Hard C-means Algorithm Research Articles

Thermographic image processing analysis in a solar concentrator with hard C-means clustering

Thermographic measurements are used to determine the temperatures reached by the focus of a modified Fresnel solar concentrator, where a container is placed to take advantage of this energy. The three steps of this investigation are: (i) the edges of each thermographic image are obtained by means of a Butterworth low-pass filter, (ii) the temperature grid in the solar concentrator is obtained by means of a feature extraction algorithm, and finally: (iii) the classification will be done through a C-means hard clustering algorithm selecting the center of each cluster to accurately find the temperature region to generate the isotherms and extract the temperatures with this algorithm. With the hard C-means algorithm, isotherm level curves and temperature graphs are obtained. Subsequently, two analyzes are carried out to validate that the original unprocessed thermographic images correspond spatially and in their spectrum with the processed images, with the aim of corroborating the acuteness of the digital image processing methodology implemented in this research. Finally, a correlation analysis is performed to validate the temperature matches of the original thermographic images.

Diagnosis and Nursing Intervention of Gynecological Ovarian Endometriosis with Magnetic Resonance Imaging under Artificial Intelligence Algorithm.

This research was aimed to study the application value of the magnetic resonance imaging (MRI) diagnosis under artificial intelligence algorithms and the effect of nursing intervention on patients with gynecological ovarian endometriosis. 116 patients with ovarian endometriosis were randomly divided into a control group (routine nursing) and an experimental group (comprehensive nursing), with 58 cases in each group. The artificial intelligence fuzzy C-means (FCM) clustering algorithm was proposed and used in the MRI diagnosis of ovarian endometriosis. The application value of the FCM algorithm was evaluated through the accuracy, Dice, sensitivity, and specificity of the imaging diagnosis, and the nursing satisfaction and the incidence of adverse reactions were used to evaluate the effect of nursing intervention. The results showed that, compared with the traditional hard C-means (HCM) algorithm, the artificial intelligence FCM algorithm gave a significantly higher partition coefficient, and its partition entropy and running time were significantly reduced, with significant differences (P < 0.05). The average values of Dice, sensitivity, and specificity of patients' MRI images were 0.77, 0.73, and 0.72, respectively, which were processed by the traditional HCM algorithm, while those values obtained by the improved artificial intelligence FCM algorithm were 0.92, 0.90, and 0.93, respectively; all the values were significantly improved (P < 0.05). In addition, the accuracy of MRI diagnosis based on the artificial intelligence FCM algorithm was 94.32 ± 3.05%, which was significantly higher than the 81.39 ± 3.11% under the HCM algorithm (P < 0.05). The overall nursing satisfaction of the experimental group was 96.5%, which was significantly better than the 87.9% of the control group (P < 0.05). The incidence of postoperative adverse reactions in the experimental group (7.9%) was markedly lower than that in the control group (24.1%), with a significant difference (P < 0.05). In short, MRI images under the artificial intelligence FCM algorithm could greatly improve the clinical diagnosis of ovarian endometriosis, and the comprehensive nursing intervention would also improve the prognosis and recovery of patients.

Infectious Disease Relational Data Analysis Using String Grammar Non-Euclidean Relational Fuzzy C-Means.

Statistical analysis in infectious diseases is becoming more important, especially in prevention policy development. To achieve that, the epidemiology, a study of the relationship between the occurrence and who/when/where, is needed. In this paper, we develop the string grammar non-Euclidean relational fuzzy C-means (sgNERF-CM) algorithm to determine a relationship inside the data from the age, career, and month viewpoint for all provinces in Thailand for the dengue fever, influenza, and Hepatitis B virus (HBV) infection. The Dunn’s index is used to select the best models because of its ability to identify the compact and well-separated clusters. We compare the results of the sgNERF-CM algorithm with the string grammar relational hard C-means (sgRHCM) algorithm. In addition, their numerical counterparts, i.e., relational hard C-means (RHCM) and non-Euclidean relational fuzzy C-means (NERF-CM) algorithms are also applied in the comparison. We found that the sgNERF-CM algorithm is far better than the numerical counterparts and better than the sgRHCM algorithm in most cases. From the results, we found that the month-based dataset does not help in relationship-finding since the diseases tend to happen all year round. People from different age ranges in different regions in Thailand have different numbers of dengue fever infections. The occupations that have a higher chance to have dengue fever are student and teacher groups from the central, north-east, north, and south regions. Additionally, students in all regions, except the central region, have a high risk of dengue infection. For the influenza dataset, we found that a group of people with the age of more than 1 year to 64 years old has higher number of influenza infections in every province. Most occupations in all regions have a higher risk of infecting the influenza. For the HBV dataset, people in all regions with an age between 10 to 65 years old have a high risk in infecting the disease. In addition, only farmer and general contractor groups in all regions have high chance of infecting HBV as well.

