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

Gingival crevicular fluid (GCF) may reflect the events associated with orthodontic tooth movement. Attempts have been conducted to identify biomarkers reflecting optimum orthodontic force, unwanted sequallea (i.e. root resorption) and accelerated tooth movement. The aim of the present study is to find out a standardized GCF collection, storage and total protein extraction method from apparently healthy gingival sites with orthodontics that is compatible with further high-throughput proteomics. Eighteen patients who required extractions of both maxillary first premolars were recruited in this study. These teeth were randomly assigned to either heavy (225g) or light force (25g), and their site specific GCF was collected at baseline and aft

The aim of this study is to investigate oxidative stress and leptinlevel in obese women. Fifty obese women with BMI value of ≥ 30.0 kg/m² and thirty healthy women with BMI<24.9 kg/m² were involved in the study during their attendance at Gastroenterology and Liver Hospital/ Medical city in Baghdad province from October 2018 to February 2019. The age range for all women was 30-50 years. Blood samples were collected from each participant for the evaluation of the levels of leptin hormone, Super Oxide Dismutase (SOD), and Malondialdehyde (MDA). The results of BMI, leptin hormone, MDA and SOD showed highly significant increase (P<0.01) in obese women in comparison with the control group.

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To determine the role of Epstien barr virus (EBV) in the pathogenesis of Rheumatoid arthritis (RA) , Reactive arthritis (ReA) and Ankylosing spondylitis (AS) patients, sixty-two patients (52 patients with RA ,five patients with ReA and five patients with AS) have been investigated. The mean age (47.3, 47.7, 34.5) of RA, ReA , AS respectively and compared to 24 apparently healthy individuals with the mean age 28.3. All the study groups were carried out to measure antibodies of EBV (VCA) IgG and IgM by enzyme-linked immunosorbent assay (ELISA) technique. The result showed that there was a highly significant elevation ( p< 0.01) in the concentration of EBV (VCA) IgG Ab compared to control group, while there was no significant different (