Fuzzy C-means Clustering Technique Research Articles

A proposed hybrid model of ANN and KNN for solar cell defects detection and temperature prediction using fuzzy image segmentation

This paper presents a novel hybrid model employing Artificial Neural Networks (ANN) and Mathematical Morphology (MM) for the effective detection of defects in solar cells. Focusing on issues such as broken corners and black edges caused by environmental factors like broken glass cover, dust, and temperature variations. This study utilizes a hybrid model of ANN and K-Nearest Neighbor (KNN) for temperature prediction. This hybrid approach leverages the strengths of both models, potentially opening up new avenues for improved accuracy in temperature forecasting, which is critical for solar energy applications. The significance lies in the interconnectedness of temperature fluctuations and solar cell efficiency, leading to defects. The proposed model aims to predict temperatures accurately, providing insights into potential solar cell efficiency problems. Subsequently, this work studies the transitions to defect detection using Fuzzy C-Means (FCM) clustering and MM techniques. The hybrid model demonstrates accurate temperature prediction with Mean Absolute Percentage Error (MAPE) values of 0.92 %, 0.72 %, and 1.3 % for average, maximum, and minimum temperatures, respectively. The defect detection process yields a detection accuracy (CR) of 96 % and sensitivity of detection (SD) of 89 %. This work is validated compared to the literature work done and by using K-fold cross validation technique. The proposed work emphasizes the improvement in defect detection accuracy and the overall quality enhancement of solar cells.

Cluster-based wireless sensor network framework for denial-of-service attack detection based on variable selection ensemble machine learning algorithms

A Cluster-Based Wireless Sensor Network (CBWSN) is a system designed to remotely control and monitor specific events or phenomena in areas such as smart grids, intelligent healthcare, circular economies in smart cities, and underwater surveillance. The wide range of applications of technology in almost every field of human activity exposes it to various security threats from cybercriminals. One of the pressing concerns that requires immediate attention is the risk of security breaches, such as intrusions in wireless sensor network traffic. Poor detection of denial-of-service (DoS) attacks, such as Grayhole, Blackhole, Flooding, and Scheduling attacks, can deplete the energy of sensor nodes. This can cause certain sensor nodes to fail, leading to a degradation in network coverage or lifetime. The detection of such attacks has resulted in significant computational complexity in the related works. As new threats arise, security attacks get more sophisticated, focusing on the target system's vulnerabilities. This paper proposed the development of Cluster-Based Wireless Sensor Network and Variable Selection Ensemble Machine Learning Algorithms (CBWSN_VSEMLA) as a security threats detection system framework for DoS attack detection. The CBWSN model is designed using a Fuzzy C-Means (FCM) clustering technique, whereas VSEMLA is a detection system comprised of Principal Component Analysis (PCA) for feature selection and various ensemble machine learning algorithms (Bagging, LogitBoost, and RandomForest) for the detection of grayhole attacks, blackhole attacks, flooding attacks, and scheduling attacks. The experimental results of the model performance and complexity comparison for DoS attack evaluation using the WSN-DS dataset show that the PCA_RandomForest IDS model outperforms with 99.999 % accuracy, followed by the PCA_Bagging IDS model with 99.78 % accuracy and the PCA_LogitBoost model with 98.88 % accuracy. However, the PCA_RandomForest model has a high computational complexity, taking 231.64 s to train, followed by the PCA_LogitBoost model, which takes 57.44 s to train, and the PCA_Bagging model, which takes 0.91 s to train to be the best in terms of model computational complexity. Thus, the models surpassed all baseline models in terms of model detection accuracy on flooding, scheduling, grayhole, and blackhole attacks.

