Hybrid Fuzzy Inference System Research Articles

Predicting child alimony under Islamic shariah law using hybrid fuzzy inference system

This paper proposes a novel approach to predicting child alimony under Islamic Shariah law using a hybrid fuzzy inference system, integrating Mamdani and Takagi-Sugeno-Kang (TSK) fuzzy systems. Machine learning algorithms have become valuable tools for legal decision-making, but judicial process delays can lead to adverse effects. Our model aims to expedite decision-making and minimize legal fees by accurately determining the proper amount of alimony for children after divorce. We collected data from 94 alimony cases and evaluated the model’s performance using accuracy, precision, recall, and F1 score metrics. The hybrid fuzzy system achieved promising results with 88% accuracy, 84% precision, 89% recall, and an 86% F1 score. Notably, the model reduced bias and standardization in decision-making, promoting fairness. However, the study suggests potential areas for improvement and emphasizes trans-parent judgment processes and coordination among judges in assessing alimony costs based on sufficiency and ma’ruf criteria. This research significantly contributes to machine learning applications in the judicial domain. It provides a valuable decision-making tool for judges and lawyers to enhance the judicial process’s efficiency and ensure children’s welfare in divorce cases under Islamic Shariah law. Further research can enhance the model’s effectiveness and reliability, opening avenues for continued exploration in this field.

On the performance and interpretability of Mamdani and Takagi-Sugeno-Kang based neuro-fuzzy systems for medical diagnosis

PurposeNeuro-fuzzy systems aim to combine the benefits of artificial neural networks and fuzzy inference systems: a neural network can learn patterns from data and achieves high performance, whereas a fuzzy system matches inputs and outputs using linguistic and interpretable rules. The combination of these two techniques yields models that can both perform well and provide interpretability in a fuzzy linguistic manner. DesignIn this paper, the performance and interpretability of five neuro-fuzzy classifiers were evaluated (three Takagi-Sugeno-Kang (TSK) classifiers: adaptive neuro-fuzzy inference system (ANFIS), dynamic evolving neuro-fuzzy system (DENFIS), self-organizing fuzzy neural network (SOFNN), and two Mamdani classifiers: hybrid fuzzy inference system (HyFIS) and neuro-fuzzy classifier (NEFCLASS)). All the empirical evaluations were over four benchmark medical datasets (Wisconsin breast cancer dataset, SPECTF heart dataset, Parkinsons dataset, and diabetic retinopathy Debrecen dataset), and used five performance criteria (accuracy, precision, recall, f score, and training time) and two interpretability criteria (number of rules and number of membership functions). FindingsResults showed that the TSK-based self-organizing fuzzy neural network classifier, in general, outperformed the others. In terms of interpretability, DENFIS and NEFCLASS were the best Takagi-Sugeno-Kang and Mamdani classifiers respectively. The findings also suggested that three classifiers: DENFIS, SOFNN, and NEFCLASS achieved a good performance-interpretability tradeoff. OriginalityTo the best of our knowledge, no study has compared the neuro-fuzzy techniques presented in this paper in terms of performance and interpretability in the medical domain.

Using Artificial Intelligence to Predict Customer Satisfaction with E‐Payment Systems during the COVID‐19 Pandemic

This study explores the impact of electronic payment systems on Saudi Arabia’s customer satisfaction during the COVID‐19 pandemic. Descriptive analytical approach of a sample of 1,025 people living in Saudi Arabia was used to answer the study questions and test its hypotheses. Then, a new hybrid fuzzy inference system (HyFIS) is proposed to predict customer satisfaction during COVID‐19 pandemic. The proposed system contemplates customer resistance (CR), access to technology (AT), privacy (PV), costs (CT), and speed of efficiency (SE) as the input variables and customer satisfaction (CS) as the output variable. Various statistical tests are utilized to determine the efficiency of input variables in the obtained data. The statistical tests are multicollinearity tests, reliability and validity, ordinal least square (OLS), fixed effect, and random development. As a result, we can determine each input variable’s direct and indirect impact on the CS. Under OLS, fixed effect, and unexpected effect, the SE, CT, PV, AT, and CR considerably impact EP. The EP has been shown to have substantial positive indirect implications. Under OLS, fixed effect, and random effect, the CT, PV, and CR are found to have a significant positive impact on CS. In addition, the AT has a substantial impact on CS in a fixed effect indirect effect. The results of HyFIS were compared to those of the adaptive network‐based fuzzy inference system (ANFIS). The results reveal that HyFIS outperforms ANFIS in predicting CS based on the error criterion.

