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  1. AI-Driven Innovations in Agriculture: Transforming Farming Practices and Outcomes.Jehad M. Altayeb, Hassam Eleyan, Nida D. Wishah, Abed Elilah Elmahmoum, Ahmed J. Khalil, Bassem S. Abu-Nasser & Samy S. Abu-Naser - 2024 - International Journal of Academic Applied Research (Ijaar) 8 (9):1-6.
    Abstract: Artificial Intelligence (AI) is transforming the agricultural sector, enhancing both productivity and sustainability. This paper delves into the impact of AI technologies on agriculture, emphasizing their application in precision farming, predictive analytics, and automation. AI-driven tools facilitate more efficient crop and resource management, leading to higher yields and a reduced environmental footprint. The paper explores key AI technologies, such as machine learning algorithms for crop monitoring, robotics for automated planting and harvesting, and data analytics for optimizing resource use. Additionally, (...)
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  2. Mango Pests Identification Expert System.Jehad M. Altayeb, Samy S. Abu-Naser, Shahd J. Albadrasawi & Mohammed M. Almzainy - 2023 - International Journal of Engineering and Information Systems (IJEAIS) 7 (6):19-26.
    Mango is an economically significant fruit crop cultivated in various tropical and subtropical regions around the world. However, the productivity and quality of mangoes can be severely impacted by a range of pests. This research paper introduces an innovative approach to identify mango pests using an expert system. The expert system integrates knowledge from entomology and plants to provide accurate identification of common mango pests. The paper outlines the development and implementation of the expert system using Clips shell, which utilizes (...)
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  3. (1 other version)Development and Evaluation of an Expert System for Diagnosing Kidney Diseases.Shahd J. Albadrasawi, Mohammed M. Almzainy, Jehad M. Altayeb, Hassam Eleyan & Samy S. Abu-Naser - 2023 - International Journal of Academic Engineering Research (IJAER) 7 (6):16-22.
    This research paper presents the development and evaluation of an expert system for diagnosing kidney diseases. The expert system utilizes a decision-making tree approach and is implemented using the CLIPS and Delphi frameworks. The system's accuracy in diagnosing kidney diseases and user satisfaction were evaluated. The results demonstrate the effectiveness of the expert system in providing accurate diagnoses and high user satisfaction.
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