Fuzzy Inference Method Research Articles

Mamdani Fuzzy Expert System for Online Learning to Diagnose Infectious Diseases

E-learning and expert systems can be implemented for learning in the health sector. Through the e-learning system, prospective health workers can analyze problems by exploring the material in the system. However, material learning alone is less effective, so case study-based learning using an expert system is needed to strengthen understanding. The research applies an expert system to online learning to diagnose several infectious diseases. The disease diagnosis process uses the backward chaining method and the Mamdani fuzzy inference system. The fuzzy Mamdani inference system determines the intensity of disease severity so that appropriate treatment recommendations can be made. The test findings on 15 test datasets yielded a backward chaining accuracy value of 100%. Three test scenarios were used to establish the test using the Mamdani fuzzy inference method. Scenario 1: Testing with the Center of Gravity defuzzification and Fuzzy Mamdani Min inference system Tests employing the Fuzzy Mamdani Min inference method and center average defuzzification are used in Scenario 2. Scenario 3 involves testing using the Fuzzy Mamdani Product Inference System with Center Average Defuzzification. The average outcome for the intensity of disease severity utilizing the Fuzzy Mamdani Min inference system with Center of Gravity defuzzification was greater than that of the two test scenarios that were suggested, which was 49.43%.

Novel approach to design matched digital filter with Abelian group and fuzzy particle swarm optimization vector quantization

This paper presents a new method for designing matched digital filters with discrete valued coefficients. The fuzzy particle swarm optimization vector quantization (FPSOVQ) has been applied to obtain the optimum codebook in design of matched wavelet function. Abelian group has been used to extract the similarity present in the input voiced signal. Fuzzy particle swarm optimization (FPSO) process is used to find approximate ideal vector quantization (VQ) codebook to be carried out for compression of data. FPSOVQ scheme utilises features of fuzzy inference method (FIM) and expert particle swarm optimization (PSO). The generated codebook consists of set of highly ideal features, which are considered as the filter coefficients. These coefficients are used in the designing of the filter. All of the phonemes in the American English language were included in the 30 sentences that were chosen from the IEEE database. The sentences were originally down-sampled from 25 kHz to 8 kHz. The magnitude responses of each filter have been drawn, which indicates the characteristics of the filter. A comparison has been provided using energy compaction ratio as a parameter to judge the performance of matched designed filter with db4 filter. Experimental results show the advantage of the developed algorithm as the average value of energy compaction ratio for sampled voice signals is 2.8046 times lower for matched designed filter.

Regional prediction and prevention analysis of rockburst hazard based on the Gaussian process for binary classification

Rockburst is a complex dynamic disaster in coal mining and affected by many factors. To accurately predict the rockburst hazard among complex influencing factors, a prediction model of rockburst hazard based on the Gaussian process for binary classification (GPC) was proposed after the identification of the intrinsic relationship between multiple factors of coal mines and rockburst. Through computerized machine learning and integrated intelligent analysis, the non-linear mapping of rockburst hazard and its influencing factors was established. The multi-factor pattern recognition model was constructed using artificial intelligence. The prediction criteria of the rockburst hazard probability and the hazard probability value of the prediction area unit were determined by applying neural network and fuzzy inference methods. In addition, the rockburst hazardous zone was classified, and the corresponding technical scheme for the prevention was put forward. The validity and feasibility of the regional prediction of rockburst hazard based on GPC were verified in the engineering practice. This method is highly targeted and can improve the accuracy and precision of rockburst prediction, thus contributing to the safe and efficient production of coal mines.

MODEL IMPLEMENTASI LOGIKA FUZZY UNTUK PENILAIAN KINERJA KARYAWAN IT SUPPORT

Human resources are the most valuable asset in a company. Employee performance is one of the company's benchmarks in assessing the productivity of these assets. Methods in measuring employee performance can be done in various forms, one of which is using the fuzzy logic method. Fuzzy Mamdani is a fuzzy inference method that provides rules that must be passed before carrying out the process of determining the final value. There are three main criteria that will be analyzed in this study, namely attendance, quality of work and communication. Each of these criteria has a value obtained from the average value of several elements. The final result after going through the employee data analysis process is the average weighted value. Conclusions that can be drawn as the basis for employee performance and the results can be seen from the order of values, starting from the highest to the lowest. MATLAB or what we call it (Matrix Laboratory) is a program for analyzing and computing numerical data, and MATLAB is also an advanced mathematical programming language, which was formed on the premise of using the properties and forms of matrices. Graphical User Interface (GUI) is a visual media that makes users give certain commands on the computer without typing the command, but using the available images.

