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

These With recent developments in machine learning, data mining and computer vision, there is great potential for improvements in early detection of lung cancer using scans and data available. This paper details the methods and techniques used in our project, where the objective is to develop algorithms to determine whether a patient has or is likely to develop lung cancer using dataset images using data mining and machine learning for the classification and examination. We explore approaches to address the problem. Cancer is the most important cause of death globally. The disease diagnosis is a major process to treat the patients who are affected by cancer disease. The diagnosis process is more difficult comparatively known about t

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

     There is poverty because of the difference in capacity and material resources, Previously poverty known on the basis of disparity between income and inadequate income. It realize later that fare wore effects of poverty is the erosion of human capital. The human poverty is the loss of food, education, health care and shelter.

     In order to provide a database that target the poor  , it have been propped a document on the features of poverty and the whereabouts of the poor and the rate of disparity between provinces.

    Here the goal of the research is the identify the factors affecti

Background: Pressure ulcers remain a serious complication for immobile patients and a burden for healthcare professionals. Objectives: To assess health behavior prevention among critical care nurses regarding pressure ulcer prevention for hospitalized patients and to find out the relationship between critical care nurses health behavior prevention and sociodemographic variables. Methods: A cross-sectional design study was carried out in critical care units at three teaching hospitals. The study period extended from November 1, 2022, to January 28, 2023. Non-probability purposive sampling, whose target population was 100 nurses who work in critical care units in Baghdad, Iraq. The data were collected using a self-administered questio