Computer Sciences Commons^™

Heart Rhythm Classification From Static And Ecg Time-Series Data Using Hybrid Multimodal Deep Learning, Ahmad Abdulrazaq Abdulla Alnajjar

Theses

Cardiovascular arrhythmia diseases are considered as the most common diseases that cause death around the world. Abnormal arrhythmia diseases can be identified by analyzing heart rhythm using an electrocardiogram (ECG). However, this analysis is done manually by cardiologists, which may be subjective and susceptible to different cardiologist observations and experiences, as well as to noise and irregularities in those signals. This can lead to misdiagnosis. Motivated by this challenge, an automated heart rhythm diagnosis approach from ECG signals using Deep Learning has been proposed. In order to achieve this goal, three research problems have been addressed. First, recognize the role …

Distfold: Distance-Guided Protein Folding, Matthew Bernardini

Theses

Protein structure prediction and its associated key sub-problems such as distance map prediction are of significance importance in biology and bioinformatics. The inter-residue distance prediction problem, or distance prediction in short, is to predict the physical distance between amino acids in a three-dimensional (3D) space, given a protein's one-dimensional sequence information. While there exist many methods to predict distance maps, there are currently no methods that can take those predicted distance maps and build 3D models from them in an ab initio way, i.e., without using any other information. This works aims to fill this gap by: a) developing a …

The Role Of Software Engineering In Bioinformatics, Brendan Sean Lawlor

Theses

This thesis proposes that by applying state-of-the-art software engineering tools, techniques and frameworks to currently recognised challenges in bioinformatics, improved outcomes can be attained in that field. It begins by decomposing software engineering into two categories, namely process and architecture, and choosing two key challenges in the practice of bioinformatics: reproducibility and scalability. The body of the thesis is an exploration of the intersection between these two software engineering categories and these two bioinformatics challenges. The question is asked: Can best practices in professional software engineering be applied to address key issues in the bioinformatics domain, creating positive outcomes? And …

Accelerating Transitive Closure Of Large-Scale Sparse Graphs, Sanyamee Milindkumar Patel

Theses

Finding the transitive closure of a graph is a fundamental graph problem where another graph is obtained in which an edge exists between two nodes if and only if there is a path in our graph from one node to the other. The reachability matrix of a graph is its transitive closure. This thesis describes a novel approach that uses anti-sections to obtain the transitive closure of a graph. It also examines its advantages when implemented in parallel on a CPU using the Hornet graph data structure.

Graph representations of real-world systems are typically sparse in nature due to lesser …

New Methods For Deep Learning Based Real-Valued Inter-Residue Distance Prediction, Jacob Barger

Theses

Background: Much of the recent success in protein structure prediction has been a result of accurate protein contact prediction--a binary classification problem. Dozens of methods, built from various types of machine learning and deep learning algorithms, have been published over the last two decades for predicting contacts. Recently, many groups, including Google DeepMind, have demonstrated that reformulating the problem as a multi-class classification problem is a more promising direction to pursue. As an alternative approach, we recently proposed real-valued distance predictions, formulating the problem as a regression problem. The nuances of protein 3D structures make this formulation appropriate, allowing predictions …

Establishing Blockchain-Related Security Controls, Maitha Ali Al Ketbi

Theses

Blockchain technology is a secure and relatively new technology of distributed digital ledgers which is based on interlinked blocks of transactions. There is a rapid growth in the adoption of the blockchain technology in different solutions and applications and within different industries throughout the world, such as but not limited to, finance, supply chain, digital identity, energy, healthcare, real estate and government. Blockchain technology has great benefits such as decentralization, transparency, immutability and automation. Like any other emerging technology, the blockchain technology has also several risks and threats associated with its expected benefits which in turns could have a negative …

Translating Counting Problems Into Computable Language Expressions, Zach Prescott

Theses

The realm of automated problem solving is a relatively new field, even in the context of natural language processing. One area where this is often demonstrated is that of creating a program that can solve word problems. The program must understand the problem, perform some processing, and then convey this information to a user in a way that is accessible and understandable. There has been quite a lot of progress in this area with simpler problems. However, when it comes to understanding problems that involve a level of NLP, the results are not conclusive. In this paper, we would like …

Disruptive Bussiness Model For Higher Education, Aya Rizik Abushawish

Theses

A business model is a plan for the successful operation of a business, identifying the source of revenue, the intended customers, value proposition, key resources, activities, and financing. It describes how organizations create, deliver and capture value. The recent developments in information and communications technology (ICT) disrupted most business models in different industries. The higher education industry is no exception, where it witnessed enormous integration of information and communications technologies. E-learning in higher education has made a tremendous shift in students’ life and raised the expectations of higher education service quality. The main objective of this thesis to develop a …

