Fuzzy C-means Approach Research Articles

Fuzzy c-mean (FCM) integration of geophysical data from an iron-oxide copper gold (IOCG) deposit under thick cover

Geophysical methods depend on a range of physical and chemical mechanisms, and each method has different sensitivities and resolution of Earth parameters, and over different scale lengths. Additionally, such geophysical methods will also have their own statistical characteristics. Thus, it is a significant challenge to combine different methods through a single inversion framework. Rather than attempting to find an optimal and single model for different geophysical responses, an alternative approach is to use FCM clustering to identify clusters of parameters from two or more different geophysical data sets or models that have similar statistical properties. In this paper, we apply the FCM approach to integrate data and model sets for an array of 100 broadband magnetotelluric (MT) and 100 passive seismic receivers spaced 1 km apart on a 10 by 10 grid above the Vulcan IOCG prospect in the Olympic Cu–Au Province, southern Australia. The challenge for exploration of the Vulcan prospect is that it lies beneath 750 m of regolith sedimentary cover, no single geophysical method provides a unique characterization of the deposit geometry, and drilling is very expensive. Fuzzy c-mean cluster analyses are undertaken in 2D for gravity data and shear-wave velocity model data for basement depths beneath cover and in 3D for shear-wave velocity and electrical resistivity model data. Fuzzy c-mean clustering is shown to provide a simple and efficient method of integrating different geophysical measurements to produce a geological framework that can be verified with drilling information.

Feature-weight and cluster-weight learning in fuzzy c-means method for semi-supervised clustering

Semi-supervised clustering aims to guide the clustering by utilizing auxiliary information about the class labels. Among the semi-supervised clustering categories, the constraint-based approach uses the available pairwise constraints in some steps of the clustering procedure, usually by adding new terms to the objective function. Considering this category, Semi-supervised FCM (SSFCM) is a semi-supervised version of the fuzzy c-means algorithm, which takes advantage of fuzzy logic and auxiliary class distribution knowledge. Despite the performance enhancement caused by incorporating this extra knowledge in the clustering process, semi-supervised fuzzy approaches still suffer from some problems. All the data attributes in the feature space are assumed to have equal importance in the cluster formation, while some features may be more informative than others. Thus the feature importance issue is not addressed in the semi-supervised category. This paper proposes a novel Semi-Supervised Fuzzy c-means approach, which is designed based on Feature-Weight, and Cluster-Weight learning, named SSFCM-FWCW. Inspired by the SSFCM, a fuzzy objective function is presented, which is composed of (1) a semi-supervised term representing the external class knowledge; (2) a feature weighting; and (3) a cluster weighting. Both feature weights and cluster weights are determined adaptively during the clustering. Considering these two techniques leads to insensitivity to the initial center selection, insensitivity to noise, and consequently helps to form an optimal clustering structure. Experimental comparisons are carried out on several benchmark datasets to evaluate the proposed approach's performance, and promising results are achieved. The Matlab implementation source code of the proposed method is publicly accessible at https://github.com/Amin-Golzari-Oskouei/SSFCM-FWCW.

Structural integrity and hybrid ANFIS-PSO modeling of the corrosion rate of ductile irons in different environments

Ductile iron (DI) samples were immersed in near-neutral, alkaline sodium hydroxide (NaOH), and sodium chloride (NaCl) environments for 180 days. The influence of microstructure on the corrosion resistance of three DI specimens was investigated. Microstructures, electrochemical measurements, and the characterization of the corroded surfaces were analyzed. The experimental results from this study were used to validate a model generated from hybrid adaptive neuro-fuzzy inferences system-particle swarm optimization (ANFIS-PSO) algorithms. The hybrid ANFIS-PSO modelling technique was improvised for a detailed evaluation of corrosion rate of ductile cast iron materials in different environments. The integrated hybrid ANFIS-PSO model revealed a sharp rise in localized corrosion caused by chloride-induced structural deterioration at the nanoscale for some of the grains. The performance results revealed that the fuzzy c-mean (FCM) clustering outperformed other clustering approach in the neuro-fuzzy model. Accuracy values of 92.9% and 93.7% were recorded for the training phase of ANFIS-FCM and ANFIS-PSO-FCM respectively for corrosion rates. The percentage error of the ANFIS-PSO predictions is significantly lower than the ANFIS-standalone prediction. This shows that the ANFIS-PSO with FCM approach is a better model for predicting corrosion rates. This will contribute to the body of knowledge for ductile iron, corrosion, and corrosion modelling using machine learning.

