Physical Sciences and Mathematics Commons^™

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 …

Magnetic Field Sensor Based On A Tri-Microfiber Coupler Ring In Magnetic Fluid And A Fiber Bragg Grating, Fangfang Wei, Dejun Liu, Arun Mallik, Gerald Farrell, Qiang Wu, Gang-Ding Peng, Yuliya Semenova

Articles

In this paper we propose and investigate a novel magnetic field sensor based on a Tri-microfiber coupler combined with magnetic fluid and a fiber Bragg grating (FBG) in a ring. A sensitivity of 1306 pm/mT was experimentally demonstrated in the range of magnetic fields from 0 to 15 mT. The reflection peak in the output spectrum associated with the FBG serves as a reference point allowing to avoid ambiguity in determining the spectral shift induced by the magnetic field. Due to its high sensitivity at low magnetic fields, the proposed structure could be of high interest in low field biosensing …

How Do Spinal Surgeons Perceive The Impact Of Factors Used In Post-Surgical Complication Risk Scores?, Enea Parimbelli, Szymon Wilk, Dympna O'Sullivan, Stephen Kingwell, Wojtek Michalowski, Martin Michalowski

Conference papers

When deciding about surgical treatment options, an important aspect of the decision-making process is the potential risk of complications. A risk assessment performed by a spinal surgeon is based on their knowledge of the best available evidence and on their own clinical experience. The objective of this work is to demonstrate the differences in the way spine surgeons perceive the importance of attributes used to calculate risk of post-operative and quantify the differences by building individual formal models of risk perceptions. We employ a preference-learning method - ROR-UTADIS - to build surgeon-specific additive value functions for risk of complications. Comparing …

Update Frequency And Background Corpus Selection In Dynamic Tf-Idf Models For First Story Detection, Fei Wang, Robert J. Ross, John D. Kelleher

Conference papers

First Story Detection (FSD) requires a system to detect the very first story that mentions an event from a stream of stories. Nearest neighbour-based models, using the traditional term vector document representations like TF-IDF, currently achieve the state of the art in FSD. Because of its online nature, a dynamic term vector model that is incrementally updated during the detection process is usually adopted for FSD instead of a static model. However, very little research has investigated the selection of hyper-parameters and the background corpora for a dynamic model. In this paper, we analyse how a dynamic term vector model …

Sure 2019 Undergraduate Science Conference Booklet, Sure Network

Group Reports

The Second Annual Science Undergraduate Research Experience (SURE) Conferences (SURE 2019) took place on 27th September 2019 in Institute of Technology, Carlow, Technological University Dublin and Institute of Technology, Sligo. The simultaneous conferences had a total of 23 oral presentations and 67 poster presentations, and were attended by over 450 students, academic staff, professional body and industry representatives.

The aims of the conference were to:

• Provide current students with an opportunity to gain an understanding of the work which has been undertaken by recent graduates, and the career opportunities that exist for graduates in Scientific disciplines.
• Provide recent graduates with …

Eavesdropping Hackers: Detecting Software Vulnerability Communication On Social Media Using Text Mining, Susan Mckeever, Brian Keegan, Andrei Quieroz

Conference papers

Abstract—Cyber security is striving to find new forms of protection against hacker attacks. An emerging approach nowadays is the investigation of security-related messages exchanged on Deep/Dark Web and even Surface Web channels. This approach can be supported by the use of supervised machine learning models and text mining techniques. In our work, we compare a variety of machine learning algorithms, text representations and dimension reduction approaches for the detection accuracies of software-vulnerability-related communications. Given the imbalanced nature of the three public datasets used, we investigate appropriate sampling approaches to boost detection accuracies of our models. In addition, we examine how …

Capturing Dialogue State Variable Dependencies With An Energy-Based Neural Dialogue State Tracker, Anh Duong Trinh, Robert J. Ross, John D. Kelleher

