Physical Sciences and Mathematics Commons^™

Neural Machine Translation, Quinn M. Lanners, Thomas Laurent

Honors Thesis

Neural Machine Translation is the primary algorithm used in industry to perform machine translation. This state-of-the-art algorithm is an application of deep learning in which massive datasets of translated sentences are used to train a model capable of translating between any two languages. The architecture behind neural machine translation is composed of two recurrent neural networks used together in tandem to create an Encoder Decoder structure. Attention mechanisms have recently been developed to further increase the accuracy of these models. In this senior thesis, the various parts of Neural Machine Translation are explored towards the eventual creation of a tutorial …

Deep Learning Based Medical Image Analysis With Limited Data, Jiaxing Tan

Dissertations, Theses, and Capstone Projects

Deep Learning Methods have shown its great effort in the area of Computer Vision. However, when solving the problems of medical imaging, deep learning’s power is confined by limited data available. We present a series of novel methodologies for solving medical imaging analysis problems with limited Computed tomography (CT) scans available. Our method, based on deep learning, with different strategies, including using Generative Adversar- ial Networks, two-stage training, infusing the expert knowledge, voting based or converting to other space, solves the data set limitation issue for the cur- rent medical imaging problems, specifically cancer detection and diagnosis, and shows very …

Improving Vix Futures Forecasts Using Machine Learning Methods, James Hosker, Slobodan Djurdjevic, Hieu Nguyen, Robert Slater

SMU Data Science Review

The problem of forecasting market volatility is a difficult task for most fund managers. Volatility forecasts are used for risk management, alpha (risk) trading, and the reduction of trading friction. Improving the forecasts of future market volatility assists fund managers in adding or reducing risk in their portfolios as well as in increasing hedges to protect their portfolios in anticipation of a market sell-off event. Our analysis compares three existing financial models that forecast future market volatility using the Chicago Board Options Exchange Volatility Index (VIX) to six machine/deep learning supervised regression methods. This analysis determines which models provide best …

Intelligent Technology Of Command And Control System In The Rts Perspective, Wenfeng Wu, Zhang Yu, Rong Ming

Journal of System Simulation

Abstract: Real-Time Strategy (RTS) games have important reference value for studying the intelligent technology of command and control systems. The similarities between RTS games and the strategic battle level command and control systems are described according to the decision process. The challenges brought by the problems of planning, learning, uncertainty and space-time reasoning in the intelligent technology of RTS games are analyzed. The key technologies and latest research progress of action sequence planning, plan recognition, state assessment, multi-agent collaboration and multi-scale AI are studied. The trend of intelligent technology development of strategic and operational level command and control systems is …

Opioid Misuse Detection In Hospitalized Patients Using Convolutional Neural Networks, Brihat Sharma

Master's Theses

Opioid misuse is a major public health problem in the world. In 2016, 11.3 million people were reported to misuse opioids in the US only. Opioid-related inpatient and emergency department visits have increased by 64 percent and the rate of opioid-related visits has nearly doubled between 2009 and 2014. It is thus critical for healthcare systems to detect opioid misuse cases. Patients hospitalized for consequences of their opioid misuse present an opportunity for intervention but better screening and surveillance methods are needed to guide providers. The current screening methods with self-report questionnaire data are time-consuming and difficult to perform in …

Deep Learning: Edge-Cloud Data Analytics For Iot, Katarina Grolinger, Ananda M. Ghosh

Electrical and Computer Engineering Publications

Sensors, wearables, mobile and other Internet of Thing (IoT) devices are becoming increasingly integrated in all aspects of our lives. They are capable of collecting massive quantities of data that are typically transmitted to the cloud for processing. However, this results in increased network traffic and latencies. Edge computing has a potential to remedy these challenges by moving computation physically closer to the network edge where data are generated. However, edge computing does not have sufficient resources for complex data analytics tasks. Consequently, this paper investigates merging cloud and edge computing for IoT data analytics and presents a deep learning-based …

