Kernel-based Fuzzy C-means Clustering Research Articles

Analysis of the Improvement of Engineering Mechanics Experimental Methods Based on IoT and Machine Learning

With the rapid development of sensor technology, machine learning, and the Internet of Things, wireless sensor networks have gradually become a research hotspot. In order to improve the data fusion performance of wireless sensor networks and ensure network security in the event of external attacks, this paper proposes a wireless sensor optimization algorithm model, involving wireless sensor networks, the Internet of Things, and other related fields. This paper first analyzes the role of the Internet of Things in wireless sensor networks, studies the localization mechanism and hierarchy of the Internet of Things based on wireless sensor networks, and improves the LE-RLPCCA (Position Estimation Robust Local Retention Criteria Correlation Analysis) localization algorithm model based on sensor grids. This paper discusses the problems of machine learning in wireless sensor networks, constructs a sensor-based machine learning model, and designs a data fusion algorithm for a wireless sensor networks' machine learning model. The application of wireless sensors in engineering mechanics experiments is summarized, and the optimization algorithm model of the wireless sensor in engineering mechanics experiments is proposed. The analysis results show that the average accuracy of the DKFCM-FSVM (Density aware Kernel-based Fuzzy C-means Clustering algorithm Fuzzy Support Vector Machine) algorithm in detecting five behaviors is 0.997, 0.992, 0.904, 0.996, and 0.946, respectively, and the accuracy in detecting different behaviors is the best, 0.005, 0.01, 0.003, and 0.006 respectively. It achieves the lowest false positive rate in the detection of different behaviors, and the average false positive rate is 0.004, 0.003, 0.003, 0.008, and 0.005, respectively, which shows that the DKFCM-FSVM algorithm model of wireless sensor networks in engineering mechanics experiments is the optimal solution. The work of this paper has good reference value for the application of wireless sensor networks and the optimization of engineering mechanics experimental methods and is helpful for further research of sensor technology.

Robust interval type-2 kernel-based possibilistic fuzzy clustering algorithm incorporating local and non-local information

Type-2 fuzzy set theory has certain potential advantage in processing high-order uncertainty, and has been more and more widely applied in pattern recognition, image processing, and system modeling. Although it has been introduced into robust fuzzy clustering modeling, most clustering methods based on type-2 fuzzy set theory have many obvious shortcomings, such as weak robustness against noise and high sensitivity to initial value. Therefore, this paper presents a novel single fuzzifier interval type-2 kernel-based possibilistic fuzzy local and non-local information c-means clustering, and it is driven by deep neighborhood information for image segmentation in the presence of high noise. Firstly, we construct the novel deep neighborhood window structure, which consists of local neighborhood window around current pixel and deep neighborhood window around the pixel in local neighborhood window around current pixel. Secondly, using the structural similarity between local neighborhood window and deep neighborhood window around current pixel, we construct a novel fuzzy local information factor to tune the impact of local neighborhood information on current pixel clustering. Thirdly, possibility theory and fuzzy local information factor are introduced into interval type-2 kernel-based fuzzy c-means clustering, and a novel single fuzzifier type-2 possibilistic fuzzy clustering with richer local and non-local information and kernel metric is proposed. Experimental results indicate that the proposed algorithm outperforms existing state-of-the-art robust fuzzy clustering-related algorithms, and significantly improve the robustness of robust fuzzy clustering algorithms against noise.

