Physical Sciences and Mathematics Commons^™

A Comparative Study Of Text Summarization On E-Mail Data Using Unsupervised Learning Approaches, Tijo Thomas

Dissertations

Over the last few years, email has met with enormous popularity. People send and receive a lot of messages every day, connect with colleagues and friends, share files and information. Unfortunately, the email overload outbreak has developed into a personal trouble for users as well as a financial concerns for businesses. Accessing an ever-increasing number of lengthy emails in the present generation has become a major concern for many users. Email text summarization is a promising approach to resolve this challenge. Email messages are general domain text, unstructured and not always well developed syntactically. Such elements introduce challenges for study …

Content-Based Filtering Recommendation Approach To Label Irish Legal Judgements, Sandesh Gangadhar

Dissertations

Machine learning approaches are applied across several domains to either simplify or automate tasks which directly result in saved time or cost. Text document labelling is one such task that requires immense human knowledge about the domain and efforts to review, understand and label the documents. The company Stare Decisis summarises legal judgements and labels them as they are made available on Irish public legal source www.courts.ie. This research presents a recommendation-based approach to reduce the time for solicitors at Stare Decisis by reducing many numbers of available labels to pick from to a concentrated few that potentially contains the …

Lewis Acid-Carbonyl Solution Interactions And Their Implications In Catalytic Systems, Carly Soren Hanson

Dissertations

The utilization of Lewis acids to activate substrates containing carbonyls is ubiquitous in organic synthetic methods. in order to facilitate the development of novel reaction pathways and understand existing methods, it is necessary to determine the solution interactions between Lewis acids and Lewis bases. Historically, the characterization of the interactions of Lewis pairs has relied on solid state infrared (IR) spectroscopy and X-ray crystallography, as well as in situ NMR. I have developed a method utilizing in situ IR spectroscopy and solution conductivity towards the identification of the solution structures formed when a range of carbonyl compounds are combined with …

Classification Of Animal Sound Using Convolutional Neural Network, Neha Singh

Dissertations

Recently, labeling of acoustic events has emerged as an active topic covering a wide range of applications. High-level semantic inference can be conducted based on main audioeffects to facilitate various content-based applications for analysis, efficient recovery and content management. This paper proposes a flexible Convolutional neural network-based framework for animal audio classification. The work takes inspiration from various deep neural network developed for multimedia classification recently. The model is driven by the ideology of identifying the animal sound in the audio file by forcing the network to pay attention to core audio effect present in the audio to generate Mel-spectrogram. …

An Examination Of The Smote And Other Smote-Based Techniques That Use Synthetic Data To Oversample The Minority Class In The Context Of Credit-Card Fraud Classification, Eduardo Parkinson De Castro

Dissertations

This research project seeks to investigate some of the different sampling techniques that generate and use synthetic data to oversample the minority class as a means of handling the imbalanced distribution between non-fraudulent (majority class) and fraudulent (minority class) classes in a credit-card fraud dataset. The purpose of the research project is to assess the effectiveness of these techniques in the context of fraud detection which is a highly imbalanced and cost-sensitive dataset. Machine learning tasks that require learning from datasets that are highly unbalanced have difficulty learning since many of the traditional learning algorithms are not designed to cope …

Customer Churn Prediction, Deepshikha Wadikar

Dissertations

Churned customers identification plays an essential role for the functioning and growth of any business. Identification of churned customers can help the business to know the reasons for the churn and they can plan their market strategies accordingly to enhance the growth of a business. This research is aimed at developing a machine learning model that can precisely predict the churned customers from the total customers of a Credit Union financial institution. A quantitative and deductive research strategies are employed to build a supervised machine learning model that addresses the class imbalance problem handled feature selection and efficiently predict the …

An Evaluation Of Text Representation Techniques For Fake News Detection Using: Tf-Idf, Word Embeddings, Sentence Embeddings With Linear Support Vector Machine., Sangita Sriram

Dissertations

In a world where anybody can share their views, opinions and make it sound like these are facts about the current situation of the world, Fake News poses a huge threat especially to the reputation of people with high stature and to organizations. In the political world, this could lead to opposition parties making use of this opportunity to gain popularity in their elections. In the medical world, a fake scandalous message about a medicine giving side effects, hospital treatment gone wrong or even a false message against a practicing doctor could become a big menace to everyone involved in …

Drug Reviews: Cross-Condition And Cross-Source Analysis By Review Quantification Using Regional Cnn-Lstm Models, Ajith Mathew Thoomkuzhy

Dissertations

Pharmaceutical drugs are usually rated by customers or patients (i.e. in a scale from 1 to 10). Often, they also give reviews or comments on the drug and its side effects. It is desirable to quantify the reviews to help analyze drug favorability in the market, in the absence of ratings. Since these reviews are in the form of text, we should use lexical methods for the analysis. The intent of this study was two-fold: First, to understand how better the efficiency will be if CNN-LSTM models are used to predict ratings or sentiment from reviews. These models are known …

