scholarly journals A Novel Approach to Generate Type-1 Fuzzy Triangular and Trapezoidal Membership Functions to Improve the Classification Accuracy

Symmetry ◽  
2021 ◽  
Vol 13 (10) ◽  
pp. 1932
Author(s):  
Muhammad Hamza Azam ◽  
Mohd Hilmi Hasan ◽  
Saima Hassan ◽  
Said Jadid Abdulkadir
Keyword(s):  
Membership Function ◽  
Clustering Algorithm ◽  
Fuzzy Membership ◽  
Fuzzy Membership Function ◽  
Membership Functions ◽  
Function Type ◽  
Fuzzy C Means ◽  
Fuzzy Membership Functions ◽  
Fuzzy C Means Algorithm

Fuzzy logic is an approach that reflects human thinking and decision making by handling uncertainty and vagueness using fuzzy membership functions. When a human is engaged in the design of a fuzzy system, symmetric properties are naturally preferred. Fuzzy c-means clustering is a clustering algorithm that can cluster datasets to produce membership matrix and cluster centers, which results in generating type-1 fuzzy membership functions. However, fuzzy c-means algorithm has a limitation of producing only a single membership function type, Gaussian MF. Generation of multiple fuzzy membership functions is of immense importance as it provides more efficient and optimal solutions to a problem. Therefore, an approach to generate multiple type-1 fuzzy membership functions through fuzzy c-means is required for the optimal and improved results of classification datasets. Hence, to overcome the limitation of the fuzzy c-means algorithm, an approach for the generation of type-1 fuzzy triangular and trapezoidal membership function through fuzzy c-means is considered in this study. The approach is used to calculate and enhance the accuracy of classification datasets called iris, banknote authentication, blood transfusion, and Haberman’s survival. The proposed approach of generating MFs using FCM produce asymmetric MFs, whose results are compared with the MFs produced from grid partitioning (GP), which are symmetric MFs. The results show that the proposed approach of generating type-1 fuzzy membership function through fuzzy c-means is effective and can be adopted.

A Study on Static Hand Gesture Recognition Using Type-1 Fuzzy Membership Function

2018 ◽  
pp. 105-140 ◽  
Author(s):  
Sriparna Saha ◽  
Amit Konar
Keyword(s):  
Membership Function ◽  
Gesture Recognition ◽  
Skin Color ◽  
Hand Gesture Recognition ◽  
Fuzzy Membership ◽  
Fuzzy Membership Function ◽  
Hand Gesture ◽  
Fuzzy Membership Functions ◽  
Sobel Edge Detection

The idea of this chapter is the use of Gaussian type-1 fuzzy membership functions based approach for automatic hand gesture recognition. The process has been carried out in five stages starting with the use of skin color segmentation for the isolation of the hand from the background. Then Sobel edge detection technique is employed to extract the contour of the hand. The next stage comprises of the calculation of eight spatial distances by locating the center point of the boundary and all distances are normalized with respect to the maximum distance value. Finally, matching based on Gaussian fuzzy membership function is used for the recognition of unknown hand gestures. This simple and effective procedure produces highest accuracy of 91.23% for Gaussian membership function and a time complexity of 2.01s using Matlab R2011b run on an Intel Pentium Dual Core Processor.

scholarly journals Using Artificial Intelligence to Predict Class Loyalty and Plagiarism in Students in an Online Blended Programming Course during the COVID-19 Pandemic

Electronics ◽  
2021 ◽  
Vol 10 (18) ◽  
pp. 2203
Author(s):  
Jain-Shing Wu ◽  
Ting-Hsuan Chien ◽  
Li-Ren Chien ◽  
Chin-Yi Yang
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Membership Function ◽  
Fuzzy Membership ◽  
Fuzzy Membership Function ◽  
Membership Functions ◽  
High Class ◽  
Fuzzy Membership Functions ◽  
Novel Method ◽  
Programming Courses