Which Features Are More Correlated to Illuminant Estimation: A Composite Substitute

Computational color constancy (CCC) is to endow computers or cameras with the capability to remove the color bias effect caused by different scene illuminations. The first procedure of CCC is illuminant estimation, i.e., to calculate the illuminant color for a given image scene. Recently, some methods directly mapping image features to illuminant estimation provide an effective and robust solution for this issue. Nevertheless, due to diverse image features, it is uncertain to select which features to model illuminant color. In this research, a series of artificial features weaved into a mapping-based illuminant estimation framework is extensively investigated. This framework employs a multi-model structure and integrates the functions of kernel-based fuzzy c-means (KFCM) clustering, non-negative least square regression (NLSR), and fuzzy weighting. By comparing the resulting performance of different features, the features more correlated to illuminant estimation are found in the candidate feature set. Furthermore, the composite features are designed to achieve the outstanding performances of illuminant estimation. Extensive experiments are performed on typical benchmark datasets and the effectiveness of the proposed method has been validated. The proposed method makes illuminant estimation an explicit transformation of suitable image features with regressed and fuzzy weights, which has significant potential for both competing performances and fast implementation against state-of-the-art methods.

MRI brain segmentation using correlation based adaptively regularized kernel-based fuzzy C-means clustering

Brain tumour detection is the most challenging aspect in the field of medical image processing and analysis. Whereas, the traditional techniques used in MRI brain tumour segmentation are extremely time consuming tasks. In this paper, the proposed approach contains two major steps: image acquisition and segmentation. Initially, the brain tumour detection was assessed by employing T1-weighted contrast enhanced magnetic resonance imaging (T1-WCEMRI) database. After image acquisition, segmentation was carried-out by using correlation based adaptively regularised kernel-based fuzzy C-means (ARKFCM) clustering along with Otsu thresholding. In conventional clustering methodologies, it was very hard to detect the ill-defined masses that highly decrease the segmentation accuracy. To address this concern, the kernel function in ARKFCM was replaced by a correlation function for localising the object in a complex template. In experimental analysis, the proposed approach distinguishes the normal brain region and brain tumour region by means of dice coefficient, Jaccard coefficient, true positive rate (TPR), false positive rate (FPR) and accuracy. The experimental outcome shows that the proposed methodology delivered average accuracy of 99.213% in brain tumour detection. The proposed methodology improved accuracy in brain tumour segmentation up to 3-3.2% compared to the existing methods: FCM and ARKFCM.

Artificial Intelligence-based Myelinated White Matter Segmentation for a Pediatric Brain - A Challenging Task

Background: Segmentation of a baby brain,in particular myelinated white matter is a very challenging and important task in medical image analysis, because of the ongoing process of myelination and structural differences present in magnetic resonance images of a baby. Most available algorithms for segmentation of a baby brain are atlas based segmentations, which may not accurate because baby brain Magnetic Resonance Images (MRI) are very subjective. Objective: Artificial intelligence based methods for myelinated white matter segmentation. Method: Fuzzy C-means Clustering with Level Set Method (FCMLSM), Adaptively Regularized Kernel-based Fuzzy C-means clustering (ARKFCM), Multiplicative Intrinsic Component Optimization (MICO) and Particle Swarm Optimization (PSO). Results: Signal to Noise Ratio (SNR), Edge Preservation Index (EPI), Structural Similarity Index (SSIM) and Peak Signal to noise Ratio (PSNR) Accuracy, Precision, Dice and Jaccard values are maintained good and Mean squared error (MSE) is less for FCMLSM. Conclusion: FCMLSM is a very suitable method for myelinated white matter segmentation when compared to ARKFCM, MICO and PSO.

A Novel Modeling Framework Based on Customized Kernel-Based Fuzzy C-Means Clustering in Iron Ore Sintering Process

This article proposes a customized kernel-based fuzzy C-means (CKFCM) clustering, which provides an original framework for data interpretation and data analysis and delivers an effective solution for an accurate division of actual run data under multiple operating conditions in an iron ore sintering process (IOSP). The improvement of CKFCM clustering is achieved by including an adjustment factor introduced into the kernel-based fuzzy C-means clustering. The adjustment factor only needs to consider a small amount of labeled data that are determined based on expert experience. Subsequently, the CKFCM clustering is applied to the modeling of the IOSP, and the Spearman’s rank correlation coefficient method is utilized to determine input variables of the model under different operating conditions. Furthermore, the broad learning model is employed to build the prediction model for each operating condition. Finally, we conducted an in-depth analysis of the presented clustering method. Its performance has been experimentally demonstrated by many publicly available datasets. Meanwhile, simulation results involving actual run data of the IOSP demonstrate the superiority and effectiveness of the developed model in carbon efficiency prediction. We show that the developed model outperforms the state-of-the-art prediction models in achieving a good balance between simplicity and accuracy.

