Fuzzy Inference Method Research Articles

Application of Structural Entropy and Spatial Filling Factor in Colonoscopy Image Classification.

For finding colorectal polyps the standard method relies on the techniques and devices of colonoscopy and the medical expertise of the gastroenterologist. In case of images acquired through colonoscopes the automatic segmentation of the polyps from their environment (i.e., from the bowel wall) is an essential task within computer aided diagnosis system development. As the number of the publicly available polyp images in various databases is still rather limited, it is important to develop metaheuristic methods, such as fuzzy inference methods, along with the deep learning algorithms to improve and validate detection and classification techniques. In the present manuscript firstly a fuzzy rule set is generated and validated. The former process is based on a statistical approach and makes use of histograms of the antecedents. Secondly, a method for selecting relevant antecedent variables is presented. The selection is based on the comparision of the histograms computed from the measured values for the training set. Then the inclusion of the Rényi-entropy-based structural entropy and the spatial filling factor into the set of input variables is proposed and assessed. The beneficial effect of including the mentioned structural entropy of the entropies from the hue and saturation (H and S) colour channels resulted in 65% true positive and 60% true negative rate of the classification for an advantageously selected set of antecedents when working with HSV images.

Applying Mamdani’s method to categorize mathematical literacy of public middle school students in Kupang

The study objective was to describe the application of Mamdani’s fuzzy inference method to the categorization of public middle school students’ mathematical literacy in Kupang, East Nusa Tenggara. Having been done not only because of the low achievement of Indonesian 15-year-old students in PISA, but the research has also become the answer to the lack of surveys regarding students’ mathematical literacy in Kupang. Consequently, this study provides valid and actual data that the government should refer to. As many as 377 respondents of ninth-grade students from public schools were selected using a two-stage cluster randomized sampling so that they could best represent six districts in Kupang. Their ability was evaluated by fifteen math literacy problems adopted from PISA 2012 and the products of some development research; as a result, the reliability could be assured. At the fuzzification stage, scores obtained from three process domains of math literacy: formulating, employing, and interpreting, became the crisp input since those domains were the input variables. After being analyzed, the scores then produced crisp output indicating student mathematical literacy–as the output variable–at the defuzzification stage. Overall, public middle school students’ mathematical literacy in Kupang was classified as a low group.

A Proposed AODV Black hole Detection Model using the Fuzzy Inference Method in MANET Topology

Mobile Adhoc Network (MANET)as a network of mobile routers that self-configures has raised some security concerns which one of them has been the Black Hole Attack (BHA). Studies have shown various detection and mitigation techniques in line with the black hole attack of MANET technology. However, some of the proposed models in this direction have paid less attention to how fuzzy systems could be integrated in Adhoc On-Demand Distance Vector (AODV)protocol, to further guide its detection abilities against a possible black hole attack. Hence the study proposed a model that integrates the Fuzzy Inference Method (FIM)on the existing AODV security measures in order to strengthen further its detection metrics. Also,an Intrusion Detection System (IDS) was introduced for an early check against possible BHA along with the Fuzzy Parameter Extraction module which performs all necessary parameter checks before passing it on to the FIM. The parameters into consideration were adapted from the study of Narang (2013), with this study modifying further its proposed algorithm using the added concepts earlier mentioned. The proposed model presented tends to perform better than the existing models in this direction with evidence from the theoretical underpinnings of the functionality of the added modules in the proposed system

A fuzzy inference method for image fusion/refinement of CT images from incomplete data

The quality of computed-tomography (CT) images deteriorates when images are reconstructed from incomplete data. This work makes use of the a priori knowledge inherent in the membership functions and the logical rules of a fuzzy inference system (FIS) to compensate for the missing data. It is shown that a fuzzy inference system can be used to improve the quality of reconstructed CT images, particularly when the images are reconstructed from incomplete data. It is proposed to reconstruct a coarser image for which the data is over-complete, and use the histograms of this image and that of the original finer image to generate the membership functions required in FIS. The two images are then fused, with the aid of logical rules based on the knowledge that the two images posses the same distinct attributes (pixel values). In order to avoid the difference in spatial resolution between the original fine image and the reconstructed coarse image, a modified FIS method is introduced to refine the fine image. Results are presented, showing visually and quantitatively that this FIS refinement process improves the quality of the original fine image.

