Computer
Basma Wael Abdullah; Hanaa Mohsin Ahmed
Abstract
since the global pandemic of COVID-19 has spread out, the use of Artificial Intelligence to analyze Chest X-Ray (CXR) image for COVID-19 diagnosis and patient treatment is becoming more important. This research hypothesized that using COVID19 radiographic changes in the X-Ray images. Artificial Intelligence ...
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since the global pandemic of COVID-19 has spread out, the use of Artificial Intelligence to analyze Chest X-Ray (CXR) image for COVID-19 diagnosis and patient treatment is becoming more important. This research hypothesized that using COVID19 radiographic changes in the X-Ray images. Artificial Intelligence (AI) systems may extract certain graphical elements regarding COVID-19 and offer a clinical diagnosis ahead of pathogenic test; therefore, saving vital time for disease prevention. Employing 2614 CXR radiographs from Kaggle data collection of verified COVID-19 cases and healthy persons, a new Convolutional Neural Network (CNN) model that is inspired by the Xception architecture was presented for the diagnosis of coronavirus pneumonia infected patients. The suggested technique reached an average validation accuracy of 0.99, precision of 0.95, recall of 0.92, and F1-score of 0. 95. Finally, such findings revealed that the Deep Learning (DL) technique has the potential to decrease frontline radiologists' stress, enhance early diagnosis, treatment, and isolation; therefore, aid in epidemic control.
H. Saeed Essad; Hanaa Mohsin Ahmed
Abstract
Due to the fact that the risk factor in the international border is very high, it causes threats affecting soldiers’ lives, border military facility and state security. In fields where there are difficulties for people to go or where human life may be endangered (such as places that contain the ...
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Due to the fact that the risk factor in the international border is very high, it causes threats affecting soldiers’ lives, border military facility and state security. In fields where there are difficulties for people to go or where human life may be endangered (such as places that contain the harmful gases and explosive things). Human guards may be substituted by a robot system that is designed for the purpose of taking care of the dangerous tasks of surveillance. The main objective of this paper is to build an intelligent surveillance robot with high accuracy to detect intrusions, easy to use and inexpensive. This paper includes a new contribution by integrating intelligent algorithms into monitoring systems and robotics technology, which is a strong addition to the research through the accuracy of the system. The system provides a modern monitoring method for detecting and recognizing faces using a robot equipped with a pi camera, sensors and a control panel. The result of the proposal is a system that uses face detection and recognition by utilizing HAAR algorithm, and CNN algorithm, the system percentage accuracy becomes 99.87%.and the loss is 0.013. The proposed have high accuracy, effective, easy to use, with low cost, can be used to guard critical infrastructures, large facilities, and national borders.
Computer
shayma Ashor; Hanaa Mohsin Ahmed
Abstract
In the last few years, many applications have viewed great development, such as smart city applications, social media, smartphones, security systems, etc. In most of these applications, facial recognition played a major role. The work of these applications begins by locating the face within the image ...
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In the last few years, many applications have viewed great development, such as smart city applications, social media, smartphones, security systems, etc. In most of these applications, facial recognition played a major role. The work of these applications begins by locating the face within the image and then recognizing the face. The circumstances surrounding the person at the moment of taking the picture greatly affect the accuracy of these applications, especially the inappropriate lighting. Therefore, the stage of preparing the images is very important in the work. To solve this problem, we proposed a system that combines the use of gamma and Histogram Equalization algorithm (HE) to improve the images before starting to detect the face using the Viola-Jones. Then extract the facial features and identify the person using convolutional neural networks. The proposed system achieved a very small error rate and an accuracy during training that reached 100%.
