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Physical Sciences and Mathematics Commons

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Full-Text Articles in Physical Sciences and Mathematics

An Environment For Developing Incremental Learning Applications For Data Streams, Farzin Sarvaramini Nov 2019

An Environment For Developing Incremental Learning Applications For Data Streams, Farzin Sarvaramini

Electronic Thesis and Dissertation Repository

Smart cities look to leverage technology, particularly sensors, and software to provide improved services for its citizenry and enhanced operational efficiencies. Cities look to develop applications that can process data from sensors and other sources to gain insights into operation, enable them to improve operations and inform city leadership. Such applications often need to process streams of data from sensors or other sources to provide city staff with insights into city operations. However, cities are faced with limited budgets and limited staff. The development of applications by third parties can be extremely expensive. One alternative is to identify tools for …


Algebraic Neural Architecture Representation, Evolutionary Neural Architecture Search, And Novelty Search In Deep Reinforcement Learning, Ethan C. Jackson Jun 2019

Algebraic Neural Architecture Representation, Evolutionary Neural Architecture Search, And Novelty Search In Deep Reinforcement Learning, Ethan C. Jackson

Electronic Thesis and Dissertation Repository

Evolutionary algorithms have recently re-emerged as powerful tools for machine learning and artificial intelligence, especially when combined with advances in deep learning developed over the last decade. In contrast to the use of fixed architectures and rigid learning algorithms, we leveraged the open-endedness of evolutionary algorithms to make both theoretical and methodological contributions to deep reinforcement learning. This thesis explores and develops two major areas at the intersection of evolutionary algorithms and deep reinforcement learning: generative network architectures and behaviour-based optimization. Over three distinct contributions, both theoretical and experimental methods were applied to deliver a novel mathematical framework and experimental …


Incorporating Figure Captions And Descriptive Text Into Mesh Term Indexing: A Deep Learning Approach, Xindi Wang May 2019

Incorporating Figure Captions And Descriptive Text Into Mesh Term Indexing: A Deep Learning Approach, Xindi Wang

Electronic Thesis and Dissertation Repository

The exponential increase of available documents online makes document classification an important application in natural language processing. The goal of text classification is to automatically assign categories to documents. Traditional text classifiers depend on features, such as, vocabulary and user-specified information which mainly relies on prior knowledge. In contrast, deep learning automatically learns effective features from data instead of adopting human-designed features. In this thesis, we specifically focus on biomedical document classification. Beyond text information from abstract and title, we also consider image and table captions, as well as paragraphs associated with images and tables, which we demonstrate to be …


Machine Learning For Stock Prediction Based On Fundamental Analysis, Yuxuan Huang Apr 2019

Machine Learning For Stock Prediction Based On Fundamental Analysis, Yuxuan Huang

Electronic Thesis and Dissertation Repository

Application of machine learning for stock prediction is attracting a lot of attention in recent years. A large amount of research has been conducted in this area and multiple existing results have shown that machine learning methods could be successfully used toward stock predicting using stocks’ historical data. Most of these existing approaches have focused on short term prediction using stocks’ historical price and technical indicators. In this thesis, we prepared 22 years’ worth of stock quarterly financial data and investigated three machine learning algorithms: Feed-forward Neural Network (FNN), Random Forest (RF) and Adaptive Neural Fuzzy Inference System (ANFIS) for …


Improving Neural Sequence Labelling Using Additional Linguistic Information, Muhammad Rifayat Samee Apr 2019

Improving Neural Sequence Labelling Using Additional Linguistic Information, Muhammad Rifayat Samee

Electronic Thesis and Dissertation Repository

Sequence Labelling is the task of mapping sequential data from one domain to another domain. As we can interpret language as a sequence of words, sequence labelling is very common in the field of Natural Language Processing (NLP). In NLP, some fundamental sequence labelling tasks are Parts-of-Speech Tagging, Named Entity Recognition, Chunking, etc. Moreover, many NLP tasks can be modeled as sequence labelling or sequence to sequence labelling such as machine translation, information retrieval and question answering. An extensive amount of research has already been performed on sequence labelling. Most of the current high performing models are neural network models. …


Applicability Of Recurrent Neural Networks To Player Data Analysis In Freemium Video Games, Jonathan Tan Apr 2019

Applicability Of Recurrent Neural Networks To Player Data Analysis In Freemium Video Games, Jonathan Tan

Electronic Thesis and Dissertation Repository

We demonstrate the applicability and practicality of recurrent neural networks (RNNs), a machine learning methodology suited for sequential data, on player data from the mobile video game My Singing Monsters. Since this data comes in as a stream of events, RNNs are a natural solution for analyzing this data with minimal preprocessing. We apply RNNs to monitor and forecast game metrics, predict player conversion, estimate lifetime player value, and cluster player behaviours. In each case, we discuss why the results are interesting, how the trained models can be applied in a business setting, and how the preliminary work can …


Extracting Scales Of Measurement Automatically From Biomedical Text With Special Emphasis On Comparative And Superlative Scales, Sara Baker Apr 2019

Extracting Scales Of Measurement Automatically From Biomedical Text With Special Emphasis On Comparative And Superlative Scales, Sara Baker

Electronic Thesis and Dissertation Repository

Abstract

In this thesis, the focus is on the topic of “Extracting Scales of Measurement Automatically from Biomedical Text with Special Emphasis on Comparative and Superlative Scales.” Comparison sentences, when considered as a critical part of scales of measurement, play a highly significant role in the process of gathering information from a large number of biomedical research papers. A comparison sentence is defined as any sentence that contains two or more entities that are being compared. This thesis discusses several different types of comparison sentences such as gradable comparisons and non-gradable comparisons. The main goal is extracting comparison sentences automatically …


Vessel Tree Reconstruction With Divergence Prior, Zhongwen Zhang Jan 2019

Vessel Tree Reconstruction With Divergence Prior, Zhongwen Zhang

Electronic Thesis and Dissertation Repository

Accurate structure analysis of high-resolution 3D biomedical images of vessels is a challenging issue and in demand for medical diagnosis nowadays. Previous curvature regularization based methods [10, 31] give promising results. However, their mathematical models are not designed for bifurcations and generate significant artifacts in such areas. To address the issue, we propose a new geometric regularization principle for reconstructing vector fields based on prior knowledge about their divergence. In our work, we focus on vector fields modeling blood flow pattern that should be divergent in arteries and convergent in veins. We show that this previously ignored regularization constraint can …