Computer Sciences Commons^™

Rnn-Based Generation Of Polyphonic Music And Jazz Improvisation, Andrew Hannum

Electronic Theses and Dissertations

This paper presents techniques developed for algorithmic composition of both polyphonic music, and of simulated jazz improvisation, using multiple novel data sources and the character-based recurrent neural network architecture char-rnn. In addition, techniques and tooling are presented aimed at using the results of the algorithmic composition to create exercises for musical pedagogy.

Recurrent Neural Networks And Their Applications To Rna Secondary Structure Inference, Devin Willmott

Theses and Dissertations--Mathematics

Recurrent neural networks (RNNs) are state of the art sequential machine learning tools, but have difficulty learning sequences with long-range dependencies due to the exponential growth or decay of gradients backpropagated through the RNN. Some methods overcome this problem by modifying the standard RNN architecure to force the recurrent weight matrix W to remain orthogonal throughout training. The first half of this thesis presents a novel orthogonal RNN architecture that enforces orthogonality of W by parametrizing with a skew-symmetric matrix via the Cayley transform. We present rules for backpropagation through the Cayley transform, show how to deal with the Cayley …

An Adaptive Machine Learning-Based Qoe Approach In Sdn Context For Video-Streaming Services, Asma Ben Letaifa

Turkish Journal of Electrical Engineering and Computer Sciences

In data service applications over the Internet, user perception and satisfaction can be assessed by quality of experience (QoE) metrics. QoE depends both on the users' perception and the used service, which together form end-to-end metrics. While network optimization has traditionally focused on optimizing network properties such as QoS, we focus in this work on optimizing end-to-end QoE metrics with the aim to deliver to the client a good QoE that can be monitored in real time. We argue that end-user QoE is a relevant measurement for network operators and service providers. In this paper, we present a machine learning …

Evaluation Of Machine Learning Techniques For Early Identification Of At-Risk Students, Mansour Hamoud Awaji

CCAC Theses and Dissertations

Student attrition is one of the long-standing problems facing higher education institutions despite the extensive research that has been undertaken to address it. To increase students’ success and retention rates, there is a need for early alert systems that facilitate the identification of at-risk students so that remedial measures may be taken in time to reduce the risk. However, incorporating ML predictive models into early warning systems face two main challenges: improving the accuracy of timely predictions and the generalizability of predictive models across on-campus and online courses. The goal of this study was to develop and evaluate predictive models …

Expanding The Artificial Intelligence-Data Protection Debate, Fred H. Cate, Christopher Kuner, Orla Lynskey, Christopher Millard, Nora Ni Loideain, Dan Jerker B. Svantesson

Articles by Maurer Faculty

No abstract provided.

Clinical Information Extraction From Unstructured Free-Texts, Mingzhe Tao

Legacy Theses & Dissertations (2009 - 2024)

Information extraction (IE) is a fundamental component of natural language processing (NLP) that provides a deeper understanding of the texts. In the clinical domain, documents prepared by medical experts (e.g., discharge summaries, drug labels, medical history records) contain a significant amount of clinically-relevant information that is crucial to the overall well-being of patients. Unfortunately, in many cases, clinically-relevant information is presented in an unstructured format, predominantly consisting of free-texts, making it inaccessible to computerized methods. Automatic extraction of this information can improve accessibility. However, the presence of synonymous expressions, medical acronyms, misspellings, negated phrases, and ambiguous terminologies make automatic extraction …

Old English Character Recognition Using Neural Networks, Sattajit Sutradhar

College of Graduate Studies: Theses & Dissertations

Character recognition has been capturing the interest of researchers since the beginning of the twentieth century. While the Optical Character Recognition for printed material is very robust and widespread nowadays, the recognition of handwritten materials lags behind. In our digital era more and more historical, handwritten documents are digitized and made available to the general public. However, these digital copies of handwritten materials lack the automatic content recognition feature of their printed materials counterparts. We are proposing a practical, accurate, and computationally efficient method for Old English character recognition from manuscript images. Our method relies on a modern machine learning …

Leveraging Overhead Imagery For Localization, Mapping, And Understanding, Scott Workman

Theses and Dissertations--Computer Science

Ground-level and overhead images provide complementary viewpoints of the world. This thesis proposes methods which leverage dense overhead imagery, in addition to sparsely distributed ground-level imagery, to advance traditional computer vision problems, such as ground-level image localization and fine-grained urban mapping. Our work focuses on three primary research areas: learning a joint feature representation between ground-level and overhead imagery to enable direct comparison for the task of image geolocalization, incorporating unlabeled overhead images by inferring labels from nearby ground-level images to improve image-driven mapping, and fusing ground-level imagery with overhead imagery to enhance understanding. The ultimate contribution of this thesis …