MRI brain segmentation using correlation based on adaptively regularised kernel-based fuzzy C-means clustering

Brain tumour detection is the most challenging aspect in the field of medical image processing and analysis. Whereas, the traditional techniques used in MRI brain tumour segmentation are extremely time consuming tasks. In this paper, the proposed approach contains two major steps: image acquisition and segmentation. Initially, the brain tumour detection was assessed by employing T1-weighted contrast enhanced magnetic resonance imaging (T1-WCEMRI) database. After image acquisition, segmentation was carried-out by using correlation based adaptively regularised kernel-based fuzzy C-means (ARKFCM) clustering along with Otsu thresholding. In conventional clustering methodologies, it was very hard to detect the ill-defined masses that highly decrease the segmentation accuracy. To address this concern, the kernel function in ARKFCM was replaced by a correlation function for localising the object in a complex template. In experimental analysis, the proposed approach distinguishes the normal brain region and brain tumour region by means of dice coefficient, Jaccard coefficient, true positive rate (TPR), false positive rate (FPR) and accuracy. The experimental outcome shows that the proposed methodology delivered average accuracy of 99.213% in brain tumour detection. The proposed methodology improved accuracy in brain tumour segmentation up to 3-3.2% compared to the existing methods: FCM and ARKFCM.

Designing RBFNs Structure Using Similarity-Based and Kernel-Based Fuzzy C-Means Clustering Algorithms

The RBF networks belong to a set of artificial neural network architectures. RBF networks have been successfully applied for solving various data mining tasks including classification, and regression. Successful implementation of the RBF network depends on numerous factors among which, the crucial is its structure. The decision on the network structure has to be taken at the network initialization stage. It requires calculating or inducing the number of centroids, and their respective locations. The above problem is known to be NP-hard, and hence, not easily solvable. The traditional approach for deciding on the number of hidden units is based on applying the k-means algorithm for calculating cluster centroids. Unfortunately, the procedure guarantees neither a satisfactory accuracy nor the required generalization level of the RBF network under development. To alleviate the problem for cluster determination, i.e. number of centroids, we propose the similarity-based algorithm (SCA) for the RBF networks initialization, as well as an alternative method for initializing RBFNs using the kernel-based fuzzy clustering algorithm (KFCM-K). In both cases, the number of resulting centroids and their initial locations are provided automatically. The next step involves applying the optimization procedure resulting in the selection of the final centroids’ location. The procedure is integrated with the output weights determination. Since the discussed optimization problem is computationally difficult it has been decided to apply the agent-based population learning algorithm (PLA) which belongs to the class of metaheuristics. A comparative study of approaches based on SCA and KFCM-K is included in the paper. Their effectiveness is demonstrated experimentally using artificial and real benchmark datasets. The results of the computational experiment have shown that both proposed approaches for designing RBFNs perform significantly better than other algorithms used for this task.

Integrating saliency with fuzzy thresholding for brain tumor extraction in MR images

The automatic detection and extraction of tumor area in Magnetic Resonance Imaging (MRI) is an important and challenging task. This paper presents a fully automatic and unsupervised method for fast and accurate extraction of brain tumor area from MR images. The proposed method named as Saliency Based Segmentation (SBS) is based on visual saliency. The saliency model detects the pathologically important area and then fuzzy thresholding is used for extraction of the detected region. The performance of SBS is compared with Adaptively Regularized Kernel-Based Fuzzy C-Means Clustering, Mean Shift and Fuzzy C-Means clustering with Level Set Method. The experimental evaluation validated on BRATS database using Jaccard index (0.84 ± 0.04), Dice Index (0.91 ± 0.02), Execution time (2.99 ± 0.29), Precision (0.82 ± 0.16), Recall (0.97 ± 0.03) and F-measure (0.88 ± 0.10) demonstrates that SBS achieves better segmentation results even in the presence of noise and uneven illumination in images.

