Please use this identifier to cite or link to this item:
|Title:||Novel Applications of Machine Learning in Pipeline Inspection and Neuroscience|
|Advisor:||Reilly, James P.|
Hasey, Gary M.
|Department:||Electrical and Computer Engineering|
|Keywords:||novel, applications, machine, learning, pipeline, neuroscience|
|Abstract:||<p> In this thesis we develop and evaluate automated "expert systems" for two applications: (i) gas/oil pipeline inspection using magnetic flux leakage information, (ii) treatment efficacy prediction and medical diagnosis using electroencephalograph (EEG) and clinical information. Both applications share the same methodology and procedure as they employ machine learning methods which learn their decision models using the training data (or past examples in real life/environment).</p> <p> The magnetic flux leakage (MFL) technique is commonly used for nondestructive testing (NDT) of oil and gas pipelines which are mostly buried underground. This testing involves the detection of metal defects and anomalies in the pipe wall, and the evaluation of the severity of these defects. The difficulty with the MFL method is the extent and complexity of the analysis of the MFL images. In this thesis we show how modern machine learning techniques can be used to considerable advantage in this respect.</p> <p> The problem of identifying in advance the most effective treatment agent for various psychiatric conditions remains an elusive goal. To address this challenge, an automated medical expert system is designed and then evaluated. The system is capable of predicting the treatment response for each individual patient at the outset of a therapy (i.e., using pre-treatment information) thus improving therapeutic efficiency and reducing personal and economic costs. Our experiments are focused on treatment planning and diagnosis of mood disorders and psychiatric illnesses. Through different experiments, we have shown that it is possible to predict treatment efficacy of a 'selective serotonin reuptake inhibitor' (SSRI) antidepressant and 'repetitive transcranial magnetic stimulation' (rTMS) therapies for patients with treatment-resistant major depressive disorder (MDD) or major depression. The predictions are based on pre-treatment quantitative EEG measurements. Also, prediction of post-treatment schizophrenia symptomatic scores, using pre-treatment EEG data, showed significant performance in patients treated with the drug clozapine. Clozapine is an antipsychotic medication of superior effectiveness in treating Schizophrenia but has several potentially severe side effects.</p> <p> Medical diagnosis is the second problem we consider in the neuroscience aspects of this thesis. In this research, an automated digital medical diagnosis methodology is developed to estimate/detect the type of a disease or illness that a patient is suffering. This intelligent diagnostic system can assist the physician/clinician by offering a second opinion on diagnosis. Several complex psychiatric illnesses may have many common symptoms and accurate diagnosis can, at times, be very difficult. Efficient diagnosis helps by avoiding prescription of wrong therapy /treatment to a patient. In our limited experiments, EEG data is used to make a diagnosis for distinguishing between various psychiatric illnesses including MDD, schizophrenia, and the depressed phase of bipolar affective disorder (BAD).</p> <p> In all problems considered in this thesis, specifically the neuroscience problem, a large number of candidate features are extracted from measurement data but most candidate features are found to be irrelevant and have little or no discriminative power. Finding a few most discriminating features that guarantee numerical efficiency and obtain a smooth and generalizable decision function, is a major challenge in this research. In this thesis, feature selection methods based on mutual information or Kullback-Leibler (KL) distance is employed to find the most statistically relevant features. For the multi-class diagnosis problem, to improve performance, a feature selection procedure denoted as feature combination feature selection is used which first finds discriminating features in all binary classification combinations, and then combines them into a larger feature subset to make a final multi-class decision. The two-dimensional (2D) representation of the feature data is also found to be useful for clustering analysis. The overall method was evaluated using a nested cross-validation procedure for which over 80% average prediction performance is obtained in all experiments. The results indicate that machine learning methods hold considerable promise in solving the challenging problems encountered in the two applications of concern.</p>|
|Appears in Collections:||Open Access Dissertations and Theses|
Files in This Item:
|Khodayari-Rostamabad_Ahmad_2010Aug_Ph.D..pdf||10.83 MB||Adobe PDF||View/Open|
Items in MacSphere are protected by copyright, with all rights reserved, unless otherwise indicated.