Digital Commons Network^™

Looping Predictive Method To Improve Accuracy Of A Machine Learning Model, Subramanyam Reddy Pogili

Theses

The topic of this project is an analysis of drug-related tweets. The goal is to build a Machine Learning Model that can distinguish between tweets that indicate drug abuse and other tweets that also contain the name of a drug but do not describe abuse. Drugs can be illegal, such as heroin, or legal drugs with a potential of abuse, such as painkillers. However, building a good Machine Learning Model requires a large amount of training data. For each training tweet, a human expert has determined whether it indicates drug abuse or not. This is difficult work for humans. …

360° View Camera Based Visual Assistive Technology For Contextual Scene Information, Mazin Ali

Theses

In this research project, a system is proposed to aid the visually impaired by providing partial contextual information of the surroundings using 360° view camera combined with deep learning is proposed. The system uses a 360° view camera with a mobile device to capture surrounding scene information and provide contextual information to the user in the form of audio. The system could also be used for other applications such as logo detection which visually impaired users can use for shopping assistance.

The scene information from the spherical camera feed is classified by identifying objects that contain contextual information of the …

Discovering A Domain Knowledge Representation For Image Grouping: Multimodal Data Modeling, Fusion, And Interactive Learning, Xuan Guo

Theses

In visually-oriented specialized medical domains such as dermatology and radiology, physicians explore interesting image cases from medical image repositories for comparative case studies to aid clinical diagnoses, educate medical trainees, and support medical research. However, general image classification and retrieval approaches fail in grouping medical images from the physicians' viewpoint. This is because fully-automated learning techniques cannot yet bridge the gap between image features and domain-specific content for the absence of expert knowledge. Understanding how experts get information from medical images is therefore an important research topic.

As a prior study, we conducted data elicitation experiments, where physicians were instructed …

Automated Quality Assessment Of Printed Objects Using Subjective And Objective Methods Based On Imaging And Machine Learning Techniques., Ritu Basnet

Theses

Estimating the perceived quality of printed patterns is a complex task as quality is subjective. A study was conducted to evaluate how accurately a machine learning method can predict human judgment about printed pattern quality.

The project was executed in two phases: a subjective test to evaluate the printed pattern quality and development of the machine learning classifier-based automated objective model. In the subjective experiment, human observers ranked overall visual quality. Object quality was compared based on a normalized scoring scale. There was a high correlation between subjective evaluation ratings of objects with similar defects. Observers found the contrast of …

Machine Learning Based Autism Detection Using Brain Imaging, Gajendra Jung Katuwal

Theses

Autism Spectrum Disorder (ASD) is a group of heterogeneous developmental disabilities that manifest in early childhood. Currently, ASD is primarily diagnosed by assessing the behavioral and intellectual abilities of a child. This behavioral diagnosis can be subjective, time consuming, inconclusive, does not provide insight on the underlying etiology, and is not suitable for early detection. Diagnosis based on brain magnetic resonance imaging (MRI)—a widely used non- invasive tool—can be objective, can help understand the brain alterations in ASD, and can be suitable for early diagnosis. However, the brain morphological findings in ASD from MRI studies have been inconsistent. Moreover, there …

Deep Learning Localization For Self-Driving Cars, Suvam Bag

Theses

Identifying the location of an autonomous car with the help of visual sensors can be a good alternative to traditional approaches like Global Positioning Systems (GPS) which are often inaccurate and absent due to insufficient network coverage. Recent research in deep learning has produced excellent results in different domains leading to the proposition of this thesis which uses deep learning to solve the problem of localization in smart cars with visual data.

Deep Convolutional Neural Networks (CNNs) were used to train models on visual data corresponding to unique locations throughout a geographic location. In order to evaluate the performance of …

Application Of Machine Learning Models To Cftr Enhancer Discovery, James M.J. Lawlor

Theses

Cystic Fibrosis is a Mendelian genetic disorder causing production of hyper- viscous mucus, which damages organs including the lungs and digestive system, and is the result of absent or defective production of the chloride transport protein CFTR. CFTR expression varies between organs, developmental stages, and individuals. We develop and optimize a machine learning pipeline to identify enhancers—binding sites for regulatory proteins—near CFTR. We segment A549, Caco-2, Calu-3, and PANC-1 genomes, train a sequence-based classifier on the predicted enhancer segments, and use the classifier score to predict sequence variants’ enhancer activities. Our optimizations appreciably improve the resulting enhancer predictions. We present …