Rainfall rate estimation over India using global precipitation measurement’s microwave imager datasets and different variants of fuzzy information system

Effective rain rate estimation using satellite-based measurement is imperative for many hydro-meteorological applications. With the recent advancement in satellite products and retrieving algorithms, rain rate estimations are continuously improving. This study provides a comparative performance appraisal of three hybrid machine learning algorithms namely Adaptive Neuro-Fuzzy Inference System (ANFIS), Dynamic Evolving Neuro-Fuzzy Inference System (DENFIS) and Hybrid Fuzzy Inference System (HYFIS) for rain rate estimation using the Global Precipitation Measurement (GPM)’s Microwave Imager (GMI) and ground-based Disdrometer data. The in situ sampling was conducted at four different locations (both land and ocean) across the Indian region and different statistical metrics were used to evaluate the performances of these models. The results showed that HYFIS algorithm has provided better rain rate estimation than ANFIS and DENFIS. The study endorses these neuro-fuzzy models for generating accurate precipitation products and can be considered as an alternative for future satellite retrieval algorithms.

Fuzzy-based computational intelligence to support screening decision in environmental impact assessment: A complementary tool for a case-by-case project appraisal

Screening is a key stage in environmental impact assessment (EIA), but the most common approach based on policy delineation are inherently arbitrary. On the other hand, a case-by-case approach can be complex, slow, and costly. This paper introduces a computational intelligence based on hybrid fuzzy inference system (h-FIS), combining data-driven and expert knowledge, in order to assess its capability of supporting a case-by-case screening in project appraisal. For empirical research, a dataset with appraisal variables of projects highway was made available by a Brazilian environmental protection agency (EPA). Firstly, using this dataset, multivariate analyses were performed to find criteria (xi) capable of indicating statistically significant differences among projects, previously screened by EPA experts into three types (simplified, preliminary, and comprehensive) of environmental impact study (EIS). Then, h-FIS was built through machine learning, using the FRBCS·W algorithm, with xi as input predictors and the type of EIS as the output target. The performances of alternative approaches were compared using cross-validation accuracy tests and the kappa index, with a significance level of 0.05. As a result, the h-FIS achieved accuracy of 92.6% and a kappa index of 0.88, which represented almost perfect agreement between the screening decision provided by the h-FIS and the one performed by the EPA experts. In conclusion, the fuzzy-based computational intelligence was capable of dealing with the complexity involved in screening decision. Therefore h-FIS be considered a promising complementary tool for a case-by-case project appraisal in EIA. For further advances, future research should assess other algorithms, such as genetic fuzzy systems, in order to strengthen the proposed system and make it generally applicable in other projects subject to EIA.

CaFIRST: A context-aware hybrid fuzzy inference system for the similarity measure of multivariate trajectories

CaFIRST: A context-aware hybrid fuzzy inference system for the similarity measure of multivariate trajectories

Hybrid fuzzy inference system for evaluating lean product development practice

In this study, a hybrid model which is based on fuzzy inference system (FIS) and fuzzy technique for order of preference by similarity to ideal solution (FTOPSIS) is proposed for the holistic evaluation, integration and implementation of the lean practice in the product development process using an automotive related company as the case company. The main contribution of the study includes; the evaluation of the effects of product quality, product value, development time and development cost on the lean practice and strategy in the product development environment which the authors believe is the first in this area. The development of a hybrid model which is based on FIS and FTOPSIS, for the holistic evaluation of lean practices in the product development process which also provide a framework for the implementation of lean practices when quality, product value, development time and cost are considered. The used of two distance methods for FTOPSIS model analysis, which effectively eliminates the bias of using a single distance method and finally the introduction of a new reflection defuzzification integration formula which serves as an interface between the FIS and FTOPSIS methods. To prove the effectiveness of the proposed hybrid model, it has been compared with a similar computational model in literature.

A Hybrid Fuzzy Inference System Based on Dispersion Model for Quantitative Environmental Health Impact Assessment of Urban Transportation Planning

Characterizing the spatial variation of traffic-related air pollution has been and is a long-standing challenge in quantitative environmental health impact assessment of urban transportation planning. Advanced approaches are required for modeling complex relationships among traffic, air pollution, and adverse health outcomes by considering uncertainties in the available data. A new hybrid fuzzy model is developed and implemented through hierarchical fuzzy inference system (HFIS). This model is integrated with a dispersion model in order to model the effect of transportation system on the PM2.5 concentration. An improved health metric is developed as well based on a HFIS to model the impact of traffic-related PM2.5 on health. Two solutions are applied to improve the performance of both the models: the topologies of HFISs are selected according to the problem and used variables, membership functions, and rule set are determined through learning in a simultaneous manner. The capabilities of this proposed approach is examined by assessing the impacts of three traffic scenarios involved in air pollution in the city of Isfahan, Iran, and the model accuracy compared to the results of available models from literature. The advantages here are modeling the spatial variation of PM2.5 with high resolution, appropriate processing requirements, and considering the interaction between emissions and meteorological processes. These models are capable of using the available qualitative and uncertain data. These models are of appropriate accuracy, and can provide better understanding of the phenomena in addition to assess the impact of each parameter for the planners.

