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The traditional educational systems in certain nations, such as those in the Arab world, use the previous year's scores to forecast academic achievement. Meanwhile, the science, technology, engineering, arts and mathematics (STEAM) educational system also considers the student's talents and interests, in addition to their test results, for each of the years of study used to forecast academic achievement. However, despite the numerous variables that could potentially impact a student's future profession, the STEAM educational system accurately predicts the academic achievement of students in five to seven broad majors. A smart model, data, generative, factors (DGF), has been proposed to assign a specific major for each student based on their academic background, interests, skills, and main influencing factors. The DGF model uses the supervised deep neural network and is trained by the generative adversarial network (GAN) algorithm. The DGF model has the capability to predict a specific major among 17 different majors for every student, with a high learning performance (1.4133), a plausible error rate (0.0046), a rational number of learning epochs (206), and an optimal accuracy of 99.99%. [ABSTRACT FROM AUTHOR]

Lung nodules are important signs that lung cancer is present. Early detection increases a patient's survival rate through allowing for timely treatment initiation. Researchers created algorithms with regard to the systems of Computer-Aided Diagnosis (CAD) to lessen the load of radiologists' challenging and time-consuming task of detecting and classifying malignancies in Computed Tomography (CT) images. Utilizing a variety of deep learning (DL) method, the systems of CAD are progressively becoming more effective at detecting lung cancers. In the presented study, we suggested a hybrid model for the purpose of enhancing the classification performance regarding pulmonary nodules in CT scan by combining a Multi-Layer Perceptron (MLP) and convolution neural network (DenseNet201). MLP was utilized as classification, while DenseNet201 as feature extraction. On the Lung Image Database Consortium Image Collection (LIDC-IDRI) dataset, the presented study was assessed using various assessment matrices, including sensitivity, accuracy, and specificity. Recall, accuracy, and specificity for the model are 99%, 99.30%, and 99%, respectively. Thus, the suggested model has produced the best results with regard to accuracy. [ABSTRACT FROM AUTHOR]