Conference papers

Dialogue state tracking requires the population and maintenance of a multi-slot frame representation of the dialogue state. Frequently, dialogue state tracking systems assume independence between slot values within a frame. In this paper we argue that treating the prediction of each slot value as an independent prediction task may ignore important associations between the slot values, and, consequently, we argue that treating dialogue state tracking as a structured prediction problem can help to improve dialogue state tracking performance. To support this argument, the research presented in this paper is structured into three stages: (i) analyzing variable dependencies in dialogue data; …

Development Of Photopolymer Material With Improved Dynamic Range And Sensitivity, Brian Rogers, Izabela Naydenova, Suzanne Martin

Conference Papers

In this study the effect of the concentration of acrylamide and the influence of different initiators in a photopolymer composition for holographic recording of diffraction gratings is investigated. Light manipulating Holographic Optical Elements (HOEs) have a number of characteristics which can be optimised for different roles. However, at the core of these devices is the refractive index modulation that has been created in the material during recording. Typically higher refractive index modulation will enable greater diffraction efficiency. Solar concentrating HOEs can particularly benefit from material that experiences higher refractive index modulation. For a solar concentrator to have a high acceptance …

Study Of The Effect Of Magnetic Nanoparticles On The Hologram Recording Capability Of Photopolymer Nanocomposite For Development Of Holographic Sensor/Actuator, Muhammad Irfan, Suzanne Martin, Izabela Naydenova

Conference Papers

Photopolymer nanocomposite materials utilising nanoparticles of varied refractive index are one of the most attractive materials for holography due to their tuneable properties. The preparation of the photopolymer nanocomposites can involve mixing a stable colloidal suspension in the photopolymer solution or printing of the nanoparticle colloidal suspension on top of a dried photopolymer layer. By following the first approach in this study a novel photopolymer nanocomposite material is prepared for holographic recording. It consists of N-isopropylacrylamide (NIPA)-based photopolymer as a host and iron oxide magnetic nanoparticles as nanodopant. The methodology of the photopolymer nanocomposite material preparation is explained in detail. …

Investigating Variable Dependencies In Dialogue States, Anh Duong Trinh, Robert J. Ross, John D. Kelleher

Conference papers

Dialogue State Tracking is arguably one of the most challenging tasks among dialogue processing problems due to the uncertainties of language and complexity of dialogue contexts. We argue that this problem is made more challenging by variable dependencies in the dialogue states that must be accounted for in processing. In this paper we give details on our motivation for this argument through statistical tests on a number of dialogue datasets. We also propose a machine learning-based approach called energy-based learning that tackles variable dependencies while performing prediction on the dialogue state tracking tasks.

Bigger Versus Similar: Selecting A Background Corpus For First Story Detection Based On Distributional Similarity, Fei Wang, Robert J. Ross, John D. Kelleher

Conference papers

The current state of the art for First Story Detection (FSD) are nearest neighbour-based models with traditional term vector representations; however, one challenge faced by FSD models is that the document representation is usually defined by the vocabulary and term frequency from a background corpus. Consequently, the ideal background corpus should arguably be both large-scale to ensure adequate term coverage, and similar to the target domain in terms of the language distribution. However, given these two factors cannot always be mutually satisfied, in this paper we examine whether the distributional similarity of common terms is more important than the scale …

Predicting The Hardness Of Turf Surfaces From A Soil Moisture Sensor Using Iot Technologies, Ann Marie Mckeon

Other

In horseracing, “the going” is a term to describe the racetrack ground conditions. In Ireland presently, a groundskeeper or course clerk walks the racecourse poking it with a blackthorn stick, assesses conditions, and declares the going – it is a subjective measurement.

This thesis will propose using remote low-cost soil moisture sensors to gather high frequency data about the soil water content in the ground and to enable informed decisions to be made. This will remove the subjective element from the ground hardness, and look at the data in an objective way.