During the COVID-19 epidemic, most programming courses were revised to distance learning. However, many problems occurred, such as students pretending to be actively learning while actually being absent and students engaging in plagiarism. In most existing systems, obtaining status updates on the progress of a student’s learning is hard. In this paper, we first define the term “class loyalty”, which means that a student studies hard and is willing to learn without using any tricks. Then, we propose a novel method combined with the parsing trees of program codes and the fuzzy membership function to detect plagiarism. Additionally, the fuzzy membership functions combined with a convolution neural network (CNN) are used to predict which students obtain high scores and high class loyalty. Two hundred and twenty-six students were involved in the experiments. The dataset was randomly separated into the training datasets and the test datasets for twenty runs. The average accuracies of the experiment in predicting which students obtain high scores using the fuzzy membership function combined with a CNN and using the duration and number of actions are 93.34% and 92.62%. The average accuracies of the experiment in predicting which students have high class loyalty are 95.00% and 92.74%. Both experiments show that our proposed method not only can detect plagiarism but also can be used to detect which students are diligent.

scholarly journals Automatic Methods for Generation of Type-1 and Interval Type-2 Fuzzy Membership Functions

2015 ◽  
Vol 11 (9) ◽  
pp. 976-987 ◽  
Author(s):  
Andréia Alves dos Santos Schwaab ◽  
Silvia Modesto Nassar ◽  
Paulo José de Freitas Filho
Keyword(s):  
Fuzzy Membership ◽  
Membership Functions ◽  
Fuzzy Membership Functions ◽  
Automatic Methods ◽  
Interval Type

Neuro-Fuzzy Approaches for Forecasting Electrical Load Using Additional Moving Average Window Data Filter on Takagi-Sugeno Type MISO Networks

2008 ◽  
Vol 12 (4) ◽  
pp. 361-369 ◽  
Author(s):  
Felix Pasila ◽  
◽  
Ajoy K. Palit ◽  
Georg Thiele ◽  
◽  
...  
Keyword(s):  
Clustering Algorithm ◽  
Moving Average ◽  
Fuzzy Membership ◽  
Membership Functions ◽  
Training Algorithm ◽  
Electrical Load ◽  
Fuzzy Membership Functions ◽  
Neuro Fuzzy ◽  
Takagi Sugeno ◽  
Fuzzy Network

The paper describes a neuro-fuzzy approach with additional moving average window data filter and fuzzy clustering algorithm that can be used to forecast electrical load using the Takagi-Sugeno (TS) type multi-input single-output (MISO) neuro-fuzzy network efficiently. The training algorithm with additional moving average filter is efficient in the sense that it can bring the performance index of the network, such as the sum squared error (SSE), down to the desired error goal much faster than the simple Levenberg-Marquardt algorithm (LMA). The fuzzy clustering algorithm allows the selection of initial parameters of fuzzy membership functions, e.g. mean and variance parameters of Gaussian membership functions of neuro-fuzzy networks, which are otherwise selected randomly. The initial parameters of fuzzy membership functions, which result in low SSE value with given training data of neuro-fuzzy network, are further fine tuned during the network training. Finally, the above training algorithm is tested on TS type MISO neuro-fuzzy structure for long-term forecasting application of electrical load time series.

Analysis of Data Generated From Multidimensional Type-1 and Type-2 Fuzzy Membership Functions

2018 ◽  
Vol 26 (2) ◽  
pp. 681-693 ◽  
Author(s):  
Desh Raj ◽  
Aditya Gupta ◽  
Bhuvnesh Garg ◽  
Kenil Tanna ◽  
Frank Chung-Hoon Rhee
Keyword(s):  
Fuzzy Membership ◽  
Membership Functions ◽  
Fuzzy Membership Functions

Analysis and Research of Direction Relation Based on Fuzzy Description

2011 ◽  
Vol 393-395 ◽  
pp. 1102-1105
Author(s):  
Yong Shan Liu ◽  
Yan Li
Keyword(s):  
Fuzzy Sets ◽  
Fuzzy Membership ◽  
Fuzzy Membership Function ◽  
Membership Degree ◽  
Membership Functions ◽  
Fuzzy Matrix ◽  
Fuzzy Membership Functions ◽  
Fuzzy Objects ◽  
Direction Relations ◽  
Fuzzy Description

A fuzzy membership function was defined for each direction to predict the membership degree that an object pertains to a certain direction. Nine fuzzy membership functions were defined to describe the direction relations between fuzzy objects and crisp objects with corresponding fuzzy sets. Direction relations were described by a 3×3 fuzzy matrix, which was computed by an aggregation operator defined on the nine fuzzy sets. The fuzzy matrices and crisp matrices of direction relations between fuzzy objects and crisp objects were computed respectively, and comparison of fuzzy matrices with crisp ones reveals that the proposed fuzzy approach is more effective than existing crisp method.

