.00 ± 292.20), Mn (168.97 ± 5.93 – 353.30 ± 20.84), Ni (5.63 ± 1.99- 25.54 ± 2.50),),Pb (4.44 ± 0.8 – 17.87 ± 2.80) and Zn (42.97 ± 413 – 147.00 ± 7.50); 40 K (76.78 ± 44.76—2647.88 ± 179.44), 238U (3.24 ± 1.82—55.42 ± 24.88),and 232Th (5.24 ± 3.90—244.36 ± 89.84). The results were modeled into a membership function matrix of three pollution classes. The FCA of NORMs revealed that 30, 10, and 60% of the sites were pristine, moderately polluted, and heavily polluted.In comparison, the FCA of PTEs confirmed 100% of the sites to be heavily polluted due to the accumulative effect of the PTEs. A high percentage of membership in the extremely impacted class is linked to a high concentration of Fe in all the sites due to the soil's geological structure and natural activities. At the same time, 40 K and 238U have high-impact membership in all the quarry sites. Based on the findings, there is a need for stringent pollution control measures, targeted monitoring of PTEs and NORMs and the development of region-specific environmental regulations to protect both public health and ecosystems.

Unit Commitment in Microgrid Considering Customer Satisfaction in Incentives-Based Demand Response Program: A Fuzzy Logic Model

Unit Commitment in Microgrid Considering Customer Satisfaction in Incentives-Based Demand Response Program: A Fuzzy Logic Model

Multimodal fuzzy logic-based gait evaluation system for assessing children with cerebral palsy

Gait analysis is crucial for identifying functional deviations from the normal gait cycle and is essential for the individualized treatment of motor disorders such as cerebral palsy (CP). The primary contribution of this study is the introduction of a multimodal fuzzy logic system-based gait index (FLS-GIS), designed to provide numerical scores for gait patterns in both healthy children and those with CP, before and after surgery. This study examines and evaluates the surgical outcomes in children with CP who have undergone Achilles tendon lengthening. The FLS-GIS utilizes hierarchical feature fusion and fuzzy logic models to systematically evaluate and score gait patterns, focusing on spatial and temporal features across the hip, knee, and ankle joints. The two FLS types-1 (FLS-GIS-T1) and type-2 (FLS-GIS-T2) indices, respectively, were implemented to comprehensively study gait profiles. Starting with the gait parameters of all subjects, the changes in gait parameters in post-surgery children reflect significant improvements in gait dynamics, bringing walking patterns in CP children closer to those of their typically healthy peers. Both FLS-GIS-T1 and FLS-GIS-T2 demonstrated significant improvements in post-surgery evaluations compared to pre-surgery assessments, with p values < 0.05 and < 0.001, respectively, when compared to traditional indices. The proposed FLS-based index offers clinicians a robust and standardized gait evaluation tool, characterized by a fixed range of values, enabling consistent assessment across various gait conditions.

Consumer Happiness in the Purchase of Electric Vehicles: a Fuzzy Logic Model

This study analyzes customer happiness in acquiring an electric vehicle, considering pleasure as an ambiguous language term that conventional models have inadequately incorporated. This research was conducted using a fuzzy Delphi method survey targeting a specific consumer group and two fuzzy inference systems: a multi-input single-output FIS model and an FIS Tree employing a hierarchical fuzzy inference structure, which leverages the survey's training data to optimize the models using different machine learning algorithms. The FIS tree model demonstrated superior efficacy in predicting the consumer satisfaction index, achieving an average forecast error of 0.65%. This approach could assist automobile agency marketers in creating accurate predictions to evaluate the purchasing decision-making process.

Comparative Analysis of Fuzzy Logic Models for Depression Prediction: Python and LabVIEW Approaches

Depression is one of the mental disorders with a significant impact on individuals' quality of life and productivity. The diagnostic process for depression, which typically relies on subjective assessment, often encounters challenges of uncertainty and variability in symptoms. This study aims to develop a fuzzy model for predicting depression levels based on five primary symptom variables: worthlessness, concentration, suicidal ideation, sleep disturbance, and hopelessness. The model is implemented on two platforms, Python and LabVIEW, to evaluate the accuracy and consistency of prediction results between these platforms. The analysis process begins with data preprocessing, input variable fuzzification, inference using 243 fuzzy rules, and defuzzification to generate a crisp output value classified into four depression levels: No Depression, Mild, Moderate, and Severe. The study results indicate a very small error margin between the two platforms, with error values below 0.01 in each trial. These findings suggest that both Python and LabVIEW can produce nearly identical and consistent predictions. This conclusion supports the effectiveness of fuzzy logic in addressing uncertainty in clinical data, especially for cases of depression with varying symptoms. Nonetheless, there are limitations related to the subjectivity in selecting membership functions and rules, as well as limitations in the number of variables used. Therefore, this study recommends expanding the developed fuzzy model with additional variables or integrating it with machine learning approaches to improve prediction accuracy. These findings are expected to serve as a foundation for the development of fuzzy-based systems in future mental health diagnostics.