Entropy-like Divergence Based Kernel Fuzzy Clustering for Robust Image Segmentation

Gaussian kernel is defined by Euclidean distance and has been widely used in many fields. In the view of Euclidean distance is sensitive to outliers or noise and it is difficult to obtain satisfactory results for complex non-convex data. Entropy-like divergence is firstly induced by combining Jenson-Shannon/Bregman divergence with convex function, and its mercer kernel function called entropy-like divergence-based kernel is also constructed in this paper. Secondly, a new fuzzy weighted factor based on entropy-like divergence-based kernel is proposed by improving the existing trade-off weighting factor of kernel-based fuzzy local information C-means clustering (KWFLICM). In the end, a weighted fuzzy local information clustering based on entropy-like divergence-based kernel (EKWFLICM) is presented, which combines a new weighted fuzzy factor and entropy-like divergence-based kernel. Experiment results show that the proposed algorithm outperforms the segmentation performance of existing state-of-the-art fuzzy clustering-related algorithms for the image in presence of high noise.

A combined kernel-based fuzzy C-means clustering and spectral centroid for instantaneous frequency estimation

An improved instantaneous frequency estimation algorithm for rotating machines based on a kernel-based fuzzy C-means clustering (KFCM) algorithm used in association with a spectral centroid algorithm is proposed in this study. The clustering algorithm is used first to discriminate the time-frequency points from the sources of the reference axis and other points. The discrete time-frequency points related to the instantaneous rotation frequency of the reference axis are then located based on the values of the time-frequency matrix elements; on the basis of these elements, the instantaneous rotation frequency is then estimated using a spectral centroid algorithm. It is demonstrated that this method effectively reduces the effects of interference and noise while achieving higher estimation precision. To validate the proposed method, numerical simulations of multi-component signals and crossover signals are performed. The results of these simulations indicate that the method can realize instantaneous frequency estimation with high precision, even when the numerical responses are contaminated by Gaussian white noise. In addition, when this method is used to analyze the vibration signal of rotating machinery in the situation of a run-up procedure, remarkable speed estimation results are obtained.

BRAIN TUMOR SEGMENTATION BASED ON SUPERPIXELS AND HYBRID CLUSTERING WITH FAST GUIDED FILTER

Brain tumor segmentation from magnetic resonance (MR) image is vital for both the diagnosis and treatment of brain cancers. To alleviate noise sensitivity and improve stability of segmentation, an effective hybrid clustering algorithm combined with fast guided filter is proposed for brain tumor segmentation in this paper. Preprocessing is performed using adaptive Wiener filtering combined with a fast guided filter. Then simple linear iterative clustering (SLIC) is utilized for pre-segmentation to effectively remove scatter. During the clustering, K-means[Formula: see text] and Gaussian kernel-based fuzzy C-means (K[Formula: see text]GKFCM) clustering algorithm are combined to segment, and the fast-guided filter is introduced into the clustering. The proposed algorithm not only improves the robustness of the algorithm to noise, but also improves the stability of the segmentation. In addition, the proposed algorithm is compared with other current segmentation algorithms. The results show that the proposed algorithm performs better in terms of accuracy, sensitivity, specificity and recall.

