Engineering 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 …

Improving The Sustainability Of The Built Environment By Training Its Workforce In More Efficient And Greener Ways Of Designing And Constructing Through The Horizon2020 Bimcert Project, Barry Mcauley, Avril Behan

Conference papers

The construction industry consumes up to 50% of mineral resources excavated from nature, generates about 33% of CO2 present in the atmosphere and is responsible for 40% of total global energy through both construction and operation of buildings. The realisation that current pervasive construction practices now face globalization, sustainability, and environmental concerns, as well as ever-changing legislation requirements and new skills needed for the information age has resulted in technologies such as Building Information Modelling (BIM) becoming a key enabler in navigating these barriers. To assist in overcoming these barriers, a number of funding initiatives have been put in place …

Centres Of Excellence And Roadmaps For Digital Transition: Lessons For Ireland’S Construction Industry, Barry Mcauley, Alan Hore, Roger West

Conference papers

Like most sectors in today’s working world, construction businesses are challenged to work in an increasingly digitised world with sophisticated demands from intelligent clients. So much has been written about the inefficiencies of the construction industry, its fragmentation, lack of collaboration, low margins, adversarial pricing, poor productivity, financial fragility, lack of research and development, poor industry image and relatively weak use of digital solutions. The Irish government recognises the importance of digital innovation to address many of the challenges the construction industry faces. With recent high profile reports of escalating spend on signature public sector projects and weak productivity performance …

Bim In Ireland 2019: A Study Of Bim Maturity And Diffusion In Ireland, Barry Mcauley, Alan Hore, Roger West

Conference papers

In 2017, the BIM Innovation Capability Programme team applied five macro BIM maturity conceptual models to capture the capability of the Irish construction industry and assess its BIM maturity. The results found that while Ireland is mature for modelling processes, it is less developed with regards to collaboration processes and policies. Ireland also ranked poorly when it came to regulatory frameworks, measurements and benchmarks compared to a number of countries which also applied the same conceptual models. At the time, the findings highlighted that Ireland’s diffusion dynamic was middle out, meaning that larger organisations or industry associations were pushing the …

An Investigation Into Current Procurement Strategies That Promote Collaboration Through Early Contractor Involvement With Regards To Their Suitability For Irish Public Work Projects, Barry Mcauley, Frederic Lefebvre

Conference papers

Previous research has established that multi-disciplinary collaboration will benefit a construction project throughout its lifecycle. While Lean Construction, Building Information Modelling (BIM), and Integrated Project Delivery (IPD) can all be viewed as separate processes which add independent value to a project, they are more effective when used in partnership with each other. In order to ensure the high levels of collaboration expected for these processes to work in unison, the early involvement of the Contractor is paramount. Early contractor involvement within the design process can ensure a more focused integrated project team, improvement of both constructability and cost certainty, as …

From Roadmap To Implementation: Lessons For Ireland’S Digital Construction Programme, Barry Mcauley, Alan Hore, Roger West

Conference papers

As part of their Future of Construction initiative in 2018 the World Economic Forum published an action plan to accelerate Building Information Modelling adoption. The WEF report highlighted actions that companies, industry organisations and governments are advised to implement to accelerate BIM adoption and better capitalise on delivering better project outcomes. According the authors of the report BIM is seen as the centrepiece of the construction industry’s digital transformation, however they acknowledged that BIM adoption globally remain slow. Anecdotal experience would suggest that BIM usage in Ireland is also very low and that a similar initiative or an adaptation of …

Using Bluetooth Low Energy Devices To Monitor Visitor Activity In Remote Amenity Spaces, Ahlam Al Anbouri, David Powell, Damon Berry, John Mcgrory, Niall Holmes, Lorraine D'Arcy

Conference Papers

Tracking of pedestrian behaviour, particularly route selection and temporal behaviours, can be difficult to undertake. This is especially true of studies at a community or campus level where the anonymity of pedestrians can be difficult to protect. The introduction of the EU’s General Data Protection Regulations 2016 (GDPR) has increased the complexity of this challenge. Advances in Bluetooth Low Energy (BLE) technology in recent years have increased the potential to monitor human behaviour by tracking and triangulating pedestrians. This paper describes an experiment undertaken along The Great South Wall at the Port of Dublin, which is considered a leading amenity …