Dedicated Hardware For Machine/Deep Learning: Domain Specific Architectures, Angel Izael Solis

Open Access Theses & Dissertations

Artificial intelligence has come a very long way from being a mere spectacle on the silver screen in the 1920s [Hml18]. As artificial intelligence continues to evolve, and we begin to develop more sophisticated Artificial Neural Networks, the need for specialized and more efficient machines (less computational strain while maintaining the same performance results) becomes increasingly evident. Though these “new” techniques, such as Multilayer Perceptron’s, Convolutional Neural Networks and Recurrent Neural Networks, may seem as if they are on the cutting edge of technology, many of these ideas are over 60 years old! However, many of these earlier models, at …

A Novel Set Of Weight Initialization Techniques For Deep Learning Architectures, Diego Aguirre

Open Access Theses & Dissertations

The importance of weight initialization when building a deep learning model is often underappreciated. Even though it is usually seen as a minor detail in the model creation cycle, this process has shown to have a strong impact on the training time of a network and the quality of the resulting model. In fact, the implications of choosing a poor initialization scheme range from leading to the creation of a poorly performing model to preventing optimization techniques (like stochastic gradient descent) from converging.

In this work, we introduce and evaluate a set of novel weight initialization techniques for deep learning …

Multi-Spectral Visual Crop Assessment Under Limited Data Constraints, Patricia O'Byrne, Patrick Jackman, Damon Berry, Hector-Hugo Franco-Penya, Michael French, Robert J. Ross

Conference papers

In an era of climate change and global population growth, deep learning based multi-spectral imaging has the potential to significantly assist in production management across a wide range of agricultural and food production domains. A key challenge however in applying state-of-the-art methods is that they, unlike classical hand crafted methods, are usually thought of as being only useful when significant amounts of data are available. In this paper we investigate this hypothesis by examining the performance of state-of-the-art deep learning methods when applied to a restricted data set that is not easily bootstrapped through pre-trained image processing networks. We demonstrate …

A Deep Dive Into The Land Development Dynamics Of A Complex Landscape, Pariya Pourmohammadi

Graduate Theses, Dissertations, and Problem Reports

Land development is a complex and dynamic process simultaneously interacting with numerous environmental, cultural and economic procedures. In this research we studied past, present and future of land transformation in Appalachia. This dissertation is organized in three-essay format and each essay is focused on one aspect of land development processes in a sub-region in the Appalachian region. In the first essay, deep learning techniques are used to build predictive models for the land development. This study presets deconvolutional neural networks models in predicting land development. On the second essay, spatial data analysis and remote sensing are used to investigate the …

Explainable Neural Attention Recommender Systems, Omer Tal

Theses and Dissertations (Comprehensive)

Recommender systems, predictive models that provide lists of personalized suggestions, have become increasingly popular in many web-based businesses. By presenting potential items that may interest a user, these systems are able to better monetize and improve users’ satisfaction. In recent years, the most successful approaches rely on capturing what best define users and items in the form of latent vectors, a numeric representation that assumes all instances can be described by their respective affiliation towards a set of hidden features. However, recommendation methods based on latent features still face some realworld limitations. The data sparsity problem originates from the unprecedented …

Quantifying Human Biological Age: A Machine Learning Approach, Syed Ashiqur Rahman

Graduate Theses, Dissertations, and Problem Reports

Quantifying human biological age is an important and difficult challenge. Different biomarkers and numerous approaches have been studied for biological age prediction, each with its advantages and limitations. In this work, we first introduce a new anthropometric measure (called Surface-based Body Shape Index, SBSI) that accounts for both body shape and body size, and evaluate its performance as a predictor of all-cause mortality. We analyzed data from the National Health and Human Nutrition Examination Survey (NHANES). Based on the analysis, we introduce a new body shape index constructed from four important anthropometric determinants of body shape and body size: body …

Towards Improving Accuracy And Interpretability Of Deep Learning Based On Satellite Image Classification, Yamile Patino Vargas

Dissertations and Theses

ABSTRACT

The study of satellite images provides a way to monitor changes in the surface of the Earth and the atmosphere. Convolutional Neural Networks (CNN) have shown accurate results in solving practical problems in multiple fields. Some of the more recognized fields using CNNs are satellite imagery processing, medicine, communication, transportation, and computer vision. Despite the success of CNNs, there remains a need to explain the network predictions further and understand what the network is determining as valuable information.

