Kernel Fuzzy C-means Algorithm Research Articles

Novel modified kernel fuzzy c-means algorithm used for cotton leaf spot detection

Image segmentation is a significant and difficult subject that is a prerequisite for both basic image analysis and sophisticated picture interpretation. In image analysis, picture segmentation is crucial. Several different applications, including those related to medicine, facial identification, Cotton disease diagnosis, and map object detection, benefit from image segmentation. In order to segment images, the clustering approach is used. The two types of clustering algorithms are Crisp and Fuzzy. Crisp clustering is superior to fuzzy clustering. Fuzzy clustering uses the well-known FCM approach to enhance the results of picture segmentation. KFCM technique for image segmentation can be utilized to overcome FCM’s shortcomings in noisy and nonlinear separable images. In the KFCM approach, the Gaussian kernel function transforms high-dimensional, nonlinearly separable data into linearly separable data before applying FCM to the data. KFCM is enhancing noisy picture segmentation results. KFCM increases the accuracy rate but ignores neighboring pixels. The Modified Kernel Fuzzy C-Means approach is employed to get over this problem. The NMKFCM approach enhances picture segmentation results by including neighboring pixel information into the objective function. This suggested technique is used to find “blackarm” spots on cotton leaves. A fungal leaf disease called “blackarm” leaf spot results in brown leaves with purple borders. The bacterium can harm cotton plants, causing angular leaf blotches that range in color from red to brown.

A generalized fuzzy clustering framework for incomplete data by integrating feature weighted and kernel learning.

Missing data presents a challenge to clustering algorithms, as traditional methods tend to pad incomplete data first before clustering. To combine the two processes of padding and clustering and improve the clustering accuracy, a generalized fuzzy clustering framework is proposed based on optimal completion strategy (OCS) and nearest prototype strategy (NPS) with four improved algorithms developed. Feature weights are introduced to reduce outliers' influence on the cluster centers, and kernel functions are used to solve the linear indistinguishability problem. The proposed algorithms are evaluated regarding correct clustering rate, iteration number, and external evaluation indexes with nine datasets from the UCI (University of California, Irvine) Machine Learning Repository. The results of the experiment indicate that the clustering accuracy of the feature weighted kernel fuzzy C-means algorithm with NPS (NPS-WKFCM) and feature weighted kernel fuzzy C-means algorithm with OCS (OCS-WKFCM) under varying missing rates is superior to that of seven conventional algorithms. Experiments demonstrate that the enhanced algorithm proposed for clustering incomplete data is superior.

Substation clustering based on improved KFCM algorithm with adaptive optimal clustering number selection

The premise and basis of load modeling are substation load composition inquiries and cluster analyses. However, the traditional kernel fuzzy C-means (KFCM) algorithm is limited by artificial clustering number selection and its convergence to local optimal solutions. To overcome these limitations, an improved KFCM algorithm with adaptive optimal clustering number selection is proposed in this paper. This algorithm optimizes the KFCM algorithm by combining the powerful global search ability of genetic algorithm and the robust local search ability of simulated annealing algorithm. The improved KFCM algorithm adaptively determines the ideal number of clusters using the clustering evaluation index ratio. Compared with the traditional KFCM algorithm, the enhanced KFCM algorithm has robust clustering and comprehensive abilities, enabling the efficient convergence to the global optimal solution

IFCEM based recognition method for target with interval-overlapped hybrid attributes

When the attributes of unknown targets are not just numerical attributes, but hybrid attributes containing linguistic attributes, the existing recognition methods are not effective. In addition, it is more difficult to identify the unknown targets densely distributed in the feature space, especially when there is interval overlap between attribute measurements of different target classes. To address these problems, a novel method based on intuitionistic fuzzy comprehensive evaluation model (IFCEM) is proposed. For numerical attributes, targets in the database are divided into individual classes and overlapping classes, and for linguistic attributes, continuous interval-valued linguistic term set (CIVLTS) is used to describe target characteristic. A cloud model-based method and an area-based method are proposed to obtain intuitionistic fuzzy decision information of query target on numerical attributes and linguistic attributes respectively. An improved inverse weighted kernel fuzzy c-means (IWK-FCM) algorithm is proposed for solution of attribute weight vector. The possibility matrix is applied to determine the identity and category of query target. Finally, a case study composed of parameter sensitivity analysis, recognition accuracy analysis. and comparison with other methods, is taken to verify the superiority of the proposed method.