Fuzzy C-mean clustering technique based visual features fusion for automatic video summarization method

Fuzzy C-mean clustering technique based visual features fusion for automatic video summarization method

Brain MRI Image Analysis and Segmentation using Machine Learning

The brain magnetic resonance imaging (MRI), analysis and segmentation plays one of the crucial roles in medical diagnosis and facilitates in an early detection of diseases in critical medical conditions, Due to the structural complexity and type of the tumor, radiologists are facing difficulties in extracting essential features of the image which are crucial in treating the patient. Therefore, correct, and meaningful segmentation of brain MRI is a challenging task and is required for further processing. This article proposes machine learning based automatic brain MRI segmentation and classification. The pre-processing step is the vital part of the algorithm, where the discrete wavelet transforms (DWT) and median filtering help in identifying and pointing the exact location of the tumor. The preprocessed image is further segmented by an improved original Fuzzy C-means (FCM) clustering technique. The feature extraction and classification is performed by support vector machine (SVM) classifier. It is found that the simulation associated with ground truth data provides better segmentation results in terms of accuracy, sensitivity, and dice coefficient.

Provincial waste generation prediction using evolutionary-based neuro-fuzzy model: A South Africa case study

The accurate prediction of waste generation at the provincial level is essential for effective waste management planning in South Africa. However, the complex and uncertain nature of waste generation patterns poses significant challenges for policymakers and waste management authorities. Existing prediction models often overlook the intricate relationships between various factors influencing waste generation. Therefore, there is a need to develop an advanced predictive model that integrates evolutionary algorithms and neuro-fuzzy systems to provide reliable and comprehensive waste generation predictions. In this study, adaptive neuro-fuzzy inference system (ANFIS) model with two evolutionary algorithms namely particle swarm optimization (PSO) and genetic algorithm (GA) were developed to predict the provincial waste quantities generated using South Africa as a case study. Relevant demographic and economic variables of nine provinces in South Africa namely population, provincial GDP, household number and employment data were set of input variables while the waste quantity generated was set as output variable. The fuzzy c-means (FCM) clustering technique developed the fuzzy inference system based on its speed boost capability. Hyper-parameters setting of the FCM-clustered neuro-fuzzy model in a range of 2–7 clusters was tested. The models’ accuracy was evaluated using known statistical metrics like root mean square error (RMSE), Average Absolute Percentage Relative Error (AAPRE), mean absolute deviation (MAD), and co-efficient of determination (R2). The most accurate model is the FCM-ANFIS-GA with 5-clusters giving RMSE, AAPRE, MAD and R2-values of 0.7335, 0.3656, 7.3359 and 0.9986 at the training phase and 0.8041, 0.3291, 8.1467 and 0.9962 at the testing phase. The developed model would assist in sustainable waste management and would be beneficial to South African Waste Information Center (SAWIC) and other countries.

Measuring lane-changing trajectories by employing context-based modified dynamic time warping

The spatial lane-changing (LC) behavior should be analyzed for the vehicles in transportation systems in order to identify the patterns of vehicles’ movements using the similarities detected in their lane-changing trajectories. The trajectory of an LC vehicle is a function of its context. The present paper utilized spatial footprints and external/internal contexts to contextualize a measure applicable to the similarities found between LC trajectories. While only the external context of the previous investigation was constrained to the surrounding vehicles on the road, this study has investigated the idea of the contribution of solar radiation to the lane-changing trajectory patterns. The similarities found between multi-dimensional trajectories were determined by offering context-based modified dynamic time warping (CMDTW), and the CMDTW technique with the Next Generation Simulation (NGSIM) dataset was carefully analyzed. The weighting framework used for each dimension made it possible to quantify the similarities between lane-changing trajectories using the AHP technique. The obtained results showed that not only the lane-changing procedure depends on the conditions of the lane changer, but this procedure also depends on the solar radiation and the surrounding vehicles offered as the external contexts. Additionally, by including different dimensions, both internal and external contexts, the similarity results of LC trajectories turn into a more realistic phenomenon. The potential of the context-based modified dynamic time warping algorithm in the detection of a trajectory with the maximum similarity is also enhanced. Furthermore, in order to determine the LC trajectories, we used the Fuzzy C-means (FCM) clustering technique. We utilized Cohen’s kappa for the evaluation of the Fuzzy C-means results, and since the calculated Kappa score exceeds 0.8, the clustering algorithm has an excellent performance. The results obtained by comparing the suggested technique with commonly used similarity measurement techniques indicated that the accuracy of the CMDTW technique outperforms other techniques in the detection of the lane-changing trajectory patterns. The suggested CMDTW method has therefore been effective in the identification of the patterns of lane-changing trajectory.