Fruit Fly-Based Artificial Neural Network Classifier with Kernel-Based Fuzzy c-Means Clustering for Satellite Image Classification

Image segmentation and classification are the major challenges to satellite imagery. Also, the identification of unique objects in the satellite image is a significant aspect in the application of remote sensing. Many satellite image classification techniques have been presented earlier. However, the accuracy of the image classification has to be further improved. So that, optimal artificial neural network with kernel-based fuzzy c-means ([Formula: see text]) clustering based satellite image classification is proposed in this paper. Initially, the images are segmented with the help of KFCM algorithm. Then, color features and gray level co-occurrence matrix (GLCM) features to be extracted from the segmented regions. Then, these extracted features are given to the OANN classifier. Based on these features, segmented regions are classified as building, road, shadow, and tree. To enhance the performance of the classifier, the weight values are optimally selected with the help of fruit fly algorithm. Simulation results show that the performance of proposed classifier outperforms that of the existing filters in terms of accuracy.

VMD-KFCM Algorithm for the Fault Diagnosis of Diesel Engine Vibration Signals

Accurate and timely fault diagnosis for the diesel engine is crucial to guarantee it works safely and reliably, and reduces the maintenance costs. A novel diagnosis method based on variational mode decomposition (VMD) and kernel-based fuzzy c-means clustering (KFCM) is proposed in this paper. Firstly, the VMD algorithm is optimized to select the most suitable K value adaptively. Then KFCM is employed to classify the feature parameters of intrinsic mode functions (IMFs). Through the comparison of many different parameters, the singular value is selected finally because of the good classification effect. In this paper, the diesel engine fault simulation experiment was carried out to simulate various faults including valve clearance fault, fuel supply fault and common rail pressure fault. Each kind of machine fault varies in different degrees. To prove the effectiveness of VMD-KFCM, the proposed method is compared with empirical mode decomposition (EMD)-KFCM, ensemble empirical mode decomposition (EEMD)-KFCM, VMD-back propagation neural network (BPNN), and VMD-deep belief network (DBN). Results show that VMD-KFCM has advantages in accuracy, simplicity, and efficiency. Therefore, the method proposed in this paper can be used for diesel engine fault diagnosis, and has good application prospects.

An Intuitionistic Kernel-Based Fuzzy C-Means Clustering Algorithm With Local Information for Power Equipment Image Segmentation

With the transformation of the national energy and power sector, the steady advancement of intelligent power grid construction and the continuous improvement of the Ubiquitous Power Internet of Things technology framework, it has further requirements for realizing state comprehensive awareness and efficient processing of information data, and has been widely used in power equipment. The infrared image recognition technology, which has been widely used in thermal fault diagnosis of power equipment, also requires deeper research. For traditional Intuitionistic Fuzzy C-means (IFCM) algorithm for image segmentation is sensitive to the clustering center lead to low final clustering precision and detail, the time complexity and the high shortage. The paper puts forward a kind of applicable to power equipment of the infrared image segmentation based on space distribution information of Intuitionistic Fuzzy clustering algorithm. Non-target objects with high intensity and uneven image intensity in infrared image have strong interference to image segmentation. The proposed algorithm can effectively suppress the interference. Firstly, the gaussian model is introduced into the global spatial distribution information of power equipment to improve the IFCM. Secondly, the spatial operator optimization membership function of local spatial information is used to solve the problem of edge blurring and uneven image intensity. Through experiments on the data set containing 300 infrared images of power equipment, the relative regional error rate is about 10%, which is less affected by the change of fuzzy factor m. The effectiveness and applicability of this algorithm are verified, which is obviously better than other comparison algorithms.

