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.

Plant regeneration protocols were developed for medicinally important anise (Pimpinella anisum L.) that successfully achieved from seeds. Seeds were sterilized and inoculated on Murashige and Skoog (MS) medium with and without gibberellins (GA3) until full germination. The highest percentage of germination (100%) was recorded on MS medium treated with 2.0 mg/L GA3 after 7 days. For shoot proliferation, different concentrations of 6- benzyl adenine BA (1, 1.5, 2 mg/L) were used. To enhance shoot induction, 0.1 mg/L of naphthalene acetic acid (NAA) and 0.01 mg/L of thidiazuron (TDZ) were tested along with BA. Direct regenerated shoots were obtained on MS medium supplemented with BA alone (2mg/L) which gave (7shoot/explant), while the presence

This paper is a part of a cytological studies of the genus Bromus L. in Iraq . Cl number for 103 specimens ropro^eming l^species determined . The course of meiosis including an analysis of chromosome configuradon for 27 collections belonging to 8 species were investigated . Meiosis was regular in most species . Tetraploid for B.scoparius L. (new cytotype ) and hexaploid ?'or " Kunth. were re^rted for the first time, cytological findings were supported the view of maintaining the closely related species B.danthoniae Trin. and B.lanceolatus Roth, as distinct species .

Some of the main challenges in developing an effective network-based intrusion detection system (IDS) include analyzing large network traffic volumes and realizing the decision boundaries between normal and abnormal behaviors. Deploying feature selection together with efficient classifiers in the detection system can overcome these problems.  Feature selection finds the most relevant features, thus reduces the dimensionality and complexity to analyze the network traffic.  Moreover, using the most relevant features to build the predictive model, reduces the complexity of the developed model, thus reducing the building classifier model time and consequently improves the detection performance.  In this study, two different sets of select

In the image processing’s field and computer vision it’s important to represent the image by its information. Image information comes from the image’s features that extracted from it using feature detection/extraction techniques and features description. Features in computer vision define informative data. For human eye its perfect to extract information from raw image, but computer cannot recognize image information. This is why various feature extraction techniques have been presented and progressed rapidly. This paper presents a general overview of the feature extraction categories for image.

Image classification is the process of finding common features in images from various classes and applying them to categorize and label them. The main problem of the image classification process is the abundance of images, the high complexity of the data, and the shortage of labeled data, presenting the key obstacles in image classification. The cornerstone of image classification is evaluating the convolutional features retrieved from deep learning models and training them with machine learning classifiers. This study proposes a new approach of “hybrid learning” by combining deep learning with machine learning for image classification based on convolutional feature extraction using the VGG-16 deep learning model and seven class

Image classification is the process of finding common features in images from various classes and applying them to categorize and label them. The main problem of the image classification process is the abundance of images, the high complexity of the data, and the shortage of labeled data, presenting the key obstacles in image classification. The cornerstone of image classification is evaluating the convolutional features retrieved from deep learning models and training them with machine learning classifiers. This study proposes a new approach of “hybrid learning” by combining deep learning with machine learning for image classification based on convolutional feature extraction using the VGG-16 deep learning model and seven class

Background: In young adults, multiple sclerosis is a prevalent chronic inflammatory demyelinating condition. It is characterized by white matter affection, but many individuals also have significant gray matter involvement. A double-inversion recovery pulse (DIR) pattern was recently proposed to improve the visibility of multiple sclerosis lesions. Objective: To find out how well a DIR sequence, FLAIR, and T2-weighted pulse sequences can find MS lesions in the supratentorial and infratentorial regions. Methods: A total of 37 patients with established diagnoses of multiple sclerosis were included in this cross-sectional study. Brain MRI was done using double inversion recovery, T2, and FLAIR sequences. The number of lesions was count

Experiment was conducted using clay pots at botanical garden of Department of Biology/ College of Education for Pure Science / Ibn –AL- Haitham, University of Baghdad during the growth season of (2013-2014).The experiment aimed to study the effect of NPK Zn fertilizer in (15-15-15-1)% with the levels 0.2, 0.4, 0.6 grams per five kilograms soil based on (80, 120, 240 kg per Hectare)in some of morphological characters and yield of Vigna radiatia. The results showed a significant increase in plant height, dry weight, biological yield, economic yield, weight of 100 seeds, harvest index, absolute growth rate (AGR) were all increased and amount of output compared with control plants.

Disturbances of lipid metabolism is a predisposing factor of cardiovascular diseases, which are accompanied by high mortality rates of the population - ischemic heart disease and ischemic stroke. Plant polysaccharides can be promising remedies for correction of lipid imbalance.

In this regard, the aim of the study was to assess the hypolipidemic activity of polysaccharides isolated from the leaves of mistletoe.

The test-object was the leaves of the white mistletoe (Viscum album L.) growing on the apple (Malus domestica Borkh.), and the pear (Pyrus communis L.). Polysaccharides (water-soluble polysaccharides and pectin substances) were quantitatively determined by the gravimetric method followed