Model-Based Deep Siamese Autoencoder For Clustering Single Cell Rna-Seq Data, Zixia Meng

Theses

In the biological field, the smallest unit of organisms in most biological systems is the single cell, and the classification of cells is an everlasting problem. A central task for analysis of single-cell RNA-seq data is to identify and characterize novel cell types. Currently, there are several classical methods, such as K-means algorithm, spectral clustering, and Gaussian Mixture Models (GMMs), which are widely used to cluster the cells. Furthermore, typical dimensional reduction methods such as PCA, t-SNE, and ZIDA have been introduced to overcome “the curse of dimensionality”. A more recent method scDeepCluster has demonstrated improved and promising performances in …

Analysis Of Gameplay Strategies In Hearthstone: A Data Science Approach, Connor W. Watson

Theses

In recent years, games have been a popular test bed for AI research, and the presence of Collectible Card Games (CCGs) in that space is still increasing. One such CCG for both competitive/casual play and AI research is Hearthstone, a two-player adversarial game where players seeks to implement one of several gameplay strategies to defeat their opponent and decrease all of their Health points to zero. Although some open source simulators exist, some of their methodologies for simulated agents create opponents with a relatively low skill level. Using evolutionary algorithms, this thesis seeks to evolve agents with a higher skill …

Food Fraud In Nigeria: Challenges, Risks And Solutions, Joy Ewomazino Opia

Theses

Food fraud is one of the most urgent and active food research and regulatory areas. It is an evolving problem in Nigeria that has led to the deaths of many people especially the vunerable groups that includes mostly children, the elderly and immunocomprised persons. Therefore the aim of this study is to investigate the current challenges of food fraud in Nigeria, identify the risks it poses on the health and wellbeing of Nigerians and propose measures to tackle food fraud at local and international levels by regulatory and government agencies. This study explored the relationship between food fraud, food security …

Deepcon-Pre: Improved Protein Contact Map Prediction Using Inverse Covariance And Deep Residual Networks, Nachammai Palaniappan

Theses

As with most domains where machine learning methods are applied, correct feature engineering is critical when developing deep learning algorithms for solving the protein folding problem. Unlike the domains such as computer vision and natural language processing, feature engineering is not rigorously studied towards solving the protein folding problem. A recent research has highlighted that input features known as precision matrix are most informative for predicting inter-residue contact map, the key for building three-dimensional models. In this work, we study the significance of the precision matrix feature when very deep residual networks are trained. Using a standard dataset of 3456 …

Protein Inter-Residue Distance Prediction Using Residual And Capsule Networks, Andrew Dillon

Theses

The protein folding problem, also known as protein structure prediction, is the task of building three-dimensional protein models given their one-dimensional amino acid sequence. New methods that have been successfully used in the most recent CASP challenge have demonstrated that predicting a protein's inter-residue distances is key to solving this problem. Various deep learning algorithms including fully convolutional neural networks and residual networks have been developed to solve the distance prediction problem. In this work, we develop a hybrid method based on residual networks and capsule networks. We demonstrate that our method can predict distances more accurately than the algorithms …

A Study Of Machine Learning And Deep Learning Models For Solving Medical Imaging Problems, Fadi G. Farhat

Theses

Application of machine learning and deep learning methods on medical imaging aims to create systems that can help in the diagnosis of disease and the automation of analyzing medical images in order to facilitate treatment planning. Deep learning methods do well in image recognition, but medical images present unique challenges. The lack of large amounts of data, the image size, and the high class-imbalance in most datasets, makes training a machine learning model to recognize a particular pattern that is typically present only in case images a formidable task.

Experiments are conducted to classify breast cancer images as healthy or …

Deep Morphological Neural Networks, Yucong Shen

Theses

Mathematical morphology is a theory and technique applied to collect features like geometric and topological structures in digital images. Determining suitable morphological operations and structuring elements for a give purpose is a cumbersome and time-consuming task. In this paper, morphological neural networks are proposed to address this problem. Serving as a non-linear feature extracting layers in deep learning frameworks, the efficiency of the proposed morphological layer is confirmed analytically and empirically. With a known target, a single-filter morphological layer learns the structuring element correctly, and an adaptive layer can automatically select appropriate morphological operations. For high level applications, the proposed …

A Comparative Study Of Russian Trolls Using Several Machine Learning Models On Twitter Data, Kannan Neten Dharan Kannan Neten Dharan