The hard c-means algorithm for clustering Indonesian plantation commodity based on metabolites composition

Indonesia is the second largest biodiversity country in the world. Indonesia has a variety of top agricultural and plantation commodities, whereas each commodity has premium products which are normally related to its specific character/flavour and are generally associated with the producing origins. Specific character/flavour is represented by composition of metabolites each origin. To find out the specific character/flavour in various Indonesian plantation commodities, clustering is needed. In this paper, we perform clustering on an Indonesian plantation commodity based on their metabolites composition. Metabolite compositions of samples of some origins of the commodity are clustered using the Hard C-Means algorithm with the Xie-Beni index as the cluster validity. Our present results confirm that each origin has a unique characteristic and belongs to a separate cluster.

Adaptive local data and membership based KL divergence incorporating C-means algorithm for fuzzy image segmentation

In this paper, a fuzzy clustering technique for image segmentation is developed by incorporating a hybrid of local spatial membership and data information into the conventional hard C-means (HCM) algorithm. This incorporation is a threefold procedure. (1) The membership function of a pixel is spatially smoothed in the pixel vicinity. (2) The Kullback-Leibler (KL) divergence between the pixel membership function and the smoothed one is added to the HCM objective function for fuzzification. (3) The resulting fuzzified HCM is regularized by adding a weighted HCM-like function where the original pixel data are replaced by locally smoothed ones. Thereby the weight is proportional to the residual of the locally smoothed membership. This residual decreases when many pixels existing in the pixel vicinity belong to the same cluster. Thus, the weighted distance decreases, allowing the pixel membership to follow the dominant membership in the pixel vicinity. The simulation results of segmenting synthetic, medical and media images have shown that the proposed algorithm provides better performance compared to several previously developed algorithms. For example, in a synthetic image, with added white Gaussian noise having a variance of 0.3, the proposed algorithm provides accuracy, sensitivity and specificity of 92%, 84% and 94.7% respectively, while the algorithm with the closest results provides 81.9% of accuracy, 62.2% of sensitivity and 86.8% of specificity. In addition, the proposed algorithm shows the capability to identify the number of clusters.

OPERATING SPEED AS A KEY FACTOR IN STUDYING THE DRIVER BEHAVIOUR IN A RURAL CONTEXT

The research aims to explore the effects of geometric road features on driver speed behaviour in order to identify unsafe road segments where high reductions in speed between successive road elements occur. The sample involves two-lane rural roads on flat terrain (vertical grade less than 5%) in Southern Italy, totalling 184 km without spiral transition curves between the tangent segments and circular elements. The testing was carried out on 567 study sites, of which 248 are on circular curves and 319 on tangents. Speed data collection was carried out in environmental and traffic conditions using a laser. The conditions were the following: dry roads, free flow conditions, daylight hours and good weather conditions. The main goal was to calibrate and validate different operating speed prediction models: a) one model on tangent segments; b) one model on circular curves; c) only one model to be used at the same time on tangents and circular curves. The validation process involved almost 10% of the total road network length, that was removed from the calibration phase. The speed measurements of each of the first two datasets (a, b) were grouped into ten homogeneous substrates while for the remaining dataset (c) sixteen substrates were defined by using a hard c-means algorithm. Two statistical criteria were used to remove anomalous operating speed values from each group of three datasets, namely, the Chauvenet criterion and the Vivatrat method. The first criterion was preferred in the final process of model selection. The results of the first filtering procedure showed more homogeneous samples that guaranteed a higher correlation coefficient and lower residuals of the predictive models during the validation phase than the Vivatrat method. The models were developed using an Ordinary Least Squares (OLS) method. The explanatory variables were total segment length, lane width, curvature of the road element, the curvature change rate on homogeneous road segments, and the number of residential driveways per km. ANOVA and additional synthetic statistical parameters were assessed to check the effectiveness of using a single general model to predict operating speeds at the same time on tangents and on circular curves alike. The results suggested the reliability of this hypothesis and its effectiveness in bringing advantages during the application phase.

Rough Clustering Method Based on Particle Swarm Optimization Algorithm

As a relatively novel clustering approach, Particle Swarm Optimization (PSO) prevents k-means algorithm from falling into local optimum effectively, and has made relatively notable successes in clustering, however, using Hard C-Means algorithm when randomly obtaining initial clustering centers is required in most existing PSOs, while no definite limit existing in these samples actually. Based on this, we utilized an improved PSO; along with effective processing methods on boundary objects of Rough Set Theory, we proposed a new rough clustering algorithm based on PSO. It can adjust the upper and lower approximations weighting factors dynamically, and coordinate the proportions of upper and lower approximations in different generations as well. Finally, we compared it with several common clustering methods using Iris dataset of UCI. It turned out that the algorithm has higher accuracy and stability, along with better comprehensive performance.