The soil moisture sensor will systematically collect high …

Estimating Distributed Representation Performance In Disaster-Related Social Media Classification, Pallavi Jain, Robert J. Ross, Bianca Schoen-Phelan

Conference papers

This paper examines the effectiveness of a range of pre-trained language representations in order to determine the informativeness and information type of social media in the event of natural or man-made disasters. Within the context of disaster tweet analysis, we aim to accurately analyse tweets while minimising both false positive and false negatives in the automated information analysis. The investigation is performed across a number of well known disaster-related twitter datasets. Models that are built from pre-trained word embeddings from Word2Vec, GloVe, ELMo and BERT are used for performance evaluation. Given the relative ubiquity of BERT as a standout language …

Energy-Based Modelling For Dialogue State Tracking, Anh Duong Trinh, Robert J. Ross, John D. Kelleher

Conference papers

The uncertainties of language and the complexity of dialogue contexts make accurate dialogue state tracking one of the more challenging aspects of dialogue processing. To improve state tracking quality, we argue that relationships between different aspects of dialogue state must be taken into account as they can often guide a more accurate interpretation process. To this end, we present an energy-based approach to dialogue state tracking as a structured classification task. The novelty of our approach lies in the use of an energy network on top of a deep learning architecture to explore more signal correlations between network variables including …

Synthetic, Yet Natural: Properties Of Wordnet Random Walk Corpora And The Impact Of Rare Words On Embedding Performance, Filip Klubicka, Alfredo Maldonado, Abhijit Mahalunkar, John D. Kelleher

Conference papers

Creating word embeddings that reflect semantic relationships encoded in lexical knowledge resources is an open challenge. One approach is to use a random walk over a knowledge graph to generate a pseudo-corpus and use this corpus to train embeddings. However, the effect of the shape of the knowledge graph on the generated pseudo-corpora, and on the resulting word embeddings, has not been studied. To explore this, we use English WordNet, constrained to the taxonomic (tree-like) portion of the graph, as a case study. We investigate the properties of the generated pseudo-corpora, and their impact on the resulting embeddings. We find …

Integration Of Technology In The Chemistry Classroom And Laboratory, Barry J. Ryan

Books/Book Chapters/ Proceedings

The role of technology in the chemistry classroom and laboratory continues to evolve, with mainstream applications such as pre-lecture/laboratory resources being supplemented by technological innovations such as immersive reality. Although the range is vast, care must be taken to select appropriate and pedagogically aligned technologies to enable learning.

In this chapter a model for the appropriate selection and application of technology enabled learning in chemistry is developed and explored in the context of two case-studies. This model, LEAPTech, is based on ten years of personal experience, informed by evidence and underpinned by the scholarly literature. This model will serve as …

Runge–Kutta–Gegenbauer Explicit Methods For Advection-Diffusion Problems, Stephen O'Sullivan

Articles

In this paper, Runge-Kutta-Gegenbauer (RKG) stability polynomials of arbitrarily high order of accuracy are introduced in closed form. The stability domain of RKG polynomials extends in the the real direction with the square of polynomial degree, and in the imaginary direction as an increasing function of Gegenbauer parameter. Consequently, the polynomials are naturally suited to the construction of high order stabilized Runge-Kutta (SRK) explicit methods for systems of PDEs of mixed hyperbolic-parabolic type.

We present SRK methods composed of L ordered forward Euler stages, with complex-valued stepsizes derived from the roots of RKG stability polynomials of degree $L$. Internal stability …

Modelling Weighted Signed Networks, Alberto Caimo, Isabella Gollini

Conference papers

In this paper we introduce a new modelling approach to analyse weighted signed networks by assuming that their generative process consists of two models: the interaction model which describes the overall connectivity structure of the relations in the network without taking into account neither the weight nor the sign of the dyadic relations; and the conditional weighted signed network model describes how the edge signed weights form given the interaction structure. We then show how this modelling approach can facilitate the interpretation of the overall network process. Finally, we adopt a Bayesian inferential approach to illustrate the new methodology by …

Comparative Study Of Feature Representations For Disaster Tweet Classification, Pallavi Jain, Bianca Schoen-Phelan, Robert J. Ross

Other resources

Twitter is a popular social media platform where users publicly broadcast short messages on a myriad of topics. In recent years it has enjoyed an increased usage around disaster events due to availability of information in near real time. Additionally, enhanced information representations to facilitate the classification of social media in terms of relevancy and type of information is currently a highly active research area (Ashktorab et al., 2014, Imran et al., 2014, Win et al., 2018). In this work we consider the usefulness and reliability of a range of representation models in the analysis of disaster related social media.