Theses

Ever since Russian trolls have been brought into light, their interference in the 2016 US Presidential elections has been monitored and studied thoroughly. These Russian trolls have fake accounts registered on several major social media sites to influence public opinions. Our work involves trying to discover patterns in these tweets and classifying them by using different machine learning approaches such as Support Vector Machines, Word2vec and neural network models, and then creating a benchmark to compare all the different models. Two machine learning models are developed for this purpose. The first one is used to classify any given specific tweet …

Machine Learning For Real-Time Data-Driven Security Practices, Shane Coleman

Theses

The risk of cyber-attacks exploiting vulnerable organisations has increased significantly over the past several years. Cyber-attacks can be described as any type of aggressive strategy which targets computer information systems, computer networks, personal computer systems or other organisational infrastructures which may originate internally or externally. These attacks may combine to exploit a vulnerability breach within a system’s protection strategy which has the potential for loss, damage or destruction of assets. Consequently, every vulnerability has an accompanying risk which is defined as the “intersection of assets, threats, and vulnerabilities”.

This research project uses various types of recommender system techniques, employed for …

Music Retrieval System Using Dynamic Time Warping, Emeka Jude Okafor

Theses

With the growth of digital audio data, various and fast access to music data is strongly desired, especially for large music databases. A more natural way to retrieve a song from a database will be to hum to the tune. To relate and compare musical pieces is a very complex task. Musical compositions usually collapse multiple information sources and complex, multifaceted interactions established between parts. Despite such degrees of complexity, humans are outstandingly good at performing individual musical judgments with little conscious effort, while a computer cannot efficiently achieve this task. In this work, we focus on one such task: …

Using Long Short-Term Memory (Lstm) Recurrent Neural Network (Rnn) To Classify Network Attacks, Pramita Sree Muhuri

Theses

Cyber-attacks have increased greatly in recent years. Therefore, the identification of various network attacks has been an important research area. An Intrusion Detection System (IDS), can identify an ongoing invasion or an intrusion which has already occurred. Intrusion Detection is a classification problem. It identifies whether the network traffic behavior is normal or anomalous or identifies the attack types. Various approaches have been proposed to improve the accuracy of classifiers for identifying the intrusion types. Recently, deep learning has emerged as a successful approach in IDSs having a high accuracy rate with its distinctive learning mechanism. In this research, Long …

Collaboration Pattern Model For Student Participation In Problem-Solving Typed Chat, Duy Quang Bui

Theses

This project measures different collaborative dialogue acts between students who are working together to solve problems in a computer programming class. In COMPS (Computer-Mediated Problem Solving) exercises students work together via online typed-chat. Transcripts of these conversations were annotated with four categories of collaborative utterance: sharing ideas, negotiating ideas, regulating problem-solving, and maintaining communication. The annotated transcripts were then applied to answer four different research questions. A) Among the several students in a conversation, there are measurable quantitative differences in dialogue behavior that correlate with the relative preparedness for solving the problem. The most prepared student not only talks more …

Polya Db3: A Database Cataloging Polyadenation Sites(Pas) Across Different Species And Their Conservation, Ram Mohan Nambiar

Theses

Polyadenation is an important process occurring in the messenger RNA that involves cleavage of 3 end nascent mRNAs and addition of poly(A) tails. For this thesis,I present PolyA DB3 ,a database cataloging cleavage and polyadenylation sites (PASs) in several genomes specifically for human,mouse,rat and chicken. This database is based on deep sequencing data. PASs are mapped by the 3’ region extraction and deep sequencing (3’READS) method, ensuring unequivocal PAS identification. Large volume of data based on diverse biological samples is used to increase PAS coverage and provide PAS usage information. Strand-specific RNA-seq data were used to extend annotated 3’ ends …

A Parallelized Implementation Of Cut-And-Solve And A Streamlined Mixed-Integer Linear Programming Model For Finding Genetic Patterns Optimally Associated With Complex Diseases, Michael Yip-Hin Chan

Theses

With the advent of genetic sequencing, there was much hope of finding the inherited elements underlying complex diseases, such as late-onset Alzheimer’s disease (AD), but it has been a challenge to fully uncover the necessary information hidden in the data. A likely contributor to this failure is the fact that the pathogenesis of most complex diseases does not involve single markers working alone, but patterns of genetic markers interacting additively or epistatically. But as we move upwards beyond patterns of size two, it quickly becomes computationally infeasible to examine all combinations in the solution space. A common solution to solving …

Detecting And Characterizing Self Hiding Behavior In Android Applications, Raina Samuel

