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.
Results
Showing 1 to 20 of 464 for “"learning approaches"”.
-
Statistical Learning Approaches to Information Filtering
… to understand users' information needs. Existing approaches either infer a user's profile by exploring his/her connections to other users, i.e.~collaborative filtering (CF), or analyzing the content descriptions of liked or disliked examples annotated by the user, ~i.e.~content-based filtering …
-
Machine Learning Approaches for Equitable Healthcare
… the equity and fairness of the resulting machine learning models. Because the observational data we collect can be noisy, incomplete, and biased, seemingly straight-forward implementation of existing methods for clinical intervention or better understanding human knowledge can lead to inaccurate …
-
Machine learning approaches in practical anxiety detection
Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2025-02-04 without embargo terms
-
Machine learning approaches to star-galaxy classification
… Thus, we explore a variety of machine learning approaches to improve star-galaxy classification in ground-based photometric surveys. In Chapter 2, we present a meta-classification framework that combines existing star-galaxy classifiers, and demonstrate that our Bayesian combination …
-
Mold Allergomics: Comparative and Machine Learning Approaches
… This dissertation presents computational approaches in characterizing the allergen repertoire in fungal genomes as part of the whole genome studies of Alternaria, an important allergenic/opportunistic human pathogenic fungus and necrotrophic plant parasite. In these studies, the genomes of …
-
Image-based deep learning approaches for plant phenotyping
… in available phenotyping methods. Deep learning could be used to build automatic tools for identifying, localizing and quantifying plant features based on agricultural images. This dissertation describes the development and evaluation of state-of-the-art deep learning approaches for …
-
Contributions to distributed MPC: coalitional and learning approaches
… works of this thesis focus on the application of learning techniques in distributed and decentralized MPC schemes, thus paving the way for a future extension to the coalitional framework. In this regard, we have focused on the use of neural networks to aid distributed negotiations, and on the …
-
Wind Turbine Parameter Calibration Using Deep Learning Approaches
<p>The inertia and damping coefficients are critical to understanding the workings of a wind turbine, especially when it is in a transient state. However, many manufacturers do not provide this information about their turbines, requiring people to estimate these values themselves. This research …
-
Machine learning approaches towards tuning ALICE TRD simulations
… gas gain is subjected to modification. A machine learning approach is taken with the use of deep learning discrimination mechanisms namely artificial neural networks and convolutional neural networks to quantify the effect that our tuning has on the improvement of the simulation results and their …
-
Machine Learning Approaches for Characterizing ALS Disease Progression
… progression. In this thesis, I describe machine learning approaches to characterizing disease progression in ALS. I first present the development of a Mixture of Gaussian Processes model to learn clusters of ALS disease progression from sparse longitudinal clinical data. I show that our learned …
-
Machine Learning Approaches that Extend Healthcare: Algorithms & Applications
… artificial intelligence (AI) and machine learning (ML) algorithms and building practical systems that use these algorithms to solve key problems in healthcare and medicine. Specifically, on the algorithms front, the thesis introduces principled ML approaches to achieve fair, unbiased, and …
-
Machine Learning Approaches to Data-Driven Transition Modeling
… flow regimes. This dissertation explores machine learning approaches to develop transition models using data from computations based on linear stability theory. Such data provide strong correlation with the underlying physics governed by linearized disturbance equations. In the proposed transition …
-
PROBABILISTIC AND DEEP LEARNING APPROACHES TO MODELING BIOLOGICAL SYSTEMS
This thesis investigates probabilistic and deep learning methods for modeling biological systems across various scales, with a specific focus on cancer. The aim is to develop models that are both quantitatively rigorous and biologically meaningful. In the first part, I present a hierarchical …
-
Discovering Biomarkers of Alzheimer's Disease by Statistical Learning Approaches
In this work, statistical learning approaches are exploited to discover biomarkers for Alzheimer's disease (AD). The contributions has been made in the fields of both biomarker and software driven studies. Surprising discoveries were made in the field of blood-based biomarker search. With the …
-
Road Collision Analysis and Prediction Using Machine Learning Approaches
… hand, data-driven methods utilizing Machine Learning (ML) approaches have been used recently to deal with the drawbacks of classical models, which showed promising results. Road accidents result from many factors, including spatial, temporal and external factors. Those factors may influence …
-
Machine Learning Approaches to Assessing Future Flood & Storm Risk
This thesis describes the application of machine learning to hydrology problems in the face of imminent and long term climate change, in particular through the lens of data minimalism. First, we note that with the dawn of the Anthropocene the world's climate is changing, primarily due to human …
-
Machine learning approaches for malware classification based on hybrid artefacts
… level. The current research uses different approaches, including static and dynamic analysis, to detect and analyze malware categories using distinct feature sets, such as imported modules, opcodes, and API calls, which can improve performance in binary and multi-class classification …
Page 1 of 24