الأهداف: تُعد الثقة في اتخاذ قرارات الاستثمار من الأدوات الرئيسية لتقييم أسواق الأسهم. يتوجب على كل مستثمر تحديد أسعار الأسهم المستقبلية قبل تطوير استراتيجيات الاستثمار لتعزيز عائداتهم. ومع ذلك، فإن الطبيعة غير الخطية والمعقدة والمتقلبة والديناميكية لبيانات الأسهم تجعل من التنبؤ بأسعار الأسهم مهمة صعبة. تهدف هذه الدراسة إلى مواجهة هذه التحديات وتحسين دقة التنبؤ باستخدام تقنيات النمذجة المتقدمة. المنهجية: اقترحنا نموذجًا هجينًا للشبكات العصبية يدمج بين وحدات التكرار المغلقة ثنائية الاتجاه (BiGRU) والشبكات العصبية الالتفافية أحادية البعد (CNNs). تم إجراء تجارب على خمس مجموعات بيانات لأسعار الأسهم، تضمنت ثلاث أدوات أسهم فردية ومؤشرين للأداء في أسواق الأسهم. شملت معايير التقييم متوسط الخطأ المطلق (MAE) وجذر متوسط مربع الخطأ (RMSE). النتائج: حقق النموذج الهجين المقترح أدنى قيم للـ MAE لمجموعات البيانات الفردية، حيث تراوحت بين 0.357 و1.747. كما أظهرت التجارب انخفاضًا يتراوح بين 42% و91% في قيم RMSE مقارنةً بالأساليب القياسية المعتمدة على CNN وGRU، مما يعكس تحسنًا كبيرًا في دقة التنبؤ. الأبعاد: تشير النتائج إلى أن النموذج الهجين القائم على...

The coronavirus (COVID-19) is a major global disaster of humankind, in the 21st century. COVID-19 initiates breathing infection, including pneumonia, common cold, sneezing, and coughing. Initial detection becomes crucial, to classify the virus and limit its spread. COVID-19 infection is similar to other types of pneumonia, and it may result in severe pneumonia, with bundles of illness onsets. This research is focused on identifying people affected by COVID-19 at a very early stage, through chest X-ray images. Chest X-ray classification is a beneficial method in the identification, follow up, and evaluation of treatment efficiency, for people with pneumonia. This research, also, considered chest X-ray classification as a basic method to evaluate the existence of lung irregularities in symptomatic patients, alleged for COVID-19 disease. The aim of this research is to classify COVID-19 samples from normal chest X-ray images and pneumonia-affected chest X-ray images of people, for early identification of the disease. This research will help people in diagnosing individuals for viruses and insisting that people receive proper treatment as well as preventive action, to stop the spread of the virus. To provide accurate classification of disease in patients’ chest X-ray images, this research proposed a novel classification model, named 2dCNN-BiCuDNNLSTM, which combines two-dimensional Convolutional Neural Network (CNN) and a Bidirectional CUDA Deep Neural Network Long Short-Term Memory (BiCuDNNLSTM). Deep learning is known for identifying the patterns in available data that will be helpful in accurate classification of disease. The proposed model (2dCNN and BiCuDNNLSTM layers, with proper hyperparameters) can differentiate normal chest X-rays from viral pneumonia and COVID-19 ones, with high accuracy. A total of 6863 X-ray images (JPEG) (1000 COVID-19 patients, 3863 normal cases, and 2000 pneumonia patients) have been engaged, to examine the achievement of the suggested neural network; 80% of the images dataset for every group is received for proposed model training, 10% is accepted for validation, and 10% is accepted for testing. It is observed that the proposed model acquires the towering classification accuracy of 93%. The proposed network is used for predictive analysis, to prompt people regarding the risk of early detection of COVID-19. X-ray images help to classify people with COVID-19 variants and to indicate the severity of disease in the future. This study demonstrates the effectiveness of the proposed CUDA-enabled hybrid deep learning models, to classify the X-ray image data, with a high accuracy of detecting COVID-19. It reveals that the proposed model can be applicable in numerous virus classifications. The chest X-ray classification is a commonly available and reasonable approach, for diagnosing people with lower respiratory signs or suspected COVID-19. Therefore, it is demonstrated that the proposed model has an efficient and promising accomplishment for classifying COVID-19 through X-ray images. The proposed hybrid model can, efficiently, preserve the comprehensive characteristic facts of the image data, for more exceptional concluding classification results than an individual neural network.