Aerosol classification using fuzzy clustering over a tropical rural site

Exact knowledge on the types of aerosols present over a region is very important in the radiative forcing estimates. Several techniques are available to classify aerosols, which have their own pros and cons. In the present study, a soft clustering technique, Fuzzy C-Means (FCM) is employed to classify aerosols using multiple aerosol parameters over a rural site Gadanki (13.48oN, 79.18°E), India. The optical and microphysical properties of the aerosols (26 parameters) derived from long-term measurements of the Sky Radiometer (from April 2008 to October 2020) are utilized for cluster analysis. The three resultant clusters were found to be distinct and compact based on high intra-cluster similarity and low inter-cluster similarity. By comparing the clustering results of different subsets of data and testing the robustness of the FCM algorithm, we found that the correct classification percentage for each scenario was about 98.4%. Even by introducing a 10% random error for each of the aerosol microphysical properties used in the cluster analysis, around 94.5% of the records were found to retain the original clusters. Based on the aerosol properties of the clusters, the three clusters were identified as mixed mode moderately absorbing, mixed mode slightly absorbing, and coarse mode slightly absorbing type of aerosols. While comparing with the global aerosol models, these three aerosol types showed similarities with biomass burning, polluted marine, and polluted continental aerosol types. Significant seasonal variability of the identified aerosol types was observed, with biomass burning type dominating in the pre-monsoon season, polluted marine during the monsoon season, and polluted continental in a relatively higher percentage compared to the other two types in winter and post-monsoon seasons. An increasing trend in polluted marine aerosol type is noticed, whilst biomass burning and polluted continental showed decreasing trends. Furthermore, the existing knowledge on various aspects of aerosols over Gadanki ascertains that the identified aerosol types are reasonable, implying that the FCM clustering technique for aerosol classification is effective and it might be extend to other locations.

A FCM-clustered neuro-fuzzy model for estimating the methane fraction of biogas in an industrial-scale bio-digester

Real-time and offline monitoring, control and optimization of significant variables of bio-digester plants is crucial for optimal yield and maximum recovery of bioenergy at an industrial scale. Methane is an energy carrier and a critical component in the total biogas generated in a digestion process, thus necessitating more attention on the fraction of methane in the biogas yield. However, most previous studies in literature had focused on the volumetric yield of methane with little or no attention given to the fractional composition of methane in the total biogas produced. The deficiency of the classical technique in controlling the process parameters for optimal yield has motivated the need for machine learning-based techniques for modelling the methane fraction of biogas in a large-scale plant. In this study, a fuzzy c-mean (FCM)-clustered adaptive neuro-fuzzy inference system (ANFIS) was developed to model the methane fraction of biogas in an industrial-scale plant. The FCM clustering technique was preferred owing to its computational speed boost capability. The model was simulated using the control parameters of the algorithms while the optimal model was selected after testing their performance using relevant statistical metrics. The best model was obtained with ANFIS-FCM model with 8 clusters giving Root Mean Square Error (RMSE), Mean Absolute Deviation (MAD), Average Absolute Percentage Relative Error (AAPRE), Relative Mean Bias Error (rMBE) and correlation coefficient (R2) values of 3.156, 2.236, 3.015, 0.306, 0.978 respectively at the training phase and 4.936, 3.245, 3.456. 0.306, 0.956 respectively at the testing phase. The statistical metrics values obtained implied that FCM-ANFIS is a satisfactory model to predict methane fraction successfully.

