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Full-Text Articles in Physical Sciences and Mathematics

A Dynamic F5 Algorithm, Candice Mitchell Apr 2020

A Dynamic F5 Algorithm, Candice Mitchell

Dissertations

Gröbner bases are a “nice” representation for nonlinear systems of polynomials, where by “nice” we mean they have good computation properties. They have many useful applications, including decidability (whether the system has a solution or not), ideal membership (whether a given polynomial is in the system or not), and cryptography. Traditional Gröbner basis algorithms require as input an ideal and an admissible term ordering. They then determine a Gröbner basis with respect to the given ordering. Some term orderings lead to a smaller basis, but finding them traditionally requires testing many orderings and hoping for better results. A dynamic algorithm ...


Huisgen 1,3-Dipolar Azide-Alkyne Cycloaddition “Click” Reaction In Polymer Synthesis And Curing, Jie Wu Apr 2020

Huisgen 1,3-Dipolar Azide-Alkyne Cycloaddition “Click” Reaction In Polymer Synthesis And Curing, Jie Wu

Dissertations

This dissertation’s key focus is on utilizing Huisgen 1,3-dipolar azide-alkyne cycloaddition (AAC) reaction in copolymer synthesis and modification, including thermoplastic block copolymer and commercially available two-component polyurethane system. It can be divided into two major projects, introduced as follows.

The first project involves the development of a modular synthetic approach toward polyisobutylene (PIB)-based triphasic pentablock thermoplastic elastomer with enhanced moisture permeability. This terpolymer consists of a poly(styrene-b-isobutylene-b-styrene) (SIBS) core and appended hydrophilic polymer blocks (HBs). The SIBS core was synthesized via living cationic polymerization (LCP) of isobutylene followed by sequential addition of ...


Geochemical Tracers Of Arctic Ocean Processes: A Study Of Gallium, Barium, And Vanadium, Laura M. Whitmore Apr 2020

Geochemical Tracers Of Arctic Ocean Processes: A Study Of Gallium, Barium, And Vanadium, Laura M. Whitmore

Dissertations

The Arctic Ocean is linked to the global oceans and climate through its connectivity with the North Atlantic Ocean and the regional thermohaline deep water formation sites. It’s also a region undergoing rapid environmental change. To inform the community of potential changes in geochemical and biogeochemical cycles, this dissertation addresses three dissolved geochemical tracers (gallium, barium, and vanadium) as indicators of Arctic Ocean processes. Gallium is tested as a replacement for nutrient-type tracers in an effort to deconvolve Pacific and Atlantic derived waters in the Arctic Ocean basins. These water masses carry different heat and salt content and can ...


Noise Reduction Of Eeg Signals Using Autoencoders Built Upon Gru Based Rnn Layers, Esra Aynali Feb 2020

Noise Reduction Of Eeg Signals Using Autoencoders Built Upon Gru Based Rnn Layers, Esra Aynali

Dissertations

Understanding the cognitive and functional behaviour of the brain by its electrical activity is an important area of research. Electroencephalography (EEG) is a method that measures and record electrical activities of the brain from the scalp. It has been used for pathology analysis, emotion recognition, clinical and cognitive research, diagnosing various neurological and psychiatric disorders and for other applications. Since the EEG signals are sensitive to activities other than the brain ones, such as eye blinking, eye movement, head movement, etc., it is not possible to record EEG signals without any noise. Thus, it is very important to use an ...


An Evaluation Of Text Representation Techniques For Fake News Detection Using: Tf-Idf, Word Embeddings, Sentence Embeddings With Linear Support Vector Machine., Sangita Sriram Jan 2020

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 ...


Classification Of Animal Sound Using Convolutional Neural Network, Neha Singh Jan 2020

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 ...


Image Instance Segmentation: Using The Cirsy System To Identify Small Objects In Low Resolution Images, Orghomisan William Omatsone Jan 2020

Image Instance Segmentation: Using The Cirsy System To Identify Small Objects In Low Resolution Images, Orghomisan William Omatsone

Dissertations

The CIRSY system (or Chick Instance Recognition System) is am image processing system developed as part of this research to detect images of chicks in highly-populated images that uses the leading algorithm in instance segmentation tasks, called the Mask R-CNN. It extends on the Faster R-CNN framework used in object detection tasks, and this extension adds a branch to predict the mask of an object along with the bounding box prediction. Mask R-CNN has proven to be effective ininstance segmentation and object de-tection tasks after outperforming all existing models on evaluation of the Microsoft Common Objects in Context (MS COCO ...