Machine Learning Assisted Gait Analysis For The Determination Of Handedness In Able-Bodied People, Hugh Gallagher

Dissertations

This study has investigated the potential application of machine learning for video analysis, with a view to creating a system which can determine a person’s hand laterality (handedness) from the way that they walk (their gait). To this end, the convolutional neural network model VGG16 underwent transfer learning in order to classify videos under two ‘activities’: “walking left-handed” and “walking right-handed”. This saw varying degrees of success across five transfer learning trained models: Everything – the entire dataset; FiftyFifty – the dataset with enough right-handed samples removed to produce a set with parity between activities; Female – only the female …

Identifying Online Sexual Predators Using Support Vector Machine, Yifan Li

Dissertations

A two-stage classification model is built in the research for online sexual predator identification. The first stage identifies the suspicious conversations that have predator participants. The second stage identifies the predators in suspicious conversations. Support vector machines are used with word and character n-grams, combined with behavioural features of the authors to train the final classifier. The unbalanced dataset is downsampled to test the performance of re-balancing an unbalanced dataset. An age group classification model is also constructed to test the feasibility of extracting the age profile of the authors, which can be used as features for classifier training. The …

Transformer Neural Networks For Automated Story Generation, Kemal Araz

Dissertations

Towards the last two-decade Artificial Intelligence (AI) proved its use on tasks such as image recognition, natural language processing, automated driving. As discussed in the Moore’s law the computational power increased rapidly over the few decades (Moore, 1965) and made it possible to use the techniques which were computationally expensive. These techniques include Deep Learning (DL) changed the field of AI and outperformed other models in a lot of fields some of which mentioned above. However, in natural language generation especially for creative tasks that needs the artificial intelligent models to have not only a precise understanding of the given …

Development Of Methods For The Removal Of Selected Pollutants From Several Matrices And Identification Of Unknown Pollutants Adsorbed Onto Plastics Collected From Freshwater, Kathryn Marie Renyer

Dissertations

Plastic pollution represents one of greatest anthropogenic threats to the environment. Five to ten billion tons of plastic are manufactured every year. Currently, Earth's ecosystem is contaminated with billions of tons of plastic debris, much of which cannot be recycled. Over time, this plastic debris decomposes into small particles. Small plastic particles are known to adsorb toxic compounds in marine environments. My research is concerned with creating novel methods for the detection and quantification of selected persistent organic pollutants from several media. Specifically, I developed methods for the detection and quantification of endosulfan sulfate (ESS) from Lumbricus terrestris tissue and …

Convex Relaxations Of A Continuum Aggregation Model, And Their Efficient Numerical Solution, Mahdi Bandegi

Dissertations

In this dissertation, the global minimization of a large deviations rate function (the Helmholtz free energy functional) for the Boltzmann distribution is discussed. The Helmholtz functional arises in large systems of interacting particles — which are widely used as models in computational chemistry and molecular dynamics. Global minimizers of the rate function (Helmholtz functional) characterize the asymptotics of the partition function and thereby determine many important physical properties such as self-assembly, or phase transitions. Finding and verifying local minima to the Helmholtz free energy functional is relatively straightforward. However, finding and verifying global minima is much more difficult since the …

Quantitative Metrics For Mutation Testing, Amani M. Ayad

Dissertations

Program mutation is the process of generating versions of a base program by applying elementary syntactic modifications; this technique has been used in program testing in a variety of applications, most notably to assess the quality of a test data set. A good test set will discover the difference between the original program and mutant except if the mutant is semantically equivalent to the original program, despite being syntactically distinct.

Equivalent mutants are a major nuisance in the practice of mutation testing, because they introduce a significant amount of bias and uncertainty in the analysis of test results; indeed, mutants …

Dimension Reduction Techniques For High Dimensional And Ultra-High Dimensional Data, Subha Datta

Dissertations

This dissertation introduces two statistical techniques to tackle high-dimensional data, which is very commonplace nowadays. It consists of two topics which are inter-related by a common link, dimension reduction.

The first topic is a recently introduced classification technique, the weighted principal support vector machine (WPSVM), which is incorporated into a spatial point process framework. The WPSVM possesses an additional parameter, a weight parameter, besides the regularization parameter. Most statistical techniques, including WPSVM, have an inherent assumption of independence, which means the data points are not connected with each other in any manner. But spatial data violates this assumption. Correlation between …

Reduction In Salt Deposition On Carbon Nano-Tube Immobilized Membrane During Desalination Via Membrane Distillation, Madihah Saud Humoud

Dissertations

As water scarcity increases globally under the stresses of increasing demand, aquifer depletion, and climate change, the market for efficient desalination technologies has grown rapidly to fill the void. One such developing technology, membrane distillation (MD), has found much interest in the scientific community. MD has also been powered by solar energy and waste heat resources because it can be operated at relatively low temperatures. Recent studies indicate that MD could potentially achieve the efficiencies of state-of-the-art mature thermal desalination technologies, although additional engineering and scientific challenges must first be overcome.

