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Holistic indoor scene understanding by context supported instance segmentation
(2020-12)
Intelligent robots require advanced vision capabilities to perceive and interact with the real physical world. While computer vision has made great strides in recent years, its predominant paradigm still focuses on building ...
Interpreting natural language processing (NLP) models and lifting their limitations
(2021-07)
There have been many advances in the artificial intelligence field due to the emergence of deep learning and big data. In almost all sub-fields, artificial neural networks have reached or exceeded human-level performance. ...
Using machine learning methods to improve healthcare delivery in diabetes management
(2022-07)
This dissertation includes three studies, all focusing on Analytics and Patients information for improving diabetes management, namely educating patients and early detection of comorbidities. In these studies, we develop ...
Physics-guided machine learning for turbulence closure and reduced-order modeling
(2022-07)
A recent advance in scientific machine learning has started to show promising results in fluid mechanics. Despite their early success, the application of data-driven methods to turbulent flow simulation is non-trivial due ...
Development of in-field data acquisition systems and machine learning-based data processing and analysis approaches for turfgrass quality rating and peanut flower detection
(2022-07)
Digital image processing and machine vision techniques provide scientists with an objective measure of crop quality that adds to the validity of study results without burdening the evaluation process. This dissertation ...