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 …

Visualising The Complex Features Of Source Code, Ivan Bacher

Doctoral

Software development is a complex undertaking composed of several activities that include reading, writing, and modifying source code. Indeed, previous studies have shown that the majority of the effort invested in software development is dedicated to understanding code. This includes understanding the static structure, dynamic behaviour, and evolution of the code. Given these particular characteristics, as well as the high complexity of source code, it is reasonable to consider how visualisation can facilitate source code understanding. This work proposes to extend existing software development tools with visualisations that can be used to encode the various complex features within a source …

A Deep Recurrent Q Network Towards Self-Adapting Distributed Microservices Architecture (In Press), Basel Magableh

Articles

One desired aspect of microservices architecture is the ability to self-adapt its own architecture and behaviour in response to changes in the operational environment. To achieve the desired high levels of self-adaptability, this research implements the distributed microservices architectures model, as informed by the MAPE-K model. The proposed architecture employs a multi adaptation agents supported by a centralised controller, that can observe the environment and execute a suitable adaptation action. The adaptation planning is managed by a deep recurrent Q-network (DRQN). It is argued that such integration between DRQN and MDP agents in a MAPE-K model offers distributed microservice architecture …

Solid Spherical Energy (Sse) Cnns For Efficient 3d Medical Image Analysis, Vincent Andrearczyk, Valentin Oreiller, Julien Fageot, Xavier Montet, Adrien Depeursinge

Session 2: Deep Learning for Computer Vision

Invariance to local rotation, to differentiate from the global rotation of images and objects, is required in various texture analysis problems. It has led to several breakthrough methods such as local binary patterns, maximum response and steerable filterbanks. In particular, textures in medical images often exhibit local structures at arbitrary orientations. Locally Rotation Invariant (LRI) Convolutional Neural Networks (CNN) were recently proposed using 3D steerable filters to combine LRI with Directional Sensitivity (DS). The steerability avoids the expensive cost of convolutions with rotated kernels and comes with a parametric representation that results in a drastic reduction of the number of …

An Investigation Of Three Subjective Rating Scales Of Mental Workload In Third Level Education, Nha Vu Thanh Nguyen

Dissertations

Mental Workload assessment in educational settings is still recognized as an open research problem. Although its application is useful for instructional design, it is still unclear how it can be formally shaped and which factors compose it. This paper is aimed at investigating a set of features believed to shape the construct of mental workload and aggregating them together in models trained with supervised machine learning techniques. In detail, multiple linear regression and decision trees have been chosen for training models with features extracted respectively from the NASA Task Load Index and the Workload Profile, well-known self-reporting instruments for assessing …

Forecasting Anomalous Events And Performance Correlation Analysis In Event Data, Sonya Leech [Thesis]

Dissertations

Classical and Deep Learning methods are quite common approaches for anomaly detection. Extensive research has been conducted on single point anomalies. Collective anomalies that occur over a set of two or more durations are less likely to happen by chance than that of a single point anomaly. Being able to observe and predict these anomalous events may reduce the risk of a server’s performance. This paper presents a comparative analysis into time-series forecasting of collective anomalous events using two procedures. One is a classical SARIMA model and the other is a deep learning Long-Short Term Memory (LSTM) model. It then …

An Investigation Into The Predictive Capability Of Customer Spending In Modelling Mortgage Default, Donal Finn [Thesis]

Dissertations

The mortgage arrears crisis in Ireland was and is among the most severe experienced on record and although there has been a decreasing trend in the number of mortgages in default in the past four years, it still continues to cause distress to borrowers and vulnerabilities to lenders. There are indications that one of the main factors associated with mortgage default is loan affordability, of which the level of disposable income is a driver. Additionally, guidelines set out by the European Central Bank instructed financial institutions to adopt measures to further reduce and prevent loans defaulting, including the implementation and …

Human Action Recognition In Videos Using Transfer Learning, Kaiqiang Huang, Sarah Jane Delany, Susan Mckeever

Session 1: Active Vision, Tracking, Motion Analysis

A variety of systems focus on detecting the actions and activities performed by humans, such as video surveillance and health monitoring systems. However, published labelled human action datasets for training supervised machine learning models are limited in number and expensive to produce. The use of transfer learning for the task of action recognition can help to address this issue by transferring or re-using the knowledge of existing trained models, in combination with minimal training data from the new target domain. Our focus in this paper is an investigation of video feature representations and machine learning algorithms for transfer learning for …

Micro Expression Classification Accuracy Assessment, Pratikshya Sharma, Sonya Coleman, Pratheepan Yogarajah, Laurenc Taggart