Prediction of Heart Attack Using Fuzzy Logic Method and Determination of Factors Affecting Heart Attacks

As a result of the researches, it has been revealed that heart attack is the number one cause of death in the world. This problem will continue to increase, especially today and in the future. In this study, a heart attack was predicted by considering the factors affecting heart attack. Due to the uncertain conditions in heart attack, the fuzzy logic method, which is frequently used in healthcare, was used and expert opinions were taken into account in the model created. 576 rules were defined using the Mamdani fuzzy inference method. The study was tested with 10 patient data and the results were compared with the actual values. In addition, multiple regression analysis was performed, variables that had a significant effect on heart attack were determined, and the relationship between dependent and independent variables was examined. It was shown in the study that dependent variables explained the independent variable by 41.9% thanks to the multiple regression analysis. The regression equation obtained in line with these results significantly predicted the heart attack and the effect levels of the independent variables were determined.

Simultaneous reconstruction of thermal boundary condition and physical properties of participating medium

A hybrid optimization technique, which combines decentralized fuzzy inference method (DFIM) and sequential quadratic programming (SQP) algorithm, is developed to reconstruct the thermal boundary condition and physical properties of participating medium simultaneously. The coupled radiation-conduction heat transfer in the medium is solved by finite volume method in combination with discrete ordinate method. The reconstruction task is formulated as an inverse problem which is solved by the hybrid DFIM-SQP technique from the knowledge of surface temperature and exit radiative intensity. Retrieval results demonstrate that the time-dependent heat flux, absorption coefficient, scattering coefficient and thermal conductivity of participating medium can be accurately retrieved by the present methodology. The proposed DFIM-SQP technique is more accurate and efficient in solving the simultaneous reconstruction problem than DFIM, conjugate gradient method, SQP, particle swarm optimization and krill herd algorithms.

Управление техногенными рисками электроустановок производственного объекта на основе нечетко-темпоральной модели

Production facilities that have complex electric power structure should be provided with an efficient system of diagnostics and control of electrical installations operating modes. Constant monitoring of electrical installations parameters is required to create a database and a knowledge base for the purpose of forecasting and managing technogenic risks at the functioning of the «Man — Electrical Installation — Environment» system. The methods used for modeling hazard of electrical installations are providing for risk assessment in the weakly structurered man-machine system «Man — Electrical Installation — Environment» with a causal relationship that interact with the risk-forming factors that are directly effecting on the technogenic risks. Methods of temporal logic with the construction of fuzzy modeling algorithms allow to determine the dynamic properties of the system under consideration. The occurrence of the risk of a dangerous situation is considered as a technogenic risk and depends on the probability of a dangerous technogenic situation with subsequent injuries and damage. In this regard, considering the peculiarities of managing technogenic risks of the electric power infrastructure of an enterprise, the compilation of a simulation model includes the stages of forming the knowledge base by an expert way using the method of fuzzy inference for dynamic descriptions of the «Man — Electrical Installation — Environment» system. They consider the influence of time-related factors between the fuzzy signs of risk-forming factors and the technogenic risk value. The interaction and mutual influence of risk-forming factors are described using the logical signs «AND» and «OR» with the fuzzy operation «Priority And». During the temporal-logical analysis, the knowledge base was formed with the given temporal relationships of risk-forming factors. As a result of fire risks calculation using fuzzy-temporal statements and without them, the corresponding values were obtained: 235·10–6 in the first case, 98·10–6 — in the second one. Given that the standard fire risk value is 1·10–6, the risk level will be acceptable.

Using Fuzzy Inference System FIS for Identifying Motion in Digital Surveillance Systems

The aim of the current study is to use the FIS Fuzzy Inference System method to determine motion in digital surveillance systems. In the analysis, there are several methods used such as segmenting, sorting, evaluating and showing the results. After capturing an online video stream with an average of 1 frame/sec, the goal of the proposed method is to translate these frames to their corresponding representation of pixels and then use these frames as inputs for the job. The output is evaluated based on these inputs. The idea is to compare the average pixel representation of the current frame boundaries (column vectors) with the corresponding column vectors in the next frame, in order to find out whether there is any motion detected by comparing the average of the calculated column vector for the ith frame with the corresponding column vector in the i+1th frame. This operation leads to extracting 8 averages and it is considered as inputs to the fuzzy inference method. There is one output that will detect whether there is any motion detected or not. By designing a set of rules and then analyzing the results, a comparison of the averages is held.