Performance Evaluation of the Impact of Clustering Methods and Parameters on Adaptive Neuro-Fuzzy Inference System Models for Electricity Consumption Prediction during COVID-19

Increasing economic and population growth has led to a rise in electricity consumption. Consequently, electrical utility firms must have a proper energy management strategy in place to improve citizens’ quality of life and ensure an organization’s seamless operation, particularly amid unanticipated circumstances such as coronavirus disease (COVID-19). There is a growing interest in the application of artificial intelligence models to electricity prediction during the COVID-19 pandemic, but the impacts of clustering methods and parameter selection have not been explored. Consequently, this study investigates the impacts of clustering techniques and different significant parameters of the adaptive neuro-fuzzy inference systems (ANFIS) model for predicting electricity consumption during the COVID-19 pandemic using districts of Lagos, Nigeria as a case study. The energy prediction of the dataset was examined in relation to three clustering techniques: grid partitioning (GP), subtractive clustering (SC), fuzzy c-means (FCM), and other key parameters such as clustering radius (CR), input and output membership functions, and the number of clusters. Using renowned statistical metrics, the best sub-models for each clustering technique were selected. The outcome showed that the ANFIS-based FCM technique produced the best results with five clusters, with the Root Mean Square Error (RMSE), Mean Absolute Error (MAE), Coefficient of Variation (RCoV), Coefficient of Variation of the Root Mean Square Error (CVRMSE), and Mean Absolute Percentage Error (MAPE) being 1137.6024, 898.5070, 0.0586, 11.5727, and 9.3122, respectively. The FCM clustering technique is recommended for usage in ANFIS models that employ similar time series data due to its accuracy and speed.

Assessing commuters’ perceptions towards improvement of intermediate public transport as access modes to metro stations

The study considers the perception of Metro commuters to investigate the priority areas of interventions for improving the service quality of Intermediate Public Transport (IPT) as an access mode to the Metro stations in Delhi. Tablet-based face-to-face surveys were conducted to collect responses from 1121 commuters towards perceived importance and satisfaction ratings of 18 service quality attributes under study on a 6-point Likert scale. Collected responses were analysed using Revised Importance Performance Analysis. Implicit importance values of all the attributes were calculated to derive the factor structures and management schemes using the Fuzzy C-means clustering technique. The priority attributes were identified by comparing the factor structures and management schemes. Finally, the study proposed a priority order of resource allocation to improve attributes influencing IPT as access modes to the Metro stations. The study findings identified that ‘Transportation Subsidy’, ‘Accessibility in Bad Weather’ and ‘Universal Design Considerations’ were top priority attributes that demanded resource allocation for improvement. ‘Access Time’, ‘In-vehicle Travel Time’, ‘Luggage Space’, ‘Hygiene’, ‘Security’, ‘Riding Comfort’, ‘Frequency’ and ‘Travel Fare’ were operating at optimum service levels, hence, occupied a second-level priority in terms of resource allocation to ensure the levels were maintained. Two low-priority attributes, ‘Information at IPT stop’ and ‘On-board Information’ occupied a third-level priority in terms of resource allocation for improvement. Attributes under ‘Possible Overkill’, namely, ‘Safety’, ‘Convenience’, ‘Waiting Time’, ‘Egress Time’ and ‘Seat Availability’, suggested transferring resources towards improving other priority service attributes. The study findings can assist transport planners and policymakers in formulating policies for improving the performance of IPT as access modes to Metro stations in Delhi and extending the approach to other contexts to supplement the mass transit systems suitably and increase their ridership.

Brain tumor segmentation of MR images using SVM and fuzzy classifier in machine learning

