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: The diagnosis of interstitial lung disease (ILD) is frequently delayed, because clinical clues are neglected and respiratory symptoms are ascribed to more common pulmonary diagnosis such as asthma and chronic obstructive pulmonary disease in the primary care setting.
Objective: To evaluate the diagnostic yield of open lung biopsy in patients with suspected ILD in relation to clinical and radiological features.
Patients and methods: Thirty-five patients were admitted with suspected interstitial lung disease (ILD), and scheduled for open lung biopsy (OLB) in Ghazi AL-Hariri hospital for surgical specialty, were included in this study. Data collected from the patient's files (who were subjected to open lung biopsies which

Background:

Expansion the engineering consultancy offices in the universities of Iraq, about (14) offices, leading to increas the competition between them, especially after the great trends of Iraqi government agencies to use the academic experiences and their efficiencies in general, due to non-existence of the engineering qualification in the government institutions to do the engineering designs ,supervision of projects and other engineering works which are practicing by the engineering consultancy offices in order to get the best performance of the work.Within this serious competition, needing a specific approach to enable government agencies to choose the optimal and alternative consultancy office to meet specific project and not rely on cronyis

The present study aimed to identify the exact location and its relation to cognitive
method (risk_caution) to university students. The sample consisted of (300) students who
were chosen randomly and equally. The study results indicated that students possess an
internal exact location and they also use risk cognitive method. The study also indicated that
there is a prophesies which is an exact location for others to reach to caution cognitive
method. Depending on these results, the study recommended to benefit from the results of the
study variables and from the measurement of the exact location and method of cognitive
(risk_caution) to identify male and female students and especially by consulting units at
college