Session 1: Active Vision, Tracking, Motion Analysis

The ability to identify and draw appropriate implications from non-verbal cues is a challenging task in facial expression recognition and has been investigated by various disciplines particularly social science, medical science, psychology and technological sciences beyond three decades. Non-verbal cues often last a few seconds and are obvious (macro) whereas others are very short and difficult to interpret (micro). This research is based on the area of micro expression recognition with the main focus laid on understanding and exploring the combined effect of various existing feature extraction techniques and one of the most renowned machine learning algorithms identified as Support …

Distance,Time And Terms In First Story Detection, Fei Wang

Doctoral

First Story Detection (FSD) is an important application of online novelty detection within Natural Language Processing (NLP). Given a stream of documents, or stories, about news events in a chronological order, the goal of FSD is to identify the very first story for each event. While a variety of NLP techniques have been applied to the task, FSD remains challenging because it is still not clear what is the most crucial factor in defining the “story novelty”. Giventhesechallenges,thethesisaddressedinthisdissertationisthat the notion of novelty in FSD is multi-dimensional. To address this, the work presented has adopted a three dimensional analysis of the …

Music Information Retrieval For Irish Traditional Music Automatic Analysis Of Harmonic, Rhythmic, And Melodic Features For Efficient Key-Invariant Tune Recognition, Pierre Beauguitte

Doctoral

Music making and listening practices increasingly rely on techno logy,and,asaconsequence,techniquesdevelopedinmusicinformation retrieval (MIR) research are more readily available to end users, in par ticular via online tools and smartphone apps. However, the majority of MIRresearchfocusesonWesternpopandclassicalmusic,andthusdoes not address specificities of other musical idioms. Irishtraditionalmusic(ITM)ispopularacrosstheglobe,withregular sessionsorganisedonallcontinents. ITMisadistinctivemusicalidiom, particularly in terms of heterophony and modality, and these character istics can constitute challenges for existing MIR algorithms. The bene fitsofdevelopingMIRmethodsspecificallytailoredtoITMisevidenced by Tunepal, a query-by-playing tool that has become popular among ITM practitioners since its release in 2009. As of today, Tunepal is the state of the art for tune recognition in ITM. The research in …

Predicting Customer Retention Of An App-Based Business Using Supervised Machine Learning, Jeswin Jose

Dissertations

Identification of retainable customers is very essential for the functioning and growth of any business. An effective identification of retainable customers can help the business to identify the reasons of retention and plan their marketing strategies accordingly. This research is aimed at developing a machine learning model that can precisely predict the retainable customers from the total customer data of an e-learning business. Building predictive models that can efficiently classify imbalanced data is a major challenge in data mining and machine learning. Most of the machine learning algorithms deliver a suboptimal performance when introduced to an imbalanced dataset. A variety …

Investigation Into The Perceptually Informed Data For Environmental Sound Recognition, Chenglin Kang

Dissertations

Environmental sound is rich source of information that can be used to infer contexts. With the rise in ubiquitous computing, the desire of environmental sound recognition is rapidly growing. Primarily, the research aims to recognize the environmental sound using the perceptually informed data. The initial study is concentrated on understanding the current state-of-the-art techniques in environmental sound recognition. Then those researches are evaluated by a critical review of the literature. This study extracts three sets of features: Mel Frequency Cepstral Coefficients, Mel-spectrogram and sound texture statistics. Two kinds machine learning algorithms are cooperated with appropriate sound features. The models are …

Comparing Defeasible Argumentation And Non-Monotonic Fuzzy Reasoning Methods For A Computational Trust Problem With Wikipedia, Ryan Kirwan

Dissertations

Computational trust is an ever-more present issue with the surge in autonomous agent development. Represented as a defeasible phenomenon, problems associated with computational trust may be solved by the appropriate reasoning methods. This paper compares two types of such methods, Defeasible Argumentation and Non-Monotonic Fuzzy Logic to assess which is more effective at solving a computational trust problem centred around Wikipedia editors. Through the application of these methods with real-data and a set of knowledge-bases, it was found that the Fuzzy Logic approach was statistically significantly better than the Argumentation approach in its inferential capacity.