Optimization of National Rice Production with Fuzzy Logic using Mamdani Method

The purpose of this article is to optimization of national rice production with fuzzy logic using Mamdani method. Based on the results of the study, it is known that four parameters need to be considered to maintain the price stability of necessities, namely production; availability; demand and distribution. Optimization of production by producers and optimization of the ordering of goods by distributors are important steps to maintain price stability for necessities. Optimization of production and ordering of staple goods will have a significant impact on the financial sector because it is closely related to the prediction of the number of raw materials used, production costs, storage costs, and also distribution costs of goods. One of the fuzzy inference methods that can be used for this optimization is the Mamdani method. To get the output on the application of the fuzzy logic of the Mamdani method, four stages are needed, formation of fuzzy sets; application of implication functions; composition of rules and defuzzification. Fuzzy logic Mamdani method can be used to predict the amount of national rice that must be produced. If it is known that the need is 21,908,784 tons of rice and the supply is 65,457,456 tons, the amount of national rice that must be produced is 14,624,592 tons.

Comparison of Type-2 Fuzzy Inference Method and Deep Neural Networks for Mass Detection from Breast Ultrasonography Images

In this study, mass detection from breast ultrasonography images was realized using deep neural networks. Dataset is a collection of publicly available ultrasonography images which were classified by their biopsy results. A total of 153 breast ultrasonography images that contain 89 malign and 64 benign tumours were used. Image augmentation and deep neural network software was developed using Python 3,5 environment on Visual Studio Community 2017 IDE. A hybrid method including Keras ImageDataGenerator Class and image preprocessing techniques was introduced. Twenty images from both classes were randomly split from the dataset for testing after the network was designed. The network had a success rate of 100% at an epoch value of 70. The result of this study was compared with the result of another study that implemented type-2 fuzzy inference system with a success rate of 99,34%. 
 As a conclusion, it can be expressed that the deep neural networks are more successful than fuzzy inference systems in tumour detection from breast ultrasonography images. Therefore, it can be more convenient to use deep neural network technology in computer aided detection systems for mass detection from breast ultrasonography images.

PSO-aided fuzzy inference of material elastic constants with resonant ultrasound spectroscopy

ABSTRACT Fuzzy inference method is applied to formulate an algorithm capable of estimating material elastic constants (ECs) of a specimen by solving an inverse problem with a group of measured resonance frequencies obtained via Resonant Ultrasound Spectroscopy (RUS). The algorithm is validated with RUS data from a specimen of polycrystalline aluminium alloy. Then the algorithm is found to be sensitive to the initial ECs by processing RUS data from a specimen of fine-grain polycrystalline Ti–6Al–4V, the same as the Levenberg–Marquardt (L–M) method popularly used in solving inverse problems. To overcome such a drawback, a hybrid method of Particle Swarm Optimization (PSO) and Density-Based Spatial Clustering of Applications with Noise (DBSCAN) is proposed. And it is used to generate several groups of initial ECs for the fuzzy inference method. There is a trade-off between computational time and accurately estimated ECs, since the hybrid method needs more time to directly find out accurate ECs.

An online operating performance evaluation approach using probabilistic fuzzy theory for chemical processes with uncertainties

Operating performance evaluation (OPE) has been playing an essential role to ensure the effective operations of chemical processes. However, most of previous research focused on the deterministic evaluation strategies, without consideration of uncertainties in the evaluation indicators of OPE. Based on probabilistic fuzzy theory, an online OPE scheme is proposed by considering the uncertainties in chemical processes. In the modeling step, on the basis of just-in-time learning and probabilistic principal component regression, a prediction model is proposed and applied to estimate the probability distribution of the evaluation indicators in real time; and a weighted cosine Mahalanobis-Taguchi system for variable selection is developed to improve the prediction accuracy of the evaluation indicators. In the evaluation step, a probabilistic fuzzy inference method is proposed to improve the accuracy of evaluation results by considering the uncertainty of evaluation indicators. The effectiveness of the proposed approach is finally tested on an industrial hydrocracking process.

