Browsing by Author "Adorneto, Carlo"

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    Expanding the Frontiers in GenAI and XAI: Innovative Architectures and Applications
    (Università della Calabria, 2024-09-27) Adorneto, Carlo; Greco, Gianluigi; Terracina, Giorgio
    Generative Artificial Intelligence (GenAI) and Explainable Artificial Intelligence (XAI) have attracted significant interest in recent years due to their potential and their capacity to drive and inspire further research. This thesis explores new frontiers in these fields by presenting a collection of innovative architectures and applications. In the realm of GenAI, this work introduces GIDnets, a generative neural network aimed at solving inverse design problems through latent space exploration, showcasing improvements over existing methods. Furthermore, the research explores the application of latent space conditioning and transformers for automatic medical report generation. The thesis also investigates the role of generative agents, based on Large Language Models (LLMs), in agent-based modeling, offering insights into their validation and the emerging challenges. One of the notable challenges addressed is the complexity of opinion diffusion in social environments, highlighting its potential as a promising application scenario for generative agents. In the domain of XAI, this thesis illustrates the impact of computational methods on data interpretation, particularly when data science and Deep Learning (DL) are employed to gain insights in the biomedical field. Despite advancements, explaining DL models remains a debated issue. SHAP (SHapley Additive exPlanations) is demonstrated as a powerful tool for extracting insights from these black-box models and its application in bankruptcy prediction and natural disaster event scenarios will be discussed. Additionally, a new deep learning algorithm based on XAI is proposed for feature selection in genomics. This algorithm utilizes a new SHAP-inspired metric to identify and quantify the impact of genes, significantly enhancing the prediction accuracy for chronic lymphocytic leukemia. The innovative approaches presented in this thesis advance the state-of-the-art in GenAI and XAI, showcasing the potential of these technologies to enable the design of practical solutions across various domains.