Assesing Completeness Of Solvency And Financial Condition Reports Through The Use Of Machine Learning And Text Classification, Ruairí Nugent

Dissertations

Text mining is a method for extracting useful information from unstructured data through the identification and exploration of large amounts of text. It is a valuable support tool for organisations. It enables a greater understanding and identification of relevant business insights from text. Critically it identifies connections between information within texts that would otherwise go unnoticed. Its application is prevalent in areas such as marketing and political science however, until recently it has been largely overlooked within economics. Central banks are beginning to investigate the benefits of machine learning, sentiment analysis and natural language processing in light of the large …

Data Visualization And Classification Of Artificially Created Images, Dmytro Dovhalets

All Master's Theses

Visualization of multidimensional data is a long-standing challenge in machine learning and knowledge discovery. A problem arises as soon as 4-dimensions are introduced since we live in a 3-dimensional world. There are methods out there which can visualize multidimensional data, but loss of information and clutter are still a problem. General Line Coordinates (GLC) can losslessly project n-dimensional data in 2- dimensions. A new method is introduced based on GLC called GLC-L. This new method can do interactive visualization, dimension reduction, and supervised learning. One of the applications of GLC-L is transformation of vector data into image data. This novel …

Smart Augmented Reality Instructional System For Mechanical Assembly, Ze-Hao Lai

Masters Theses

"Quality and efficiency are pivotal indicators of a manufacturing company. Many companies are suffering from shortage of experienced workers across the production line to perform complex assembly tasks such as assembly of an aircraft engine. This could lead to a significant financial loss. In order to further reduce time and error in an assembly, a smart system consisting of multi-modal Augmented Reality (AR) instructions with the support of a deep learning network for tool detection is introduced. The multi-modal smart AR is designed to provide on-site information including various visual renderings with a fine-tuned Region-based Convolutional Neural Network, which is …

Artificial Intelligence And It Professionals, Sunil Mithas, Thomas Kude, Jonathan W. Whitaker

Management Faculty Publications

How will continuing developments in artificial intelligence (AI) and machine learning influence IT professionals? This article approaches this question by identifying the factors that influence the demand for software developers and IT professionals, describing how these factors relate to AI, and articulating the likely impact on IT professionals.

Application Of Acoustic Emission And Machine Learning To Detect Codling Moth Infested Apples, Mengxing Li, Nader Ekramirad, Ahmed Rady, Akinbode A. Adedeji

Biosystems and Agricultural Engineering Faculty Publications

Incidence of codling moth (CM) (Cydia pomonella L.) infestation in apples has been a major concern in North America for decades. CM larvae bore deep into the fruit, making it unmarketable. An effective noninvasive method to detect larvae-infested apples is necessary to ensure that apples are CM-free in post-harvest processing. In this study, a novel approach using an acoustic emission (AE) system and subsequent machine learning methods was applied to classify larvae-infested apples from intact apples. 'GoldRush‘ apples were infested with CM neonates and stored at the same conditions as intact apples. The AE system was used to collect …

Mapping Opioid Mortality Rates Across Treatment Capacity To Identify Need And Access, Garrett K. Wong, Justin R. Chang, Chase Greco, Yadunandan Pillai, Mohammad A. Shahrezaei, Melissa H. Burton, Rob Lawrence, Alan Dow

Graduate Research Posters

Background: The opioid and heroin overdose epidemic is a public health emergency in the state of Virginia, resulting in the death of more than 1,100 people in 2016. In order to overcome this epidemic, we need to match the places with the greatest need for services related to substance use disorders with the appropriate healthcare workforce.

Aims: As the data about the overdose outbreak and related socioeconomic factors grow in size and complexity, data scientists have attempted to utilize big data techniques to identify communities and risk factors contributing to addiction.

Methods: Using data obtained from the …

Visualizing The Opioid Overdose With A Dynamic Heat Map To Identify And Predict Vulnerable Communities, Justin R. Chang, Garrett K. Wong, Chase Greco, Yadunandan Pillai, Mohammad A. Shahrezaei, Melissa H. Burton, Rob Lawrence, Alan Dow

Graduate Research Posters

Background: Opioid and heroin overdose epidemic is a public health emergency in the state of Virginia. In order to prevent overdose deaths, we need the target expertise in substance use disorders to areas with high rates of overdose. In particular, an area with an acute spike in overdoses might represent an urgent need for intervention.