A kernelized-bias-corrected fuzzy C-means approach with moment domain filtering for segmenting brain magnetic resonance images

A kernelized-bias-corrected fuzzy C-means approach with moment domain filtering for segmenting brain magnetic resonance images

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.

Kernel possibilistic fuzzy c-means clustering algorithm based on morphological reconstruction and membership filtering

A lot of research has been conducted on many variants of the Fuzzy c-means (FCM) clustering algorithm incorporating local spatial neighborhood information to improve segmentation accuracy and robustness to noise. Among these variants, a fast and robust FCM (FRFCM) clustering algorithm performs fast and robustly to noise for both grayscale and color images. Though, FRFCM is fast but segmentation performance needs improvement. This work presents an improved variant of the FRFCM algorithm, based on the kernel metric and possibilistic fuzzy c-means approach. The proposed method named Kernel Possibilistic Fast-Robust Fuzzy c-means (KPFRFCM) algorithm overcomes the disadvantages of FRFCM i.e. the poor segmentation performance and less robustness to noise, for both grayscale and color images. Experiments performed on various types of images without noise and images degraded by different types of noises with different degrees, prove that proposed KPFRFCM is more efficient and more robust to noise when compared with existing state-of-the-art algorithms for image segmentation.

A CAD system design using iteratively reweighted fuzzy c-means and deep tree training for Alzheimer's disease diagnosis

Alzheimer's disease (AD) is a neurodegenerative disorder characterized by memory loss and other cognitive abnormalities. In this research work, an MRI-based Computer-Aided Diagnosis (CAD) system is presented for clinical practice. While current algorithms used for detecting Alzheimer's disease perform inefficiently. Those algorithms are unable to detect minor changes in the disease due to discrepancies in feature extraction, segmentation, and classification methods. To overcome the issues in algorithm, a new iteratively reweighted adaptive spatial fuzzy c-means approach (IRW-ASFCM) is used to segment the brain tissue efficiently. Initially, the Magnetic resonance images (MRI) will pre-processed by using the Statistical parametric mapping (SPM) tool. After pre-processing, the segmentation is done using IRW-ASFCM to get effective tissue segments. Then, a 2-level selective Wavelet Kernel (2LS-WaveCNN ensemble) based CNN ensemble model will be used to extract the feature map from the Gray matter (GM), White matter (WM) and Cerebrospinal fluid (CSF) regions after segmenting the images. The Wave CNN networks built from hidden layers are trained using Deep Tree Training (DTT) for AD classification. The simulation results demonstrate that the suggested CAD system is superior to the state-of-the-art systems in terms of accuracy (99.54 %), sensitivity (99.26 %), and specificity (99.64 %) for multi-class problems.

Automated segmentation of meningioma from contrast-enhanced T1-weighted MRI images in a case series using a marker-controlled watershed segmentation and fuzzy C-means clustering machine learning algorithm