Multi-Sensory Deep Learning Architectures For Slam Dunk Scene Classification, Paul Minogue

Dissertations

Basketball teams at all levels of the game invest a considerable amount of time and effort into collecting, segmenting, and analysing footage from their upcoming opponents previous games. This analysis helps teams identify and exploit the potential weaknesses of their opponents and is commonly cited as one of the key elements required to achieve success in the modern game. The growing importance of this type of analysis has prompted research into the application of computer vision and audio classification techniques to help teams classify scoring sequences and key events using game footage. However, this research tends to focus on classifying …

The "Invisible Hand" Of Peer Review: The Implications Of Author-Referee Networks On Peer Review In A Scholarly Journal, Pierpaolo Dondio, Niccolo Casnici, Nigel Gilbert, Francisco Grimaldo, Flaminio Squazzoni

Articles

Peer review is not only a quality screening mechanism for scholarly journals. It also connects authors and referees either directly or indirectly. This means that their positions in the network structure of the community could influence the process, while peer review could in turn influence subsequent networking and collaboration. This paper aims to map these complex network implications by looking at 2232 author/referee couples in an interdisciplinary journal that uses double blind peer review. By reconstructing temporal co-authorship networks, we found that referees tended to recommend more positively submissions by authors who were within three steps in their collaboration network. …

Persistence Pays Off: Paying Attention To What The Lstm Gating Mechanism Persists, John D. Kelleher, Giancarlo Salton

Articles

Language Models (LMs) are important components in several Natural Language Processing systems. Recurrent Neural Network LMs composed of LSTM units, especially those augmented with an external memory, have achieved state-of-the-art results. However, these models still struggle to process long sequences which are more likely to contain long-distance dependencies because of information fading and a bias towards more recent information. In this paper we demonstrate an effective mechanism for retrieving information in a memory augmented LSTM LM based on attending to information in memory in proportion to the number of timesteps the LSTM gating mechanism persisted the information.

An Evaluation Of The Information Security Awareness Of University Students, Alan Pike

Dissertations

Between January 2017 and March 2018, it is estimated that more than 1.9 billion personal and sensitive data records were compromised online. The average cost of a data breach in 2018 was reported to be in the region of US$3.62 million. These figures alone highlight the need for computer users to have a high level of information security awareness (ISA). This research was conducted to establish the ISA of students in a university. There were three aspects to this piece of research. The first was to review and analyse the security habits of students in terms of their own personal …

Noise Reduction In Eeg Signals Using Convolutional Autoencoding Techniques, Conor Hanrahan

Dissertations

The presence of noise in electroencephalography (EEG) signals can significantly reduce the accuracy of the analysis of the signal. This study assesses to what extent stacked autoencoders designed using one-dimensional convolutional neural network layers can reduce noise in EEG signals. The EEG signals, obtained from 81 people, were processed by a two-layer one-dimensional convolutional autoencoder (CAE), whom performed 3 independent button pressing tasks. The signal-to-noise ratios (SNRs) of the signals before and after processing were calculated and the distributions of the SNRs were compared. The performance of the model was compared to noise reduction performance of Principal Component Analysis, with …

Predicting Violent Crime Reports From Geospatial And Temporal Attributes Of Us 911 Emergency Call Data, Vincent Corcoran

Dissertations

The aim of this study is to create a model to predict which 911 calls will result in crime reports of a violent nature. Such a prediction model could be used by the police to prioritise calls which are most likely to lead to violent crime reports. The model will use geospatial and temporal attributes of the call to predict whether a crime report will be generated. To create this model, a dataset of characteristics relating to the neighbourhood where the 911 call originated will be created and combined with characteristics related to the time of the 911 call. Geospatial …

Enhancing Partially Labelled Data: Self Learning And Word Vectors In Natural Language Processing, Eamon Mcentee

Dissertations

There has been an explosion in unstructured text data in recent years with services like Twitter, Facebook and WhatsApp helping drive this growth. Many of these companies are facing pressure to monitor the content on their platforms and as such Natural Language Processing (NLP) techniques are more important than ever. There are many applications of NLP ranging from spam filtering, sentiment analysis of social media, automatic text summarisation and document classification.

Detection Of Offensive Youtube Comments, A Performance Comparison Of Deep Learning Approaches, Priyam Bansal

Dissertations

Social media data is open, free and available in massive quantities. However, there is a significant limitation in making sense of this data because of its high volume, variety, uncertain veracity, velocity, value and variability. This work provides a comprehensive framework of text processing and analysis performed on YouTube comments having offensive and non-offensive contents.

YouTube is a platform where every age group of people logs in and finds the type of content that most appeals to them. Apart from this, a massive increase in the use of offensive language has been apparent. As there are massive volume of new …