Applications of non‐invasive sensor devices to personalised health care

The application of non-invasive devices in personal health care is becoming more and more widespread, especially sleep quality, which is a critical part of personal health because it is often associated with many diseases. Using some sensor devices such as pressure sensors, photoplethysmography and heart rate devices, the authors can collect a lot of physiological signals. In this work, they provide a method of fuzzy inference to evaluate the sleep phase, which uses the values of heart rate, heart rate variation, and body movement as input parameters that collected by the sensor devices. This method has been applied to the actual product. The results show that the measurement results of this method are consistent with polysomnography, which is recognised as the best method for measuring sleep quality currently. At the same time, the device can make some additional contributions to monitoring personal health. Combining personal activity information collected by GPS with heart rate information collected by heart rate sensors and using process mining to analyse those data, they can provide good recommendations for personal health care.

Determination of Factors that Influence the Noise Pollution in Malaysia using Fuzzy Logic

Nowadays, the economy of our country has increased, so the use of transport, whether on land or by air, has also increased. This will lead to noise pollution, which will have an impact on human health. Noise pollution was an unpleasant sound that could have a negative impact on human health, such as sleep disturbance, hearing loss, annoyance, and stress. There are many factors that can cause noise pollution such as road traffic, aircraft, commercial construction, industrial and manufacturing noise. Therefore, the aim of this study is to determine the factor that influences noise pollution in Malaysia using Fuzzy Logic. The determination of factors that affect noise pollution in Malaysia using the Fuzzy Logic approach includes the determination of input and output variables, Fuzzification, Fuzzy Ru le - Based, Fuzzy Inference Method and Defuzzification. In Defuzzification which is the last stage for Fuzzy logic, Centroid method used since it can give a result with more accurate and flexible. This study used road traffic noise, aircraft noise, industria l and manufacturing noise and commercial construction as factors for noise pollution, as well as input variables. This method was used to determine which factors have the most influence on noise pollution. The results of this study show that road traffic n oise and industrial and manufacturing noise were factors which had an impact on noise pollution with a maximum value of 82. This shows that the aim of this study has been achieved.

Sugeno Fuzzy Inference Method and MatLab Application Program for Simulation of Student Performance Evaluation in the Elementary Mathematics Learning Process

Sugeno Fuzzy Inference Method and MatLab Application Program for Simulation of Student Performance Evaluation in the Elementary Mathematics Learning Process

RETRACTED ARTICLE: ANN and fuzzy based household energy consumption prediction with high accuracy

Timeline Data is gathered according to different time intervals, which are day after day after week or month after month, for updating properties and rationing institutional resilience it is important to consider the usage of systems and lead to reduced lifespan Such details illustrate the use of the system as well as its interaction with time, like day, week, month and time of year, and the relation between the equipment and a relative, essential factors for the effects of the usage of their potency and the expected movement by customers. This is because it is not significant to determine the various relations between specific devices utilizing concurrent data. In addition, precise relations between interval-based instances in which specific system usage continue for certain duration cannot be calculated. To address these difficulties, we propose supervised energy time series data clustering and frequent pattern mining analysis as well as a Bayesian network forecast for energy use. However, the AI model is a univariate construct based on past use-values. Neural Networks have the favored position that can estimate nonlinear limits. Everything together they have an approximate usage of vitality, the ANN adds in a planning knowledge table between the use of vitality (EC) and its determinants. SVM is capable of reliably calculating knowledge on time structure while the basic system mechanism is frequently nonlinear and not set. Also, certain nonlinear mechanisms such as multilayer perceptron have been shown to flank SVM. The single data has been converted into a multivariate and the ANFIS has been selected as it transmits both the AI (ANN) and Fuzzy Inference Method (FIS) points of concern. ANFIS yields the accuracy, RMSE, and MAPE among genuine and anticipated power utilization of 91.19%, 0.4076 and 0.9049 which is moderately low.