MD can be used to treat high salinity water …

Early Detection Of Fake News On Social Media, Yang Liu

Dissertations

The ever-increasing popularity and convenience of social media enable the rapid widespread of fake news, which can cause a series of negative impacts both on individuals and society. Early detection of fake news is essential to minimize its social harm. Existing machine learning approaches are incapable of detecting a fake news story soon after it starts to spread, because they require certain amounts of data to reach decent effectiveness which take time to accumulate. To solve this problem, this research first analyzes and finds that, on social media, the user characteristics of fake news spreaders distribute significantly differently from those …

Investigation Of Small-Scale Energy Release And Transfer Processes In The Solar Atmosphere With High-Resolution Observations In Infrared, Xu Yang

Dissertations

Solar spectrum in the infrared (IR) contains abundant information of solar activities, however, it has not spectral lines in the solar IR spectrum provide different tools to probe the solar atmosphere in various heights. This radiation band in such relatively long wavelength includes various atom and molecule spectral lines that are generated by relatively small energy level transitions. The temperature-sensitive and highly dynamic spectral lines could reveal the energy transmission process more easily than those in the visible wavelength of solar emission. Moreover, the better magnetic sensitivities for the infrared lines resulting from their longer wavelength make them detect the …

Bio-Inspired Learning And Hardware Acceleration With Emerging Memories, Shruti R. Kulkarni

Dissertations

Machine Learning has permeated many aspects of engineering, ranging from the Internet of Things (IoT) applications to big data analytics. While computing resources available to implement these algorithms have become more powerful, both in terms of the complexity of problems that can be solved and the overall computing speed, the huge energy costs involved remains a significant challenge. The human brain, which has evolved over millions of years, is widely accepted as the most efficient control and cognitive processing platform. Neuro-biological studies have established that information processing in the human brain relies on impulse like signals emitted by neurons called …

Amyloid Proteins And Fibrils Stability, Farbod Mahmoudinobar

Dissertations

Compared to globular proteins that have a stable native structure, intrinsically disordered peptides (IDP) sample an ensemble of structures without folding into a native conformation.One example of IDP is the amyloid-beta(Abeta) protein which is the main constituent of senile plaques in the brain of Alzheimer's patients.Understanding the process by which IDPs undergo structural changes to form oligomers that eventually aggregate into senile plaques/amyloid fibrils may significantly advance the development of novel therapeutic methods to treat neurodegenerative diseases, for which there is no cure to date. This dissertation has two main objectives. The first one is to investigate and identify structural …

Mitochondria Imaging And Targeted Cancer Treatment, Tinghan Zhao

Dissertations

Mitochondria are essential organelles as the site of respiration in eukaryotic cells and are involved in many crucial functions in cell life. Dysfunction of mitochondrial metabolism and irregular morphology have been frequently found in human cancers. The capability of imaging mitochondria as well as regulating their microenvironment is important both scientifically and clinically. Mitochondria penetrating peptides (MPPs), certain peptides that are composed of cationic and hydrophobic amino acids, are good candidates for mitochondria targeting. Herein, a novel MPP, D-argine-phenylalanine-D-argine-phenylalanine-D-argine-phenylalanine-NH₂ (rFrFrF), is conjugated with a rhodamine-based fluorescent chromophore (TAMRA). The TAMRA-rFrFrF probe exhibits advantageous properties for long-term mitochondria tracking of …

Topics On High Dimensional Selective Inference, Yan Zhang

Dissertations

In such applications as identifying differentially expressed genes in micro-array experiments or assessing safety and efficacy of drugs in clinical trials, researchers often report confidence intervals (CIs) and p-values only for the selected parameters, which is called selective inference. While constructing multiple CIs for the selected parameters, it is common practice to ignore issue of selection and multiplicity. Although protection against the effect of selection is sufficient in some cases, simultaneous coverage should be also needed in real applications. For example, in clinical trials, multiple endpoints are considered to assess effects of a drug and the ultimate decision often depends …

Cancer Risk Prediction With Whole Exome Sequencing And Machine Learning, Abdulrhman Fahad M Aljouie

Dissertations

Accurate cancer risk and survival time prediction are important problems in personalized medicine, where disease diagnosis and prognosis are tuned to individuals based on their genetic material. Cancer risk prediction provides an informed decision about making regular screening that helps to detect disease at the early stage and therefore increases the probability of successful treatments. Cancer risk prediction is a challenging problem. Lifestyle, environment, family history, and genetic predisposition are some factors that influence the disease onset. Cancer risk prediction based on predisposing genetic variants has been studied extensively. Most studies have examined the predictive ability of variants in known …

Using Machine Learning Classification Methods To Detect The Presence Of Heart Disease, Nestor Pereira

Dissertations

Cardiovascular disease (CVD) is the most common cause of death in Ireland, and probably, worldwide. According to the Health Service Executive (HSE) cardiovascular disease accounting for 36% of all deaths, and one important fact, 22% of premature deaths (under age 65) are from CVD.