Aims: The CDC urges the use of near real-time surveillance to effectively identify overdose incidence, and to coordinate community responses in the states affected by the epidemic, including Virginia. However, current opioid overdose datasets for Virginia lack adequate consistency, granularity, and temporality for …

Estimating The Optimal Cutoff Point For Logistic Regression, Zheng Zhang

Open Access Theses & Dissertations

Binary classification is one of the main themes of supervised learning. This research is concerned about determining the optimal cutoff point for the continuous-scaled outcomes (e.g., predicted probabilities) resulting from a classifier such as logistic regression. We make note of the fact that the cutoff point obtained from various methods is a statistic, which can be unstable with substantial variation. Nevertheless, due partly to complexity involved in estimating the cutpoint, there has been no formal study on the variance or standard error of the estimated cutoff point.

In this Thesis, a bootstrap aggregation method is put forward to estimate the …

Deep Learning Methods For Visual Object Recognition, Zeyad Hailat

Wayne State University Dissertations

Convolutional neural networks (CNNs) attain state-of-the-art performance on various classification tasks assuming a sufficiently large number of labeled training examples. Unfortunately, curating sufficiently large labeled training dataset requires human involvement, which is expensive, time-consuming, and susceptible to noisy labels. Semi-supervised learning methods can alleviate the aforementioned problems by employing one of two techniques. First, utilizing a limited number of labeled data in conjunction with sufficiently large unlabeled data to construct a classification model. Second, exploiting sufficiently large noisy label training data to learn a classification model. In this dissertation, we proposed a few new methods to mitigate the aforementioned problems. …

Measuring Goal Similarity Using Concept, Context And Task Features, Vahid Eyorokon

Browse all Theses and Dissertations

Goals can be described as the user's desired state of the agent and the world and are satisfied when the agent and the world are altered in such a way that the present state matches the desired state. For physical agents, they must act in the world to alter it in a series of individual atomic actions. Traditionally, agents use planning to create a chain of actions each of which altering the current world state and yielding a new one until the final action yields the desired goal state. Once this goal state has been achieved, the goal is said …

The Feasibility Of Dementia Caregiver Task Performance Measurement Using Smart Gaming Technology, Garrett G. Goodman

Browse all Theses and Dissertations

Dementia caregiver burnout is detrimental to both the familial caregiver and their loved ones with dementia. As the population of older adults increases, both the number of individuals with dementia and their corresponding caregivers increase as well. Thus, we are interested in developing a potential tool to non-invasively detect signs of caregiver burnout using a mobile application combined with machine learning. Hence, the mobile application "Caregiver Assessment using Smart Technology" (CAST) was developed which personalizes a word scramble game. The CAST application utilizes a heuristically constructed Fuzzy Inference System (FIS) optimized via a Genetic Algorithm (GA) to provide an individualized …

Quantitative Forecasting Of Risk For Ptsd Using Ecological Factors: A Deep Learning Application, Nuriel S. Mor, Kathryn L. Dardeck

Journal of Social, Behavioral, and Health Sciences

Forecasting the risk for mental disorders from early ecological information holds benefits for the individual and society. Computational models used in psychological research, however, are barriers to making such predictions at the individual level. Preexposure identification of future soldiers at risk for posttraumatic stress disorder (PTSD) and other individuals, such as humanitarian aid workers and journalists intending to be potentially exposed to traumatic events, is important for guiding decisions about exposure. The purpose of the present study was to evaluate a machine learning approach to identify individuals at risk for PTSD using readily collected ecological risk factors, which makes scanning …

Bringing Defensive Artificial Intelligence Capabilities To Mobile Devices, Kevin Chong, Ahmed Ibrahim

Australian Information Security Management Conference

Traditional firewalls are losing their effectiveness against new and evolving threats today. Artificial intelligence (AI) driven firewalls are gaining popularity due to their ability to defend against threats that are not fully known. However, a firewall can only protect devices in the same network it is deployed in, leaving mobile devices unprotected once they leave the network. To comprehensively protect a mobile device, capabilities of an AI-driven firewall can enhance the defensive capabilities of the device. This paper proposes porting AI technologies to mobile devices for defence against today’s ever-evolving threats. A defensive AI technique providing firewall-like capability is being …

Fuzziness-Based Active Learning Framework To Enhance Hyperspectral Image Classification Performance For Discriminative And Generative Classifiers, Muhammad Ahmad, Stanislav Protasov, Adil Mehmood Khan, Rasheed Hussain, Asad Masood Khattak, Wajahat Ali Khan

All Works

© 2018 Ahmad et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Hyperspectral image classification with a limited number of training samples without loss of accuracy is desirable, as collecting such data is often expensive and time-consuming. However, classifiers trained with limited samples usually end up with a large generalization error. To overcome the said problem, we propose a fuzziness-based active learning framework (FALF), in which we implement the idea of selecting optimal …

A Study Into The Feasibility Of Using Natural Language Processing And Machine Learning For The Identification Of Alcohol Misuse In Trauma Patients, Andrew Phillips

Master's Theses

Alcohol misuse is a leading cause of premature death in the United States, with nearly a third of trauma patients found to have elevated blood alcohol levels upon admission. However, timely intervention has been shown to reduce this. It is thus important to be able to quickly screen patients to identify alcohol misuse. Many medical centers use standardized questionnaires to identify alcohol misuse, but since these instruments are not usually a part of routine care, there are many cases where it is not done.