Development of an Optimized Neural Network Model using Hyperparameters Optimization for Electrical Load Prediction

Electrical load prediction has become essential to the efficient operation, control and management of modern electric power systems. Various machine learning prediction models have been developed for electrical load prediction, this includes Support Vector Regression, Fuzzy Logic and Neural Network (NN) modelling approaches. However, incorrect selection of model hyperparameters, which are parameters that affect the output of the prediction models, could result in low prediction accuracy of machine learning models. Hence, the development of an optimized NN model for electrical load prediction was presented in this study. Historical daily data of temperature, rainfall, relative humidity and windspeed for Osogbo, Nigeria was obtained from the National Aeronautics and Space Administration website; while electrical load data for the same location was collected from the Transmission Company of Nigeria. The data captured a period of five years (2017 to 2021). The NN models were developed with MATLAB R2022a software, and two hyperparameters, hidden layers and neuron counts, were optimized using the Bayesian optimization technique to enhance the quality of the models. The models were evaluated using mean absolute error (MAE), and root mean square error (RMSE). The MAE and RMSE for the non-optimized NN model were 6.5247 and 8.2725 respectively. Meanwhile, for the optimized NN model, the MAE and RMSE were 5.6571 and 7.4289 respectively. The obtained results show that the optimized NN performed better than the non-optimized NN models. Therefore, for more accurate load prediction, the method developed of this research is suggested for use by utility providers.

THE PRACTICE OF USING FUZZY LOGIC FOR SIMULATION OF COMPLEX SYSTEMS

Simulation modeling is one of the popular methods for studying complex systems of different nature. The paper analyzes the existing experience of using the fuzzy logic apparatus for simulation modeling of processes and systems. The main directions of integration of fuzzy logic and simulation modeling are considered. Examples of computer models from different subject areas, built on the basis of fuzzy logic, in the context of simulation modeling methods are given. The ways of technical implementation of the integration of the two considered means are noted. The authors conclude that fuzzy logic can be used to model the resilience of regional socio-economic systems.

A CASE STUDY: FUZZY LOGIC BASED DECISION-MAKING SYSTEM FOR ELECTRIC VEHICLE CHARGING

Recently, the use of environmentally friendly electric vehicles instead of traditional internal combustion engine vehicles continues to be widespread due to threats to world life such as global warming and climate change. However, the biggest disadvantages of this technology are the limited range of electric vehicles, long charging times, low number of charging stations, and different charging costs. There is a need for more studies on the problem of finding charging stations, especially for long-distance traveling with electric vehicles. In this paper, a fuzzy logic based decision making system is designed for electric vehicle users to find the most suitable one among the charging stations on a long travelling route. In this study, a traveling route of 1779 km between Izmir and Van provinces in Turkey is selected. The current charging station locations obtained from different charging station companies on this route were processed on Google Earth, and charging stations that were too far from the route were not taken into consideration. A fuzzy logic model was created for 56 charging stations on the route, which performs weight calculation according to the current charging cost and the distance of the station to the normal route. The fuzzy control system is expected to decide on the most appropriate charging station in accordance with the specified rule table, and the results are evaluated.

