Modified Fuzzy C-means Algorithm Research Articles

Lifespan Enhancement of WSN for IoT - Modified Fuzzy Grey Wolf Optimizer (MFGWO) Approach

Wireless Sensor Networks (WSNs) are gaining prominence for diverse applications, including environmental monitoring and industrial automation. Yet, their energy constraint poses a significant challenge. Clustering, a prevalent technique, optimizes energy utilization by grouping nodes into clusters and appointing a cluster head (CH) to aggregate data and communicate with the base station (BS). This paper presents a novel clustering and CH selection algorithm for a energy varied WSNs, leveraging modified fuzzy c-means (FCM) clustering and Grey Wolf Optimization (GWO). Modified FCM partitions nodes based on their similarity, while GWO identifies CHs in each cluster, considering energy levels, centrality, distance from the BS, and dynamic node distribution. Simulation results demonstrate the superior energy efficiency and network lifetime of our proposed approach compared to existing algorithms. Key Word: Wireless Sensor Networks, Modified Fuzzy C Means algorithm (MFCM), Grey Wolf Optimizer (GWO)

Fuzzy clustering and optimal routing in wireless sensor network

A wireless sensor network (WSN) is a collection of numerous independent sensor nodes that can sense, process, and manipulate data. WSN is grouped into clusters for energy-efficient data collection. A clustering and aggregation technique automatically extends the lifetime of a WSN by collecting data within the cluster to the cluster head, reduces the amount of data through processing, and transmitting. WSN routing protocols are also required for completing all types of operations in a Internet of things (IOT) environment, such as sensing, controlling, and transmitting packets. In this paper, a novel Fuzzy Clustering and Optimal Routing (FCOR) method is proposed in order to lessen the energy consumption, delay, and improve network lifetime and node density. The proposed FCOR method is executed in two stages. The initial stage consists of clustering and cluster head selection using modified Fuzzy c-means algorithm (MFCM). This algorithm will efficiently cluster the nodes and select the optimal cluster head. The second phase consists of optimal routing using a normalized whale optimization algorithm (NWOA), that select the optimal route and thus improve the lifetime of the nodes. The efficiency of the proposed FCOR approach has been determined using the evaluation metrics such as energy efficiency, packet delivery, and network lifetime. The experimental results reveals that the proposed FCOR model achieves less energy consumption of 67.8%, 54.4%, 60% and 6.67% than existing FRNSEER, E-ALWO, ACI-GSO and CRSH respectively.

An advanced fuzzy C-Means algorithm for the tissue segmentation from brain magnetic resonance images in the presence of noise and intensity inhomogeneity

ABSTRACT Segmentation of brain Magnetic Resonance Images (MRIs) into various brain tissues such as white matter (WM), grey matter (GM), and cerebrospinal fluid (CSF) is very important to detect and diagnose different brain-related disorders at the primitive level. Accurate segmentation of brain MRIs is very difficult because of the intricate anatomical structure of the tissues, the existence of Intensity Inhomogeneity (IIH), noise, and Partial Volume Effects (PVE). Clustering-based methods are generally used to segment brain images. This work proposes a Chaotic based Enhanced Firefly Algorithm Integrated with Fuzzy C-Means (CEFAFCM) for the segmentation of brain tissues WM, GM, and CSF from brain MRIs. The proposed method can handle IIH, PVE, and noise. CEFAFCM is a spatially modified FCM algorithm combined with the Firefly Algorithm (FA) along with a chaotic map for the initialization of the population of fireflies. The algorithm is tested with brain MRIs acquired from the BrainWeb database. The experimental results demonstrate that the proposed technique is producing better results in comparison with some existing brain MRI segmentation methods such as FCM, BCFCM, FAFCM, and En-FAFCM.