Applying Fuzzy Inference and Machine Learning Methods for Prediction with a Small Dataset: A Case Study for Predicting the Consequences of Oil Spills on a Ground Environment

Applying machine learning (ML) and fuzzy inference systems (FIS) requires large datasets to obtain more accurate predictions. However, in the cases of oil spills on ground environments, only small datasets are available. Therefore, this research aims to assess the suitability of ML techniques and FIS for the prediction of the consequences of oil spills on ground environments using small datasets. Consequently, we present a hybrid approach for assessing the suitability of ML (Linear Regression, Decision Trees, Support Vector Regression, Ensembles, and Gaussian Process Regression) and the adaptive neural fuzzy inference system (ANFIS) for predicting the consequences of oil spills with a small dataset. This paper proposes enlarging the initial small dataset of an oil spill on a ground environment by using the synthetic data generated by applying a mathematical model. ML techniques and ANFIS were tested with the same generated synthetic datasets to assess the proposed approach. The proposed ANFIS-based approach shows significant performance and sufficient efficiency for predicting the consequences of oil spills on ground environments with a smaller dataset than the applied ML techniques. The main finding of this paper indicates that FIS is suitable for prediction with a small dataset and provides sufficiently accurate prediction results.

A meta-analysis approach to measure the impact of project-based learning outcome with program attainment on student learning using fuzzy inference systems

This paper presents a fuzzy inference method to investigate the impact of project-based assessment on the desirable outcomes by analyzing students creative and critical thinking, collaborative decision-making, and communication skills with realistic constraints and standards through theory and practical implementation in (a) course attainment and (b) on overall program attainment carried out in engineering discipline. This paper uses twelve specific parameters to capture program attainment parameters (PAPs). It proposes three main parameters to define various assessment system elements required for assessing course attainment parameters (CAPs), correlated with each other. To the best of the author's knowledge, to date, there is no defined mathematical tool to map CAPs to PAPs. Thus, this paper proposes assessment pedagogy to evaluate the PAPs corresponding to CAPs to handle the vague correlation mapping using fuzzy logic. The methodology and the preliminary results conducted for one year are promising, helping educators evaluate a candidate's performance individually or in a group on several assessment criteria, assisting in attaining the knowledge, values, attitude, deep learning, and skills needed for sustainable education development.

Fault diagnosis method for proton exchange membrane fuel cells based on EIS measurement optimization

AbstractPoor durability and reliability are key barriers to the application of proton‐exchange membrane fuel cells (PEMFCs). The timely detection and isolation of faults can improve the performance and durability of PEMFCs. This paper proposes a PEMFC fault diagnostic method based on rapid electrochemical impedance spectroscopy (EIS) measurements. To shorten the EIS measurement time, the characteristic frequency bands were separated by a fuzzy inference method to remove interference frequency bands and low‐frequency invalid frequency bands. Then, they were optimized for the corresponding characteristic frequency band points. The parameters of the improved equivalent circuit model were identified according to the electrochemical impedance spectrum, and four of the model parameters were selected as characteristic variables for fault diagnosis. Based on this, a multifault diagnosis algorithm with an improved K‐nearest neighbor classifier applied to PEMFC was proposed. The experimental results showed that the proposed fault diagnostic method accurately and quickly distinguished four health states, that is, flooding, membrane drying, air starvation, and normal state.

Reconstruction of surface laser power and internal temperature of biological tissue during laser-induced thermal therapy

In this article, the decentralized fuzzy inference method (DFIM) is applied to reconstruct the distributions of surface laser power and internal temperature of the biological tissue during laser-induced thermal therapy. The Pennes bioheat equation is used to describe the heat transfer in the tissue with laser irradiation, and it is solved by finite volume method. The reconstruction task is formulated as an inverse problem which is solved by the DFIM. Reconstruction results demonstrate that the DFIM can accurately reconstruct the surface laser power and internal temperature distribution of the tissue. Furthermore, the influences of the number of measurement points, laser irradiance form and measurement error on reconstruction results are discussed. Retrieval results showed that the DFIM is robust to reconstruct different forms of laser irradiances and internal temperature fields, even with noisy data.