A Comparative Study Of Text Summarization On E-Mail Data Using Unsupervised Learning Approaches, Tijo Thomas Jan 2020

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 ...


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 Jan 2020

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 ...


Brain Disease Detection From Eegs: Comparing Spiking And Recurrent Neural Networks For Non-Stationary Time Series Classification, Hristo Stoev Jan 2020

Brain Disease Detection From Eegs: Comparing Spiking And Recurrent Neural Networks For Non-Stationary Time Series Classification, Hristo Stoev

Dissertations

Modeling non-stationary time series data is a difficult problem area in AI, due to the fact that the statistical properties of the data change as the time series progresses. This complicates the classification of non-stationary time series, which is a method used in the detection of brain diseases from EEGs. Various techniques have been developed in the field of deep learning for tackling this problem, with recurrent neural networks (RNN) approaches utilising Long short-term memory (LSTM) architectures achieving a high degree of success. This study implements a new, spiking neural network-based approach to time series classification for the purpose of ...


Drug Reviews: Cross-Condition And Cross-Source Analysis By Review Quantification Using Regional Cnn-Lstm Models, Ajith Mathew Thoomkuzhy Jan 2020

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 ...


Content-Based Filtering Recommendation Approach To Label Irish Legal Judgements, Sandesh Gangadhar Jan 2020

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 ...


Customer Churn Prediction, Deepshikha Wadikar Jan 2020

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 ...


Machine Learning Assisted Gait Analysis For The Determination Of Handedness In Able-Bodied People, Hugh Gallagher Jan 2020

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 samples; Male ...


Identifying Online Sexual Predators Using Support Vector Machine, Yifan Li Jan 2020

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 Jan 2020

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 ...


Quantitative Metrics For Mutation Testing, Amani M. Ayad Dec 2019

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 ...


Cancer Risk Prediction With Whole Exome Sequencing And Machine Learning, Abdulrhman Fahad M Aljouie Dec 2019

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 ...


Reduction In Salt Deposition On Carbon Nano-Tube Immobilized Membrane During Desalination Via Membrane Distillation, Madihah Saud Humoud Dec 2019

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 ...


Dimension Reduction Techniques For High Dimensional And Ultra-High Dimensional Data, Subha Datta Dec 2019

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 ...


Investigation Of Small-Scale Energy Release And Transfer Processes In The Solar Atmosphere With High-Resolution Observations In Infrared, Xu Yang Dec 2019

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 ...


Early Detection Of Fake News On Social Media, Yang Liu Dec 2019

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 ...


Convex Relaxations Of A Continuum Aggregation Model, And Their Efficient Numerical Solution, Mahdi Bandegi Dec 2019

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 Helmholtz ...


Using Machine Learning Classification Methods To Detect The Presence Of Heart Disease, Nestor Pereira Dec 2019

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 Dec 2019

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 ...


Developing A Computational Framework For A Construction Scheduling Decision Support Web Based Expert System, Feroz Ahmed Dec 2019

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 Dec 2019

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 ...


Adsorption Of Polyisobutylene-Based Dispersants Onto Carbon Black, Travis Paul Holbrook Dec 2019

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 ...


Simultaneous X-Ray Emission Accompanying Two Electron Capture For Fluorine On Gas Targets, David S. La Mantia Dec 2019

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 ...


Toward Self-Reconfigurable Parametric Systems: Reinforcement Learning Approach, Ting-Yu Mu Dec 2019

Toward Self-Reconfigurable Parametric Systems: Reinforcement Learning Approach, Ting-Yu Mu

Dissertations

For the ongoing advancement of the fields of Information Technology (IT) and Computer Science, machine learning-based approaches are utilized in different ways in order to solve the problems that belong to the Nondeterministic Polynomial time (NP)-hard complexity class or to approximate the problems if there is no known efficient way to find a solution. Problems that determine the proper set of reconfigurable parameters of parametric systems to obtain the near optimal performance are typically classified as NP-hard problems with no efficient mathematical models to obtain the best solutions. This body of work aims to advance the knowledge of machine ...