Modelling of wastewater treatment, microalgae growth and harvesting by flocculation inside photo bioreactor using machine learning technique

Biological wastewater treatment with the use of algae-bacteria consortia for the uptake of nutrient and recovery of resource is considered as the ‘paradigm shift’ from the process of mainstream wastewater treatment plants (WWTPs) so as to mitigate the pollution and thus promoting the circular economy. In this regard, the application of machine learning algorithms (MLAs) was found to be effectual and beneficial for the prediction of uncertain performances in the process of treatment and it shows a satisfactory result for the effective optimization, monitoring, uncertainty prediction and so on in the environment systems. The proposed approach aims at modelling the treatment of wastewater, growth of micro algae and flocculation harvesting at the photobioreactor (PBR) along with the utilization of machine learning techniques. Initially, the raw data from the PBR was taken and is pre-processed using z-score normalization technique followed by extraction and selection of features that are more appropriate. The Adaptive neuro-fuzzy inference system (ANFIS) model is built along with the modified Fuzzy C-Means algorithm (MFCM) so as to cluster the huge amount of data. ANFIS is employed for the estimation of controller output parameters and for controlling the temperature inside the reactor. The output controller parameter performance can be enhanced by the use of optimization approach. The discrete Multilayer perceptron (DMLP) with the hyper tuning parameters of Iterative Levi’s Flight Dependent Cuckoo search optimization algorithm (ILF-CSO) is employed for the prediction purpose of attained cultivation growth rate and the pH of treated wastewater. The optimization technique based on machine learning model in turn offers the best possible solution needed for the estimation of output parameters. Thus, the removal rate of effluent T-N concentrations from the wastewater treatment is predicted with some intervals of day. At last, the performance is estimated in terms of growth rate, temperature variations, biomass, nitrate and phosphate concentrations, and error rates (RMSE, APE), and determination coefficient (R2). The attained outcome shows that the presented model is effectual and has the potential to apply for controlling and predicting the biological wastewater treatment plants.

Iris Recognition Based on Multilevel Thresholding Technique and Modified Fuzzy c-Means Algorithm

Iris Recognition Based on Multilevel Thresholding Technique and Modified Fuzzy c-Means Algorithm

An automatic self‐initialized clustering method for brain tissue segmentation and pathology detection from magnetic resonance human head scans with graphics processing unit machine

AbstractThe proposed work introduces a fully automatic modified fuzzy c‐means (MFCM) algorithm for segmenting brain tissue into gray matter (GM), white matter (WM), and cerebrospinal fluid (CSF) which identifies the pathological conditions of magnetic resonance human head scans. The present work implements histogram smoothing using Gaussian distribution for finding the number of clusters (K) and cluster centers (C) to initialize modified FCM algorithm (MFCM). The modification includes the local impact of each pixel based on the median of local neighborhoods. This needs more computational power to reduce the processing time and requires a parallel programming environment like the Graphics Processing Unit. The parallel MFCM is performed with the help of compute unified device architecture language and reduced the processing time up to 80 speedup folds than the serial implementation in Matlab and 20 speedup folds than C programming implementation. The method is examined with the Internet Brain Segmentation Repository (IBSR20) T1W dataset. The quantitative and qualitative results of the proposed method are compared with state‐of‐the‐art‐methods using the Dice coefficient (DC). Proposed method yields high DC 0.84 ± 0.03 for GM, 0.83 ± 0.04 for WM, and 0.41 ± 0.12 for CSF segmentation. In post‐processing, 3D volumes of segmented regions have been constructed and compared with the gold standard quantitatively and qualitatively.

A Point Cloud Simplification Method Based on Modified Fuzzy C-Means Clustering Algorithm with Feature Information Reserved

To further improve the performance of the point cloud simplification algorithm and reserve the feature information of parts point cloud, a new method based on modified fuzzy c-means (MFCM) clustering algorithm with feature information reserved is proposed. Firstly, the normal vector, angle entropy, curvature, and density information of point cloud are calculated by combining principal component analysis (PCA) and k-nearest neighbors (k-NN) algorithm, respectively; Secondly, gravitational search algorithm (GSA) is introduced to optimize the initial cluster center of fuzzy c-means (FCM) clustering algorithm. Thirdly, the point cloud data combined coordinates with its feature information are divided by the MFCM algorithm. Finally, the point cloud is simplified according to point cloud feature information and simplified parameters. The point cloud test data are simplified using the new algorithm and traditional algorithms; then, the results are compared and discussed. The results show that the new proposed algorithm can not only effectively improve the precision of point cloud simplification but also reserve the accuracy of part features.

Classification of Electromyographic Hand Gesture Signals Using Modified Fuzzy C-Means Clustering and Two-Step Machine Learning Approach.