Granular Symmetric Implicational Method

As a means of describing realistic problems, fuzzy sets can be included into the category of information granules from a broader perspective. Then the interval-valued fuzzy set itself is an expression of information granule which has more elaborate and stronger characterization abilities than generic fuzzy sets. In this study, facing up with modeling situations involving the use of interval-valued fuzzy sets, we come up with the granular symmetric implicational (GSI) method of fuzzy inference in view of the symmetric implicational idea and granular computing, which includes the basic GSI method and the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\zeta (w, z)$</tex-math></inline-formula> -GSI method. First, complete residuated lattices are employed as the structures of truth-values for interval-valued fuzzy sets. Second, unified expressions of optimal solutions to two GSI methods are gained for R-implications and (S, N)-implications. Lastly, it is shown through examples that the GSI method is superior over corresponding interval-valued fully implicational method. The originality of this work is three-fold. To begin with, the interval-valued fuzzy operators are introduced to the symmetric implicational mechanism, and novel symmetric implicational principles are presented which ameliorate the previous ones. Moreover, we offer a new construction method for interval-valued implications and corresponding adjoint couples, and on the strength of it we validate the reversibility and continuous properties of the GSI method. Finally, the hierarchical granular inference strategy is established for the GSI method in allusion to the circumstance of multiple rules.

Diagnosis and prediction of failures in maintenance systems using fuzzy inference and Z-number method

In this research, a timely diagnosis and prediction mechanism for drill failure are provided to improve the maintenance process in drilling through fuzzy inference systems. Failures and decisions are based on information and reliability as well, and that affects the quality of decision-making. We apply the potential of if-then rules and a new approach called Z-number that considers fuzzy constraints and reliability at the same time. Exerting Z-number in this research took maximum advantage of reducing uncertainty for predicting failures. Additionally, this research has a practical aspect in maintenance systems by using if-then rules that rely on Z-number. The proposed approach can cover the expert idea during drill operation time simultaneously. This approach also helps experts encounter ambiguous situations and formulate uncertainties. Experts or drill operators can consider key factors of drilling collapse along with the reliability of these factors. The proposed approach can be applied to a real-life situation of human inference with probability for the purpose of predicting failures during drilling. Hence, this method has excellent flexibility for implementation in various maintenance systems.

A Global Path Planning Method for Unmanned Ground Vehicles in Off-Road Environments Based on Mobility Prediction

In a complex off-road environment, due to the low bearing capacity of the soil and the uneven features of the terrain, generating a safe and effective global route for unmanned ground vehicles (UGVs) is critical for the success of their motion and mission. Most traditional global path planning methods simply take the shortest path length as the optimization objective, which makes it difficult to plan a feasible and safe route in complex off-road environments. To address this problem, this research proposes a global path planning method, which considers the influence of terrain factors and soil mechanics on UGV mobility. First, we established a high-resolution 3D terrain model with remote sensing elevation terrain data, land use and soil type distribution data, based on a geostatistical method. Second, we analyzed the vehicle mobility by the terramechanical method (i.e., vehicle cone index and Bakker’s theory), and then calculated the mobility cost based on a fuzzy inference method. Finally, based on the calculated mobility cost, the probabilistic roadmap method was used to establish the connected matrix and the multi-dimensional traffic cost evaluation matrix among the sampling nodes, and then an improved A* algorithm was proposed to generate the global route.

A voice of the customer real-time strategy: An integrated quality function deployment approach

In the era of Industry 4.0, the rapid development of information technology in the last decade has provided new challenges for product improvement by enabling users to give their feedback and sentiments in real time. In this paper, combining with genetic algorithm back propagation neutral network, fuzzy inference method, and entropy-based synthesis evaluation method, we raise a full-process product improvement solution driven by online reviews, from the initial online review collection to the final engineering characteristic prioritization. The proposed novel integrated quality function deployment-based approach adheres to the customer-oriented design principle, allowing manufacturers to strengthen the launched product based on the spontaneously-articulated voice of customer, rather than the traditional expertise. In this way, an off-the-shelf product improvement strategy is available for enterprises, and its special advantages like fast adaptation and real-time responsiveness, would significantly reduce management costs, shorten response time to market dynamics, and enhance customer satisfaction. In addition, a case study in smartphone industry is conducted for illustration, and the results clearly demonstrate the effectiveness and practicability of the treatment.