Brain Tumour Region Extraction Using Novel Self-Organising Map-Based KFCM Algorithm

Medical professionals need help finding tumours in the ground truth image of the brain because the tumours’ location, contrast, intensity, size, and shape vary between images because of different acquisition methods, modalities, and the patient’s age. The medical examiner has difficulty manually separating a tumour from other parts of a Magnetic Resonance Imaging (MRI) image. Many semi- and fully automated brain tumour detection systems have been written about in the literature, and they keep improving. The segmentation literature has seen several transformations throughout the years. An in-depth examination of these methods will be the focus of this investigation. We look at the most recent soft computing technologies used in MRI brain analysis through several review papers. This study looks at Self-Organising maps (SOM) with K-means and the kernel Fuzzy c-means (KFCM) method for segmenting them. The suggested SOM networks were first compared to K-means analysis in an experiment based on datasets with well-known cluster solutions. Later, the SOM is combined with KFCM, reducing time complexity and producing more accurate results than other methods. Experiments show that skewed data improves networks’ performance with more SOMs. Finally, performance measures in real-time datasets are analysed using machine learning approaches. The results show that the proposed algorithm has good sensitivity and better accuracy than k-means and other state-of-art methods.

A Hybrid Method to Diagnose 3D Rotor Eccentricity Faults in Synchronous Generators Based on ALIF_PE and KFCM

This paper proposed a new hybrid diagnosis method for the generator’s 3D static eccentricity faults which include the axial eccentricity, the radial eccentricity, and the mixed eccentricity composed of the former two. Firstly, adaptive local iterative filtering (ALIF) method was used to decompose the vibration signals of the generator under eccentricity faults. Then, in order to figure out the intrinsic mode function (IMF) components with the upmost feature information, the correlation coefficient was calculated. Finally, the components’ permutation entropy (PE) is extracted to construct the eigenvector matrix which can be used to input the kernel fuzzy C-means (KFCM) algorithm to obtain the result of clustering. The result indicates that the classification coefficient based on ALIF and KFCM behaves closer to 1, while the average fuzzy entropy (FE) is closer to 0, showing that this method is able to detect different eccentricity faults more accurately.

Moving Object Segmentation Using Level Set Algorithm with GWO-KFCM Clustering

Video analytics is widely used to automatically analyze the videos to extract the required information and detect the various events object identification and traffic analysis. The segmentation of the image is referring to extracting the required region from an image. The major objective of the segmentation process is to cluster the images without being affected by the noises. The detection of the moving objects is a challenging task in video analysis due to the dynamic background of the video. The major drawback of the existing Kernel Fuzzy C-Means (KFCM) clustering is initialization of random centroids which increases the execution time to identify the segmented portions. In this research, the Grey Wolf Optimization (GWO) algorithm used to initialize the centroids of required clusters in KFCM and Level Set (LS) Algorithm is used to segment the objects in video sequence. The proposed KFCM-GWO-LS is implemented for moving and static object detection in the videos obtained from SBM-RGBD dataset. For object detection, determining central clusters are important which is performed by using KFCM. GWO helps in finding the best centroids clusters by matching with KFCM. The centroids clusters are segmented by using LS algorithm which undergoes over segmentation problem that is overcome by GWO. As all the three techniques are dependent on one another hybrid of all these techniques obtains better results. The proposed KFCM-GWO-LS is evaluated in terms of Recall, Specificity, Precision and F-measure and the experimental results showed that the proposed method improved the system performance from 0.3 % to 14.35 % compared to existing methods Multi-Sensor Scheduler Algorithm and Statistical Inference Theory model.

Optimal object detection and tracking in occluded video using DNN and gravitational search algorithm

Moving object tracking is an effective optimization procedure based on the impermanent relevant information associated with the original frames. Suggesting a method with efficient accuracy in convoluted atmospheres is a difficulty for scientists in the area of research study. In this research, powerful object detection and movement tracking videos are proposed. Here, we are considering the input video sequence PETS and Hall monitor videos. Initially, the background and foreground separations are done by modified kernel fuzzy c-means algorithm. The object detection and tracking are done by gravitational search algorithm-based deep belief neural network. The implementation will be in MATLAB. The effectiveness of the recommended strategy is assessed with means of precision, recall, F-measure, FPR, FNR, PWC, FAR, similarity, specificity, and accuracy. From the experimental results, the proposed work outperforms the state of artwork. Here, the proposed method attains maximum precision and recall value for both PETS and Hall monitor video when compared to the existing algorithm.

