The aim of the present study is to evaluate the effectiveness of using Art as therapy to reduce the symptoms of Attention Deficit Hyper Activity Disorder (ADHD), in primary school children.
A clinical approach was used to test the validity of the hypothesis of our study, conducted on two second and fourth-year primary school pupils from Algiers, aged 7 and 9 years respectively.
In addition to the clinical observation and interview, we made use of the "Conners" scale for a (pre and post intervention) ADHD assessment, consisting of a combination of Art media in the form of mosaic works on purposely prepared panels. After 10 therapy sessions, results revealed the effectiveness of Art therapy in reducing ADHD in primary education

Ethnographic research is perhaps the most common applicable type of qualitative research method in psychology and medicine. In ethnography studies, the researcher immerses himself in the environment of participants to understand the cultures, challenges, motivations, and topics that arise between them by investigating the environment directly. This type of research method can last for a few days to a few years because it involves in-depth monitoring and data collection based on these foundations. For this reason, the findings of the current study stimuli the researchers in psychology and medicine to conduct studies by applying ethnographic research method to investigate the common cultural patterns language, thinking, beliefs, and behavior

Medicine is one of the fields where the advancement of computer science is making significant progress. Some diseases require an immediate diagnosis in order to improve patient outcomes. The usage of computers in medicine improves precision and accelerates data processing and diagnosis. In order to categorize biological images, hybrid machine learning, a combination of various deep learning approaches, was utilized, and a meta-heuristic algorithm was provided in this research. In addition, two different medical datasets were introduced, one covering the magnetic resonance imaging (MRI) of brain tumors and the other dealing with chest X-rays (CXRs) of COVID-19. These datasets were introduced to the combination network that contained deep lea

Objective: The aims of research to identify sample of websites of Arabian medical periodicals and exam applying to
standards for publishing on the internet.
Methodology: A survey method is applied about nine medical periodicals websites and data are collected through
forms include five international standards to assessing websites.
Results: of data collected, the following findings are obtained:
1. Through examining website addresses, unsuitability was found in using Universal Resources Locater, because six of
periodicals use com. in URL. While, all of them not relevance commercial but scientific aim.
2. To measure Credibility Standard by adopting numbers values, the results found, four of periodicals obtained (level

Conditional logistic regression is often used to study the relationship between event outcomes and specific prognostic factors in order to application of logistic regression and utilizing its predictive capabilities into environmental studies. This research seeks to demonstrate a novel approach of implementing conditional logistic regression in environmental research through inference methods predicated on longitudinal data. Thus, statistical analysis of longitudinal data requires methods that can properly take into account the interdependence within-subjects for the response measurements. If this correlation ignored then inferences such as statistical tests and confidence intervals can be invalid largely.

Problem: Cancer is regarded as one of the world's deadliest diseases. Machine learning and its new branch (deep learning) algorithms can facilitate the way of dealing with cancer, especially in the field of cancer prevention and detection. Traditional ways of analyzing cancer data have their limits, and cancer data is growing quickly. This makes it possible for deep learning to move forward with its powerful abilities to analyze and process cancer data. Aims: In the current study, a deep-learning medical support system for the prediction of lung cancer is presented. Methods: The study uses three different deep learning models (EfficientNetB3, ResNet50 and ResNet101) with the transfer learning concept. The three models are trained using a

—Medical images have recently played a significant role in the diagnosis and detection of various diseases. Medical imaging can provide a means of direct visualization to observe through the human body and notice the small anatomical change and biological processes associated by different biological and physical parameters. To achieve a more accurate and reliable diagnosis, nowadays, varieties of computer aided detection (CAD) and computer-aided diagnosis (CADx) approaches have been established to help interpretation of the medical images. The CAD has become among the many major research subjects in diagnostic radiology and medical imaging. In this work we study the improvement in accuracy of detection of CAD system when comb

This study seeks to identify the role that the leadership trend plays in the management of health institutions in Iraq and its impact on improving the quality of the health service provided by analyzing some opinions of affiliates working in the Iraqi health sector where a survey list was used as a main tool for collecting primary data, as it was subjected to this analysis ( 60) of the medical staff, of whom (40) are doctors and (20) are affiliated with the rank of assistant physician, and (60) members of the administrative cadre have undergone their various job ranks and administrative specializations (department manager, auditor, observer, accountant, statistician, secretary). Reliance on statistical software (spss) in data ana

In this paper, membrane-based computing image segmentation, both region-based and edge-based, is proposed for medical images that involve two types of neighborhood relations between pixels. These neighborhood relations—namely, 4-adjacency and 8-adjacency of a membrane computing approach—construct a family of tissue-like P systems for segmenting actual 2D medical images in a constant number of steps; the two types of adjacency were compared using different hardware platforms. The process involves the generation of membrane-based segmentation rules for 2D medical images. The rules are written in the P-Lingua format and appended to the input image for visualization. The findings show that the neighborhood relations between pixels o

This paper considers and proposes new estimators that depend on the sample and on prior information in the case that they either are equally or are not equally important in the model. The prior information is described as linear stochastic restrictions. We study the properties and the performances of these estimators compared to other common estimators using the mean squared error as a criterion for the goodness of fit. A numerical example and a simulation study are proposed to explain the performance of the estimators.