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

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

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

The present study attempts to find out the effect of some fish preservatives in the laboratory, such as alcohol and dilute formalin, on some biological characteristics related to the body measurements of those fish preserved in these materials. The fish used in this study were the local Planiliza abu. The processes of expansion and contraction of the bodies of fish preserved in diluted formalin solution at a concentration of 10% and diluted ethyl alcohol solution at a concentration of 70%. As that the standard length of the specimens of this study, which are separately preserved in formalin 10% and alcohol 70%, in a completely isolated are fluctuating in change. Constant shrinkage in head length in both diluted formalin and alcohol.

This article discusses some linguistic problems that arise when translating the Holy Quran from Arabic to Russian. We analyze lexical, syntactic and semantic problems and support them with Examples of verses from the Qur'an, since the Qur'an is the word of Allah. It contains prayers and instructions full of both literal representations and figurative comparisons. The identification of linguistic and rhetorical features challenges translators of the Holy Qur'an, especially when translating such literary devices as metaphor, assonance, epithet, irony, repetition, polysemy, metonymy, comparisons, synonymy and homonymy. The article analyzes: metaphor, metonymy, ellipsis, polysemy.

Comparative morphological study has been treated for two species of the genus Chaenorhinum (D.C.) Richb., These species were: 1. Chaenorhinum calycinum 2. Chaenorhinum rubrifolium (Robill. & cast. Ex Lam. & DC.) Fourr. The genus belong to the family Scorphulariaceae. Morphological characters has been studies for: root, stem, leaves, flowers (calyx, corolla, androcium including filaments and anthers, gynocium including ovary, style and stigma), fruits and seeds also has been characterized. Key for there two species presented using some quantitative characters. Other characters like shape of fruits and seeds were used too, and they were of a useful taxonomic value

The present study deals with the morphological and histological aspects of the forebrain(Cerebrum) in the Columba livia domestica (Gmelin, 1789) to identify the histoarchitecture of its layers. This bird' has a large head found as perpendicular to the longitudinal axis. The morphological results reveal that for brain (Cerebrum) pear shaped, its outer surface is smooth without folds or deep grooves. Cerebrum is made up of two regions, the Pallium and the Subpallium. The Cerebral cortex includes four layers of hyperpallium (Wulst) , Dorsolateral corticoid area (CDL), Hippocampus, Piriform cortex. The internal cortex of cerebrum consists of Dorsal Ventricle ridge which includes the mesopallium, nidopallium, and archospallium. All these reg

Comparative morphological study has been treated for two species of the genus Chaenorhinum (D.C.) Richb., These species were: 1. Chaenorhinum calycinum 2. Chaenorhinum rubrifolium (Robill. & cast. Ex Lam. & DC.) Fourr. The genus belong to the family Scorphulariaceae. Morphological characters has been studies for: root, stem, leaves, flowers (calyx, corolla, androcium including filaments and anthers, gynocium including ovary, style and stigma), fruits and seeds also has been characterized. Key for there two species presented using some quantitative characters. Other characters like shape of fruits and seeds were used too, and they were of a useful taxonomic value