An intelligent fuzzy inference rule‐based expert recommendation system for predictive diabetes diagnosis

AbstractDiabetes is one of the most common and hazardous diseases, which can affect almost every organ in the body. Diagnosis of diabetes requires determining all vital parameters related to the disease. However, the nature of the data from those parameters is very uncertain, affecting the process of disease diagnosis. This article proposes an intelligent fuzzy inference rule‐based predictive diabetes diagnosis model (IFIR_PDDM), providing content recommendations to patients with diabetes. The suggested model employs an inference technique that medical specialists have validated for recommendations. IFIR_PDDM comprises three elements used to forecast the risk of diabetes disease. Initially, a fuzzy membership function utilizes medical recommendations and statistical methodologies. Medical specialists then validate the mining‐based rules using a decision tree rule induction technique. The proposed model predicts the risk of diabetes disease using fuzzy inference based on Mamdani's technique. Based on this information, the recommendations for a normal life, nutrition, exercise, and medications are given to patients. We used an electronic health record (EHR) medical and clinical dataset from the PIMA Indian Diabetes dataset to develop our proposed model and assess its performance. The proposed model takes less time for diabetes diagnosis, and the expert recommendation system uses the fuzzy inference method.

Modified Fuzzy Inference Method for Heat Flux Inversion of Geothermal Reservoir Heat Source

The key to determine the performance of fuzzy inference inversion is to select a reasonable domain. However, there is no universal method for selecting domain at present. According to the characteristics of heat flux of geothermal heat source and the research of fuzzy inference inversion process, this paper modified the fuzzy inference method from two aspects of domain setting and iteration termination condition. The recommended domain and selection scheme for solving the problem of geothermal heat flux are given, and the modified fuzzy inference inversion method is applied to Rucheng geothermal field to verify the method. The results showed that the modified fuzzy inference inversion method could overcome the problem that the solution of the traditional method fell into a cycle, and the relative error of the verification term was less than 5%. Compared with the traditional method, the modified method greatly improved the computational efficiency, and the number of iterations was reduced to only 7. This method has a good application prospect for geothermal heat source inversion and resource evaluation.

Text Tone Determination Using Fuzzy Logic

Abstract The study proposes the text tone detection system based on sentiment dictionaries and fuzzy rules. Computer analysis of texts from different sources has been performed in emotional categories: anger, anticipation, disgust, fear, joy, sadness, surprise and trust. A synonym dictionary has been used to expand the vocabulary. To increase the accuracy and validity of sentiment analysis, the authors of the study have used coefficients that take into account different emotional loads of words of various parts of speech and the action of intensifying or softening adverbs. A quantitative value of the text tone has been obtained as a result of an aggregation of normalized data on all emotional categories by the fuzzy inference methods. It has been found that emotional words have a greater impact on the text tone value in the case of analysis of short messages. The proposed approach makes it possible to contribute to all emotional categories in the final text evaluation.

Fuzzy logic assessment of engineering properties of granular soil with wastes for environment protection and road base use

Afyonkarahisar is a city in Turkey and is rich especially with marble production. Therefore, marble dust piles occupy places in residential fields and agricultural lands or the vicinity of the marble manufacturing facilities. So, the use of those wastes with some amount of fly ashes with granular soils as admixtures for road constructions around the city will be great to protect the environment pollution and also to handle waste storage problems besides also improving the engineering properties and strength of these soils. However, assessing appropriate percentages of admixtures is a difficult task and requires performing many laboratory experiments considering different combinations and rates for each type of soil. So, the purpose of this study was to develop a practical method taking each possible marble dust and fly ash rates and their combinations into account for assessing the strength and compaction properties of granular soil used in road bases. The developed method may also reduce the number of laboratory experiments, lessen labor costs, and save experiment time. The model presented in this study is based upon the fuzzy logic inference method. The model contains two fuzzy input sets as the marble dust and fly ash and four fuzzy output sets as California Bearing Ratio (CBR), unconfined compressive strength (qu), dry unit weight (γdry) and water content (ωopt). The study results showed that the strength parameters as CBR and qu and compaction properties as γdry and ωopt of the granular soil with different combinations and percentages of marble dust and fly ash admixtures can be determined with high rate of confidences by using this model.