Understanding and classifying electromyogram (EMG) signals is of significance for dexterous prosthetic hand control, sign languages, grasp recognition, human-machine interaction, etc.. The existing research of EMG-based hand gesture classification faces the challenges of unsatisfied classification accuracy, insufficient generalization ability, lack of training data and weak robustness. To address these problems, this paper combines unsupervised and supervised learning methods to classify an EMG dataset consisting of 10 classes of hand gestures. To lessen the difficulty of classification, clustering methods including subtractive clustering and fuzzy c-means (FCM) clustering algorithms are employed first to obtain the initial partition of the inputs. In particular, modified FCM algorithm is proposed to accustom the conventional FCM to the multi-class classification problem. Based on the grouping information obtained from clustering, a type of two-step supervised learning approach is proposed. Specifically, a top-classifier and three sub-classifiers integrated with windowing method and majority voting are employed to accomplish the two-step classification. The results demonstrate that the proposed method achieves 100% test accuracy and the strongest robustness compared to the conventional machine learning approaches, which shows the potential for industrial and healthcare applications, such as movement intention detection, grasp recognition and dexterous prostheses control.

Segmentation of Brain Subjects in MR Images using Hybrid Segmentation Technique

Image segmentation takes place a vital role in the area of biomedical applications. Magnetic resonance brain images with and without Alzheimer’s disease have been preferred for the detection and staging the AD. Clustering is one of the extensively implemented image segmentation principle which differentiates group in such a way that samples of the relevant group are related to each other than samples associated to various groups. There has been significant concern recently in the utilization of fuzzy clustering methods, which keep additional information from the input image than the clustering principle. Modified Fuzzy C Means (MFCM) algorithm is extensively preferable because of its flexibility which leads the pixels to exist to various classes with changing the degrees of membership. Cluster initialization process has been done with MFCM and the performance of the segmentation algorithm has enhanced with Binary Gravitational search algorithm. Various brain subjects such as White Matter (WM), Grey matter (GM), hippocampus region, Cerebrospinal Fluid (CSF) are segmented for the detection of AD. The BGSA with MFCM algorithm has achieved better outcomes and it is compared with various existing techniques. The accuracy of the proposed technique is about 93.3%.

Fuzzy c-means clustering using Jeffreys-divergence based similarity measure

In clustering, similarity measure has been one of the major factors for discovering the natural grouping of a given dataset by identifying hidden patterns. To determine a suitable similarity measure is an open problem in clustering analysis for several years. The purpose of this study is to make known a divergence based similarity measure. The notion of the proposed similarity measure is derived from Jeffrey-divergence. Various features of the proposed similarity measure are explained. Afterwards we develop fuzzy c-means (FCM) by making use of the proposed similarity measure, which guarantees to converge to local minima. The various characteristics of the modified FCM algorithm are also addressed. Some well known real-world and synthetic datasets are considered for the experiments. In addition to that two remote sensing image datasets are also adopted in this work to illustrate the effectiveness of the proposed FCM over some existing methods. All the obtained results demonstrate that FCM with divergence based proposed similarity measure outperforms three latest FCM algorithms.

Stage Classification of Alzheimer‟s disease in MR Brain Image using Hybrid Clustering Technique and Ensemble Classifier

Alzheimer’s Disease (AD) can be identified in the structural variation of MR brain image. Accurate segmentation of brain subjects is an essential requirement to the quantitative analysis for the pre-surgical planning. Brain network takes place a vital role in the staging of AD. Cortical thickness, hippocampus volume and the volume reduction in Grey Matter (GM) have been taken so far in the discrimination of AD subjects from the normal control. The segmentation task has been performed for grouping various brain tissues using Modified Fuzzy C Means algorithm (MFCM) with Particle Swarm Optimization (PSO) and this optimization technique very useful to enhance the segmentation accuracy. Volumetric measure of all the segmented brain tissues have been preferred as one of the feature as well as some structural features also have obtained using Grey Level Co-occurrence Matrix (GLCM) to improve the performance of classifier. In this study, ensemble of classifier has been implemented to classify the severity of the disease. An ensemble of classifier works depends upon the majority voting principle and the error rate of classifier is reduced when compared to the base classifier. The performance of proposed classifier has compared with the base classifiers and verified that the accuracy is improved.

