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Full-Text Articles in Engineering

Dual-Cure Benzoxazine Networks For Additive Manufacturing, Jeremy Weigand Apr 2020

Dual-Cure Benzoxazine Networks For Additive Manufacturing, Jeremy Weigand

Dissertations

The research presented addresses the design of novel materials for additive manufacturing through a dual-cure approach that combines UV initiated free-radical polymerization of an acrylic network combined with the thermally initiated ring opening polymerization of a benzoxazine network. This work is split into three primary sections: the first and second sections focus on the synthesis and characterization of networks based on dual-cure BOX monomers, while the third develops simulation tools to further investigate thermoset networks prepared via additive manufacturing. A novel 3D printing formulation based on a multifunctional benzoxazine (BOX) monomer possessing both photo and thermally polymerizable functional groups is ...


Team Creation Methods In Practice: Understanding The Potential Effects Of Nonverbal Communication In The Leadership Of Team Formation, Troy Allen Robertson Apr 2020

Team Creation Methods In Practice: Understanding The Potential Effects Of Nonverbal Communication In The Leadership Of Team Formation, Troy Allen Robertson

Dissertations

Teams require leadership, even if they are self-managed. The group of individuals who make up a team must be gathered in some form or another. For self managed teams to function successfully, the first step is the process of creating the team. Many aspects may factor into the creation process. Often time is of the essence and methods to quickly assess and form teams show merit. First impressions in general are based largely on nonverbal communication. The focus of this mixed-methods concurrent embedded study is to analyze the potential effects of nonverbal communication on influencing team creation. A group of ...


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 ...


Perivascular Waste Metabolites Clearance In Central Nervous System (Cns), Yiming Cheng Dec 2019

Perivascular Waste Metabolites Clearance In Central Nervous System (Cns), Yiming Cheng

Dissertations

Efficient clearance of interstitial waste metabolites is essential for normal brain homeostasis. Such effective clearance is hampered by the lack of a lymphatic system in the brain, and the cerebrospinal fluid (CSF) is unable to clear large size waste metabolites in the brain. Here, a novel idea that brain arterial endothelium and smooth muscle cells reactivity regulates the clearance of these water-insoluble large size waste metabolites through the perivascular dynamic exchange, and that low dose ethanol promotes this perivascular clearance is proposed.

In Aim 1, the biodistribution of a large size waste metabolite (Amyloid-β protein mimic) in rat perivascular space ...


Customized Boron And Magnesium-Based Reactive Materials Prepared By High Energy Mechanical Milling, Xinhang Liu Dec 2019

Customized Boron And Magnesium-Based Reactive Materials Prepared By High Energy Mechanical Milling, Xinhang Liu

Dissertations

New reactive materials need to be developed having biocidal combustion products. When ignited, such material can add chemical biocidal effects to the common effects of high temperature and pressure. Biocidal combustion products are capable of deactivating harmful spores or bacteria, which can be released by targets containing biological weapons of mass destruction. Research showed that halogens, especially iodine, are effective as biocidal components of reactive material formulations. Recently, magnesium combustion product MgO is also found to have a biocidal effect. Thus, advanced formulations containing both magnesium and iodine are of interest; such formulations are prepared and investigated here.

Reactive materials ...


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 ...


Multiple Ssid Framework For Rss-Fingerprint Based Indoor Positioning Systems, Ahmed Kareem Abed Dec 2019

Multiple Ssid Framework For Rss-Fingerprint Based Indoor Positioning Systems, Ahmed Kareem Abed

Dissertations

Location-Based Indoor positioning systems significance stems from the bloom of recent applications in various fields such as in tracking services for an elder or a patient within large living communities, mobile robot localization, and several other security applications. Currently, Global Positioning Systems (GPS) are the most widely used location-sensing technique. However, satellite-based GPS signals require line of sight (LOS) to work correctly, which is something cannot be achieved inside buildings. Fortunately, wireless LAN can be employed in indoor positioning systems (IPS), and since all large buildings such as malls, hospitals, airports, schools, and museums have hundreds of Wi-Fi access points ...