Theses

Applications (apps) that conceal their activities are fundamentally deceptive; app marketplaces and end-users should treat such apps as suspicious. However, due to its nature and intent, activity concealing is not disclosed up-front, which puts users at risk. This study focuses on characterization and detection of such techniques, e.g., hiding the app or removing traces, known as 'self hiding' (SH) behavior. SH behavior has not been studied per se - rather it has been reported on only as a byproduct of malware investigations. This gap is addressed via a study and suite of static analyses targeted at SH in Android apps. …

Hypoxic And Viral Contributions To The Etiopathogenesis Of Schizophrenia: A Whole Transcriptome Analysis, Kathryn A. Gorski

Theses

Schizophrenia is a mental illness with a complex and as of yet unclear etiology. It is highly heritable and has a strong polygenic character, however, studies examining the genetics of schizophrenia have not sufficiently explained all variability in its prevalence. Environmental causes are theorized to have a non trivial contribution to the pathoetiology of schizophrenia, including interactions with genetic components, but these mechanisms remain unclear. Analyzing schizophrenia dysfunction using transcriptomic approaches is a paradigm still in its infancy, and fewer studies still have examined non neurological contributions to schizophrenia pathology with next generation sequencing technologies. This pilot study uses several …

Efficient Reduced Bias Genetic Algorithm For Generic Community Detection Objectives, Aditya Karnam Gururaj Rao

Theses

The problem of community structure identification has been an extensively investigated area for biology, physics, social sciences, and computer science in recent years for studying the properties of networks representing complex relationships. Most traditional methods, such as K-means and hierarchical clustering, are based on the assumption that communities have spherical configurations. Lately, Genetic Algorithms (GA) are being utilized for efficient community detection without imposing sphericity. GAs are machine learning methods which mimic natural selection and scale with the complexity of the network. However, traditional GA approaches employ a representation method that dramatically increases the solution space to be searched by …

Social Media: On Tech-Caves, Virtual Panopticism, And The Science Fiction-Like State In Which We Unwittingly Find Ourselves, Michael Major

Theses

Making use of three historic philosophical thought experiments, this paper blends psychological perspectives with philosophical reasoning to show how social media is corrupting our perception of reality, the result of which is ultimately detrimental to society as a whole. This is accomplished by first using Plato’s “Allegory of the Cave” to analyze and discuss the ways in which social media is limiting humanity’s access to real knowledge. Next, Michel Foucault’s analysis of punishment in its social context, Discipline and Punish, is used to discuss the ways in which social media is adversely affecting our behavior. Finally, Robert Nozick’s “Experience …

An Investigation Of Low Frequency Noise In Server Rooms, Ahmed Al Naami

Theses

Noise in a server room can have a major impact on the performance and well-being of the occupants. Sound level and low frequency noise are considered factors that influence the hearing ability of workers. This thesis is an investigation of low-frequency noise in server rooms. We conducted the field study in a server room for a large financial institution. Some employees in this study indicated that they experienced headaches from the noise and requested an analysis of the sound to determine if there were any potential adverse health effects. Attributes of the noise were investigated by evaluating the sound pressure …

Automating The Crowd-Mapping Workflow With Deep Learning, Lasith Niroshan

Theses

Maintaining updated maps in an ever-changing built environment is important for supporting modern society in many ways. The usage of online crowdsourced maps in particular has gained importance in a wide range of recent location-based applications (route planning/navigation, urban planning, real estate, tourism, etc). However, both traditional map production methods and updating today’s online maps suffer from early obsolescence due to their largely manual map production/update workflows. Significant research efforts have focused on refining techniques to identify changes in raster satellite images, aiming to improve and streamline map production processes. Concurrently, the surge in Internet usage has led to a …

The Use Of Machine Learning To Detect Suckling In Pre-Weaned Calves, Sukumar Katamreddy

Theses

The weaning of cattle is a process which is known to be labour intensive and to have stressful effects on both cow and calf Common methods used in the weaning process include the temporary removal of a mother from the calf and manual observation and intervention. Early and speedy weaning is known to have a number of benefits, including health benefits for both cow and calf, additional weight gains for the calves as well as reduced labour and feed requirements. The process known as Two-Stage Weaning is recognised to be an effective low-stress approach to weaning in which the calf …

Characteristics Of Different Deep Neural Networks And Application Of Pre-Trained Model Without Transfer Learning, Zhiqi Peng

Theses

Deep neural networks have been successful in many areas, some of them even surpass human performances. The goal of this thesis is using data simulations to present different characteristics of three deep neural networks: fully connected deep neural network, convolutional neural network, recurrent neural network, which will perform best when dealing with different feature patterns. By using these characteristics to design a deep neural network on top of an adopted pre-trained model with untrainable layers, achieved an averagely 11.1% improvement than a model with transfer learning method.