Using data from the Heart Disease UCI Data Set (UCI Machine Learning), we use machine learning techniques to detect the presence or absence of heart disease in the patient according to 14 features provide for this dataset. The different results are compared based on accuracy performance, confusion matrix and area under the Receiver Operating Characteristics (ROC) …

Factor Analysis Of Mixed Data (Famd) And Multiple Linear Regression In R, Nestor Pereira

Dissertations

In the previous projects, it has been worked to statistically analysis of the factors to impact the score of the subjects of Mathematics and Portuguese for several groups of the student from secondary school from Portugal.

In this project will be interested in finding a model, hypothetically multiple linear regression, to predict the final score, dependent variable G3, of the student according to some features divide into two groups. One group, analyses the features or predictors which impact in the final score more related to the performance of the students, means variables like study time or past failures. The second …

Simultaneous X-Ray Emission Accompanying Two Electron Capture For Fluorine On Gas Targets, David S. La Mantia

Dissertations

The collision between a charged ion and an atom resulting in the capture of two electrons, simultaneous with the emission of a single photon is referred to as radiative double electron capture (RDEC). For ion-atom collisions, this process can be considered the inverse of double photoionization. The study of either process, where just two electrons are involved without influence from neighboring electrons, promises new insight into electron correlation and the role it plays in quantum mechanics. Such a study for photoionization has not yet been done experimentally for two-electron ions because the only target system for which two electrons are …

Adsorption Of Polyisobutylene-Based Dispersants Onto Carbon Black, Travis Paul Holbrook

Dissertations

The formation of carbonaceous by-products (e.g. soot) during the operation of an internal combustion engine is unavoidable and the aggregation of this soot leads to deleterious effects including abrasive wear of the engine, increased oil viscosities, and sludge deposition. Dispersants, which are composed of a hydrophobic tail and a polar headgroup, are used as oil additives to aid in the suspension and stabilization of the soot particles. Polyisobutylene succinimide (PIBSI) is the most well-studied class of dispersants and is characterized by a linear architecture and polyamine headgroup that interacts with soot by acid-base and dipole-dipole interactions. As such, there remains …

The Antimicrobial Activity And Cellular Targets Of Plant Derived Aldehydes And Degradable Pro-Antimicrobial Networks In Pseudomonas Aeruginosa, Yetunde Adewunmi

Dissertations

Essential oils (EOs) are plant-derived products that have been long exploited for their antimicrobial activities in medicine, agriculture, and food preservation. EOs represent a promising alternative to conventional antibiotics due to the broad-range antimicrobial activity, low toxicity to human commensal bacteria, and the capacity to kill microorganisms without promoting resistance. Despite the progress in the understanding of the biological activity of EOs, many aspects of their mode of action remain inconclusive. The overarching aim of this work was to address these gaps by studying molecular interactions between antimicrobial plant aldehydes and the opportunistic human pathogen Pseudomonas aeruginosa. We initiated …

Developing A Computational Framework For A Construction Scheduling Decision Support Web Based Expert System, Feroz Ahmed

Dissertations

Decision-making is one of the basic cognitive processes of human behaviors by which a preferred option or a course of action is chosen from among a set of alternatives based on certain criteria. Decision-making is the thought process of selecting a logical choice from the available options. When trying to make a good decision, all the positives and negatives of each option should be evaluated. This decision-making process is particularly challenging during the preparation of a construction schedule, where it is difficult for a human to analyze all possible outcomes of each and every situation because, construction of a project …

Establishing The Role Of The Mississippi-Alabama Barrier Islands In Mississippi Sound And Bight Circulation Using Observational Data Analysis And A Coastal Model, Laura Hode

Dissertations

The Mississippi-Alabama barrier islands restrict exchange between the Mississippi Sound and Mississippi Bight in the northern Gulf of Mexico. The islands also act as storm breaks for tropical cyclones, so their continued existence sustains marine ecosystems and protects coastal communities. However, the chain has undergone extensive segmentation, erosion, and westward migration in the past two hundred years. The islands are now more susceptible to further erosion (Pendleton et al., 2013; Morton, 2007). Additional reduction in island subaerial land extent would alter circulation in the Mississippi Sound and Bight.

Consequently, this study targeted the two most vulnerable barrier islands in the …