scholarly journals A Multi-Gaussian Fuzzy Membership Function to the Algorithm Fuzzy GrowCut Applied to Segment Lesions in Mammography Images

2018 ◽  
Author(s):  
Filipe R. Cordeiro ◽  
Beatriz Albuquerque ◽  
Valmir Macario
Keyword(s):  
Breast Cancer ◽  
Membership Function ◽  
State Of The Art ◽  
Accurate Diagnosis ◽  
Fuzzy Membership ◽  
Fuzzy Membership Function ◽  
Tumor Segmentation ◽  
Membership Functions ◽  
Low Contrast

Segmentation of masses in mammography images is an important task to aid the accurate diagnosis of breast cancer. Although the quality of segmentation is crucial to avoid misdiagnosis, the segmentation process is a challenging task even for specialists, due to the presence of ill-defined edges and low contrast images. One of the techniques of state of the art for tumor segmentation is the Fuzzy GrowCut algorithm. In this work a study is performed on the behavior of this algorithm when using different membership functions for segmentation. Moreover, this research proposes a new membership function, called Multi-Gaussian, which improves the results of Fuzzy GrowCut with respect to those obtained through the use of classical functions.

scholarly journals SUM NORMAL OPTIMIZATION OF FUZZY MEMBERSHIP FUNCTIONS

2002 ◽  
Vol 10 (04) ◽  
pp. 363-384 ◽  
Author(s):  
DAN SIMON
Keyword(s):  
Fuzzy Logic ◽  
Membership Function ◽  
Optimization Problem ◽  
Fuzzy Membership ◽  
Optimization Approach ◽  
Membership Functions ◽  
Fuzzy Logic Systems ◽  
Fuzzy Membership Functions ◽  
Normal Constraint ◽  
Logic System

Given a fuzzy logic system, how can we determine the membership functions that will result in the best performance? If we constrain the membership functions to a certain shape (e.g., triangles or trapezoids) then each membership function can be parameterized by a small number of variables and the membership optimization problem can be reduced to a parameter optimization problem. This is the approach that is typically taken, but it results in membership functions that are not (in general) sum normal. That is, the resulting membership function values do not add up to one at each point in the domain. This optimization approach is modified in this paper so that the resulting membership functions are sum normal. Sum normality is desirable not only for its intuitive appeal but also for computational reasons in the real time implementation of fuzzy logic systems. The sum normal constraint is applied in this paper to both gradient descent optimization and Kalman filter optimization of fuzzy membership functions. The methods are illustrated on a fuzzy automotive cruise controller.

scholarly journals MRI Image Segmentation Using Conditional Spatial FCM Based on Kernel-Induced Distance Measure

2018 ◽  
Vol 8 (3) ◽  
pp. 2985-2990 ◽  
Author(s):  
B. Gharnali ◽  
S. Alipour
Keyword(s):  
Image Segmentation ◽  
Membership Function ◽  
Clustering Algorithm ◽  
Spatial Information ◽  
Distance Measure ◽  
Auxiliary Variable ◽  
Medical Image Segmentation ◽  
Intensity Inhomogeneity ◽  
Fuzzy Membership Function ◽  
Fuzzy C Means

Fuzzy C-means (FCM) clustering is the widest spread clustering approach for medical image segmentation because of its robust characteristics for data classification. But, it does not fully utilize the spatial information and is therefore very sensitive to noise and intensity inhomogeneity in magnetic resonance imaging (MRI). In this paper, we propose a conditional spatial kernel fuzzy C-means (CSKFCM) clustering algorithm to overcome the mentioned problem. The approach consists of two successive stages. First stage is achieved through the incorporation of local spatial interaction among adjacent pixels in the fuzzy membership function imposed by an auxiliary variable associated with each pixel. The variable describes the involvement level of each pixel for construction of membership functions and different clusters. Then, we adapted a kernel-induced distance to replace the original Euclidean distance in the FCM, which is shown to be more robust than FCM. The problem of sensitivity to noise and intensity inhomogeneity in MRI data is effectively reduced by incorporating a kernel-induced distance metric and local spatial information into a weighted membership function. The experimental results show that the proposed algorithm has advantages in accuracy and robustness against noise in comparison with the FCM, SFCM and CSFCM methods on MRI brain images.

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