Olfactory-Based Navigation via Model-Based Reinforcement Learning and Fuzzy Inference Methods

This article presents an olfactory-based navigation algorithm for using a mobile robot to locate an odor source in a turbulent flow environment. We analogize the odor source localization as a reinforcement learning problem. During the odor plume tracing process, the belief state in a partially observable Markov decision process model is adapted to generate a source probability map that estimates possible odor source locations, and a hidden Markov model is employed to produce a plume distribution map that premises plume propagation areas. Both source and plume estimations are fed to the robot, and a decision-making approach based on fuzzy inference is designed to dynamically fuse information from two maps and to balance the exploitation and exploration of the search. After assigning the fused information to reward functions, a value iteration based path planning algorithm is presented to solve for the optimal action policy. Comparing to other commonly used olfactory-based navigation algorithms, such as moth-inspired and Bayesian inference methods, simulation results show that the proposed method is more intelligent and efficient.

Analysis of Air Pollution Levels in DKI Jakarta Province Using the Mamdani Fuzzy Inference System Method

This study aims to measure the level of air pollution determined by pollutant gases contained in the air. Pollutants that measure air pollution are PM10 (Special Material), SO2 (Sulfur), NO2 (Nitrogen Oxide), CO (Carbon Monoxide, O3 (Ozone), and NO2 (Nitrogen Oxide), which are related to vehicle use and, according to the choice this pollutant threshold, we will discuss the level of air pollution with the fuzzy mamdani inference method. The results of the pollutant threshold study will then be applied to the rules / rules that are applied using the if-then rules and then the input variables are arranged using weighted averages, variable averages weighted will be determined higher into three levels: low, medium and high.Keywords Decision Tree, Feature Selection, Optimization of Lecturer Assistant Performance, Particle Swarm Optimization.

An efficient inverse approach for reconstructing time- and space-dependent heat flux of participating medium* *Project supported by the Natural Science Foundation of Chongqing (CSTC, Grant No. 2019JCYJ-MSXMX0441).

The decentralized fuzzy inference method (DFIM) is employed as an optimization technique to reconstruct time- and space-dependent heat flux of two-dimensional (2D) participating medium. The forward coupled radiative and conductive heat transfer problem is solved by a combination of finite volume method and discrete ordinate method. The reconstruction task is formulated as an inverse problem, and the DFIM is used to reconstruct the unknown heat flux. No prior information on the heat flux distribution is required for the inverse analysis. All retrieval results illustrate that the time- and space-dependent heat flux of participating medium can be exactly recovered by the DFIM. The present method is proved to be more efficient and accurate than other optimization techniques. The effects of heat flux form, initial guess, medium property, and measurement error on reconstruction results are investigated. Simulated results indicate that the DFIM is robust to reconstruct different kinds of heat fluxes even with noisy data.

Multi-criteria group decision-making approach to the modernization of hold baggage security screening system at an airport

The decisions on the modernization of a hold baggage screening system (HBSS) at an airport require taking into account multiple assessment criteria of the considered variants. These criteria are both objective - precisely quantifiable, and subjective - requiring descriptive assessment by experts. The purpose of this article was to make a multi-criteria assessment of variants of the modernization of the HBSS, considering various points of view of experts and the context of decisions made. Due to the existence of both the criteria described using linguistic variables and those dependent on complex random processes, both fuzzy inference methods and simulation methods based on microscale models were used. The developed method was implemented to assess the modernization variants for Katowice International Airport. The highest score resulting from the group decision-making was obtained by the modernization variant consisting in reducing the number of baggage control levels with the simultaneous increase in the number of people performing manual checks. If we consider a different decision-making process environment, the group's preferred variant changes. For example, for the scenario involving a significant increase in the size of the traffic being handled, the variant of doubling X-ray devices at the first level of control becomes the most advantageous. The change in the group preference, depending on the context of the decision-making situation, clearly indicates that this context must be taken into account in the decision-making process. The proposed method allows one not only to indicate the preferred variant in each scenario but also to numerically determine the strength of preferences for individual variants, which may facilitate making the right decision considering the chances for implementation of individual scenarios.