Text based-image clustering (TBIC) is an insufficient approach for clustering related web images. It is a challenging task to abstract the visual features of images with the support of textual information in a database. In content-based image clustering (CBIC), image data are clustered on the foundation of specific features like texture, colors, boundaries, shapes. In this paper, an effective CBIC) technique is presented, which uses texture and statistical features of the images. The statistical features or moments of colors (mean, skewness, standard deviation, kurtosis, and variance) are extracted from the images. These features are collected in a one dimension array, and then genetic algorithm (GA) is applied for image clustering.

Most recognition system of human facial emotions are assessed solely on accuracy, even if other performance criteria are also thought to be important in the evaluation process such as sensitivity, precision, F-measure, and G-mean. Moreover, the most common problem that must be resolved in face emotion recognition systems is the feature extraction methods, which is comparable to traditional manual feature extraction methods. This traditional method is not able to extract features efficiently. In other words, there are redundant amount of features which are considered not significant, which affect the classification performance. In this work, a new system to recognize human facial emotions from images is proposed. The HOG (Histograms of Or

In this article the nanoparticles synthesis of ZnO (Nps) by using the precipitation method at concentrations range (0.5, 0.25, 0.125, 0.0625, 0.03125) mg/mL and then activity was examined against Streptococcus spp that causing dental caries in vitro by well diffusion method, find these concentrations effected in these bacteria and better concentration is 0.03125. ZnO Nps were characterization by EDS to prove this particles are ZnO, and also characterized by atomic force microscope (AFM), X-ray Diffraction (XRD) and TEM, from these technic found that the average size about 30.52 nm and hexagonal shape. The UV-visible result reveals that the large band is observed at 340.8 nm, Zeta potential show that the surface charge is 30.19 mv an

Intended for getting good estimates with more accurate results, we must choose the appropriate method of estimation. Most of the equations in classical methods are linear equations and finding analytical solutions to such equations is very difficult. Some estimators are inefficient because of problems in solving these equations. In this paper, we will estimate the survival function of censored data by using one of the most important artificial intelligence algorithms that is called the genetic algorithm to get optimal estimates for parameters Weibull distribution with two parameters. This leads to optimal estimates of the survival function. The genetic algorithm is employed in the method of moment, the least squares method and the weighted

In this study, genetic algorithm was used to predict the reaction kinetics of Iraqi heavy naphtha catalytic reforming process located in Al-Doura refinery in Baghdad.  One-dimensional steady state model was derived to describe commercial catalytic reforming unit consisting of four catalytic reforming reactors in series process.

The experimental information (Reformate composition and output temperature) for each four reactors collected at different operating conditions was used to predict the parameters of the proposed kinetic model. The kinetic model involving 24 components, 1 to 11 carbon atoms for paraffins and 6 to 11 carbon atom for naphthenes and aromatics with 71 reactions. The pre-exponential Arrhenius constants and a

The estimation of the parameters of linear regression is based on the usual Least Square method, as this method is based on the estimation of several basic assumptions. Therefore, the accuracy of estimating the parameters of the model depends on the validity of these hypotheses. The most successful technique was the robust estimation method which is minimizing maximum likelihood estimator (MM-estimator) that proved its efficiency in this purpose. However, the use of the model becomes unrealistic and one of these assumptions is the uniformity of the variance and the normal distribution of the error. These assumptions are not achievable in the case of studying a specific problem that may include complex data of more than one model. To

In this research, the focus was on estimating the parameters on (min- Gumbel distribution), using the maximum likelihood method and the Bayes method. The genetic algorithmmethod was employed in estimating the parameters of the maximum likelihood method as well as  the Bayes method. The comparison was made using the mean error squares (MSE), where the best  estimator  is the one who has the least mean squared error. It was noted that the best estimator was (BLG_GE).

A genetic algorithm model coupled with artificial neural network model was developed to find the optimal values of upstream, downstream cutoff lengths, length of floor and length of downstream protection required for a hydraulic structure. These were obtained for a given maximum difference head, depth of impervious layer and degree of anisotropy. The objective function to be minimized was the cost function with relative cost coefficients for the different dimensions obtained. Constraints used were those that satisfy a factor of safety of 2 against uplift pressure failure and 3 against piping failure.
Different cases reaching 1200 were modeled and analyzed using geo-studio modeling, with different values of input variables. The soil wa

Genetics play a major role in diabetic retinopathy DR, which is a leading cause of blindness worldwide. Although the factors such as how long a person has had diabetes and high blood sugar are important, the wide variation and progression of DR indicates to a clear genetic influence. Early studies focusing on a few candidate genes (such as VEGF) produced conflicting and population-dependent results. This confirmed the multifactorial nature of the disease and the limitations of small-scale studies. Conversely, genome wide association studies (GWAS) have provided more consistent findings in detection a new genes related to DR. For example, rs2239785 variant which is located in the APOL1 gene was found to be a high risk factor for diabetic mac

Introduction and Aim: Kruppel Like Factor 14 (KLF14) gene plays an important role in metabolic illnesses and is also involved in the regulation of many other biological processes. This study's objective was to determine whether or not the KLF14 single-nucleotide-polymorphism (SNP) known as rs972283 was linked to an increased risk of peptic ulcer disease in the population that was being investigated.   Materials and Methods: Participants in this study included 71 people who had been diagnosed with peptic ulcers and 50 people who were considered to be healthy controls. In order to genotype the KLF14 SNP rs972283, an amplification refractory mutation system-polymerase chain reaction (ARMS-PCR) was carried out, and the PCR results were