Medical image processing is a rapidly growing and concentrating topic today. Medical image analysis techniques are used to diagnose and cure illnesses. One such fundamental and potentially fatal illness is brain tumor, which is an abnormal growth of brain cells within the brain. Due to the complexity of the brain's anatomy. To improve efficiency and reduce the complexity of the picture segmentation process, this work investigated computer tomography (CT)-based brain tumor segmentation. CT scans are often used to diagnose head traumas, malignancies, and skull fractures. The images from the brain tumor database are evaluated in this study effort, and a preprocessing approach called adaptive median filter is used to increase the image's clarity. The preprocessing stage eliminates noise and high-frequency artifacts from the pictures. The median filter is a type of nonlinear digital filter commonly used to reduce noise in a picture or signal. Regardless of the preprocessing technique used, feature extraction techniques are updated, and then classification procedures such as the Adaptive Neuro-Fuzzy Inference System (ANFIS) and Support Vector Machine (SVM) classifier are applied to the picture to classify it as normal or abnormal. Following classification, aberrant images are tracked and selected for segmentation using the Fuzzy C-Means (FCM) clustering technique and associated optimization techniques. In the suggested technique, centroid optimizations such as Grey Wolf Optimization (GWO) and Social Spider Optimization (SSO) combined with a Genetic Algorithm (GA) are utilized to improve the accuracy of the FCM centroid. The suggested work produces the most extreme execution in tumour picture segmentation evaluation appears differently from other works. The conclusion indicates that the hybrid technique (SSO-GA) obtains the highest accuracy of 99.24% compared to other individual algorithms. MATLAB 2014 is utilized to implement the brain tumor classification and segmentation algorithms in this research effort.

Fault detection and diagnosis strategy based on k-nearest neighbors and fuzzy C-means clustering algorithm for industrial processes

Fault detection and diagnosis is crucial in recent industry sector to ensure safety and reliability, and improve the overall equipment efficiency. Moreover, fault detection and diagnosis based on k-nearest neighbor rule (FDD-kNN) has been effectively applied in industrial processes with characteristics such as multi-mode, non-linearity, and non-Gaussian distributed data. The main challenge associated with FDD-kNN is the on-line computational complexity and storage space that are needed for searching neighbors. To deal with these issues, this paper proposes a monitoring approach where the Fuzzy C-Means clustering technique is used to decrease the overall on-line computations and required storage by measuring the neighbors of the clusters’ centres rather than the raw data. After building the monitoring model off-line based on the data clusters’ centres, the faults are detected by comparing the average squared Euclidean distance between the on-line data sample and the clusters’ centres with a predefined threshold. Then, the detected faults can be diagnosed by calculating the contribution of each variable in the fault detection index. Furthermore, for easily analysing the fault diagnosis results, the relative contribution for each sample data vector is considered. A numerical example and the Tennessee Eastman chemical process are used to demonstrate the performance of the proposed FCM-kNN for fault detection and diagnosis.

A novel framework for retinal vessel segmentation using optimal improved frangi filter and adaptive weighted spatial FCM

Medical attention has long been focused on diagnosing diseases through retinal vasculature. However, due to the image intensity inhomogeneity and retinal vessel thickness variability, segmenting the vessels from retinal images is still a tough matter. In this paper, we suggest an optimal improved Frangi-based multi-scale filter for enhancement. The parameters of the Frangi filter are optimised using a modified enhanced leader particle swarm optimization (MELPSO). The enhanced image is segmented using a novel adaptive weighted spatial fuzzy c-means (AWSFCM) clustering technique. The suggested approach is tested on three freely available databases. The results obtained are compared with state-of-the-art procedures. It is observed that the suggested approach outperforms other methods and may serve as an effective approach for retinal vessel segmentation.

Minimally parametrized segmentation framework with dual metaheuristic optimisation algorithms and FCM for detection of anomalies in MR brain images

BackgroundEarly prognosis of a brain tumour may offer better life expectancy. Magnetic Resonance Imaging (MRI) coupled with an efficient machine learning segmentation technique has proven to be a reliable way of assessing tumours. In addition to the segmentation, the image is needed to optimise to achieve the desired results. In many cases, single-stage optimisation could not complete the search target owing to algorithm-specific limitations. To overcome this hindrance, the dual metaheuristic optimisation technique is widely used to detect tumour affected tissues. AimThis research emphasises brain tumour region detection using Fuzzy C-Means (FCM) clustering techniques and the segmented output enhancement using two different optimisation techniques, namely, Artificial Bee Colony (ABC) and the JAYA algorithm. MethodsThis methodology first deploys the FCM clustering technique to segment the tumour region in the MRI. Then, the initial stage of optimisation is done using the ABC algorithm with the help of texture features extracted from the segmented image through the Gray Level Co-occurrence Matrix (GLCM) technique. Lastly, a novel JAYA algorithm is deployed for the second stage of optimisation to provide precise segmentation with the support of global and local best solutions.Result and Conclusion: The proposed framework delivers high accuracy in tumour detection. Besides, it has been proven by renowned evaluation metrics, such as Tanimoto Coefficient Index, and Dice Coefficient Index, which are up to 70.12% and 82.56%, respectively, competing with the contemporary methods used for the evaluations of MR brain images.

