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  1. Revolutionizing Drug Discovery: The Role of Artificial Intelligence in Accelerating Pharmaceutical Innovation".Alaa Soliman Abu Mettleq, Alaa N. Akkila, Mohammed A. Alkahlout, Suheir H. A. ALmurshidi, Bassem S. Abu-Nasser & Samy S. Abu-Naser - 2024 - Information Journal of Academic Engineering Research (Ijaer) 8 (10):45-53.
    Abstract: The integration of artificial intelligence (AI) into drug discovery is revolutionizing the pharmaceutical industry by accelerating the development of novel therapeutics. AI-powered tools enable researchers to process vast datasets, identify drug candidates, and predict their efficacy and safety with unprecedented speed and accuracy. This paper explores the transformative impact of AI on drug discovery, highlighting key advancements in machine learning algorithms, deep learning, and predictive modeling. Additionally, it addresses the challenges associated with AI implementation, including data quality, regulatory hurdles, (...)
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  • Breakthroughs in Breast Cancer Detection: Emerging Technologies and Future Prospects.Ola I. A. Lafi, Rawan N. A. Albanna, Dina F. Alborno, Raja E. Altarazi, Amal Nabahin, Bassem S. Abu-Nasser & Samy S. Abu-Naser - 2024 - International Journal of Academic Health and Medical Research (IJAHMR) 8 (9):8-15.
    Abstract: Early detection of breast cancer is vital for improving patient outcomes and reducing mortality rates. Technological advancements have significantly enhanced the accuracy and efficiency of screening methods. This paper explores recent innovations in early detection, focusing on the evolution of digital mammography, the benefits of 3D mammography (tomosynthesis), and the application of advanced imaging techniques such as molecular imaging and MRI. It also examines the role of artificial intelligence (AI) in diagnostic tools, showing how machine learning algorithms are improving (...)
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  • Image-Based Nuts Detection Using Deep Learning.Altarazi Altarazi, Malak Said Hammad, Fadi Naeem Qanoo & Samy S. Abu-Naser - 2025 - International Journal of Academic Information Systems Research (IJAISR) 3 (1):28-34.
    Abstract: Abstract: The classification of nuts is crucial for food security; nevertheless, accurate and swift identification continues to be a challenge in numerous areas due to insufficient infrastructure. The rise in smartphone utilization, along with advancements in computer vision driven by deep learning, has facilitated smartphone-assisted nut classification. We trained a deep convolutional neural network to categorize five distinct nut types (Chestnut, Hazelnut, Nut Forest, Nut Pecan, and Walnut) using a public dataset of 2,850 photos gathered under controlled conditions. The (...)
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