Electricity theft detection in low-voltage stations based on similarity measure and DT-KSVM

The theft of electricity affects power supply quality and safety of grid operation, and non-technical losses (NTL) have become the major reason of unfair power supply and economic losses for power companies. For more effective electricity theft inspection, an electricity theft detection method based on similarity measure and decision tree combined K-Nearest Neighbor and support vector machine (DT-KSVM) is proposed in the paper. Firstly, the condensed feature set is devised based on feature selection strategy, typical power consumption characteristic curves of users are obtained based on kernel fuzzy C-means algorithm (KFCM). Next, to solve the problem of lack of stealing data and realize the reasonable use of advanced metering infrastructure (AMI). One dimensional Wasserstein generative adversarial networks (1D-WGAN) is used to generate more simulated stealing data. Then the numerical and morphological features in the similarity measurement process are comprehensively considered to conduct preliminary detection of NTL. And DT-KSVM is used to perform secondary detection and identify suspicious customers. At last, simulation experiments verify the effectiveness of the proposed method.

Active Contour-Based Method for Finger-Vein Image Segmentation

Suffering from uneven illumination and variation of finger position, it is still a tough challenge to effectively distinguish the vein networks and nonvenous regions in a finger-vein image. Methods based on active contour have achieved an excellent result in medical image segmentation, despite facing several challenges such as vulnerable to the initial contour and prone to local minimum. In this article, we propose a novel method which is effective for finger-vein image segmentation based on active contour. Since venous and nonvenous areas in captured finger-vein images are hard to distinguish, we design a dehazing algorithm and an edge fitting term to improve the segmentation procedure. Moreover, we employ the kernel fuzzy C-means (KFCM) algorithm to conduct the initialization, which is able to solve the problem that the active contour-based methods are susceptible to initial contours. The experimental results show that compared with latest methods, the proposed method achieves a better performance in segmenting finger-vein images and is able to improve the recognition accuracy of finger-vein identification system.

An Improved Dolphin Swarm Algorithm Based on Kernel Fuzzy C-Means in the Application of Solving the Optimal Problems of Large-Scale Function

The solution of high dimensional function has always been a hot topic. In this paper, a novel algorithm based on Kernel Fuzzy C-means and dolphin swarm algorithm are proposed to solve high-dimensional functions more accurately. First, to improve the global convergence ability of dolphin swarm algorithm, Kernel Fuzzy C-means is introduced into the algorithm, named as Kernel Fuzzy C-means dolphin swarm algorithm (KFCDSA); Second, the five typical high-dimensional functions are applied to test the performance of the combination of KFCDSA. Finally, some indicators are used to evaluate the performance of different meta-heuristic algorithms. The results show that: the performance of the proposed algorithm exceeds that of the dolphin swarm algorithm and some advanced metaheuristic algorithms considered for comparison based on five different evaluating indicators. Through the test results, it can be concluded that introducing Kernel Fuzzy C-means into dolphin swarm algorithm is an effective improvement and provides a possibility for obtaining global optimal solutions for high-dimensional functions.

Semi-supervised OTSU based hyperbolic tangent Gaussian kernel fuzzy C-mean clustering for dental radiographs segmentation

Dental periapical X-ray image (DXRI) segmentation is an important process to examine dental images towards diagnosing medical systems is an essential operation within practical dentistry for periodontitis recognition. However, traditional clustering algorithms in image processing frequently accept deficiencies in finding teeth sample boundaries and parameters. The presentation related to clustering is improved while further data produced with the user. In DXRI segmentation, semi supervised fuzzy clustering which is a new collective scheme. Initially, pre-processing is done for the input X-Ray image in order to minimize error. Specifically, Otsu’s method divide the dental X-Ray image into background and foreground regions. Here, the chosen FCM to separate the teeth regions commencing on the preceding steps. A Semi-supervised Hyperbolic Tangent Gaussian kernel Fuzzy C-Means algorithm (HTGkFCM) is preferred so as to increase an outcome that is optimum than compared to the traditional methods. So, current method is less sensitive to noise with robustness. Real datasets for the implementation on the proposed framework with cluster validity computation such as, Davies–Bouldin (DB), Segmentation Accuracy (SA), Simplified Silhouete Width Criterion (SSWC), processing time, PBM and Mean Absolute Error (MAE). So the proposed work has been enhanced in terms of 7%, 2%, 5%, 6%, 3% and 30% than the state-of-the-art works. Simulation outcome shows the quality of clustering in the framework have higher accuracy and more reliable than other clustering methods.