Fire-fly based MKFCM Segmentation and Hybrid Feature Extraction for Lung Cancer Detection

The most serious and broad infections considered lung disease that sets up a principal general wellbeing risky and has a high demise level. In this worry, appropriate division of lung tumor from X-beam, CT output or, MRI is the moving stone to accomplishing totally electronic analysis framework for lung disease location. With the advancement of innovation and attainable quality of information, the regarded time of a radiologist can be secured by methods for PC apparatuses for tumor division. This paper, to improve the Lung cancer segmentation and classification a new model is introduce. To overawed the existing segmentation limitations in this proposed system for lung nodes detectionModified kernel-based Fuzzy c-means clustering (MKFCM) technique is used. The proposed method segmentation includes two modules, the fire-fly clustering module and the MKFCM clustering module. For feature Extraction feature of this paper a (Gray-Level Co-Occurrence Matrix), Local binary patterns (LBP) and Histogram of oriented gradients (HOG) based hybrid system is used. To select the best feature fire fly base Feature Selection (FS) technique is used. For proposed Lung cancer classification long short-term memory (LSTM) classifier is used. The proposed system is also named as FF-MKFCM-FF-FS-LSTM system. Finally the performances are evaluated. From that analysis the proposed module provide 96.55% of segmentation accuracy and the proposed classification provides 98.95% of classification accuracy.

Low Speed Bearing Fault Diagnosis Based on EMD-CIIT Histogram Entropy and KFCM Clustering

In view of weak defect signals and large acoustic emission (AE) data in low speed bearing condition monitoring, we propose a bearing fault diagnosis technique based on a combination of empirical mode decomposition (EMD), clear iterative interval threshold (CIIT) and the kernel-based fuzzy c-means (KFCM) eigenvalue extraction. In this technique, we use EMD-CIIT and EMD to complete the noise removal and to extract the intrinsic mode functions (IMFs). Then we select the first three IMFs and calculate their histogram entropies as the main fault features. These features are used for bearing fault classification using KFCM technique. The result shows that the combined EMD-CIIT and KFCM algorithm can accurately identify various bearing faults based on AE signals acquired from a low speed bearing test rig.

Segmentation of Medical Images using Adaptively Regularized Kernel-based Fuzzy C-Means Clustering

Segmentation of Medical Images using Adaptively Regularized Kernel-based Fuzzy C-Means Clustering

Histopathological Image Segmentation Using Modified Kernel-Based Fuzzy C-Means and Edge Bridge and Fill Technique

Abstract Histopathological lung cancer segmentation using region of interest is one of the emerging research area in the field of health monitoring system. In this paper, the histopathological images were collected from the database Stanford Tissue Microarray Database (TMAD). After image collection, pre-processing was performed using a normalization technique, which enhances the quality of the histopathological image by eliminating unwanted noise. After pre-processing, segmentation was carried out using the modified kernel-based fuzzy c-means clustering (KFCM) approach along with the edge bridge and fill technique (EBFT). It was a flexible high-level machine learning technique to localize the object in a complex template. The experimental result shows that the proposed approach segments the normal and abnormal cancer regions by means of precision, recall, specificity, accuracy, and Jaccard coefficient. The proposed methodology improved the classification accuracy in lung cancer segmentation up to 2.5–5% compared to the existing methods deep convolutional neural network (DCNN) and diffusion-weighted approach.

Adaptive Kernel-Based Fuzzy C-Means Clustering with Spatial Constraints for Image Segmentation

In order to resolve the disadvantages of fuzzy C-means (FCM) clustering algorithm for image segmentation, an improved Kernel-based fuzzy C-means (KFCM) clustering algorithm is proposed. First, the reason why the kernel function is introduced is researched on the basis of the classical KFCM clustering. Then, using spatial neighborhood constraint property of image pixels, an adaptive weighted coefficient is introduced into KFCM to control the influence of the neighborhood pixels to the central pixel automatically. At last, a judging rule for partition fuzzy clustering numbers is proposed that can decide the best clustering partition numbers and provide an optimization foundation for clustering algorithm. An adaptive kernel-based fuzzy C-means clustering with spatial constraints (AKFCMS) model for image segmentation approach is proposed in order to improve the efficiency of image segmentation. Various experiment results show that the proposed approach can get the spatial information features of an image accurately and is robust to realize image segmentation.