Employing Neural Networks, Fuzzy Logic, and Weibull Analysis for the Evaluation of Recycled Brick Powder in Concrete Compositions

Using construction and demolition (C&amp;D) waste in concrete production is a promising step toward environmental resilience amid the construction industry’s ecological footprint. The extensive history of using bricks in the construction of buildings has resulted in a considerable amount of waste associated with this commonly used material. This study aimed to assess the quality of concrete by examining the effect of replacing cement with varying percentages of recycled brick powder (RBP—0% to 50%). The primary objectives include evaluating the mechanical properties of concrete and establishing the feasibility of using RBP as a partial cement substitute. The investigation of target concrete can be divided into two phases: (i) laboratory investigation, and (ii) numerical investigation. In the laboratory phase, the performance of concrete with RBP was assessed under short-term dynamic and various static loads. The drop-weight test recommended by the ACI 544 committee was used to assess the short-term dynamic behavior (352 concrete discs). Furthermore, the behavior under static load was analyzed through compressive, flexural, and tensile strength tests. During the numerical phase, artificial neural network models (ANN) and fuzzy logic models (FL) were used to predict the results of 28-day compressive strength. The impact life with different failure probabilities was predicted based on the impact resistance results, by combining the Weibull distribution model. Additionally, an impact damage evolution equation was presented for mixtures containing RBP. The results show that the use of RBP up to 15% caused a slight decrease in compressive, flexural, and tensile strength (about 3–5%). Also, by replacing RBP up to 15%, the first crack strength decreased by 7.15% and the failure strength decreased by 6.46%. The average error for predicting 28-day compressive strength by FL and ANN models was recorded as 4.66% and 0.87%, respectively. In addition, the results indicate that the impact data follow the two-parameter Weibull distribution, and the R2 value for different mixtures was higher than 0.9275. The findings suggest that incorporating RBP in concrete can contribute to sustainable construction practices by reducing the reliance on cement and utilizing waste materials. This approach not only addresses environmental concerns but also enhances the quality assessment of concrete, offering potential cost savings and resource efficiency for the construction industry. Real-world applications include using RBP-enhanced concrete in non-structural elements, such as pavements, walkways, and landscaping features, where high strength is not the primary requirement.

Utilizing fuzzy logic, particle swarm optimization, desirability function and NSGA-II for multi-objective optimization of sponge Gourd–Bagasse polymer composite properties

The process of developing mathematical models to forecast material properties can be intricate and time-consuming. Consequently, there has been a shift towards simpler and more precise computational models founded on artificial intelligence. In this particular research, fuzzy logic (FL) was employed to anticipate the mechanical properties of a newly formulated hybrid composite material, which combines sponge gourd, bagasse, and epoxy resin for golf club applications. Furthermore, a multi-objective optimization of these properties was conducted using a modified desirability function (DF) and the Non-Dominated Sorting Genetic Algorithm (NSGA-II). The FL model took into account three input factors: the weight percentage of bagasse, the weight percentage of sponge gourd, and the fiber size measured in micrometers. The response variables examined were tensile strength, hardness, flexural strength, modulus, elongation, and impact strength. The FL model was combined with the modified DF algorithm and the NSGA-II algorithm, respectively. To optimize the DF, the particle swarm optimization (PSO) algorithm was employed. The results demonstrated that the FL model accurately predicted the mechanical properties of the hybrid composite material. The lowest correlation coefficient (R) between experimental responses and FL predictions was found to be 0.9529. The modified algorithms addressed specific characteristics in the desirability properties, such as elongation, where the desirability remained constant within a range. When the modified algorithm was utilized, the optimized properties exhibited a desirability value of 0.84, surpassing the values of 0.83 and 0.82 obtained without modifying the optimization algorithms. In conclusion, the FL model in conjunction with the modified DF algorithm and the NSGA-II algorithm successfully predicted the mechanical properties of the hybrid composite material. The modifications made to the algorithms contributed to enhancing the optimization process of these properties.

Urban flood risk assessment using fuzzy logic and real-time flood simulation model – a geomatics techniques

Urban flood risk assessment using fuzzy logic and real-time flood simulation model – a geomatics techniques

PROMOVENDO DESENVOLVIMENTO E CONSCIENTIZAÇÃO AMBIENTAL EM CRIANÇAS COM DEFICIÊNCIA: UMA ABORDAGEM COM EDUCAÇÃO FÍSICA ADAPTADA E LÓGICA FUZZY

This study aimed to develop a fuzzy logic model to evaluate the impact of interventions in Adapted Physical Education and Environmental Education on the development of children with disabilities, covering physical, cognitive and environmental awareness aspects. The research sought to transcend conventional assessment approaches, implementing scenario simulations that mimic real conditions and incorporating variables such as motor coordination and understanding of environmental concepts. The interventions were evaluated using fuzzy rules, to measure the impacts on the various dimensions of development. Although the model has not been applied in a practical context with children, validation tests suggest its future applicability in practical studies. The results indicate that integrated and balanced interventions can effectively improve both physical and cognitive development. The use of fuzzy logic proved to be a robust tool for the detailed evaluation of progress, indicating that the developed model can contribute significantly to inclusive pedagogical practices and guide educational policies aimed at the needs of children with disabilities, reinforcing the importance of sustainability in environmental education.

