Autorías: Nadeema Badr Mohammed, Missaa Nadem, Huda Badwe Shbeeb, Iqbal Abdul Hussein Neamah, Najlaa Abbas AL Zuhairi, Nihad Mohammed Alwan. Localización: Retos: nuevas tendencias en educación física, deporte y recreación. Nº. 63, 2025. Artículo de Revista en Dialnet.

Objective: The objective of this study is to develop instructional materials that teach fundamental basketball skills to female students enrolled in the first year of the College of Physical Education and Sports Sciences for Girls at the University of Baghdad while also encouraging reflective thinking (RT). Research methodology: the researcher chose the experimental methodology with the two equal group's method. Finding the community and choosing the right sample for the study's kind and goals are essential to the accomplishment of any research project. A sample for the primary experiment was picked at random after the researcher confirmed the students' first-year basketball curriculum. This study community consisted of (40) fourth-

       In the pandemic era of COVID19, software engineering and artificial intelligence tools played a major role in monitoring, managing, and predicting the spread of the virus. According to reports released by the World Health Organization, all attempts to prevent any form of infection are highly recommended among people. One side of avoiding infection is requiring people to wear face masks. The problem is that some people do not incline to wear a face mask, and guiding them manually by police is not easy especially in a large or public area to avoid this infection. The purpose of this paper is to construct a software tool called Face Mask Detection (FMD) to detect any face that does not wear a mask in a specific

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

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