هدف البحث إعداد تدريبات تزامن ما بين السايكو وتمرينات القوة الوظيفية للاعبي كرة القدم، والتعرف على تأثيرها في القدرة الانفجارية للرجلين، وفي مهارتي المناولة الطويلة والركلة الركنية للاعبي كرة القدم، ليفترض الباحث بإنه توجد فروق ذات دلالة إحصائية بين نتائج الاختبارات القبلية والبعدية لمجموعتي البحث التجريبية والضابطة في كل من القدرة الانفجارية للرجلين لمهارتي المناولة الطويلة والركلة الركنية في كرة القد

يهدف البحث الى تطبيق تزامن تصميم عملية انتاج معجون الاسنان في مصنع المامون التابع للشركة العامة للمنتوجات الغذائية مع نظام تكاليف الجودة المطبق في الشركة لتحقيق الميزة التنافسية. وتمثلت مشكلة البحث في أن الشركة عينة البحث لا تستخدم نظام تكاليف الجودة بالتزامن مع تصميم عملية إنتاج هذا المنتج لاغراض تحقيق الميزة التنافسية حيث تواجه منتجات الشركة منافسة عالية في الاسواق المحلية. إن الشركة تطبق نظام تكاليف الج

يهدف هذا البحث الى تقويم الأنشطة والأسئلة التقويمية المتضمنة في محتوى كتاب الحاسوب للصف الخامس الإعدادي وفق التفكير الإبداعي ومقترحات تطويرها، وذلك بالإجابة عن السؤال الآتي: ما نسبة توافر مهارات التفكير الإبداعي في الأنشطة والأسئلة التقويمية المتضمنة في محتوى كتاب الحاسوب المقرر لطلبة الصف الخامس الإعدادي (الاحيائي والتطبيقي، والادبي) من وزارة التربية العراقية/ المديرية العامة للمناهج في العام الدراسي (2

       In this paper, we focus on designing feed forward neural network (FFNN) for solving Mixed Volterra – Fredholm Integral Equations (MVFIEs) of second kind in 2–dimensions. in our method, we present a multi – layers model consisting of a hidden layer which has five hidden units (neurons) and one linear output unit. Transfer function (Log – sigmoid) and training algorithm (Levenberg – Marquardt) are used as a sigmoid activation of each unit. A comparison between the results of numerical experiment and the analytic solution of some examples has been carried out in order to justify the efficiency and the accuracy of our method.

Eight different Dichloro(bis{2-[1-(4-R-phenyl)-1H-1,2,3-triazol-4-yl-κN3]pyridine-κN})iron(II) compounds, 2–9, have been synthesised and characterised, where group R=CH3 (L2), OCH3 (L3), COOH (L4), F (L5), Cl (L6), CN (L7), H (L8) and CF3 (L9). The single crystal X-ray structure was determined for the L3 which was complemented with Density Functional Theory calculations for all complexes. The structure exhibits a distorted octahedral geometry, with the two triazole ligands coordinated to the iron centre positioned in the equatorial plane and the two chloro atoms in the axial positions. The values of the FeII/III redox couple, observed at ca. −0.3 V versus Fc/ Fc+ for complexes 2–9, varied over a very small potential range of 0.05 V.

 

    The combination of wavelet theory and neural networks has lead to the development of wavelet networks. Wavelet networks are feed-forward neural networks using wavelets as activation function. Wavelets networks have been used in classification and identification problems with some success.

  In this work we proposed a fuzzy wavenet network (FWN), which learns by common back-propagation algorithm to classify medical images. The library of medical image has been analyzed, first. Second, Two experimental tables’ rules provide an excellent opportunity to test the ability of fuzzy wavenet network due to the high level of information variability often experienced with this type of images.

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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

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

Deep learning techniques are used across a wide range of fields for several applications. In recent years, deep learning-based object detection from aerial or terrestrial photos has gained popularity as a study topic. 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 andclassification probabilities for an image. In layman's terms, it is a technique for instantly identifying and rec