Gender classification is a critical task in computer vision. This task holds substantial importance in various domains, including surveillance, marketing, and human-computer interaction. In this work, the face gender classification model proposed consists of three main phases: the first phase involves applying the Viola-Jones algorithm to detect facial images, which includes four steps: 1) Haar-like features, 2) Integral Image, 3) Adaboost Learning, and 4) Cascade Classifier. In the second phase, four pre-processing operations are employed, namely cropping, resizing, converting the image from(RGB) Color Space to (LAB) color space, and enhancing the images using (HE, CLAHE). The final phase involves utilizing Transfer lea

Diabetes is one of the increasing chronic diseases, affecting millions of people around the earth. Diabetes diagnosis, its prediction, proper cure, and management are compulsory. Machine learning-based prediction techniques for diabetes data analysis can help in the early detection and prediction of the disease and its consequences such as hypo/hyperglycemia. In this paper, we explored the diabetes dataset collected from the medical records of one thousand Iraqi patients. We applied three classifiers, the multilayer perceptron, the KNN and the Random Forest. We involved two experiments: the first experiment used all 12 features of the dataset. The Random Forest outperforms others with 98.8% accuracy. The second experiment used only five att

Professional learning societies (PLS) are a systematic method for improving teaching and learning performance through designing and building professional learning societies. This leads to overcoming a culture of isolation and fragmenting the work of educational supervisors. Many studies show that constructing and developing strong professional learning societies - focused on improving education, curriculum and evaluation will lead to increased cooperation and participation of educational supervisors and teachers, as well as increases the application of effective educational practices in the classroom.

The roles of the educational supervisor to ensure the best and optimal implementation and activation of professional learning soci

يَهدف البحث الحالي الى تعرف نمطي الشخصية (أ.ب) وكذلك اسلوبي التفكير (اللفظي – التصوري) لدى طلبة كلية الفنون الجميلة – قسم التربية الفنية .

ومن اجل تحقيق اهداف البحث والتعرف على نمطي الشخصية (أ.ب) فقد اختار الباحث (مقياس كلازر ، 1978) الذي تم استعماله في دراسة ( العاني ، 2013) والذي يتمتع بصدق وثبات جيدين ، ومقياس اسلوبي التفكير (اللفظي والتصوري) لـ(الطائي ،2018) المبني وفق نظرية التشفير الثنائي لمنظرها ( بايفيو – PIAV

Sentiment analysis is one of the major fields in natural language processing whose main task is to extract sentiments, opinions, attitudes, and emotions from a subjective text. And for its importance in decision making and in people's trust with reviews on web sites, there are many academic researches to address sentiment analysis problems. Deep Learning (DL) is a powerful Machine Learning (ML) technique that has emerged with its ability of feature representation and differentiating data, leading to state-of-the-art prediction results. In recent years, DL has been widely used in sentiment analysis, however, there is scarce in its implementation in the Arabic language field. Most of the previous researches address other l

يتمتع العراق بموارد بشرية هائلة حيث يعد من البلدان الفتية، إلا أنه يعاني من أزمة رأس مال بشري تغذيها أزمة التعليم، ولكون التعليم أبرز مكونات رأس المال البشري فقد ذلك بشكل كبير على مؤشر رأس المال البشري في العراق، من هذا المنطلق وللدور الكبير الذي يلعبه الانفاق العام في أي مجال، جاءت هذه الدراسة للبحث في موضوع "الانفاق العام على التعليم ودوره في تحسين مؤشرات راس المال البشري التعليمية في العراق"، حيث هدف ه

Deep learning techniques are applied in many different industries for a variety of purposes. Deep learning-based item detection from aerial or terrestrial photographs has become a significant research area in recent years. The goal of object detection in computer vision is to anticipate the presence of one or more objects, along with their classes and bounding boxes. The YOLO (You Only Look Once) modern object detector can detect things in real-time with accuracy and speed.  A neural network from the YOLO family of computer vision models makes one-time predictions about the locations of bounding rectangles and classification probabilities for an image. In layman's terms, it is a technique for instantly identifying and recognizing

The huge amount of documents in the internet led to the rapid need of text classification (TC). TC is used to organize these text documents. In this research paper, a new model is based on Extreme Machine learning (EML) is used. The proposed model consists of many phases including: preprocessing, feature extraction, Multiple Linear Regression (MLR) and ELM. The basic idea of the proposed model is built upon the calculation of feature weights by using MLR. These feature weights with the extracted features introduced as an input to the ELM that produced weighted Extreme Learning Machine (WELM). The results showed   a great competence of the proposed WELM compared to the ELM. 

After the outbreak of COVID-19, immediately it converted from epidemic to pandemic. Radiologic images of CT and X-ray have been widely used to detect COVID-19 disease through observing infrahilar opacity in the lungs. Deep learning has gained popularity in diagnosing many health diseases including COVID-19 and its rapid spreading necessitates the adoption of deep learning in identifying COVID-19 cases. In this study, a deep learning model, based on some principles has been proposed for automatic detection of COVID-19 from X-ray images. The SimpNet architecture has been adopted in our study and trained with X-ray images. The model was evaluated on both binary (COVID-19 and No-findings) classification and multi-class (COVID-19, No-findings