A New Technique for Integrating MEMS-Based Low-Cost IMU and GPS in Vehicular Navigation

In providing acceptable navigational solutions, Location-Based Services (LBS) in land navigation rely mostly on integration of Global Positioning System (GPS) and Inertial Navigation System (INS) measurements for accuracy and robustness. The GPS/INS integrated system can provide better land-navigation solutions than the ones any standalone system can provide. Low-cost Inertial Measurement Units (IMUs), based on Microelectromechanical Systems (MEMS) technology, revolutionized the land-navigation system by virtue of their low-cost miniaturization and widespread availability. However, their accuracy is strongly affected by their inherent systematic and stochastic errors, which depend mainly on environmental conditions. The environmental noise and nonlinearities prevent obtaining optimal localization estimates in Land Vehicular Navigation (LVN) while using traditional Kalman Filters (KF). The main goal of this paper is to effectively eliminate stochastic errors of MEMS-based IMUs. The proposed solution is divided into two main components: (1) improving noise cancellation, using advanced stochastic error models in MEMS-based IMUs based on combined Autoregressive Processes (ARP) and first-order Gauss-Markov Process (1GMP), and (2) modeling the low-cost GPS/INS integration, using a hybrid Fuzzy Inference System (FIS) and Second-Order Extended Kalman Filter (SOEKF). The results obtained show that the proposed methods perform better than the traditional techniques do in different stochastic and dynamic situations.

A novel robust liquid level controller for coupled-tanks systems using artificial hydrocarbon networks

This paper proposes a robust liquid-level controller for coupled-tanks systems when dealing with variable discharge rates at the secondary tank, based on a hybrid fuzzy inference system that uses artificial hydrocarbon networks at the defuzzification step, so-called fuzzy-molecular control. The design methodology of the proposed controller is presented and discussed. In addition, a case study was run over the CE105 TecQuipment coupled-tanks system in order to implement and validate the fuzzy-molecular controller proposed in that work. A comparative evaluation with the proposed controller, a conventional PID controller specifically designed for this system and a QFT robust controller, was done. Also, a performance evaluation in terms of robustness, reference-tracking in a fixed operating point and reference-tracking in a variable operating point on-the-fly was run and analyzed. Results conclude that the proposed fuzzy-molecular controller deals with uncertainty and noise, can handle dynamics in operating point, a model of the plant is not required, and it is easy and simple to implement in comparison with other controllers in literature. To this end, the proposed fuzzy-molecular liquid-level controller inherits characteristics from fuzzy controllers and artificial hydrocarbon networks in order to implement an advanced robust and intelligent control system.

Artificial hydrocarbon networks fuzzy inference systems for CNC machines position controller

This paper proposes a novel position controller for computer numerical control (CNC) machines based on a hybrid fuzzy inference system that uses artificial hydrocarbon networks in its defuzzification step, so-called fuzzy-molecular inference system. The fuzzy-molecular-based position controller is characterized to improve the accuracy in position and the time machining. In order to prove these characteristics, a case study was run over a reconfigurable micromachine tool (RmMT) assembly in lathe configuration. In addition, a workpiece machining in the RmMT assembly serves to realize a comparative analysis between the proposed controller and three other controllers: a classical PID controller manually tuned, a PID controller auto-tuned, and a fuzzy Mamdani controller. Experimental results validate the performance and the implementability of the proposed fuzzy-molecular position controller against the others.

Autonomous corrosion detection in gas pipelines: a hybrid-fuzzy classifier approach using ultrasonic nondestructive evaluation protocols

In this paper, a customized classifier is presented for the industry-practiced nondestructive evaluation (NDE) protocols using a hybrid-fuzzy inference system (FIS) to classify the corrosion and distinguish it from the geometric defects or normal/healthy state of the steel pipes used in the gas/petroleum industry. The presented system is hybrid in the sense that it utilizes both soft computing through fuzzy set theory, as well as conventional parametric modeling through H(infinity) optimization methods. Due to significant uncertainty in the power spectral density of the noise in ultrasonic NDE procedures, the use of optimal H(2) estimators for defect characterization is not so accurate. A more appropriate criterion is the H(infinity) norm of the estimation error spectrum which is based on minimization of the magnitude of this spectrum and hence produces more robust estimates. A hybrid feature set is developed in this work that corresponds to a) geometric features extracted directly from the raw ultrasonic A-scan data (which are the ultrasonic echo pulses in 1-Dtraveling inside the metal perpendicular to its 2 surfaces) and b) mapped features from the impulse response of the estimated model of the defect waveform under study. An experimental strategy is first outlined, through which the necessary data are collected as A-scans. Then, using the H(infinity) estimation approach, a parametric transfer function is obtained for each pulse. In this respect, each A-scan is treated as output from a defining function when a pure/healthy metal's A-scan is used as its input. Three defining states are considered in the paper; healthy, corroded, and defective, where the defective class represents metal with artificial or other defects. The necessary features are then calculated and are then supplied to the fuzzy inference system as input to be used in the classification. The resulting system has shown excellent corrosion classification with very low misclassification and false alarm rates.