Background: Cardiopulmonary resuscitation (CPR) is a technique or procedure that combined chest compression and rescue breathing to maintain enough circulation that prevents brain damage until other essential steps are taken to control the main cause of cardiac and respiratory arrest. The health care personnel should be qualified in the performing of cardiopulmonary resuscitation (CPR) to improve the survival rate of the victims. Therefore; it is necessary to use new methods for learning [1]. Objectives: the study aims to compare the effectiveness of self-instructional teaching strategy and traditional teaching approach on student’s knowledge toward cardiopulmonary resuscitation. Methods: A randomized comparative trial (RCT) design was ca

The present research aims to test the effect of cognitive complexity as an independent variable in organizational agility as a responsive variable among the leaders working at the headquarters of the Iraqi Petroleum Products Distribution Company.

 To conclude a number of recommendations that contribute in the organizational agility in the company, and due to the importance of this research in public organizations and its notable role in community organizations. The research was carried out on a random sample of 101 individuals out of a total of 308, which represents the high leaders in the company (general managers, head of departments, and division officials). A questionnaire was used as information

Objectives: To identify the impact of the brain consensus model on the acquisition of Arabic grammar concepts among students in the fourth grade, methodology: The pilot curriculum was used, and a partial control pilot design was adopted. There were 30 female students in the pilot group, 30 female students in the control group, and the two researchers were statistically rewarded among the two groups' students in some variables and used appropriate statistical means to analyse the results, including the test for two independent samples, the square (c2) and the Alpha Kronbach equation.Results: The pilot group outperformed the control group. The results showed that there is a significant statistical difference at the indicative level (0.05) for

The convergence speed is the most important feature of Back-Propagation (BP) algorithm. A lot of improvements were proposed to this algorithm since its presentation, in order to speed up the convergence phase. In this paper, a new modified BP algorithm called Speeding up Back-Propagation Learning (SUBPL) algorithm is proposed and compared to the standard BP. Different data sets were implemented and experimented to verify the improvement in SUBPL.

Early diagnosis and clinical decision-making depend on accurate brain tumor classification using magnetic resonance imaging (MRI). However, traditional deep learning methods usually rely on centralized medical data, which raises privacy concerns and limits the use of distributed clinical data. This research proposes a privacy-preserving federated learning framework for MRI image-based binary brain tumor classification using a decentralized ResNet-18 architecture that enables collaborative training without sharing raw patient data. To reflect realistic clinical conditions, the framework integrates heterogeneous multi-source datasets in different image formats (PNG and JPG) and evaluates performance under both IID and non-IID settings

Breast cancer is a heterogeneous disease characterized by molecular complexity. This research utilized three genetic expression profiles—gene expression, deoxyribonucleic acid (DNA) methylation, and micro ribonucleic acid (miRNA) expression—to deepen the understanding of breast cancer biology and contribute to the development of a reliable survival rate prediction model. During the preprocessing phase, principal component analysis (PCA) was applied to reduce the dimensionality of each dataset before computing consensus features across the three omics datasets. By integrating these datasets with the consensus features, the model's ability to uncover deep connections within the data was significantly improved. The proposed multimodal deep

Computer-aided diagnosis (CAD) has proved to be an effective and accurate method for diagnostic prediction over the years. This article focuses on the development of an automated CAD system with the intent to perform diagnosis as accurately as possible. Deep learning methods have been able to produce impressive results on medical image datasets. This study employs deep learning methods in conjunction with meta-heuristic algorithms and supervised machine-learning algorithms to perform an accurate diagnosis. Pre-trained convolutional neural networks (CNNs) or auto-encoder are used for feature extraction, whereas feature selection is performed using an ant colony optimization (ACO) algorithm. Ant colony optimization helps to search for the bes