There are several frameworks and methodologies developed to explain how CNNs predict outputs and what their internal representations are [1, 4, …

Efficient Detection Of Diseases By Feature Engineering Approach From Chest Radiograph, Avishek Mukherjee

Legacy Theses & Dissertations (2009 - 2024)

Deep Learning is the new state-of-the-art technology in Image Processing. We applied Deep Learning techniques for identification of diseases from Radiographs made publicly available by NIH. We applied some Feature Engineering approach to augment the data from Anterior-Posterior position to Posterior-Anterior position and vice-versa for all the diseases, at the same point we suppressed ‘No Finding’ radiographs which contributed to more than 50% (approximately 60,000) of the dataset to top 1000 images. We also prepared a model by adding a huge amount of noise to the augmented data, which if need be can be deployed at rural locations which lack …

Emotion Forecasting In Dyadic Conversation : Characterizing And Predicting Future Emotion With Audio-Visual Information Using Deep Learning, Sadat Shahriar

Legacy Theses & Dissertations (2009 - 2024)

Emotion forecasting is the task of predicting the future emotion of a speaker, i.e., the emotion label of the future speaking turn–based on the speaker’s past and current audio-visual cues. Emotion forecasting systems require new problem formulations that differ from traditional emotion recognition systems. In this thesis, we first explore two types of forecasting windows(i.e., analysis windows for which the speaker’s emotion is being forecasted): utterance forecasting and time forecasting. Utterance forecasting is based on speaking turns and forecasts what the speaker’s emotion will be after one, two, or three speaking turns. Time forecasting forecasts what the speaker’s emotion will …

Deep Neural Network Learning-Based Classifier Design For Big-Data Analytics, Krishnan Raghavan

Doctoral Dissertations

"In this digital age, big-data sets are commonly found in the field of healthcare, manufacturing and others where sustainable analysis is necessary to create useful information. Big-data sets are often characterized by high-dimensionality and massive sample size. High dimensionality refers to the presence of unwanted dimensions in the data where challenges such as noise, spurious correlation and incidental endogeneity are observed. Massive sample size, on the other hand, introduces the problem of heterogeneity because complex and unstructured data types must analyzed. To mitigate the impact of these challenges while considering the application of classification, a two step analysis approach is …

Rule Mining And Sequential Pattern Based Predictive Modeling With Emr Data, Orhan Abar

Theses and Dissertations--Computer Science

Electronic medical record (EMR) data is collected on a daily basis at hospitals and other healthcare facilities to track patients’ health situations including conditions, treatments (medications, procedures), diagnostics (labs) and associated healthcare operations. Besides being useful for individual patient care and hospital operations (e.g., billing, triaging), EMRs can also be exploited for secondary data analyses to glean discriminative patterns that hold across patient cohorts for different phenotypes. These patterns in turn can yield high level insights into disease progression with interventional potential. In this dissertation, using a large scale realistic EMR dataset of over one million patients visiting University of …

Knowledge Graph Reasoning Over Unseen Rdf Data, Bhargavacharan Reddy Kaithi

Browse all Theses and Dissertations

In recent years, the research in deep learning and knowledge engineering has made a wide impact on the data and knowledge representations. The research in knowledge engineering has frequently focused on modeling the high level human cognitive abilities, such as reasoning, making inferences, and validation. Semantic Web Technologies and Deep Learning have an interest in creating intelligent artifacts. Deep learning is a set of machine learning algorithms that attempt to model data representations through many layers of non-linear transformations. Deep learning is in- creasingly employed to analyze various knowledge representations mentioned in Semantic Web and provides better results for Semantic …