Nurse-Led Design And Development Of An Expert System For Pressure Ulcer Management, Débora Abranches, Dympna O'Sullivan, Jon Bird

Conference papers

The use of Clinical Practice Guidelines (CPGs) is known to enable better care outcomes by promoting a consistent way of treating patients. This paper describes a user-centered design approach involving nurses, to develop a prototype expert system for modelling CPGs for Pressure Ulcer management. The system was developed using Visirule, a software tool that uses a graphical approach to modeling knowledge. The system was evaluated by 5 staff nurses and compared nurses’ time and accuracy to assess a wound using CPGs accessed via the Intranet of an NHS Trust and the expert system. A post task qualitative evaluation revealed that …

Phase-Only Digital Encryption, Jonathan Blackledge, Western Govere, Dumisani Sibanda

Articles

Abstract—We study then-dimensional deconvolution prob-lem associated with an impulse response function and an(additive) noise function that are both characterised by thesame phase-only stochastic spectrum. In this case, it is shownthat the deconvolution problem becomes well-posed and has ageneral solution that is both exact and unique, subject to are-normalisation condition relating to the scale of the solution.While the phase-only spectral model considered is of limitedvalue in general (in particular, problems arising in the fieldsof digital signal processing and communications engineering,specifically with regard to the retrieval of information fromnoise), its application to digital cryptography has potential.One of the reasons for this (as …

Examining The Limits Of Predictability Of Human Mobility, Vaibhav Klukarni, Abhijit Mahalunkar, Benoit Garbinato, John D. Kelleher

Articles

We challenge the upper bound of human-mobility predictability that is widely used to corroborate the accuracy of mobility prediction models. We observe that extensions of recurrent-neural network architectures achieve significantly higher prediction accuracy, surpassing this upper bound. Given this discrepancy, the central objective of our work is to show that the methodology behind the estimation of the predictability upper bound is erroneous and identify the reasons behind this discrepancy. In order to explain this anomaly, we shed light on several underlying assumptions that have contributed to this bias. In particular, we highlight the consequences of the assumed Markovian nature of …

An Investigation Of The Detection Capability Of Pulsed Wave Duplex Doppler Of Low Grade Stenosis Using Ultrasound Contrast Agent Microbubbles – An In-Vitro Study, Jacinta Browne, Deirdre King, Andrew Fagan, Deepa Chari, Carmel Moran

Articles

Objective: The objective of the study was to investigate whether clinically used ultrasonic contrast agents improved the accuracy of spectral Doppler ultrasound in the detection of low grade (< 50%) renal artery stenosis. Low grade stenoses in the renal artery are notoriously difficult to reliably detect using Doppler ultrasound due to difficulties such as overlying fat and bowel gas.

Methods: A range of anatomically-realistic renal artery phantoms with varying low degrees of stenosis (0, 30 and 50%) were constructed and peak velocity data was measured from within the pre-stenotic and mid-stenotic regions in each phantom, for both unenhanced and contrast-enhanced spectral Doppler data acquisitions. The effect of a 20mm overlying fat layer on the ultrasound beam distortion and phase aberration, and hence on the measured peak velocity data, was also …

Monitoring Meaningful Activities Using Small Low-Cost Devices In A Smart Home, Jordan Tewell, Dympna O'Sullivan, Neil Maiden, James Lockerbie, Simone Stumpf

Articles

A challenge associated with an ageing population is increased demand on health and social care, creating a greater need to enable persons to live independently in their own homes. Ambient assistant living technology aims to address this by monitoring occupants’ ‘activities of daily living’ using smart home sensors to alert caregivers to abnormalities in routine tasks and deteriorations in a person’s ability to care for themselves. However, there has been less focus on using sensing technology to monitor a broader scope of so-called ‘meaningful activities’, which promote a person’s emotional, creative, intellectual, and spiritual needs. In this paper, we describe …