Performance Analysis and Critical Review on Segmentation Techniques for Brain Tumor Classification

An irregular growth in brain cells causes brain tumors. In recent years, a considerable rate of increment in medical cases regarding brain tumors has been observed, affecting adults and children. However, it is highly curable in recent times only if detected in the early time of tumor growth. Moreover, there are many sophisticated approaches devised by researchers for predicting the tumor regions and their stages. In addition, Magnetic Resonance Imaging (MRI) is utilized commonly by radiologists to evaluate tumors. In this paper, the input image is from a database, and brain tumor segmentation is performed using various segmentation techniques. Here, the comparative analysis is performed by comparing the performance of segmentation approaches, like Hybrid Active Contour (HAC) model, Bayesian Fuzzy Clustering (BFC), Active Contour (AC), Fuzzy C-Means (FCM) clustering technique, Sparse (Sparse FCM), and Black Hole Entropy Fuzzy Clustering (BHEFC) model. Moreover, segmentation technique performance is evaluated with the Dice coefficient, Jaccard coefficient, and segmentation accuracy. The proposed method shows high Dice and Jaccard coefficients of 0.7809 and 0.6456 by varying iteration with the REMBRANDT dataset and a better segmentation accuracy of 0.9789 by changing image size in the Brats-2015 database.

Prediction and classification of ventricular arrhythmia based on phase-space reconstruction and fuzzy c-means clustering

Background and objectivePrediction and classification of Ventricular Arrhythmias (VA) may allow clinicians sufficient time to intervene for stopping its escalation to Sudden Cardiac Death (SCD). This paper proposes a novel method for predicting VA and classifying its type, in particular, the fatal VA even before the event occurs. MethodsA statistical index J based on the combination of phase-space reconstruction (PSR) and box counting has been used to predict VA. The fuzzy c-means (FCM) clustering technique is applied for the classification of impending VA. Results32 healthy and 32 arrhythmic subjects from two open databases - PTB Diagnostic database (PTBDB) and CU Ventricular Tachyarrhythmia (CUDB) database respectively; were used to validate our proposed method. Our method showed average prediction time of approximately 5 min (4.97 min) for impending VA in the tested dataset while classifying four types of VA (VA without ventricular premature beats (VPBs), ventricular fibrillation (VF), ventricular tachycardia (VT), and VT followed by VF) with an average 4 min (approximately) before the VA onset, i.e., after 1 min of the prediction time point with average accuracy of 98.4%, a sensitivity of 97.5% and specificity of 99.1%. ConclusionsThe results obtained can be used in clinical practice after rigorous clinical trial to advance technologies such as implantable cardioverter defibrillator (ICD) that can help to preempt the occurrence of fatal ventricular arrhythmia - a main cause of SCD.

Diagnosis of Multiple Sclerosis Disease in Brain Magnetic Resonance Imaging Based on the Harris Hawks Optimization Algorithm.