Application of Hard C-Means and Fuzzy C-Means in Data Fusion

This article describes two kinds of Fuzzy clustering algorithm based on partition,Fuzzy C-means algorithm is on the basis of the hard C-means algorithm, and get a big improvement, making large data similarity as far as possible together. As a result of Simulation, FCM algorithm has more reasonable than HCM method on convergence, data fusion, and so on.

Fuzzy C-Means Clustering Algorithm for Image Segmentation Based on Improved Particle Swarm Optimization

A novel image segmentation algorithm based on fuzzy C-means (FCM) clustering and improved particle swarm optimization (PSO) is proposed. The algorithm takes global search results of improved PSO as the initialized values of the FCM, effectively avoiding easily trapping into local optimum of the traditional FCM and the premature convergence of PSO. Meanwhile, the algorithm takes the clustering centers as the reference to search scope of improved PSO algorithm for global searching that are obtained through hard C-means (HCM) algorithm for improving the velocity of the algorithm. The experimental results show the proposed algorithm can converge more quickly and segment the image more effectively than the traditional FCM algorithm.

Special issue on pattern recognition and information processing using neural networks

Neural network techniques have proven to be flexible in pattern recognition and information processing in complex environments. They typically include BP networks, RBF networks, support vector machine (SVM) and other similar biologically motivated models. The neural network techniques are able to enhance recognition accuracy, and have found applications in real-world environments. This special issue addresses neural network techniques in pattern recognition and information processing problems. The first paper ‘‘Kernel based improved discriminant analysis and its application to face recognition,’’ coauthored by Dake Zhou and Zhenmin Tang, presents a variant of KDA called kernel-based improved discriminant analysis (KIDA). In the proposed framework, original samples are projected firstly into a feature space by an implicit nonlinear mapping. After reconstructing betweenclass scatter matrix in the feature space by weighted schemes, the kernel method is used to obtain a modified Fisher criterion. Finally, simultaneous diagonalization technique is employed to find lower dimensional nonlinear features with significant discriminant power. The second paper ‘‘A Novel Application of a Self-Organizing Network for Recognition of Facial Expressions from Contours,’’ coauthored by W.F. Gu, Y.V. Venkatesh, and C. Xiang, proposes a self-organizing network for recognizing facial expressions using biologically plausible features: contours of face and its components. Experimental results show that the recognition accuracy of the presented algorithm is superior to that of other algorithms in the literature on the Japanese Female Facial Expression (JAFFE) database. The third paper ‘‘An Automatic Fuzzy C-Means Algorithm for Image Segmentation,’’ co-authored by Yanling Li and Yi Shen, proposes a novel fuzzy clustering algorithm for automatically grouping the pixels of an image into different homogeneous regions when the number of clusters is not known a priori. The new algorithm initiates the first two centroids of clusters by a method based on hard c-means algorithm and automatically determines the appropriate cluster number for image segmentation. The fourth paper ‘‘Content Based Image Classification with Wavelet Relevance Vector Machines,’’ co-authored by Arvind Tolambiya, S. Venkataraman, and Prem K. Kalra, introduces the use of relevance vector machines (RVM) for content-based image classification and compares it with the conventional SVM approach. The authors also propose a new wavelet-based feature extraction method that extracts less number of features as compared to other wavelet-based feature extraction methods. The fifth paper ‘‘A Three-layer Back-propagation Neural Network for Spam Detection Using Artificial Immune Concentration,’’ co-authored by Guangchen Ruan and Ying Tan, uses a three-layer back-propagation neural network F.-C. Sun (&) State Key Lab of Intelligent Technology and Systems, Department of Computer Science and Technology, Tsinghua University, 100084 Beijing, China e-mail: fcsun@tsinghua.edu.cn

Optimal partition algorithm of the RBF neural network and its application to financial time series forecasting

A novel neural-network-based method of time series forecasting is presented in this paper. The method combines the optimal partition algorithm (OPA) with the radial basis function (RBF) neural network. OPA for ordered samples is used to perform the clustering for the samples. The centers and widths of the RBF neural network are determined based on the clustering. The difference of the objective functions of the clustering is used to adjust the structure of the neural network dynamically. Thus, the number of the hidden nodes is selected adaptively. The method is applied to stock price prediction. The results of numerical simulations demonstrate the effectiveness of the method. Comparisons with the hard c-means (HCM) algorithm show that the proposed OPA method possesses obvious advantages in the precision of forecasting, generalization, and forecasting trends. Simulations also show that the OPA–orthogonal least squares (OPA–OLS) algorithm, which combines OPA with the OLS algorithm, results in better performance for forecasting trends.

Maximum-entropy clustering algorithm and its global convergence analysis

Constructing a batch of differentiable entropy functions to uniformly approximate an objective function by means of the maximum-entropy principle, a new clustering algorithm, called maximumentropy clustering algorithm, is proposed based on optimization theory. This algorithm is a soft generalization of the hardC-means algorithm and possesses global convergence. Its relations with other clustering algorithms are discussed.