Acoustic Deep-Sea Seafloor Characterization Accounting for Heterogeneity Effect

An algorithm is described and tested to provide accurate and robust deep-sea seafloor classification based on the backscatter data derived from a multibeam bathymetry system. This article focuses on significant heterogeneity in the deep-sea backscatter strength (BS) data. The angular response curve information is decomposed into different units, and BS data are grouped on the basis of the incidence angle to address the heterogeneity in the across-ship direction. Subsequently, a sliding window is applied on BS data in each group, and a robust estimation method is used to address the potential heterogeneity in the window during feature extraction. Thereafter, the extracted features are learned by fuzzy c-means (FCM) to obtain a clustering solution. In the learning process, the features of each group are learned by an independent FCM. The modified FCM algorithm is used to cluster each group of data to handle unbalanced backscatter data sets. With this procedure, heterogeneity in BS data can be accounted for, which is universal in deep-sea survey application. Finally, the results of the different groups are merged to obtain a global label set for the survey region. The method was tested on the multibeam data collected from an offshore region around the Kyushu–Palau Ridge. Monte Carlo simulation was performed to evaluate the performance of the robust method. Computational results demonstrate that the improved algorithm can address the heterogeneity in BS data efficiently and provide an accurate classification solution in the deep-sea survey environment.

Binarization of optical fringe patterns with intensity inhomogeneities based on modified FCM algorithm

Binarization of fringe patterns is a commonly used process in the fringe skeleton method for optical fringe pattern analysis. Most of the existing binarization algorithms of fringe patterns are based on the determination of gray level threshold. However, spatial intensity inhomogeneity induced by the uneven illumination makes the gray level threshold hard to determine. In this paper, we present a method for the binarization of fringe patterns based on a modified Fuzzy c-means clustering (FCM) algorithm. This method permits the clustering of pixels influenced by their immediate surroundings. The pixels in the fringe pattern are clustered into white fringes and black fringes without calculating thresholds. We evaluate the performance of this method by applying it to computer-simulated and real fringe patterns. Experimental results demonstrate that the proposed method can achieve the desired binarization results and perform better than the previous binarization methods, under non-uniform illumination conditions.

An efficient approach for Defect Detection in Texture analysis using Improved Support Vector Machine

Texture defect detection can be defined as the process of determining the location and size of the collection pixels in a textured image which deviate in their intensity values or spatial in compression to a background texture. The detection of abnormalities is a very challenging problem in computer vision. In our proposed method we have designed a method for detecting the defect of pattern texture analysis. Initially, features are extracted from the input image using the grey level co-occurrence matrix (GLCM) and grey level run-length matrix (GLRLM). Then the extracted features are fed to the input of classification stage. Here the classification is done by improved support vector machine (ISVM). The proposed pattern analysis showed that the traditional support vector machine is improved by means of kernel methods. In the final stage, the classified features are segmented using the modified fuzzy c means algorithm (MFCM).

Hybrid resource allocation and task scheduling scheme in cloud computing using optimal clustering techniques

In diverse parallel and distributed computing systems, resource allocation is the progression of distributing consumer tasks for processing elements during execution in which some performance intentions are optimised. This research explains about the innovative resource allocation algorithm for the computing grid environment. In the scheduling problem of independent task in cloud computing, summarise other scheduling algorithms introduce a modified fuzzy C-means (MFCM) clustering algorithm. Our algorithm abstract resource into a model to analyse these characteristics of resources with the MFCM algorithm. From that our proposed technique could decrease a execution time and memory space allocation of the system. For the optimal selection of virtual machines hybrid whale genetic (HWGA) optimisation algorithm is used. Since the virtual machines are optimally selected on the basis of feature values, our proposed method provides reduced load balancing as well as improved parallel execution of tasks.

Defect detection in pattern texture analysis using improved support vector machine

The detection of abnormalities is a very challenging problem in computer vision. In our proposed method designed for detecting the defect of pattern texture analysis. The preprocessing input image features are extracted using the Gray level co-occurrence matrix (GLCM) and Gray level run-length matrix (GLRLM). Then the extracted features are fed to the input of classification stage. Here the classification is done by improved support vector machine (ISVM) based on kernel analysis. Based on the improved support vector machine the features are classified. Final stage is segmentation; here the classified features are segmented using the modified fuzzy c means algorithm (MFCM).