Introduction and importanceAccurate segmentation of meningiomas from contrast-enhanced T1-weighted (CE T1-w) magnetic resonance imaging (MRI) is crucial for diagnosis and treatment planning. Manual segmentation is time-consuming and prone to variability. To evaluate an automated segmentation approach for meningiomas using marker-controlled watershed segmentation (MCWS) and fuzzy c-means (FCM) algorithms. Case presentation and methodsCE T1-w MRI of 3 female patients (aged 59, 44, 67 years) with right frontal meningiomas were analyzed. Images were converted to grayscale and preprocessed with Otsu's thresholding and FCM clustering. MCWS segmentation was performed. Segmentation accuracy was assessed by comparing automated segmentations to manual delineations. Clinical discussionThe approach successfully segmented meningiomas in all cases. Mean sensitivity was 0.8822, indicating accurate identification of tumors. Mean Dice similarity coefficient between Otsu's and FCM1 was 0.6599, suggesting good overlap between segmentation methods. ConclusionThe MCWS and FCM approach enables accurate automated segmentation of meningiomas from CE T1-w MRI. With further validation on larger datasets, this could provide an efficient tool to assist in delineating meningioma boundaries for clinical management.

Spatiotemporal clustering for the impact region caused by a traffic incident: an improved fuzzy C-means approach with guaranteed consistency

Traffic incidents disrupt the normal flow of vehicles and induce nonrecurrent traffic congestion. It has been well accepted that the shape of the spatiotemporal region impacted by a traffic incident should be consistent with the propagation of shockwaves. Although there has been a variety of approaches that attempt to estimate the impact region of traffic incidents, most of them are not capable of producing results with guaranteed consistency. In this research, we propose an improved fuzzy clustering approach that integrates the domain knowledge of shockwave theory for freeway incidents to address this issue, which is new to the literature. Compared to the general clustering approaches, our improved fuzzy clustering approach takes control of the clustering process by leveraging the directional propagation of shockwaves in the form of constraints, which can guarantee the consistency. In addition, unlike existing studies that employ discrete variables to distinguish traffic status in case of traffic incidents, the fuzzy clustering approach uses the continuous variable to indicate the incident impact on vehicle speed. This can help to reduce the information loss and estimate the impact region more accurately. Numerical experiments are conducted to evaluate the performance of our approach using both simulation and real data. Results show that our approach is able to guarantee that the shape of the impact region is consistent with the propagation of shockwaves and achieve higher accuracy of the estimated delay induced by the incident than the current state-of-the-art approach.

Application of KRR, K-NN and GPR Algorithms for Predicting the Soaked CBR of Fine-Grained Plastic Soils

AbstractCalifornia bearing ratio (CBR) test is one of the comprehensive tests used for the last few decades to design the pavement thickness of roadways, railways and airport runways. Laboratory-performed CBR test is considerably rigorous and time-taking. In a quest for an alternative solution, this study utilizes novel computational approaches, including the kernel ridges regression, K-nearest neighbor and Gaussian process regression (GPR), to predict the soaked CBR value of soils. A vast quantity of 1011 in situ soil samples were collected from an ongoing highway project work site. Two data divisional approaches, i.e., K-Fold and fuzzy c-means (FCM) clustering, were used to separate the dataset into training and testing subsets. Apart from the numerous statistical performance measurement indices, ranking and overfitting analysis were used to identify the best-fitted CBR prediction model. Additionally, the literature models were also tried to validate through present study datasets. From the results of Pearson’s correlation analysis, Sand, Fine Content, Plastic Limit, Plasticity Index, Maximum Dry Density and Optimum Moisture Content were found to be most influencing input parameters in developing the soaked CBR of fine-grained plastic soils. Experimental results also establish the proficiency of the GPR model developed through FCM and K-Fold data division approaches. The K-Fold data division approach was found to be helpful in removing the overfitting of the models. Furthermore, the predictive ability of any model is considerably influenced by the geological location of the soils/materials used for the model development.

Comparison Approaches of the Fuzzy C-Means and Gaussian Mixture Model in Clustering the Welfare of the Indonesian People

We compared the previous study about clustering the welfare of the Indonesian people using the Fuzzy C-Means (FCM) approach to a recent study, the Gaussian mixture model (GMM). Both of which were soft clustering. The case analyzes by classifying 34 provincial data in Indonesia, based on eight welfare of people indicator variables in 2017, which the Central Statistics Agency had issued. We compared the FCM and the GMM approaches to determine a better level of accuracy in clustering data using the Silhouette index, the Davies-Bouldin index, and the Calinski-Harabasz index values as a validity test method. The FCM and GMM methods found that the optimal clusters were 2 and 6. When we observed the consistency of the three tests’ validity results, the GMM method was preferable to the FCM clustering method.
 Keywords: fuzzy, Gaussian mixture model, clustering

Ant Colony Optimization-Enabled CNN Deep Learning Technique for Accurate Detection of Cervical Cancer.

