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 this paper, a new equivalent lumped parameter model is proposed for describing the vibration of beams under the moving load effect. Also, an analytical formula for calculating such vibration for low-speed loads is presented. Furthermore, a MATLAB/Simulink model is introduced to give a simple and accurate solution that can be used to design beams subjected to any moving loads, i.e., loads of any magnitude and speed. In general, the proposed Simulink model can be used much easier than the alternative FEM software, which is usually used in designing such beams. The obtained results from the analytical formula and the proposed Simulink model were compared with those obtained from Ansys R19.0, and very good agreement has been shown. I

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

The 3D electro-Fenton technique is, due to its high efficiency, one of the technologies suggested to eliminate organic pollutants in wastewater. The type of particle electrode used in the 3D electro-Fenton process is one of the most crucial variables because of its effect on the formation of reactive species and the source of iron ions. The electrolytic cell in the current study consisted of graphite as an anode, carbon fiber (CF) modified with graphene as a cathode, and iron foam particles as a third electrode. A response surface methodology (RSM) approach was used to optimize the 3D electro-Fenton process. The RSM results revealed that the quadratic model has a high R2 of 99.05 %. At 4 g L-1 iron foam particles, time of 5 h, and

This study aims to study some morphological and reproductional characteristics in eleven species of two genera belonging to the family of Asparagaceae, which are Bellevalia Lapeyrouse, 1808 and Ornithogalum Linnaeus, 1753 and the species are: Bellevalia chrisii Yildirim and Sahin, 2014; Bellevalia flexuosa Boissier, 1854; Bellevalia kurdistanica Feinbrun, 1940; Bellevalia longipes Post, 1895; Bellevalia macrobotrys Boissier, 1853; Bellevalia paradoxa Boissier, 1882; Bellevalia parva Wendelbo, 1973; Bellevalia saviczii Woronow, 1927; Ornithogalum brachystachys C. Koch, 1849; Ornithogalum neurostegium Boissier, 1882 and Ornithogalum pyrenaicum Linnaeus, 1753. These species were identified and compared with each other; the results showed th

Among more than 200 different human papilloma viral genotypes, the association of low oncogenic risk-HPV genotypes have been recognized with a variety of oral, oropharyngeal, nasopharyngeal benign tumors as well as non-neoplastic polyposis and papillomas and adenoid hypertrophy. This prospective case- control study aims to determine the rate of DNA detection of HPV genotype 6/11 in nasopharyngeal adeno- tonsillar tissues from a group of patients subjected to adenoctomy for adenoid hypertrophy . A total number of nasopharyngeal adeno-tonsillar tissue specimens from pediatric patients with adenoid hypertrophy were enrolled; 40 nasopharyngeal adeno-tonsillar tissues from patients with adenoid hypertrophy, and 20 normal nasal tissue specimen

The heavy metal cadmium is extremely harmful to both humans and animals. Zinc supplementation protects the biological system and reduces cadmium-induced toxicity. This study aimed to determine whether zinc chloride (ZnCl2) could protect male mice with the damaged liver induced by cadmium chloride (CdCl2). The protective role of zinc chloride and expression of the metallothionein (MT), Ki-67, and Bcl-2 apoptotic proteins in hepatocytes were studied after subchronic exposure of mice to cadmium chloride for 21 days. Thirty male mice were randomly categorized into 6 groups (5 mice/group) as follows: a control group that did not receive any treatment, a group given ZnCl2 at 10 mg/kg alone, and two groups received ZnCl2 (10 mg/kg) i

Background: Congenital cardiac defects have a wide spectrum of severity in infants. About 30-40% of patients with congenital cardiac defects will be symptomatic in the 1^st year of life, while the diagnosis was established in 60% of patients by the 1^st month of age.

Objectives: To identify the occurrence of specific types of CHD among hospitalized patients and to evaluate of growth of patients by different congenital heart lesions.

Methods: A retrospective study, done on ninety-six patients (51 male and 45 female) with congenital heart disease (CHD) admitted to central teaching hospital of pediatrics, Baghdad from 1^st September 2009 to 30