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  1. Gender Prediction From Retinal Fundus Using Deep Learning.Ashraf M. Taha, Qasem M. M. Zarandah, Bassem S. Abu-Nasser, Zakaria K. D. AlKayyali & Samy S. Abu-Naser - 2022 - International Journal of Academic Information Systems Research (IJAISR) 6 (5):57-63.
    Deep learning may transform health care, but model development has largely been dependent on availability of advanced technical expertise. The aim of this study is to develop a deep learning model to predict the gender from retinal fundus images. The proposed model was based on the Xception pre-trained model. The proposed model was trained on 20,000 retinal fundus images from Kaggle depository. The dataset was preprocessed them split into three datasets (training, validation, Testing). After training and cross-validating the proposed model, (...)
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  2. Prediction of Student Adaptability Level in E-Learning Using Machine and Deep Learning Techniques.Zakaria K. D. AlKayyali, Bassem S. Abu-Nasser, Ashraf M. Taha, Qasem M. M. Zarandah & Samy S. Abu-Naser - 2022 - International Journal of Academic and Applied Research (IJAAR) 6 (5):84-96.
    E-learning is an educational model in which the lectures can be taught at the same time using technical material without time and space barriers. E-learning has gained its popularity during the Covid-19 pandemic era and has been applied as a valid educational model in all educational levels. Due to the sudden pandemic measures, e-learning has brought about a lot of technical problems at unprepared educational institutions against the pandemic. It is important for the decision makers of educational institutions to get (...)
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  3.  76
    Predicting Whether Student Will Continue to Attend College or Not Using Deep Learning.Samy S. Abu-Naser, Qasem M. M. Zarandah, Moshera M. Elgohary, Zakaria K. D. AlKayyali, Bassem S. Abu-Nasser & Ashraf M. Taha - 2022 - International Journal of Engineering and Information Systems (IJEAIS) 6 (6):33-45.
    According to the literature review, there is much room for improvement of college student retention. The aim of this research is to evaluate the possibility of using deep and machine learning algorithms to predict whether students continue to attend college or will stop attending college. In this research a feature assessment is done on the dataset available from Kaggle depository. The performance of 20 learning supervised machine learning algorithms and one deep learning algorithm is evaluated. The algorithms are trained using (...)
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