Global ETD Search
Search theses and dissertations gathered from participating repositories worldwide. Every result links back to the library that holds it. No account is needed.
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Showing 1 to 16 of 16 for “"Explainable artificial intelligence (XAI)"”.
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Multi-scale local explanation approach for image analysis using model-agnostic explainable artificial intelligence (XAI)
… networks has generated a remarkable growth in Artificial Intelligence (AI) research, and it received much interest over the past few years. However, one of the main challenges for the broad adoption of deep learning based models such as Convolutional Neural Networks (CNN) is the lack of …
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Advancing Pattern Detection, Theory Development and Decision Making with Explainable AI
The application of explainable artificial intelligence (XAI) methods in data-driven decision-making and computationally intensive theory development (CTD) is a subject of ongoing debate, particularly concerning how and whether these methods can be effectively employed, and how the reliability of …
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Explainable AI: A Unified Approach Based on Cooperative Game Theory
Explainable Artificial Intelligence (XAI) aims to enhance the interpretability of machine learning (ML) models, yet existing feature-based explanation methods remain fragmented across local and global approaches. This thesis presents a unified framework based on cooperative game theory to …
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ARTIFICIAL INTELLIGENCE (AI) APPROACHES IN DRUG DISCOVERY: DEVELOPMENT AND VALIDATION OF NEW STRATEGIES IN VIRTUAL SCREENING CAMPAIGNS
… is increasingly benefitting from the advent of artificial intelligence (AI), which has proven to enhance both the efficiency and accuracy of Computer-Aided Drug Design (CADD), while significantly reducing the associated costs and timelines. This Work examines various case studies in which AI has …
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Analysis of bankruptcy prediction of shipping industry - Machine Learning Approach
… across 1, 3, and 5-year horizons, with Explainable Artificial Intelligence (XAI) techniques employed to interpret the impact of each variable. The findings reveal that non-financial and macroeconomic variables, such as LIBOR interest rates and trade volume growth rates, are significant …
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Prediction of Gravel Streambed Embeddedness Using Explainable AI and Machine Learning Techniques
… learning models using remotely sensed data and explainable artificial intelligence (XAI) to uncover important variables related to physical processes affecting embeddedness in Virginia (VA) and the United States (U.S.). The VA model used 1,125 embeddedness measurements from 906 sites provided by …
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A Feasible Situation Awareness-Based Evaluation Framework for Quality of Machine Learning Explanations
EXplainable Artificial Intelligence (XAI) has emerged as a critical domain, with the aim of enhancing the transparency and interpretability of advanced machine learning (ML) models. As the need to introduce more complicated ML in broader industries surged, especially for industries with high …
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From local explanations to comprehensive mechanistic understanding of deep vision models
… and accountable AI systems. The field of eXplainable Artificial Intelligence (XAI) has introduced techniques like attribution maps and feature visualizations to illuminate singular aspects of model behavior. Yet, achieving a comprehensive understanding that enables validation and control …
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A novel approach by integrating physically and Machine leaning-based models for landslide susceptibility assessment
… models. In comparison, ML-based models are not explainable and not always effective, depending on the availability and characteristics of the inventory data. This thesis proposes an integrated method based on physically and ML models to address these challenges. Moreover, the proposed method …
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Help Wanted: Robust Concept Interventions for Interpretable Deep Neural Networks
Artificial Intelligence (AI) systems are at their most powerful not when they replace humans, but when they collaborate with them. Yet, in critical domains such as healthcare and law, where expert knowledge is abundant, powerful AI systems driven by Deep Neural Networks (DNNs) operate under the …
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NETWORKS OF GROUP EQUIVARIANT NON-EXPANSIVE OPERATORS FOR ARTIFICIAL INTELLIGENCE. MODELS, APPLICATIONS AND INTERPRETABILITY.
Artificial Intelligence (AI) and Machine Learning (ML) are increasingly integrated into our daily lives, yet we often encounter opaque, black-box algorithms driving these systems, which can be deceptive or counterfeit. The pursuit of eXplainable Artificial Intelligence (XAI) aims to develop methods …
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Effects of Logic-Style Explanations and Uncertainty on Users’ Decisions
The spread of innovative Artificial Intelligence (AI) algorithms assists many individuals in their daily life decision-making tasks but also sensitive domains such as disease diagnosis and credit risk. However, a great majority of these algorithms are of a black-box nature, bringing the need to …
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Explainability of non-deterministic solvers: explanatory feature generation from the data mining of the search trajectories of population-based metaheuristics.
… decisions can be difficult. In optimisation and artificial intelligence (AI), the field of explainable AI (XAI) has grown significantly as machine learning, systems that mimic human reasoning and other AI systems have continued to be adopted into more and more user-critical applications. XAI as a …
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Natural counterfactual explanations with causal awareness and actionable recourse for black-box models.
The escalating complexity of artificial intelligence (AI) models, particularly black-box systems, poses substantial challenges to transparency, user trust, and actionable recourse, especially in high-stakes decision-making domains. Counterfactual (CF) explanations, which articulate the minimal …
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Enhancing Android application security through source code vulnerability mitigation using artificial intelligence: a privacy-preserved, community-driven, federated-learning-based approach.
… and community-driven approach that utilises artificial intelligence (AI) techniques to detect Android source code vulnerabilities in real time, with a focus on continuous model improvement. To train the initial AI model, a dataset has been curated, containing labelled Android source code …
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The Role of Explainable Artificial Intelligence in Data Science [Il Ruolo dell'Intelligenza Artificiale Esplicabile nella Scienza dei Dati]
… in particolare quelli basati su Intelligenza Artificiale (IA) e apprendimento automatico (ML), come imperativi strategici. La crescente complessità degli algoritmi di apprendimento automatico accelera la domanda parallela di modelli trasparenti e interpretabili. La tesi inizia con …