A NOVEL IMAGE PROCESSING ALGORITHM TO AUTOMATICALLY EXTRACT AND CLASSIFY LIVER BEARING TUMORS USING PATTERN RECOGNITION AND FUZZY TRANSFORM

This study presents a novel liver disease classification method by applying pattern recognition technique to automatically segmented liver from the images of computed tomographic (CT) scans. The methodology comprises of disease classification by the extraction of textural features from focal liver region bearing tumors. Two types of liver textures are investigated in this study for classification accuracy judgement. First, original liver texture is considered for feature extraction. Second, liver is used for feature extraction. The CT image dataset comprises 308 liver samples with 193 samples of malignant tumor and 115 samples of benign tumor. The entire liver tissue bearing tumor is segmented from the CT image automatically in the pre-processing stage using fuzzy transformation function and morphological processing. Four sets of textural feature matrices are applied to the liver for feature extraction. Gray level co-occurrence matrix (GLCM), standard deviation gray level co-occurrence matrix (SDGLCM), seven-moment matrix (7MM) and seven-moment gray level co-occurrence matrix (7MGLCM) are the combinational feature matrices applied to classify the liver as malignant or benign using support vector machines (SVMs). The best classification accuracy is achieved for original liver texture by 7MGLCM, which is 97% with AUC[Formula: see text]0.99 for training dataset and 97.8% with AUC[Formula: see text]1 for test dataset.

Neuro-Fuzzy Transformation with Minimize Entropy Principle to Create New Features for Particulate Matter Prediction

Air pollution is a major global issue. In Thailand, this issue continues to increase every year, similar to other countries, especially during the dry season in the northern region. In this period, particulate matter with aerodynamic diameters smaller than 10 and 2.5 micrometers, known as PM10 and PM2.5, are important pollutants, most of which exceed the national standard levels, the so-called Thailand air quality index (T-AQI). Therefore, this study created a prediction model to classify T-AQI calculated from both types of PM. The neuro-fuzzy model with a minimum entropy principle model is proposed to transform the original data into new informative features. The processes in this model are able to discover appropriate separation points of the trapezoidal membership function by applying the minimum entropy principle. The membership value of the fuzzy section is then passed to the neural section to create a new data feature, the PM level, for each hour of the day. Finally, as an analytical process to obtain new knowledge, predictive models are created using new data features for better classification results. Various experiments were utilized to find an appropriate structure with high prediction accuracy. The results of the proposed model were favorable for predicting both types of PM up to three hours in advance. The proposed model can help people who are planning short-term outdoor activities.

Discharge estimation for trapezoidal open channels using fuzzy transformation method to a flow equation

The aim of this paper is to implement the fuzzy logic theory in order to estimate the discharge for open channels, which is a well-known physical problem affected by many factors. The problem can be solved by Manning equation but the parameters present uncertainties as to their true-real values. Especially, the Manning n roughness coefficient, which is an empirically derived coefficient, presents quite high variation for different substrates. With the help of fuzzy logic and utilizing a fuzzy transformation method, it is possible to include the uncertainties of the problem in the calculation process. In this case, it is feasible to estimate the discharge, giving more emphasis on different uncertainty rates of the Manning roughness coefficient, while the rest of the parameters remain with constant or zero uncertainty level. By taking different a-cut levels, it was shown that the methodology gives realistic and reliable results, presenting with great accuracy the variations of the water discharge for trapezoidal open channels. This way, a possible underestimation or overestimation of the actual physical condition is avoided, by helping the engineers and researchers to obtain a more comprehensive view of the real physical conditions, thus making better management plans.