BRCA Variations Risk Assessment in Breast Cancers Using Different Artificial Intelligence Models.

Artificial intelligence provides modelling on machines by simulating the human brain using learning and decision-making abilities. Early diagnosis is highly effective in reducing mortality in cancer. This study aimed to combine cancer-associated risk factors including genetic variations and design an artificial intelligence system for risk assessment. Data from a total of 268 breast cancer patients have been analysed for 16 different risk factors including genetic variant classifications. In total, 61 BRCA1, 128 BRCA2 and 11 both BRCA1 and BRCA2 genes associated breast cancer patients’ data were used to train the system using Mamdani’s Fuzzy Inference Method and Feed-Forward Neural Network Method as the model softwares on MATLAB. Sixteen different tests were performed on twelve different subjects who had not been introduced to the system before. The rates for neural network were 99.9% for training success, 99.6% for validation success and 99.7% for test success. Despite neural network’s overall success was slightly higher than fuzzy logic accuracy, the results from developed systems were similar (99.9% and 95.5%, respectively). The developed models make predictions from a wider perspective using more risk factors including genetic variation data compared with similar studies in the literature. Overall, this artificial intelligence models present promising results for BRCA variations’ risk assessment in breast cancers as well as a unique tool for personalized medicine software.

Assessing the quality of international trade and economic relations in the agricultural sector

Purpose of the research. The main purpose of the article is to improve the methodical support for assessing the quality of international trade and economic relations in the agricultural sector. Methodology. In the course of the research, the following methods were applied: fuzzy logic inference, generalization, comparison, graphical, expert assessments, etc. Results. The role of international trade and economic relations in the development of the domestic economy has been substantiated. The expediency of using forecasting as a tool for assessing the prospects for expanding cooperation in the field of agriculture with other countries has been proved. The main indicators have been determined that are used in the process of measuring the relationships quality. The necessity of developing tools for assessing the quality of international trade and economic relations has been substantiated. It has been proposed to carry out an assessment using the fuzzy sets theory based on the formed thesaurus, which includes a fuzzy set, membership function, fuzzy variable, linguistic variable, fuzzy knowledge base, fuzzy logic inference. The scheme of the procedure for assessing the quality of international trade and economic relations using the method of fuzzy logic inference and description of the fuzzy system for assessing the quality of international trade and economic relations of the agricultural sector of Ukraine have been developed. An analysis has been carried out within the research framework of bilateral agreements between Ukraine and partners in the field of agriculture and it has been determined that an example of high-quality international trade and economic relations is cooperation with China, India, Germany, the United Arab Emirates; moderate – with Georgia, Poland, France, Korea; low – with Japan, Iran, Kazakhstan, etc. Practical meaning. Based on the results of the assessment, in accordance with the quality level of international trade and economic relations, priority areas of cooperation for Ukraine with partner countries to promote the development of its economy have been identified. Prospects for further research by the author are to develop a mechanism for increasing the quality of international trade and economic relations between Ukraine and partner countries.

A Fuzzy Interpolation Cross-Correlation Method for Time-Delay Estimation of Partial Discharge UHF Pulse Signals

Time-delay estimation of partial discharge (PD) ultrahigh-frequency (UHF) pulse signals is one of the effective means to diagnose the local defect of electrical equipment. In order to improve the time-delay estimation accuracy of a multielement PD UHF pulse signal sensor array and reduce the cost of the PD detection system, a fuzzy interpolation cross-correlation method for time-delay estimation of PD UHF pulse signals was proposed in this paper. Improving the signal sampling rate is an effective way to improve the time-delay estimation accuracy. In this work, the fuzzy interpolation inference method was applied to interpolate intermediate values into the feature area of the PD UHF signal to improve the sampling rate, and then the cross-correlation method was used to estimate the time delay. This method not only improves the system sampling rate by fuzzy interpolation inference, which can make up for the deficiency of system equipment sampling performance, but also reduces the estimation error caused by noninteger multiple sampling intervals of signal time delay. The comparative experiment results demonstrate the power of the proposed method in improving the accuracy of the time-delay estimation of PD UHF pulse signals.

Symmetric implicational restriction method of fuzzy inference

Symmetric implicational restriction method of fuzzy inference