In this study, large quantities of notes were processed with natural language processing and machine learning …

Estimating Meteorological Visibility Range Under Foggy Weather Conditions: A Deep Learning Approach, Hazar Chaabani, Naoufel Werghi, Faouzi Kamoun, Bilal Taha, Fatma Outay, Ansar Ul Haque Yasar

All Works

© 2018 The Authors. Published by Elsevier Ltd. Systems capable of estimating visibility distances under foggy weather conditions are extremely useful for next-generation cooperative situational awareness and collision avoidance systems. In this paper, we present a brief review of noticeable approaches for determining visibility distance under foggy weather conditions. We then propose a novel approach based on the combination of a deep learning method for feature extraction and an SVM classifier. We present a quantitative evaluation of the proposed solution and show that our approach provides better performance results compared to an earlier approach that was based on the combination …

Modeling Engagement Of Programming Students Using Unsupervised Machine Learning Technique, Hua Leong Fwa, Lindsay Marshall

Research Collection School Of Computing and Information Systems

Engagement is instrumental to students’ learning and academic achievements. In this study, we model the engagement states of students who are working on programming exercises in an intelligent tutoring system. Head pose, keystrokes and action logs of students automatically captured within the tutoring system are fed into a Hidden Markov Model for inferring the engagement states of students. With the modeling of students’ engagement on a moment by moment basis, intervention measures can be initiated automatically by the system when necessary to optimize the students’ learning. This study is also one of the few studies that bypass the need for …

Anatomy Of Online Hate: Developing A Taxonomy And Machine Learning Models For Identifying And Classifying Hate In Online News Media, Joni Salminen, Hind Almerekhi, Milica Milenkovic, Soon-Gyu Jung, Haewoon Kwak, Haewoon Kwak, Bernard J. Jansen

Research Collection School Of Computing and Information Systems

Online social media platforms generally attempt to mitigate hateful expressions, as these comments can be detrimental to the health of the community. However, automatically identifying hateful comments can be challenging. We manually label 5,143 hateful expressions posted to YouTube and Facebook videos among a dataset of 137,098 comments from an online news media. We then create a granular taxonomy of different types and targets of online hate and train machine learning models to automatically detect and classify the hateful comments in the full dataset. Our contribution is twofold: 1) creating a granular taxonomy for hateful online comments that includes both …

Modified Stacking Ensemble Approach To Detect Network Intrusion, Necati̇ Demi̇r, Gökhan Dalkiliç

Turkish Journal of Electrical Engineering and Computer Sciences

Detecting intrusions in a network traffic has remained an issue for researchers over the years. Advances in the area of machine learning provide opportunities to researchers to detect network intrusion without using a signature database. We studied and analyzed the performance of a stacking technique, which is an ensemble method that is used to combine different classification models to create a better classifier, on the KDD'99 dataset. In this study, the stacking method is improved by modifying the model generation and selection techniques and by using different classifications algorithms as a combiner method. Model generation is performed using subsets of …

Deep Recurrent Learning For Efficient Image Recognition Using Small Data, Mahbubul Alam

Electrical & Computer Engineering Theses & Dissertations

Recognition is fundamental yet open and challenging problem in computer vision. Recognition involves the detection and interpretation of complex shapes of objects or persons from previous encounters or knowledge. Biological systems are considered as the most powerful, robust and generalized recognition models. The recent success of learning based mathematical models known as artificial neural networks, especially deep neural networks, have propelled researchers to utilize such architectures for developing bio-inspired computational recognition models. However, the computational complexity of these models increases proportionally to the challenges posed by the recognition problem, and more importantly, these models require a large amount of data …

Applying Machine Learning To Advance Cyber Security: Network Based Intrusion Detection Systems, Hassan Hadi Latheeth Al-Maksousy

Computer Science Theses & Dissertations

Many new devices, such as phones and tablets as well as traditional computer systems, rely on wireless connections to the Internet and are susceptible to attacks. Two important types of attacks are the use of malware and exploiting Internet protocol vulnerabilities in devices and network systems. These attacks form a threat on many levels and therefore any approach to dealing with these nefarious attacks will take several methods to counter. In this research, we utilize machine learning to detect and classify malware, visualize, detect and classify worms, as well as detect deauthentication attacks, a form of Denial of Service (DoS). …

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. …