Optimizing Software Cost Estimation: A Hybrid Approach Integrating Soft Computing Techniques for Enhanced Accuracy and Efficiency

In software engineering, software cost estimate is a necessary process directly affecting project deadlines, budget planning, and resource allocation. Conventional estimating methods could overlook the inherent complexity and uncertainty of software development, therefore causing mistakes and maybe project failures. To optimise software cost estimation and so address these challenges, this work proposes a hybrid framework incorporating fuzzy logic, artificial neural networks (ANN), and genetic algorithms (GA). Although fuzzy logic models imprecise and ambiguous information to control uncertainty, ANN is employed for its ability to learn and generalise patterns from past data. GA guarantees accurate and effective assessment of the process by strengthening already used techniques with strong optimising characteristics. Combining these techniques makes use of their respective benefits to generate a synergistic plan beyond more traditional methods. The proposed framework is investigated using industry-standard datasets exhibiting appreciable increases in prediction accuracy, computational efficiency, and flexibility to varied project situations. Comparatively, for complex and large-scale projects the hybrid approach not only reduces estimating errors but also enhances scalability and dependability. Additionally included in the paper is a case application for a practical project, therefore stressing the relevant usefulness of the framework. By addressing the limitations of present approaches, this work reduces the gap between theoretical breakthroughs in soft computing and their pragmatic use in software engineering. stressing the need of applying contemporary computing techniques to solve evolving challenges in cost estimation, the outcomes provide useful information for scholars and business practitioners. At the end of this work is explored the possibility of new machine learning models and the investigation of domain-specific customisations to increase the applicability of the framework.

Experimental investigation of the friction stir welding process of AA5059 and AA7079 and prediction using fuzzy approach

Aluminum alloys AA 5059 and AA7079 have high strength, excellent weldability, and corrosion resistance, widely used in train and truck bodies, armored vehicles, and defense applications. Recent materials engineering tasks call for minimizing material weight and to achieve this objective, the solid-state joining approach of Friction stir welding (FSW) is utilized for material joining. FSW has produced sound welds with high joint strength and good ductility. This research investigates the weldability of AA5059 and AA7079 dissimilar aluminum alloys. In this FSW process, a threaded tapered cylindrical tool pin profile was selected, and welding trials were conducted at three different speeds of 500, 750, and 1000 rpm, with the 75 mm/min constant feed rate. The Taguchi L9 orthogonal array was selected to design the process parameters, that is, tool traversing speed, rotational speed, axial force, feed rate, and effect on dissimilar aluminum weld joints. The Mamdani-type fuzzy logic model predicted welded specimens’ ultimate tensile strength and yield strength. Microstructural analysis revealed that the grains in the stirring zone had undergone a full transformation into homogenous, evenly distributed, refined grains, which had a positive impact on the remarkable mechanical properties.

Development of eDART based online diameter prediction systems in injection molding with multiple linear regression and fuzzy logic

Abstract Injection molding is a versatile technique for processing a wide range of thermoplastic and thermosetting polymers, as well as their composites. Dimensional defects are a critical issue in injection molding. This research focuses on developing online diameter prediction systems via multiple linear regression (MLR) and fuzzy logic. The systems are developed using Delrin 311 DP material, with the Taguchi methodology employed to define optimized process parameters that ensure adequate process capability. Processing data were collected from the sensors embedded in the surface of mold cavity by the eDART system. Regression analysis was employed to build and test the relationship between the real-time data from in-mold sensors and the diameter of molded part. The real time data from the sensor-based monitoring system, including end of cavity, hydraulic injection pressure, and efficient viscosity, were selected as the inputs for the predictive model. Both MLR and fuzzy logic models were established to predict the outcomes, based on the data retrieved from the sensors, achieving the prediction accuracies of 99.09% for MLR and 99.98% for fuzzy logic, respectively. Fuzzy logic demonstrated its reliability in predicting diameters and minimizing dimensional defects in injection molding.