The damaged areas of brain tissues can be extracted by using segmentation methods, most of which are based on the integration of machine learning and data mining techniques. An important segmentation method is to utilize clustering techniques, especially the fuzzy C-means (FCM) clustering technique, which is sufficiently accurate and not overly sensitive to imaging noise. Therefore, the FCM technique is appropriate for multiple sclerosis diagnosis, although the optimal selection of cluster centers can affect segmentation. They are difficult to select because this is an NP-hard problem. In this study, the Harris Hawks optimization (HHO) algorithm was used for the optimal selection of cluster centers in segmentation and FCM algorithms. The HHO is more accurate than other conventional algorithms such as the genetic algorithm and particle swarm optimization. In the proposed method, every membership matrix is assumed as a hawk or an HHO member. The next step is to generate a population of hawks or membership matrices, the most optimal of which is selected to find the optimal cluster centers to decrease the multiple sclerosis clustering error. According to the tests conducted on a number of brain MRIs, the proposed method outperformed the FCM clustering and other techniques such as the k-NN algorithm, support vector machine, and hybrid data mining methods in accuracy.

Non-technical loss detection in limited-data low-voltage distribution feeders

Non-technical loss (NTL) is the major source of energy loss in distribution networks. Since the direct calculation of NTL is impossible, it is mostly being obtained by calculating the difference between total loss and technical loss (TL). This is rather not possible without adequate equipment for measuring the TL. However, time-worn distribution networks with high limitations and lack of equipment could identify the regions with a high share of NTL to reduce/eradicate the sources of NTL. This strategy can increase efficiency and reduce costs compared to the one-by-one inspection of customers. This paper presents a novel NTL detection procedure based on load estimation in highly limited distribution networks. First, the low-voltage (LV) distribution network will be divided into a few smaller hypothetical sub-networks through a fuzzy c-means (FCM) clustering technique. Then, a meter placement based on the maximum likelihood criterion is deployed to find the best places for installing the existing meters in each cluster. In the next stage, a linear system of equations is utilized to extract the pattern load profiles (PLPs) related to each class of customers for each hypothetical sub-network separately. These PLPs will be used to estimate the transformers’ load in all LV feeders based on their share in the total consumption of the entire network. Finally, NTL possibility per capita will be achieved for each LV feeder by defining an index that estimates the degree of NTL in those areas.

Enhancement of medical images using fuzzy logic

Image enhancement is one of the most critical subjects in computer vision and image processing fields. It can be considered as means to enrich the perception of images for human viewers. All kinds of images typically suffer from different problems such as weak contrast and noise. The primary purpose of image enhancement is to change an image's visual appearance. Many algorithms have recently been proposed for enhancing medical images. Image enhancement is still deemed a challenging task. In this paper, the fuzzy c-means clustering (FCM) technique is utilized to enhance the medical images. The method of enhancement consists of two stages. The proposed algorithm conducts a cluster test on the image pixels. It then increases the difference of gray level between the diverse objects to accomplish the enhancement purpose of the medical images. The experimental results have been tested using various images. The algorithm enhanced the small target of the image to a reasonable limit and revealed favorable performance. The results of image enhancement techniques were evaluated by using terms of different criteria such as peak signal to noise ratio (PSNR), mean square error (MSE) and average information contents (AIC), showing promising performance.

Multi-Model Approach and Fuzzy Clustering for Mammogram Tumor to Improve Accuracy

Breast Cancer is one of the most common diseases among women which seriously affect health and threat to life. Presently, mammography is an uttermost important criterion for diagnosing breast cancer. In this work, image of breast cancer mass detection in mammograms with 1024×1024 pixels is used as dataset. This work investigates the performance of various approaches on classification techniques. Overall support vector machine (SVM) performs better in terms of log-loss and classification accuracy rate than other underlying models. Therefore, further extensions (i.e., multi-model ensembles method, Fuzzy c-means (FCM) clustering and SVM combination method, and FCM clustering based SVM model) and comparison with SVM have been performed in this work. The segmentation by FCM clustering technique allows one piece of data to belong in two or more clusters. The additional parts are due to the segmented image to enhance the tumor-shape. Simulation provides the accuracy and the area under the ROC curve for mini-MIAS are 91.39% and 0.964 respectively which give the confirmation of the effectiveness of the proposed algorithm (FCM-based SVM). This method increases the classification accuracy in the case of a malignant tumor. The simulation is based on R-software.