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Articles 1 - 4 of 4
Full-Text Articles in Entire DC Network
Using Machine Learning And Traditional Statistics To Explore Retention And Knowledge Structure In Stem With An Emphasis On Physics, Cabot Alexander Zabriskie
Using Machine Learning And Traditional Statistics To Explore Retention And Knowledge Structure In Stem With An Emphasis On Physics, Cabot Alexander Zabriskie
Graduate Theses, Dissertations, and Problem Reports
Retention of Science, Technology, Engineering, and Mathematics (STEM) students is a serious problem as STEM graduation rates continue to lag the growing demand for the skills taught by these degree programs. Critical to fixing this “leaky pipeline” is investment in improving retention in the first two years of college study and increasing and maintaining the interest of K-12 students in STEM. This thesis will address this in three parts. The first is through evaluation of conceptual tests used to evaluate course improvements to determine the structure student knowledge measured by them. The second part uses machine learning to construct early …
Predictors And Health Outcomes Of Treatment-Resistant Depression Among Adults With Chronic Non-Cancer Pain Conditions And Major Depressive Disorder, Drishti Shah
Graduate Theses, Dissertations, and Problem Reports
Understanding major depressive disorder (MDD) as a comorbidity in patients with chronic non-cancer pain conditions (CNPC) is of importance because of the high prevalence and well documented bi-directional relationship between MDD and pain. Furthermore, presence of CNPC among adults with MDD often reduces benefits of antidepressant therapy, thereby increasing the possibility of treatment resistance. Treatment-resistant depression (TRD) commonly defined as insufficient response to multiple antidepressant trials, often worsens depression and pain symptoms and can amplify the clinical and economic burden among adults with CNPC and MDD. Additionally, long-term opioid therapy (LTOT) may be prescribed at a higher rate to adults …
On Matching Faces With Temporal Variations Using Representation Learning, Daksha Yadav
On Matching Faces With Temporal Variations Using Representation Learning, Daksha Yadav
Graduate Theses, Dissertations, and Problem Reports
Developing automatic face recognition algorithms which are robust to intra-subject variations is a challenging research problem in the computer vision research community. Apart from the well-studied covariates such as pose and expression, temporal variations in the facial appearance also lead to a decline in the performance of face recognition systems. This research focuses on analyzing the temporal variations in facial features due to facial aging, facial plastic surgeries, and prolonged illicit drug abuse. The contributions of this dissertation are fivefold: (i) behavioral and neuroimaging studies are conducted to understand the human perception of faces affected by temporal variations, specifically facial …
Crown-Level Mapping Of Tree Species And Health From Remote Sensing Of Rural And Urban Forests, Fang Fang
Crown-Level Mapping Of Tree Species And Health From Remote Sensing Of Rural And Urban Forests, Fang Fang
Graduate Theses, Dissertations, and Problem Reports
Tree species composition and health are key attributes for rural and urban forest biodiversity, and ecosystem services preservation. Remote sensing has facilitated extraordinary advances in estimating and mapping tree species composition and health. Yet previous sensors and algorithms were largely unable to resolve individual tree crowns and discriminate tree species or health classes at this essential spatial scale due to the low image spectral and spatial resolution. However, current available very high spatial resolution (VHR) remote sensing data can begin to resolve individual tree crowns and measure their spectral and structural qualities with unprecedented precision. Moreover, various machine learning algorithms …