Project Delivery of Goods With Limited Resources and Minimum Time Using Fuzzy Logic Method

In solving the problem of fuzzy application for scheduling delivery of goods with a limited power source and minimal time, the problems that are still not considered in the RCPS problem modeling are the uncertainty characteristics of the parameters of the timing of activities in the delivery project. Even though this can be solved by using the PERT (Project Evaluation and Review Technoloque) method with a probabilistic approach, this technique still ignores the limitations of the supply of resources. Actually, a probabilistic approach can be used, if previously provided data about the experience in completing similar projects. But if the project is a new project or the techniques and methodologies used to complete a new project, such as new techniques and methodologies in software engineering, among others: object-oriented design and programming, computer-aided software design, user interface management systems, fourth generation languages, etc., then the probabilistic approach is not suitable. In this situation, the decision maker must be able to estimate the cost and time duration, of all activities in the project based on existing experience, related to the level of knowledge they have, about new techniques and methodologies to be applied, and the level of human resource expertise. which are available. This method of estimating project costs and time, which is more precise, uses representations in the form of fuzzy numbers, namely fuzzy sets in the set of real numbers that are normal, convex, and closed intervals. The delivery time is modeled as a fuzzy number of LR types IKiri, IKanan, α, β, with three values ​​of α-cuts E = 0.3, L = 0.7, and I = 1.0. The fuzzy transformation model is based on three pairs of inferior and superior values ​​from α-cuts. The priority for scheduling delivery of goods is based on the smallest early start time EST value. Resources are solved by serial and parallel models. The smallest makespan value is used to determine the best solution. Goods delivery settlement uses fuzzy operations, namely arithmetic operations and relation operations. Analysis of the output oftware based on testing with test scenarios (table ^ .50) for some input data shows that the parallel method is better than the serial method. This is indicated by the large difference in makespan value generated from the two methods. Based on input data from a software development project, the serial method gives makespan values ​​in the range between 675.0 and 867.0, while the parallel method gives makespan values ​​in the range between 116.0 and 259.0. The analysis of the output software in a fuzzy Gantt Chart representation shows that an activity can be scheduled with varying degrees of optimism. The degree of optimistic activity scheduling can be graded in linguistic terminology between two extreme pessimistic and optimistic values, namely very pessimistic, pessimistic, slightly pessimistic, slightly optimistic, optimistic, very optomistic.

Colored spun fabric texture representation and application by combining spatial features with frequency features

Colored spun fabrics are difficult to accurately characterize with a local binary pattern due to texture anisotropy caused by the uneven distribution of dyed fibers. In this paper, we present a texture representation model based on spatial and frequency characteristics. The proposed model takes advantage of the local binary pattern and local phase quantization to extract the texture of woven fabric. Then, the two features are connected in series, and the features of dimension reduction by principal component analysis are used to represent the texture of the fabric image. Finally, the hierarchical hybrid classifier is applied to classify the fabric structure. The experimental results show that the local phase quantization feature is robust to the fuzzy transformation and the texture representation model has a stronger ability of texture description than the single local binary pattern feature, with the average classification accuracy of 97.59% on 336 samples. In addition, compared with the deep learning algorithm, the texture representation algorithm can ensure a high classification accuracy.

On Bijective Correspondence between Fuzzy Reflexive Approximation Spaces and Fuzzy Transformation Systems

The objective of this paper is to establish the relationship between fuzzy approximation operators and fuzzy transformation systems. We show that for each upper fuzzy transformation system there exists a fuzzy reflexive approximation space and vice-versa. We further establish such relationship between lower fuzzy transformation systems and fuzzy reflexive approximation spaces under the condition that the underline lattice structure satisfies double negation law.

A Fuzzy Transformation of the Classic Stream Sediment Transport Formula of Yang

The objective of this study is to transform the arithmetic coefficients of the total sediment transport rate formula of Yang into fuzzy numbers, and thus create a fuzzy relationship that will provide a fuzzy band of in-stream sediment concentration. A very large set of experimental data, in flumes, was used for the fuzzy regression analysis. In a first stage, the arithmetic coefficients of the original equation were recalculated, by means of multiple regression, in an effort to verify the quality of data, by testing the closeness between the original and the calculated coefficients. Subsequently, the fuzzy relationship was built up, utilizing the fuzzy linear regression model of Tanaka. According to Tanaka’s fuzzy regression model, all the data must be included within the produced fuzzy band and the non-linear regression can be concluded to a linear regression problem when auxiliary variables are used. The results were deemed satisfactory for both the classic and fuzzy regression-derived equations. In addition, the linear dependence between the logarithmized total sediment concentration and the logarithmized subtraction of the critical unit stream power from the exerted unit stream power is presented. Ultimately, a fuzzy counterpart of Yang’s stream sediment transport formula is constructed and made available to the readership.