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    • A global taxonomy of interpretable AI: unifying the terminology for the technical and social sciences
    Authors: Mara, Graziani; Lidia, Dutkiewicz; Davide, Calvaresi;  Advisor: -;  Co-Author: - (2023)

    Since its emergence in the 1960s, Artificial Intelligence (AI) has grown to conquer many technology products and their fields of application. Machine learning, as a major part of the current AI solutions, can learn from the data and through experience to reach high performance on various tasks. This growing success of AI algorithms has led to a need for interpretability to understand opaque models such as deep neural networks. Various requirements have been raised from different domains, together with numerous tools to debug, justify outcomes, and establish the safety, fairness and reliability of the models.
    • Hepatitis C Virus prediction based on machine learning framework a real-world case study in Egypt
    Authors: Heba Mamdouh, Farghaly; Mahmoud Y., Shams; Tarek Abd, El-Hafeez;  Advisor: -;  Co-Author: - (2023)

    Prediction and classification of diseases are essential in medical science, as it attempts to immune the spread of the disease and discover the infected regions from the early stages. Machine learning (ML) approaches are commonly used for predicting and classifying diseases that are precisely utilized as an efficient tool for doctors and specialists. This paper proposes a prediction framework based on ML approaches to predict Hepatitis C Virus among healthcare workers in Egypt. We utilized real-world data from the National Liver Institute, founded at Menoufiya University (Menoufiya, Egypt). The collected dataset consists of 859 patients with 12 different features. To ensure the robustness and reliability of the proposed framework, we performed two scenarios: the first without featur...