The focus of this thesis is on automation in road asset inspection using deep neural networks. Even though some progress has been made in automation of data collection an[...]

Feedback driven deep reinforcement learning methodologies are widely favoured approaches to solving artificial intelligence problems. The algorithms navigate complex deci[...]

To automatically classify biological images, machine learning techniques have been widely used to train the classifiers from labeled images. For a new category of biologi[...]

The Context-Aware Conditional Tabular Generative Adversarial Network (CA-CTGAN) introduces an innovative architecture for the generation of synthetic tabular data, distin[...]

Deep reinforcement learning has demonstrated its capability to solve a diverse array of challenging problems, which were not able to solve previously. It has been able to[...]