This study aims to find out the effect of the mediator on scaffolding fourth yearstudent- teachers' teaching competencies and their self-efficacy. The present study combines scaffolding and self-efficacy by using a mediator on scaffolding students affects teaching competencies and selfefficacy and from the results of which the existence of student-teachers’ selfawareness was ensured as an effect of the same independent variable. The model affects their teaching competencies and led them to be aware of the needs of their pupils and themselves. 

Acute decompensated heart failure (ADHF) is a leading cause of hospital admission and many factors are known to precipitate decompensation. We aimed to assess the decompensating factors of heart failure and the management of patients admitted to the emergency department (ED). A total of 107 patients were examined, all diagnosed with ADHF in the ED of the Baghdad Teaching Hospital, from June 2017 to December 2017, and presenting with decom¬pensation (pulmonary oedema, peripheral oedema, and fatigue). The mean patient age was 62.5 ± 9.8 years (range: 43–85 years); the majority of them were in their 7th decade (37.4%), and men were slightly more than women. Hy¬pertension was the most commonly associated comorbidity (68.2%), follow

During COVID-19, wearing a mask was globally mandated in various workplaces, departments, and offices. New deep learning convolutional neural network (CNN) based classifications were proposed to increase the validation accuracy of face mask detection. This work introduces a face mask model that is able to recognize whether a person is wearing mask or not. The proposed model has two stages to detect and recognize the face mask; at the first stage, the Haar cascade detector is used to detect the face, while at the second stage, the proposed CNN model is used as a classification model that is built from scratch. The experiment was applied on masked faces (MAFA) dataset with images of 160x160 pixels size and RGB color. The model achieve

Recurrent strokes can be devastating, often resulting in severe disability or death. However, nearly 90% of the causes of recurrent stroke are modifiable, which means recurrent strokes can be averted by controlling risk factors, which are mainly behavioral and metabolic in nature. Thus, it shows that from the previous works that recurrent stroke prediction model could help in minimizing the possibility of getting recurrent stroke. Previous works have shown promising results in predicting first-time stroke cases with machine learning approaches. However, there are limited works on recurrent stroke prediction using machine learning methods. Hence, this work is proposed to perform an empirical analysis and to investigate machine learning al

During COVID-19, wearing a mask was globally mandated in various workplaces, departments, and offices. New deep learning convolutional neural network (CNN) based classifications were proposed to increase the validation accuracy of face mask detection. This work introduces a face mask model that is able to recognize whether a person is wearing mask or not. The proposed model has two stages to detect and recognize the face mask; at the first stage, the Haar cascade detector is used to detect the face, while at the second stage, the proposed CNN model is used as a classification model that is built from scratch. The experiment was applied on masked faces (MAFA) dataset with images of 160x160 pixels size and RGB color. The model achieve