Abstract

Balance is a psychological need and a requirement of importance as the individual seeks to maintain it.  The research problem is summarized in the question: do students in the kindergarten department have a cognitive balance? The research aims to identify the cognitive balance of students in the kindergarten department and to identify if there are differences among students of the four stages according to the cognitive balance. The research community was limited to the students in the kindergarten department at the University of Baghdad / College of Education for Women and Al-Mustansiriya University / College of Basic Education and Iraqi University / College of Education for Women for the academic y

Background: Practicing self-medication is common and a worrisome issue because of irrational drug use. This study aimed to evaluate self-medication knowledge and views among the final year pharmacy students in Iraq.  Methods: A cross-sectional descriptive study was conducted from December 2018 to January 2019. A pre-validated and self-administered questionnaire was recruited to survey pharmacy students at the University of Baghdad and Al-Rafedain University College. The Statistical Package for the Social Sciences version 20 (SPSS v. 20) software used to save and analyze the data. Results expressed as numbers and percentages. Results:  A total of 344 students (response rate: 94.24%) with a mean age of 22.10 years includ

Background: Dental anomalies are abnormal tooth condition occurs due to certain genes or environmental factor disturb tooth development during morphodifferentiation stages, affecting group of teeth or entire dentition. The aim of the study is to evaluate the frequency of occurrence of dental anomalies among dental student. Materials and Methods: Three hundred dental students with age ranged (18-23 years) were examined clinically and radiographically searching for dental anomalies in shape, number and position. Results: Only 25 students (8.3 %) from the total sample (300) had dental anomalies, female form the higher percentage of anomalies (about 68%) compared to male (32%). The age group (22 years) which was the larger group had a higher

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

A Wearable Robotic Knee (WRK) is a mobile device designed to assist disabled individuals in moving freely in undefined environments without external support. An advanced controller is required to track the output trajectory of a WRK device in order to resolve uncertainties that are caused by modeling errors and external disturbances. During the performance of a task, disturbances are caused by changes in the external load and dynamic work conditions, such as by holding weights while performing the task. The aim of this study is to address these issues and enhance the performance of the output trajectory tracking goal using an adaptive robust controller based on the Radial Basis Function (RBF) Neural Network (NN) system and Hamilton

The deep learning algorithm has recently achieved a lot of success, especially in the field of computer vision. This research aims to describe the classification method applied to the dataset of multiple types of images (Synthetic Aperture Radar (SAR) images and non-SAR images). In such a classification, transfer learning was used followed by fine-tuning methods. Besides, pre-trained architectures were used on the known image database ImageNet. The model VGG16 was indeed used as a feature extractor and a new classifier was trained based on extracted features.The input data mainly focused on the dataset consist of five classes including the SAR images class (houses) and the non-SAR images classes (Cats, Dogs, Horses, and Humans). The Conv

The Machine learning methods, which are one of the most important branches of promising artificial intelligence, have great importance in all sciences such as engineering, medical, and also recently involved widely in statistical sciences and its various branches, including analysis of survival, as it can be considered a new branch used to estimate the survival and was parallel with parametric, nonparametric and semi-parametric methods that are widely used to estimate survival in statistical research. In this paper, the estimate of survival based on medical images of patients with breast cancer who receive their treatment in Iraqi hospitals was discussed. Three algorithms for feature extraction were explained: The first principal compone

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Distributed Denial of Service (DDoS) attacks on Web-based services have grown in both number and sophistication with the rise of advanced wireless technology and modern computing paradigms. Detecting these attacks in the sea of communication packets is very important. There were a lot of DDoS attacks that were directed at the network and transport layers at first. During the past few years, attackers have changed their strategies to try to get into the application layer. The application layer attacks could be more harmful and stealthier because the attack traffic and the normal traffic flows cannot be told apart. Distributed attacks are hard to fight because they can affect real computing resources as well as network bandwidth. DDoS attacks