Using a knowledge representation logic to estimate the availability of Imbrasia epimethea (Lepidoptera: Saturniidae), an important edible insect in Subsaharan Africa

Based on monthly abundance patterns, we model the density of Imbrasia epimethea (Drury, 1773), an important edible insect in Africa. Categorical data was collected from various regions in Cameroon, and data analysis techniques were used to infer relationships between environmental variables and the level of insect abundance. Through fuzzy logic modeling, we identified the key environmental factors and rules that influence the density of the insect. To visualize the distribution of I. epimethea across African landscapes, interpolation techniques were used on the study area matrix of geographical coordinates based on the corresponding monthly predictor variables for the most recent available year (2022). The results suggested a clear dynamic across Africa through the different months of the year with potentially overlapping generations with relatively high accuracy (>90 %). A clear relationship between regional climatic conditions and the density of I. epimethea could be established across Africa. The models provide insights into the complex dynamics of insect populations and sheds light on the stability and transferability of our results across different African regions (during stability analysis). This research offers a foundation for further investigations on sustainable food production and the promotion of edible insects as a viable protein source.

Optimizing coastal groundwater quality predictions: A novel data mining framework with cross-validation, bootstrapping, and entropy analysis.

Investigating the potential of novel data mining algorithms (DMAs) for modeling groundwater quality in coastal areas is an important requirement for groundwater resource management, especially in the coastal region of Bangladesh where groundwater is highly contaminated. In this work, the applicability of DMA, including Gaussian Process Regression (GPR), Bayesian Ridge Regression (BRR) and Artificial Neural Network (ANN), for predicting groundwater quality in coastal areas was investigated. The optuna-based optimized hyperparameter is proposed to improve the accuracy of the models, including optuna-GPR and optuna-BRR as benchmark models. Combined cross-validation (CV) and bootstrapping (B) methods were used to build six predictive models. The entropy-based coastal groundwater quality index (ECWQI) was converted into a normalized index (ECWQIn), which was divided into five classes from very poor to excellent. The self-organizing map (SOM), spatial autocorrelation and fuzzy logic model were used to identify spatial groundwater quality patterns based on 12 physicochemical variables collected from 67 groundwater wells. The SOM analysis identified four distinct spatial patterns, including EC-TDS-Cl-, MgpH, Ca2+K+NO₃-, and HCO₃-SO₄2-Na+F-. The results showed that both the ANN (CV) and ANN (B) models performed better than other optuna-based models during the test phase (RMSE=0.041, MAE=0.026, R2=0.971, RAE=0.15=21 and CC=0.986) and (RMSE=0.041, MAE=0.025, R2=0.969, RAE=0.119 and CC=0.975), respectively. SO42-, Cl- and F- played an important role in the prediction accuracy. F- and SO42- showed higher spatial autocorrelation, which affected groundwater quality degradation. In addition, the ANN (CV) and ANN (B) models showed a Gaussian distribution of model errors (small standard error, <1%), indicating the stability of the model. These results indicate the efficiency of the ANN model in predicting groundwater quality in coastal areas, which would help regional water managers in real-time monitoring and management of sustainable groundwater resources.

Hydrothermal fluid pathways and mineralization potential in the High Atlas Massif (Morocco) using fuzzy logic and multifractal modeling

Hydrothermal fluid pathways and mineralization potential in the High Atlas Massif (Morocco) using fuzzy logic and multifractal modeling

Optimizing Mutual Fund Selection with Fuzzy Logic: A Case Study of Nasdaq Exchange

This study examines mutual fund performance through advanced methodologies, emphasizing fuzzy logic and the Fuzzy Sharpe Ratio. A thorough literature review highlights recent developments, such as the integration of machine learning techniques with fuzzy logic models. The research utilizes data from the Bombay Stock Exchange spanning January 1, 2024, to October 31, 2024, applying the Fuzzy Risk-Adjusted Return method. Moreover, an analysis of major technology companies reveals Alphabet Inc as the top-ranked firm, demonstrating its robust performance metrics and establishing it as a premier investment choice. Conversely, Tesla Inc ranked lowest, reflecting comparatively weaker performance. These results illustrate the competitive dynamics and varied performance levels among leading tech companies, influenced by market strategy, innovation, and financial stability. This analysis underscores the value of fuzzy logic in enhancing mutual fund evaluations and positions Alphabet Inc as a strong candidate for investor consideration.

Does the fuzzy logic model explains more? Moderating effect of personality on the relationship between SR-HRM and job performance

Does the fuzzy logic model explains more? Moderating effect of personality on the relationship between SR-HRM and job performance