Cancer is characterized by abnormal cell growth and proliferation, which are both diagnostic indicators of the disease. When cancerous cells enter one organ, there is a risk that they may spread to adjacent tissues and eventually to other organs. Cancer of the cervix of the uterus often initially manifests itself in the uterine cervix, which is located at the very bottom of the uterus. Both the growth and death of cervical cells are characteristic features of this condition. False-negative results provide a significant moral dilemma since they may cause women to get an incorrect diagnosis of cancer, which in turn can result in the woman's premature death from the disease. False-positive results do not raise any significant ethical concerns; but they do require a patient to go through an expensive and time-consuming treatment process, and they also cause the patient to experience tension and anxiety that is not warranted. In order to detect cervical cancer in its earliest stages in women, a screening procedure known as a Pap test is often performed. This article describes a technique for improving images using Brightness Preserving Dynamic Fuzzy Histogram Equalization. To individual components and find the right area of interest, the fuzzy c-means approach is applied. The images are segmented using the fuzzy c-means method to find the right area of interest. The feature selection algorithm is the ACO algorithm. Following that, categorization is carried out utilizing the CNN, MLP, and ANN algorithms.

Comparison of the probabilistic model of a fuzzy Bayesian classifier and the Gaussian mixture problem

The article shows that the problem of fuzzy classification and the problem of separation of Gaussian mixtures have different theoretical solution models based on the results of the classical probability theory. A probabilistic model of the clustering and classification process based on the Bayesian approach is the joint distribution of multidimensional data points across clusters and cluster prototypes. The model is built for datasets with a normal distribution. Unknown membership values for each data point are assumed to be a random variable with some empirical distribution. The cluster prototype is also assumed to be a normally distributed random variable. The model is compared with the Gaussian mixture separation model and the main difference between them is highlighted: in the Bayesian fuzzy classification model, the product of all component probabilities is used, which means taking into account the possibility of the xn value falling into different clusters. The computational algorithm of this model may have a wider application compared to the traditional fuzzy c-means approach, due to the lack of restrictions on clustering parameters. Some numerical results are presented that confirm the model’s operability for an artificial data set both with parameters within the traditional limitations of the c-means method and for an extended range of parameter values.

Classification of Valvular Regurgitation Using Echocardiography

Echocardiography (echo) is a commonly utilized tool in the diagnosis of various forms of valvular heart disease for its ability to detect types of cardiac regurgitation. Regurgitation represents irregularities in cardiac function and the early detection of regurgitation is necessary to avoid invasive cardiovascular surgery. In this paper, we focussed on the classification of regurgitations from videographic echo images. Three different types of regurgitation are considered in this work, namely, aortic regurgitation (AR), mitral regurgitation (MR), and tricuspid regurgitation (TR). From the echo images, texture features are extracted, and classification is performed using Random Forest (RF) classifier. Extraction of keyframe is performed from the video file using two approaches: a reference frame keyframe extraction technique and a redundant frame removal technique. To check the robustness of the model, we have considered both segmented and nonsegmented frames. Segmentation is carried out after keyframe extraction using the Level Set (LS) with Fuzzy C-means (FCM) approach. Performances are evaluated in terms of accuracy, precision, recall, and F1-score and compared for both reference frame and redundant frame extraction techniques. K-fold cross-validation is used to examine the performance of the model. The performance result shows that our proposed approach outperforms other state-of-art machine learning approaches in terms of accuracy, precision, recall, and F1-score.

