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

Evaluation of trace elements in Iraqi chewing gums are unavailable, particularly pollution of toxic elements, materials which change the values of PH in the Oral. Atomic Absorption Spectroscopy (AAS) were successfully employed to determine the concentration of 7 trace elements (essentially toxic and nonessential) and the PH, in thirteen different brands of chewing gum generally consumed in Iraq. Combined wet and dry digestion procedures were applied. Two types of heated graphite tubes were used, coated and uncoated tubes treated with tungsten solution. Result showed that Cu, Al and Zn were at very high levels in almost all brands whereas Mn was found to be high in brands A and O only.

Generation of new clones of multidrug resistant infectious foci of environmental, zoonotic and reverse zoonotic Enterococcus faecalis & faecium strains from cases of mastitis in cows privileged in Baghdad ecosystem reveal unsafe tricks. Medically important plants Blackcurrant, London Plane and Pine Buds are widely used for resolving these public health threats and hazards as powerful antimicrobial preparations. Totally sixty pooled milk samples were collected from apparently healthy (30 units) and infected (30 units) Cows from different regions of Abu-Ghraib during March until July (2019). Integrated units were checked by California mastitis test and a designed scheme was dependent for recovery of pathogenic Enterococci. Gram staining and c

Rotaviruses, a major cause of gastroenteritis in children worldwide accounts for around half a million deaths annually. Vaccine against the virus has been recommended by the WHO to be involved in the national immunization program.To evaluate the genetic characterization of rotavirus among children with acute gastroenteritis in Babylon province is warranted. Children complained of diarrhea with rotavirus infection detected in their stool were involved in the study. The age range was 10 months to 60 months and with retrogradechecking of rotavirus vaccine history. Rotavirus genotypes were detected by reverse transcription-polymerase chain reaction (RT-PCR).A total number of 40 children with rotavirus gastroenteritis were used to examine the

The research aimed to modeling a structural equation for tourist attraction factors in Asir Region. The research population is the people in the region, and a simple random sample of 332 individuals were selected. The factor analysis as a reliable statistical method in this phenomenon was used to modeling and testing the structural model of tourism, and analyzing the data by using SPSS and AMOS statistical computerized programs. The study reached a number of results, the most important of them are: the tourist attraction factors model consists of five factors which explain 69.3% of the total variance. These are: the provision of tourist services, social and historic factors, mountains, weather and natural parks. And the differenc