Date of Award
Master of Science
Pattern recognition has its origins in engineering while machine learning developed from computer science. Today, artificial intelligence (AI) is a booming field with many practical applications and active research topics that deals with both pattern recognition and machine learning. We now use softwares and applications to automate routine labor, understand speech (using Natural Language Processing) or images (extracting hierarchical features and patterns for object detection and pattern recognition), make diagnoses in medicine, even intricate surgical procedures and support basic scientific research.
This thesis deals with exploring the application of a specific branch of AI, or a specific tool, Deep Learning (DL) to real world engineering problems which otherwise had been difficult to solve using existing methods till date. Here we focus on different Deep Learning based methods to deal with two such engineering problems. We also explore the inner workings of such models through an explanation stage for each of the applied DL based strategies that gives us a sense of how such typical black box models work, or as we call it, an explanation stage for the DL model.
This explanation framework is an important step as previously, Deep Learning based models were thought to be frameworks which produce good results (classification, object detection, object recognition to name a few), but with no explanations or immediately visible causes as to why it achieves the results it does. This made Deep Learning based models hard to trust amongst the scientific community. In this thesis, we aim to achieve just that by deploying two such explanation frameworks, one for a 2D image study case and another for a 3D image voxel study case, which will be discussed later in the subsequent chapters.
Ghosal, Sambuddha, "Engineering analytics through explainable deep learning" (2017). Graduate Theses and Dissertations. 16277.