A NEW HYBRID HARD-FUZZY (K-MFCM) DATA CLUSTERING METHOD FOR FINDING CLUSTER CENTROID

Data mining is a collection of methods used to extract useful information from large data bases. Cluster Analysis refers to the grouping of a set of data points into clusters. Most widely used partitioning methods are K-means and Fuzzy c-means (FCM) algorithms. However, they suffer from the difficulties such as random selection of initial centre values and handling outlier data points. Most of the existing clustering methods use the Euclidean distance metric. The modified fuzzy c-means algorithm (MFCM) is efficient in handling outlier data points. In this paper, a new hybrid algorithm is proposed to solve the limitations of the traditional clustering methods. The hybrid K-MFCM algorithm is tested on four real world bench mark data sets from UCI machine learning repository with various distance metrics including Euclidean, City Block and Chessboard. The cluster centroid values of hybrid algorithm are calculated for various data sets. The experimental results show that the hybrid algorithm gives good results in terms of objective function value and better fuzzy cluster validity results for chessboard distance metric than other distance metrics.

Rapid brain tissue segmentation process by modified FCM algorithm with CUDA enabled GPU machine

AbstractThe proposed work introduces a modified method of fuzzy c means (FCM) algorithm using bias field correction and partial supervision techniques. The proposed method is named as bias corrected partial supervision FCM (BCPSFCM). The modified membership function takes the advantage of available knowledge from labeled patterns with the bias field correction. The experiment is tested on internet brain segmentation repository with their gold standard. The performance of the method is compared with three existing methods and 12 state of the art methods using dice coefficient, sensitivity, specificity, and accuracy. Accuracy of the proposed method reached upto 98%, 98%, and 99% of GM, WM, and CSF segmentation but required additional computation power from graphics processing unit (GPU). Further parallel BCPSFCM is proposed with the help of compute unified device architecture enabled GPU machine and the processing time is reduced up to 49 times than the serial implementation.

A modified fuzzy C-means method for segmenting MR images using non-local information.

In recent years, MR images have been increasingly used in therapeutic applications such as image-guided radiotherapy (IGRT). However, images with low contrast values and noises present challenges for image segmentation. The objective of this study is to develop a robust method based on fuzzy C-means (FCM) method which can segment MR images polluted with Gaussian noise. A modified FCM algorithm accommodating non-local pixel information via Hausdorff distance was developed for segmenting MR images. The membership and objective functions were modified accordingly. Segmentations with different weights of the Hausdorff distance were compared. Segmentation tests using synthetic and MR images showed that the proposed algorithm was better at resolving boundaries and more robust to Gaussian noise. By segmenting a sample MR image of a tumor, we further showed the capability of the method in capturing the centroid of the target region. The modified FCM algorithm with neighboring information can be used to segment blurry images with potential applications in segmenting motion MR images in image-guided radiotherapy (IGRT).

A Modified Fuzzy C-Means Algorithm for Brain MR Image Segmentation and Bias Field Correction

In quantitative brain image analysis, accurate brain tissue segmentation from brain magnetic resonance image (MRI) is a critical step. It is considered to be the most important and difficult issue in the field of medical image processing. The quality of MR images is influenced by partial volume effect, noise, and intensity inhomogeneity, which render the segmentation task extremely challenging. We present a novel fuzzy c-means algorithm (RCLFCM) for segmentation and bias field correction of brain MR images. We employ a new gray-difference coefficient and design a new impact factor to measure the effect of neighbor pixels, so that the robustness of anti-noise can be enhanced. Moreover, we redefine the objective function of FCM (fuzzy c-means) by adding the bias field estimation model to overcome the intensity inhomogeneity in the image and segment the brain MR images simultaneously. We also construct a new spatial function by combining pixel gray value dissimilarity with its membership, and make full use of the space information between pixels to update the membership. Compared with other state-of-the-art approaches by using similarity accuracy on synthetic MR images with different levels of noise and intensity inhomogeneity, the proposed algorithm generates the results with high accuracy and robustness to noise.