This work implements an Electroencephalogram (EEG) signal classifier. The implemented method uses Orthogonal Polynomials (OP) to convert the EEG signal samples to moments. A Sparse Filter (SF) reduces the number of converted moments to increase the classification accuracy. A Support Vector Machine (SVM) is used to classify the reduced moments between two classes. The proposed method’s performance is tested and compared with two methods by using two datasets. The datasets are divided into 80% for training and 20% for testing, with 5 -fold used for cross-validation. The results show that this method overcomes the accuracy of other methods. The proposed method’s best accuracy is 95.6% and 99.5%, respectively. Finally, from the results, it

One of the most important features of the Amazon Web Services (AWS) cloud is that the program can be run and accessed from any location. You can access and monitor the result of the program from any location, saving many images and allowing for faster computation. This work proposes a face detection classification model based on AWS cloud aiming to classify the faces into two classes: a non-permission class, and a permission class, by training the real data set collected from our cameras. The proposed Convolutional Neural Network (CNN) cloud-based system was used to share computational resources for Artificial Neural Networks (ANN) to reduce redundant computation. The test system uses Internet of Things (IoT) services th

Diabetes imposes a substantial public health burden; according to the International Diabetes Federation, there were about 3.4 million diabetes related deaths worldwide in 2024, and in Iraq, the Federation reports that one in nine adults lives with diabetes in 2024, with 14,683 adult deaths attributable to diabetes and a total diabetes related health expenditure of 2,078 million United States dollars. The dataset analyzed in this study contains 1,000 records collected in 2020 from two Iraqi teaching hospitals and includes multiple clinical and laboratory measurements with three outcome classes, namely Non diabetic, Pre diabetic, and Diabetic, with a low prevalence of the Pre diabetic class and an imbalanced overall class distribution; the da

Deep learning convolution neural network has been widely used to recognize or classify voice. Various techniques have been used together with convolution neural network to prepare voice data before the training process in developing the classification model. However, not all model can produce good classification accuracy as there are many types of voice or speech. Classification of Arabic alphabet pronunciation is a one of the types of voice and accurate pronunciation is required in the learning of the Qur’an reading. Thus, the technique to process the pronunciation and training of the processed data requires specific approach. To overcome this issue, a method based on padding and deep learning convolution neural network is proposed to

The support vector machine, also known as SVM, is a type of supervised learning model that can be used for classification or regression depending on the datasets. SVM is used to classify data points by determining the best hyperplane between two or more groups. Working with enormous datasets, on the other hand, might result in a variety of issues, including inefficient accuracy and time-consuming. SVM was updated in this research by applying some non-linear kernel transformations, which are: linear, polynomial, radial basis, and multi-layer kernels. The non-linear SVM classification model was illustrated and summarized in an algorithm using kernel tricks. The proposed method was examined using three simulation datasets with different sample

The study aimed to identify the awareness degree of teacher students in the department of Arabic language and their supervisors at Al-aqsa University for their future roles in the age of knowledge. To achieve this objective, descriptive- analytical approach was used. The instruments of this study were two questionnaires: first one consist of (20) item for teacher students, and the second consist of (27) item for educational supervisors which covered three roles: professional, technological, and humanitarian. The sample was (120) student selected randomly, and (39) supervisors of Arabic language. The result revealed that the mean of degree awareness of teacher students and their supervisors of future role are (3.857), (3.472) respectively

هدفت الدراسة الحالية إلى ( معرفة اثر استراتيجية مقترحة وفقا لنظرية الرياضيات الواقعية في التحصيل والترابط الرياضي لدى طالبات الصف الثالث المتوسط)، تم اختيار مجموعتين من صف الثالث المتوسط في مدرسة تابعة للمديرية العامة لتربية بغداد- الرصافة الاولى للعام الدراسي(2022-2021) تمثل المجموعة التجريبية والتي تدرس على وفق الاستراتيجية المقترحة وبلغ عدد طالباتها(30)، والمجموعة الضابطة التي تدرس بالطريقة الاعتيادية وبلغ

تعتبر لعبة كرة اليد من الالعاب الفرقية، وان هذه اللعبة تستند الى قاعدة اساسية وهي الاداء الصحيح للمهارات، وللتقدم في هذه اللعبة يجب تعلم وأتقان اداء المهارات للوصول الى مستوى افضل للتعلم، من خلال الدور الذي يؤديه اسلوب نموذج كولب لأنماط التعلم (التكيفي) الذي يعمل على تحديد اسلوب التفكير والمفضل لكل فرد وتاثيره في تعلم مهارة الطبطبة للطالبات، اذ يعمل على ايصال المعلومات والافكار الخاصة باداء المهارة وحس