Fractional-Order Modeling and Fuzzy Clustering of Improved Artificial Bee Colony Algorithms

This article proposes an improved artificial bee colony algorithm (IABC) for maintaining a balance between the exploratory and exploitative abilities of the algorithm. Therefore, unmodeled dynamic characteristics can be effectively estimated, which can improve the estimation accuracy of the system and create conditions for later system modeling and control. The proposed IABC algorithm uses two new search expressions to generate new alternative solutions and the global optimal solution is considered in the search process. Exploiting the simple structure and fast global convergence of artificial bee colony algorithm, an improved clustering algorithm combining IABC and Kernel fuzzy c-means (KFCM) iteration is also proposed. KFCM algorithm is sensitive to the initial clustering center and can easily fall into local optimum; and the improved clustering algorithm solves this issue. Compared with other algorithms, IABC algorithm can converge faster and more accurately for a given parameter structure and number of cycles. Three sets of benchmark test functions and six sets of UC Irvine standard datasets were used in the simulation experiments conducted. Experimental results show that KFCM-IABC can aggregate datasets faster than generalized fuzzy c-means (GFCM)-IABC because of the use of IABC. The proposed algorithm improves class validity index from 1 to 4%, thereby exhibiting the advantages of strong robustness and high clustering accuracy.

Robust Gaussian‐base radial kernel fuzzy clustering algorithm for image segmentation

To perform the image segmentation task, in this Letter, a kernel fuzzy C-means algorithm is introduced, strengthened by a robust Gaussian radial basis function kernel based on M-estimators. It is well-known that these kernels consider the squared difference as a similarity measure, which is not robust to atypical data. In this regard, the main motivation of this contribution is to improve the atypical information tolerance of these kernels, in order to make a better clustering of pixels. Experimental tests were developed considering colour images. The robustness and effectiveness of this proposal are verified by quantitative and qualitative results.

A trusted waterfall framework based moving object detection using FACO-MKFCM techniques

Object detection and tracking are necessary and challenging activities in several pc visual scene applications such as security, motor vehicle navigating and independent robotic navigating. Video scenery security in an energetic atmosphere, particularly for people and motor vehicles, is just one of the present difficult research study subjects in computer system vision. It is a vital modern technology to attack in opposition to violence, criminal offense and publicised security as well as for effective administration of heavy traffic. In this area, monitoring of motionless forefront objects is just one of the most essential needs for security systems based upon the monitoring of deserted or taken objects or parked motor vehicles. It is really challenging for the existing approach to efficiently identify the objects as they occur. In this research suggested an effective method, Fuzzy based Ant Colony Optimization (FACO) integrated with modified kernel fuzzy c-means algorithm (MKFCM) for object segmentation. Here FACO algorithm situates optimal initial cluster centroid for the MKFCM, thus improve all applications affiliated fuzzy clustering such as foreground segmentation in image processing. The recommended new method is intellectual and additionally dynamic clustering technique for the dividing of motion objects. After the segmentation, the object tracking can be done using particle filtering method. The morphological operation helps the particle filter to effectively track the objects. Here the flow is based on the traditional waterfall approach. From the empirical impacts, the recommended method outshines than the state of artwork. Below the suggested method attains maximum efficiency for both PETS and also Hall monitor videos when examined to the current algorithm.

Optimal Kernel Parameter Setting for Faults Detection with Stochastic Methods and Data Preprocessing

In this paper, an indirect optimization criterion for parameter setting the kernel-based fault detection process is applied. The procedure analyzed involves the data preprocessing through the Kernel Independent Component Analysis (KICA) method, and the fault detection by using a classifier based on the Kernel Fuzzy C-means (KFCM) algorithm to reduce the classification errors. The main objective of the paper is the adjustment of the kernel parameters to obtain the best possible performance in the fault detection. To achieve this, two different metaheuristic algorithms are used: Differential Evolution and Particle Swarm Optimization. The proposed approach was evaluated by using the Tennessee Eastman (TE) process benchmark.

