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Full-Text Articles in Engineering
Improving Benign Paroxysmal Positional Vertigo Diagnosis, Sida Zhang
Improving Benign Paroxysmal Positional Vertigo Diagnosis, Sida Zhang
Master's Theses (2009 -)
Benign Paroxysmal Positional Vertigo (BPPV) is one of the most common causes of dizziness. Especially for people over 45, the risk of BPPV is substantial. On the other hand, BPPV is often misdiagnosed and may require expensive examinations. This thesis introduces a prediction model based on machine learning to quickly, inexpensively, and accurately diagnose BPPV. The thesis starts by introducing BPPV and the statistics of BPPV misdiagnosis. Then, a patient survey is introduced. The patient survey includes 50 BPPV-related questions, which are used as training data for the machine learning model. Logistic Regression, Decision Tree, and Naïve Bayes were compared …
Deep Learning Approach For Dynamic Sampling For High-Throughput Nano-Desi Msi, David Helminiak
Deep Learning Approach For Dynamic Sampling For High-Throughput Nano-Desi Msi, David Helminiak
Master's Theses (2009 -)
Mass Spectrometry Imaging (MSI) extracts molecular mass data to form visualizations of molecular spatial distributions. The involved scanning procedure is conducted by moving a probe across and around a rectilinear grid, as in the case of nanoscale Desorption Electro-Spray Ionization (nano-DESI) MSI, where singular measurements can take up to ~5 seconds to acquire high-resolution (better than 10 μm) results. This temporal expense creates a high inefficiency in sample processing and throughput. For example, in a high-resolution nano-DESI study, a single mouse uterine tissue section (2.5 mm by 1.7 mm) had an acquisition time of ~4 hours to acquire 104,400 pixels. …
Hierarchical Bayesian Data Fusion Using Autoencoders, Yevgeniy Vladimirovich Reznichenko
Hierarchical Bayesian Data Fusion Using Autoencoders, Yevgeniy Vladimirovich Reznichenko
Master's Theses (2009 -)
In this thesis, a novel method for tracker fusion is proposed and evaluated for vision-based tracking. This work combines three distinct popular techniques into a recursive Bayesian estimation algorithm. First, semi supervised learning approaches are used to partition data and to train a deep neural network that is capable of capturing normal visual tracking operation and is able to detect anomalous data. We compare various methods by examining their respective receiver operating conditions (ROC) curves, which represent the trade off between specificity and sensitivity for various detection threshold levels. Next, we incorporate the trained neural networks into an existing data …
Automation Of Energy Demand Forecasting, Sanzad Siddique
Automation Of Energy Demand Forecasting, Sanzad Siddique
Master's Theses (2009 -)
Automation of energy demand forecasting saves time and effort by searching automatically for an appropriate model in a candidate model space without manual intervention. This thesis introduces a search-based approach that improves the performance of the model searching process for econometrics models. Further improvements in the accuracy of the energy demand forecasting are achieved by integrating nonlinear transformations within the models. This thesis introduces machine learning techniques that are capable of modeling such nonlinearity. Algorithms for learning domain knowledge from time series data using the machine learning methods are also presented. The novel search based approach and the machine learning …