Lung cancer is the most common dangerous disease that, if treated late, can lead to death. It is more likely to be treated if successfully discovered at an early stage before it worsens. Distinguishing the size, shape, and location of lymphatic nodes can identify the spread of the disease around these nodes. Thus, identifying lung cancer at the early stage is remarkably helpful for doctors. Lung cancer can be diagnosed successfully by expert doctors; however, their limited experience may lead to misdiagnosis and cause medical issues in patients. In the line of computer-assisted systems, many methods and strategies can be used to predict the cancer malignancy level that plays a significant role to provide precise abnormality detectio

Diabetic retinopathy is an eye disease in diabetic patients due to damage to the small blood vessels in the retina due to high and low blood sugar levels. Accurate detection and classification of Diabetic Retinopathy is an important task in computer-aided diagnosis, especially when planning for diabetic retinopathy surgery. Therefore, this study aims to design an automated model based on deep learning, which helps ophthalmologists detect and classify diabetic retinopathy severity through fundus images. In this work, a deep convolutional neural network (CNN) with transfer learning and fine tunes has been proposed by using pre-trained networks known as Residual Network-50 (ResNet-50). The overall framework of the proposed

This study aims to examine the relationship between emotional intelligence and academic adaptation among a sample of gifted students in intermediate and high schools in Jeddah, Saudi Arabia. The study also seeks to examine the differences between group means in emotional intelligence and academic adaptation due to demographic variables (gender and school level). In addition, the study aims to examine the role of emotional intelligence in predicting the level of academic adaptation. The researcher performed the descriptive, correlational, predictive, and comparative approaches to collect the data from a sample comprised of (309) gifted students using the emotional intelligence scale developed by Bar-on (2000), whi

The present study aims at identifying the effect of Renzulli model on achievement and holistic thinking among the fifth grade students in the Holy Quran and Islamic Education. The experimental method with partial control was used, and the sample was chosen randomly. The sample consisted of (62) students distributed into experimental and control groups. 164 behavioral goals were formulated based on Bloom's first three levels taxonomy (knowledge and comprehension, application, analysis and installation, and evaluation). The researchers designed a post-test to measure the achievement of students in the subject of Holy Quran and Islamic Education which consisted of (40) objective items and a measure of holistic thinking which consisted of (3

The objective of the research is to identify the effect of an instructional design according to the active learning modelsالباحثين in the achievement of the students of the fifth grade, the instructional design was constructed according to the active learning models for the design of education. The research experience was applied for a full academic year (the first & the second term of 2017-2018). The sample consisted of 58 students, 28 students for the experimental group and 30 students for the control group. The experimental design was adopted with partial and post-test, the final achievement test consisted of (50) objectives and essays items on two terms, the validity of the test was verified by the adoption of the Kudoric

The proliferation of many editing programs based on artificial intelligence techniques has contributed to the emergence of deepfake technology. Deepfakes are committed to fabricating and falsifying facts by making a person do actions or say words that he never did or said. So that developing an algorithm for deepfakes detection is very important to discriminate real from fake media. Convolutional neural networks (CNNs) are among the most complex classifiers, but choosing the nature of the data fed to these networks is extremely important. For this reason, we capture fine texture details of input data frames using 16 Gabor filters indifferent directions and then feed them to a binary CNN classifier instead of using the red-green-blue

Analyzing sentiment and emotions in Arabic texts on social networking sites has gained wide interest from researchers. It has been an active research topic in recent years due to its importance in analyzing reviewers' opinions. The Iraqi dialect is one of the Arabic dialects used in social networking sites, characterized by its complexity and, therefore, the difficulty of analyzing sentiment. This work presents a hybrid deep learning model consisting of a Convolution Neural Network (CNN) and the Gated Recurrent Units (GRU) to analyze sentiment and emotions in Iraqi texts. Three Iraqi datasets (Iraqi Arab Emotions Data Set (IAEDS), Annotated Corpus of Mesopotamian-Iraqi Dialect (ACMID), and Iraqi Arabic Dataset (IAD)) col

The effect of cognitive trips via the Internet (web quest) accompanying practical lessons in learning some basic handball skills for female students

The study aims to examine the classroom activities of the developed English course (Flying High) for the high school first-grade students, identify creative thinking skills appropriate for this grade, and show the extent the classroom activities involve these skills from the female- teachers ‘point of view. The study adopted the descriptive survey method. The study community consists of all (50) English female-teachers who teach high school first grade in Arar city during the academic year (1440 -1441 A.H, the first semester). The study was applied to all respondents. The researcher used a questionnaire as a study tool. The study revealed that the female-teachers reported their disagreement and refusal of the classroom activities in th