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 13 of 13 for “"explainable machine learning"”.
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Predictive Maintenance in Rail Transportation: An Explainable Machine Learning Approach
… by investigating interpretable supervised machine learning techniques for predicting and classifying passenger door failures in Gibela’s X’Trapolis Mega trainset using event-driven data from Alstom’s TrainTracer system. Both failure and non-failure incidences are analysed under time-aware …
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Explainable Machine Learning Prediction of Antimicrobial Peptide Targeting Streptococcus mutans
… leads to dental caries. This thesis developed an explainable machine learning pipeline to design antimicrobial peptides targeting S. mutans in responsiveness to the acidic conditions of the cariogenic microenvironments. Our explainable ML pipeline approach transforms the targeted peptide design …
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Toward explainable machine learning methods for stroke patient outcomes in Tennessee
… we have combined statistical analysis and machine learning (ML) algorithms to enhance the prediction of three patient outcomes — i.e. 30-day readmission, LOS, and mortality — for stroke patients in Tennessee. Since typically such a dataset is imbalanced, due to a small fraction of those …
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Toward Designing Active ORR Catalysts via Interpretable and Explainable Machine Learning
… of factors. Researchers have recently turned to machine learning (ML) to find and design heterogeneous catalysts faster with emerging catalysis databases. Black-box models make up a lot of the ML models that are used in the field to predict the properties of catalysts that are important to their …
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Investigating Potentially Arrhythmogenic Atrial Substrate using Computational Simulations and Explainable Machine Learning
… applications of computational simulations and explainable machine learning (xML) on the mechanistic understanding of arrhythmogenic atrial substrate. We review the current understanding of atrial arrhythmia mechanisms in Chapter 1. Then, we explore how computational simulations, in highly …
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Hierarchy Aligned Commonality Through Prototypical Networks: Discovering Evolutionary Traits over Tree-of-Life
… in biology and advances in the field of explainable machine learning (ML) such as ProtoPNet and other prototype-based methods, there is a tremendous opportunity to discover evolutionary traits directly from images in the form of a hierarchy of prototypes learned at internal nodes of the …
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Why Are Some Watersheds More Sediment-Productive Than Others? An Explainable AI Approach
… aquatic sensing, sediment load estimation, and explainable machine learning. SY and SDR were quantified at 134 U.S. Geological Survey (USGS) stations and modeled using a random forest algorithm trained on 20 basin attributes, spanning climate, land cover, soils and geology, and topography. The …
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Advanced data analysis methods to optimize crop management decisions
… third chapter integrates domain knowledge and explainable machine learning methods to optimize management decisions using observational data. The data comes from the Sustainable Modernization of Traditional Agriculture (MasAgro) project in the southern state of Chiapas - Mexico. The dataset was …
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Data Science in Investment Management
… In the next part, we focus on the use of explainable Machine Learning for an important problem of consumer credit risk. In the final part, we conclude with the discussion about the future of Artificial Intelligence and Data Science in Finance.
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Towards Effective Tools for Debugging Machine Learning Models
… of detecting and fixing the errors of a machine learning (ML) model—model debugging. Current ML models, especially overparametrized deep neural networks (DNNs) trained on crowd-sourced data, easily latch onto spurious signals, underperform for small subgroups, and can be derailed by …
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Explainable and Robust Data-Driven Machine Learning Methods for Digital Healthcare Monitoring
… to track diverse human data and behaviors. Machine learning can promote an individual's well-being through more efficient and accurate health status monitoring. However, challenges hinder precise monitoring, such as privacy concerns, varied subjects, diverse sensors, and different …
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Efficient and Faithful Algorithms for Interpretable Machine Learning
As deep learning models continue to grow in complexity and scale, the demand for interpretable machine learning (ML) methods becomes increasingly critical across a wide range of applications. This thesis addresses the challenges of interpreting deep neural networks (DNNs) by designing efficient and …