Temperature-Compensated Magnetic Field Sensing With A Dual-Ring Structure Consisting Of Microfiber Coupler-Sagnac Loop And Fiber Bragg Grating - Assisted Resonant Cavity, Fangfang Wei, Dejun Liu, Arun Kumar Mallik, Gerald Farrell, Qiang Wu, Gang-Ding Peng, Yuliya Semenova

Articles

A novel temperature-compensated magnetic field sensor based on a ring erbium-doped fiber laser combined with a fiber Bragg grating (FBG) and a Sagnac loop containing a microfiber coupler (MFC) and magnetic fluid is proposed and investigated. Thanks to the dual-ring structure of the MFC-Sagnac loop and the FBG-assisted resonant cavity, the output of the structure has two distinct laser peaks. In addition to the magnetic field sensing capability, the proposed structure can simultaneously provide temperature information. The maximum experimentally demonstrated sensitivity to a magnetic field determined from the spectral shift of one laser peak is 102 pm/mT in the magnetic …

Air Quality Modelling For Ireland, Aoife Donnelly, Bruce Misstear, Brian Broderick

Reports

Air pollution is the primary environmental cause of premature death in the EU (European Commission, 2013) and the most problematic pollutants across Europe have consistently been oxides of nitrogen (e.g. nitrogen dioxide (NO₂)), particulate matter (e.g. PM₁₀, PM_2.5) and ozone (O₃). While measurements form an important aspect of air quality assessment, on their own they are unlikely to be sufficient to provide an accurate spatial and temporal description of the pollutant concentrations for exposure assessment and moreover they cannot provide information regarding future air quality. Annex XVI of 2008/50/EC requires member states …

A U-Net Deep Learning Framework For High Performance Vessel Segmentation In Paitents With Cerebrovascular Disease, Michelle Livne, Jana Rieger, Orhun Utku Aydin, Abdel Aziz Taha, Ela Maria Akay, Tabea Kossen, Jan Sobesky, John D. Kelleher, Kristian Hildebrand, Dietmar Frey, Vince I. Madai

Articles

Brain vessel status is a promising biomarker for better prevention and treatment in cerebrovascular disease. However, classic rule-based vessel segmentation algorithms need to be hand-crafted and are insufficiently validated. A specialized deep learning method—the U-net—is a promising alternative. Using labeled data from 66 patients with cerebrovascular disease, the U-net framework was optimized and evaluated with three metrics: Dice coefficient, 95% Hausdorff distance (95HD) and average Hausdorff distance (AVD). The model performance was compared with the traditional segmentation method of graph-cuts. Training and reconstruction was performed using 2D patches. A full and a reduced architecture with less parameters were trained. We …

Sustainable Energy Governance In South Tyrol (Italy): A Probabilistic Bipartite Network Model, Jessica Belest, Laura Secco, Elena Pisani, Alberto Caimo

Articles

At the national scale, almost all of the European countries have already achieved energy transition targets, while at the regional and local scales, there is still some potential to further push sustainable energy transitions. Regions and localities have the support of political, social, and economic actors who make decisions for meeting existing social, environmental and economic needs recognising local specificities.

These actors compose the sustainable energy governance that is fundamental to effectively plan and manage energy resources. In collaborative relationships, these actors share, save, and protect several kinds of resources, thereby making energy transitions deeper and more effective.

This research …

Automatic Acquisition Of Annotated Training Corpora For Test-Code Generation, Magdalena Kacmajor, John D. Kelleher

Articles

Open software repositories make large amounts of source code publicly available. Potentially, this source code could be used as training data to develop new, machine learning-based programming tools. For many applications, however, raw code scraped from online repositories does not constitute an adequate training dataset. Building on the recent and rapid improvements in machine translation (MT), one possibly very interesting application is code generation from natural language descriptions. One of the bottlenecks in developing these MT-inspired systems is the acquisition of parallel text-code corpora required for training code-generative models. This paper addresses the problem of automatically synthetizing parallel text-code corpora …