A Reinforcement Learning Approach To Spacecraft Trajectory Optimization, Daniel S. Kolosa Dec 2019

A Reinforcement Learning Approach To Spacecraft Trajectory Optimization, Daniel S. Kolosa

Dissertations

This dissertation explores a novel method of solving low-thrust spacecraft targeting problems using reinforcement learning. A reinforcement learning algorithm based on Deep Deterministic Policy Gradients was developed to solve low-thrust trajectory optimization problems. The algorithm consists of two neural networks, an actor network and a critic network. The actor approximates a thrust magnitude given the current spacecraft state expressed as a set of orbital elements. The critic network evaluates the action taken by the actor based on the state and action taken. Three different types of trajectory problems were solved, a generalized orbit change maneuver, a semimajor axis change maneuver ...


Analyzing Impacts Of Transportation And Non-Transportation Activities On Human Health With An Advanced Platform For Collecting Travel And Physical Activity Data, Raed Abdullah Hasan Dec 2019

Analyzing Impacts Of Transportation And Non-Transportation Activities On Human Health With An Advanced Platform For Collecting Travel And Physical Activity Data, Raed Abdullah Hasan

Dissertations

Recently much attention is paid to the lack of physical activities that may cause the health problems in many counties. Travel activities provide a certain amount of physical activities, and the active transportation, such as walking and cycling, becomes more important as an essential element of transportation. The active transportation is expected to contribute to improving human health by reducing cardiovascular disease, obesity, and premature death. However, detailed relationship between the transportation choices and human health has not been well understood. Therefore, there is a need for investigating traveler behaviors and how their choices affect physical activities and public health ...


Analyzing Evolution Of Rare Events Through Social Media Data, Xiaoyu Lu Aug 2019

Analyzing Evolution Of Rare Events Through Social Media Data, Xiaoyu Lu

Dissertations

Recently, some researchers have attempted to find a relationship between the evolution of rare events and temporal-spatial patterns of social media activities. Their studies verify that the relationship exists in both time and spatial domains. However, few of those studies can accurately deduce a time point when social media activities are most highly affected by a rare event because producing an accurate temporal pattern of social media during the evolution of a rare event is very difficult. This work expands the current studies along three directions. Firstly, we focus on the intensity of information volume and propose an innovative clustering ...


Graphene Channels Interfaced With Distributed Quantum Dots, Xin Miao Aug 2019

Graphene Channels Interfaced With Distributed Quantum Dots, Xin Miao

Dissertations

Previous research has elucidated the remarkable electrical and optical characteristics of graphene and pointed to the various applications of graphene-based devices. One of such applications is electro-optical graphene-based elements. In this work, the optoelectronic properties of field-effect transistors are explored. These are composed of surface graphene guides, which are interfaced with an array of individual semiconductor quantum dots. The graphene guide also serves as a channel for the field-effect transistor (FET) while the dots provide for fluorescence markers. They may be placed either within the capacitor formed between the graphene and the gate electrode, or on top of the graphene ...


Developmental And Sex Modulated Neurological Alterations In Autism Spectrum Disorder, Azeezat Azeez Aug 2019

Developmental And Sex Modulated Neurological Alterations In Autism Spectrum Disorder, Azeezat Azeez

Dissertations

Autism Spectrum Disorder (ASD) was first described in 1943 by Dr. Leo Kranner in a case study published in The Nervous Child. It is a neurodevelopment disorder, with a range of clinical symptoms. According to the Diagnostic and Statistical Manual of Mental Disorders (DSM-5), used by clinicians to diagnose mental disorders, a child needs to have persistent social deficits, language impairments, and repetitive behaviors, that cannot be explained by neurological damage or intellectual disability. It is known that children diagnosed with ASD are often are developmentally delayed therefore alterations in the typical developmental trajectory should be a major factor in ...