Using Multivariate Quality Statistic for Maintenance Decision Support in a Bearing Ring Grinder

Grinding processes’ stochastic nature poses a challenge in predicting the quality of the resulting surfaces. Post-production measurements for form, surface roughness, and circumferential waviness are commonly performed due to infeasibility in measuring all quality parameters during the grinding operation. Therefore, it is challenging to diagnose the root cause of quality deviations in real-time resulting from variations in the machine’s operating condition. This paper introduces a novel approach to predict the overall quality of the individual parts. The grinder is equipped with sensors to implement condition-based maintenance and is induced with five frequently occurring failure conditions for the experimental test runs. The crucial quality parameters are measured for the produced parts. Fuzzy c-means (FCM) and Hotelling’s T-squared (T2) have been evaluated to generate quality labels from the multi-variate quality data. Benchmarked random forest regression models are trained using fault diagnosis feature set and quality labels. Quality labels from the T2 statistic of quality parameters are preferred over FCM approach for their repeatability. The model, trained from T2 labels achieves more than 94% accuracy when compared to the measured ring disposition. The predicted overall quality using the sensors’ feature set is compared against the threshold to reach a trustworthy maintenance decision.

Towards the use of cybernetics for an enhanced cervical cancer care strategy

BackgroundCervical cancer is a prominent disease in women, with a high mortality rate worldwide. This cancer continues to be a challenge to concisely diagnose, especially in its early stages. The aim of this study was to propose a unique cybernetic system which showcased the human-machine collaboration forming a superintelligence framework that ultimately allowed for greater clinical care strategies. MethodsIn this work, we applied machine learning (ML) models on 650 patients’ data collected from Hospital Universitario de Caracas in Caracas, Venezuela, where ethical approval and informed consent were granted. The data were hosted at the University of California at Irvine (UCI) database for cancer prediction by using data purely from a patient questionnaire that include key cervical cancer drivers such as questions on sexually transmitted diseases and time since first intercourse in order to design a clinical prediction machine that can predict various stages of cervical cancer. Two contrasting methods are explored in the design of a ML-driven prediction machine in this study, namely, a probabilistic method using Gaussian mixture models (GMM), and fuzziness-based reasoning using the fuzzy c-means (FCM) clustering on the data from 650 patients. ResultsThe models were validated using a K-Fold validation method, and the results show that both methods could be feasibly deployed in a clinical setting, with the probabilistic method (produced accuracies of 80+%/classifier dependent) allowing for more detail in the grading of a potential cervical cancer prediction, albeit at the cost of greater computation power; the FCM approach (produced accuracies around 90+%/classifier dependent) allows for a more parsimonious modelling with a slightly reduced prediction depth in comparison. As part of the novelty of this work, a clinical cybernetic system is also proposed to host the prediction machine, which allows for a human-machine collaborative interaction and an enhanced decision support platform to augment overall care strategies. ConclusionThe present study showcased how the use of prediction machines can contribute towards early detection and prioritised care of patients with cervical cancer, while also allowing for cost-saving benefits when compared with routine cervical cancer screening. Further work in this area would now involve additional validation of the proposed clinical cybernetic loop and further improvement to the prediction machine by exploring non-linear dimensional embedding and clustering methods.

Effects of Training Parameter Concept and Sample Size in Possibilistic c-Means Classifier for Pigeon Pea Specific Crop Mapping

This research work aims to study the effect of training parameter concept and sample size in the process of classification by using a fuzzy Possibilistic c-Means (PCM) approach for Pigeon Pea specific crop mapping. For specific class extraction, the “mean” of the training data is considered as a training parameter of the classification algorithm. In this study, we proposed an “Individual Sample as Mean” (ISM) approach where the individual training sample is accounted as a mean parameter for the fuzzy PCM classifier. In order to avoid the spectral overlap of target Pigeon pea crop with other crops in the study area, a temporal indices database was generated from Sentinel 2A/2B satellite images acquired during the 2019–2020 Pigeon Pea crop cycle. The spectral dimensionality of temporal data was reduced to extract the required bands to achieve maximum enhancement of the target crop class in the temporal data. Further, the training sample size was increased to study the heterogeneity within the class in the classified output. The proposed ISM approach delivered a higher mean membership difference (MMD) between the Pigeon Pea crop and the co-cultivated Cotton crop as compared to the conventional mean method. This indicated that a better separation was achieved between the target crop and the spectrally similar crop grown, that were cultivated in the same study area. When the sample size was gradually increased from 5 to 60, the MMD values within the Pigeon Pea test fields remained in the range 0.013–0.02, thereby implying that the proposed algorithm works better even with a small number of training samples. The heterogeneity was better handled using the proposed ISM approach since the variance obtained within Pigeon Pea field was only 0.008, as compared to that of 0.02 achieved using the conventional mean approach.