Fuzzy Clustering with the Structural α‐Entropy

We study fuzzy clustering with the structural α-entropy and present a unified framework for fuzzy clustering with fuzzy entropy, which can be regarded fuzzy clustering with fuzzy entropy as its special case. Then, aiming at weighting exponent m equal to the structural α-entropy index α in the presented unified framework, we obtain the fuzzy membership degrees and cluster centers using Lagrange method. Further, we propose the Structural α-entropy based fuzzy c-means (SEFCM) algorithm. Moreover, to solve clustering of the complicated data, we also present the Structural α-entropy based kernel fuzzy c-means (SEKFCM) algorithm. In experiment, some University of California Irvine (UCI) data sets and synthetic data sets are used to test the performance of the presented algorithms and the role of the structural α-entropy. The experimental results show that the presented algorithms obtain better clustering result.

Kernel Possibilistic Fuzzy c-Means Clustering with Local Information for Image Segmentation

The kernel weighted fuzzy c-means clustering with local information (KWFLICM) algorithm performs robustly to noise in research related to image segmentation using fuzzy c-means (FCM) clustering algorithms, which incorporate image local neighborhood information. However, KWFLICM performs poorly on images contaminated with a high degree of noise. This work presents a kernel possibilistic fuzzy c-means with a local information (KWPFLICM) algorithm to overcome the noise-related deficiencies of KWFLICM. The proposed approach leverages the robustness to noise of the kernel possibilistic fuzzy c-means (KPFCM) algorithm, which is a hybridization of the kernel possibilistic c-means (KPCM) and kernel FCM (KFCM), rather than relying on the kernel FCM algorithm. Experiments performed on the various types of images degraded by different degrees of noises prove that proposed algorithm is effectual and efficient, and more robust to noise.

An approach to fault diagnosis with online detection of novel faults using fuzzy clustering tools

This paper presents an approach to fault diagnosis with online detection of novel faults and automatic learning using fuzzy clustering techniques. In the off-line learning stage, the classifier is trained to diagnose the known faults and the normal operation state using the Density Oriented Fuzzy C-Means and the Kernel Fuzzy C-Means algorithms. In this stage, the historical data previously selected by experts, are firstly pre-processed to eliminate outliers and reduce the confusion in the classification process by using the Density Oriented Fuzzy C-Means algorithm. Later on, the Kernel Fuzzy C-Means algorithm is used for achieving greater separability among the classes and reducing the classification errors. Finally, the optimization of the two parameters used by these algorithms in the training stage is developed by using a bio-inspired optimization algorithm, namely the differential evolution. After the training, the classifier is used online (online diagnosis stage) in order to classify the new observations that are collected from the process. In this stage, the detection of novel faults based on density by using the DOFCM algorithm is applied. The algorithm analyzes the observations belonging to a window of time which were not classified into the known classes and it is determined if they are a new class or outliers. If a new class is identified, a procedure is developed to incorporate it to the known classes set. The proposed approach was validated using the Development and Application of Methods for Actuator Diagnosis in Industrial Control Systems (DAMADICS) benchmark. The results obtained indicate the feasibility of the proposed method.

Stationarity test and Bayesian monitoring strategy for fault detection in nonlinear multimode processes

Both of multimode and nonlinear characteristics have been common in modern industrial processes. In the present work, an improved monitoring strategy on the basis of a modified discriminant locality preserving projections (DLPP) algorithm and stationary test is proposed for nonlinear multimode process monitoring to analyze both within-mode and cross-mode information. First, the adjoined multiple local models are constructed in high-dimensional kernel space to extract within-mode information through kernel fuzzy c-means algorithm and kernel principal components. Afterwards, the weighted local representations are defined by integrating the local latent vectors within each mode and the corresponding posterior probabilities of data samples. Then, a modified DLPP algorithm is developed to preserve the local structure within each mode and separate different modes as far as possible by merging local models into a low-dimensional coordinated space. Augmented Dickey Fuller test (ADF test) strategy is utilized to decompose the final low-dimensional space into two subspaces including mode-specific subspace and mode-common subspace by checking the stability of the final global data. A Bayesian monitoring strategy is presented in mode-specific subspace and residual subspace to provide more reliable monitoring results. Finally, to illustrate the feasibility and effectiveness, the proposed algorithm is illustrated with multimode data generated from the Tennessee Eastman (TE) benchmark process and Three-phase flow process.