Modelling In Vitro Dissolution And Release Of Sumatriptan Succinate From Polyvinylpyrrolidone-Based Microneedles Aided By Iontophoresis, James Paul Ronnander Aug 2019

Modelling In Vitro Dissolution And Release Of Sumatriptan Succinate From Polyvinylpyrrolidone-Based Microneedles Aided By Iontophoresis, James Paul Ronnander

Dissertations

A novel dissolving microneedle array system is developed to investigate permeation of a sumatriptan succinate formulations through the skin aided by iontophoresis. Three formulations consisting of hydrophilic, positively charged drug molecules encapsulated in a water-soluble biologically suitable polymer, polyvinylpyrrolidone (PVP), have been accepted by the U.S. Food and Drug Administration (FDA). The microneedle systems are fabricated with 600 pyramid-shaped needles, each 500 µm tall, on a 0.785-cm2 circular array. In vitro transdermal studies with minipig skin and vertical Franz diffusion cells show > 68% permeation of sumatriptan over a 24-hour period. A combination of microneedle and electrical current ...


Optimal Sampling Paths For Autonomous Vehicles In Uncertain Ocean Flows, Andrew J. De Stefan Aug 2019

Optimal Sampling Paths For Autonomous Vehicles In Uncertain Ocean Flows, Andrew J. De Stefan

Dissertations

Despite an extensive history of oceanic observation, researchers have only begun to build a complete picture of oceanic currents. Sparsity of instrumentation has created the need to maximize the information extracted from every source of data in building this picture. Within the last few decades, autonomous vehicles, or AVs, have been employed as tools to aid in this research initiative. Unmanned and self-propelled, AVs are capable of spending weeks, if not months, exploring and monitoring the oceans. However, the quality of data acquired by these vehicles is highly dependent on the paths along which they collect their observational data. The ...


Improving Boron For Combustion Applications, Kerri-Lee Annique Chintersingh Aug 2019

Improving Boron For Combustion Applications, Kerri-Lee Annique Chintersingh

Dissertations

Boron has received much attention as a potential additive to explosives and propellants due to its high theoretical gravimetric and volumetric heating values. The challenge, however, is that boron particles tend to agglomerate, have lengthy ignition delays and very low combustion rates. Prior research indicates that boron’s long ignition delays are due to its inhibiting naturally occurring oxide layer, impeding the diffusion of reactants for oxidation. For combustion, current studies report that boron particles have two consecutive stages, but the actual reaction mechanism is poorly understood. Despite many years of relevant research, quantitative combustion data on micron-sized boron particles ...


Modulation Of Corticospinal Excitability Induced By Paired Associative Stimulation Combined With Movement, Ahmad O. Alokaily Aug 2019

Modulation Of Corticospinal Excitability Induced By Paired Associative Stimulation Combined With Movement, Ahmad O. Alokaily

Dissertations

An essential feature of the brain is its capacity to undergo long-lasting morphological or functional changes in response to experiences or trauma. Advances in noninvasive brain stimulation techniques have led to increased interest in understanding neural mechanisms of neuroplasticity at the network level. Paired associative stimulation (PAS) is one of the most commonly used applications for noninvasive brain stimulation because of its clinical potential as an adjuvant rehabilitative intervention. However, the optimal method for incorporating PAS into rehabilitative activities remains unknown. This dissertation explores different approaches to combining PAS with movement and investigates the enhancement of the specificity of conventional ...


Effects Of Electro-Osmotic Consolidation Of Clays And Its Improvement Using Ion Exchange Membranes, Lucas Martin Aug 2019

Effects Of Electro-Osmotic Consolidation Of Clays And Its Improvement Using Ion Exchange Membranes, Lucas Martin

Dissertations

Electro-osmosis is an established method of expediting consolidation of soft, saturated clayey soils compared to commonly used methods, such as preloading with wick drains. In electro-osmotic consolidation a direct current (DC) is applied via inserted electrodes. This causes hydrated ions in the interstitial fluid to migrate to oppositely charged electrodes. Because the clay particles have a negative surface charge, the majority of ions in the interstitial fluid are positively charged. Therefore, the net flow will be towards the negatively charged electrode (cathode), where the water can be removed and thus consolidation is achieved. Certain problems, such as pH changes in ...