FCMCP: Fuzzy C-Means for Controller Placement in Software Defined Networking

One of the most critical factors in SDN networks is the controller placement. The controller’s location determines the network delay. Reduction in network latency can help to scale the network effectively. A Fuzzy C-Means approach is used for placement of controller in optimized manner to improve the network latency. A topology-zoo network data-set, such as OS3E and Chinanet, were used to validate the proposed FCMCP. This technique offers faster response time than K-Clustering methods such as K-Center, K-Mean, etc. Finally, the Fuzzy C-Means method reduces latency by 18% when compared to the K-Clustering methods.

Undercover karst imaging using a Fuzzy c-means data clustering approach (Costa Brava, NE Spain)

Identification of undercover karst areas can be essential for engineering or hydrogeological studies. Geological reconnaissance of karst areas can be challenging due to their complex and highly heterogeneous internal structure. Herein, we present a case study carried out at the carbonate coastal Montgrí Massif in Costa Brava (Spain), which, in the study area, is characterized by a well-developed karst system covered by Quaternary sediments. The objectives of the work are to delineate and characterize karst structures using geophysical techniques and to produce a subsurface geological model that goes beyond the individual physical properties determined independently by each geophysical method. We acquired electrical resistivity and seismic datasets for five collocated profiles. We analyzed the seismic datasets to retrieve P-wave (Vp) and S-wave (Vs) velocity models. Clay-filled karst formations may produce seismic and electrical signatures that are difficult to interpret due to the wide range of values that can be present depending on the properties of the karst's infill. In this type of environment, the Fuzzy c-means (FCM) data clustering approach is a way of producing an integrated model that can help to constrain the lithological interpretation. In this study, FCM is first applied to an electrical resistivity-Vs dataspace in order to characterize near surface sediments. Four different groups are recognized corresponding to different lithologies, fluid conductivities, and saturations in the unconsolidated sediments. Secondly, FCM analysis of the resistivity-Vp datasets provides integrated models that help to delineate the karst areas. The interpretive constraints used derive from the results of the first FCM application and the lithological description of a single borehole that reaches the karst below the Quaternary sediments. In this way, karst is identified in all five profiles. This study shows that the integration of seismic and electrical resistivity information using the FCM approach can extend borehole information throughout the study area and thus provide comprehensive geological models in karst environments.

An effective motion object detection using adaptive background modeling mechanism in video surveillance system

Automatic moving object detection has gained increased research interest due to its widespread applications like security provision, traffic monitoring, and various types of anomalies detection, etc. In the video surveillance system, the video is processed for the detection of motion objects in a step-by-step process. However, the detection has become complex and less effective due to various complex constraints. To obtain an effective performance in the detection of motion objects, this research work focuses to develop an automatic motion object detection system based on the statistical properties of video and supervised learning. In this paper, a novel Background Modeling mechanism is proposed with the help of a Biased Illumination Field Fuzzy C-means algorithm to detect the moving objects more accurately. Here, the non-stationary pixels are separated from stationary pixels through the Background Subtraction. Afterward, the Biased Illumination Field Fuzzy C-means approach has accomplished to improve the segmentation accuracy through clustering under noise and varying illumination conditions. The performance of the proposed algorithm compared with conventional methods in terms of accuracy, precision, recall, and F- measure.