Physical Sciences and Mathematics Commons^™

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

Dissertations

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

Identifying Online Sexual Predators Using Support Vector Machine, Yifan Li

Dissertations

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

Transformer Neural Networks For Automated Story Generation, Kemal Araz

Dissertations

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

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 …

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 …

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 …

Performance Comparison Of Hybrid Cnn-Svm And Cnn-Xgboost Models In Concrete Crack Detection, Sahana Thiyagarajan

Dissertations

Detection of cracks mainly has been a sort of essential step in visual inspection involved in construction engineering as it is the commonly used building material and cracks in them is an early sign of de-basement. It is hard to find cracks by a visual check for the massive structures. So, the development of crack detecting systems generally has been a critical issue. The utilization of contextual image processing in crack detection is constrained, as image data usually taken under real-world situations vary widely and also includes the complex modelling of cracks and the extraction of handcrafted features. Therefore the …

An Evaluation Of Learning Employing Natural Language Processing And Cognitive Load Assessment, Mrunal Tipari

Dissertations

One of the key goals of Pedagogy is to assess learning. Various paradigms exist and one of this is Cognitivism. It essentially sees a human learner as an information processor and the mind as a black box with limited capacity that should be understood and studied. With respect to this, an approach is to employ the construct of cognitive load to assess a learner's experience and in turn design instructions better aligned to the human mind. However, cognitive load assessment is not an easy activity, especially in a traditional classroom setting. This research proposes a novel method for evaluating learning …

Comparing Procedural Content Generation Algorithms For Creating Levels In Video Games, Zina Monaghan

Dissertations

Procedural Content Generation (PCG) is used frequently in games to increase replayability by introducing variety to playghrough of a game and reduce development time by allowing complex game worlds to be developed by a smaller team over a more limited amount of time.

Hierarchical Cluster Analysis: A New Type Of Ranking Criteria Based On Arwu Ranking Data, Zhengshuo Li

Dissertations

The advent of big data leads to many applications of Machine Learning techniques. University rankings is one of the applicable domains, which is currently playing a crucial role in the assessment of the universities' performance. Currently, the rankings are usually carried out by some authoritative ranking institutions by means of weighting techniques and the results are conveyed in numerical rankings. Three of the most famous university ranking institutions have been introduced from a technical perspective. However, these institutions have been proven to be subjective in relation to their data selection and weighting method.

Using Supervised Learning To Predict English Premier League Match Results From Starting Line-Up Player Data, Runzuo Yang

Dissertations

Soccer is one of the most popular sports around the world. Many people, whether they are a fan of a soccer team, a player of online soccer games or even the professional coach of a soccer team, will attempt to use some relevant data to predict the result of a match. Many of these kinds of prediction models are built based on data from the match itself, such as the overall number of shots, yellow or red cards, fouls committed, etc. of the home and away teams. However, this research attempted to predict soccer game results (win, draw or loss) …

Is There A Correlation Between Wikidata Revisions And Trending Hashtags On Twitter?, Paula Dooley [Thesis]

Dissertations

Twitter is a microblogging application used by its members to interact and stay socially connected by sharing instant messages called tweets that are up to 280 characters long. Within these tweets, users can add hashtags to relate the message to a topic that is shared among users. Wikidata is a central knowledge base of information relying on its members and machines bots to keeping its content up to date. The data is stored in a highly structured format with the added SPARQL protocol and RDF Query Language (SPARQL) endpoint to allow users to query its knowledge base.

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 …

Image Classification Using Bag-Of-Visual-Words Model, Kaiqiang Huang

Dissertations

Recently, with the explosive growth of digital technologies, there has been a rapid proliferation of the size of image collection. The technique of supervised image clas sification has been widely applied in many domains in order to organize, search, and retrieve images. However, the traditional feature extraction approaches yield the poor classification accuracy. Therefore, the Bag-of-visual-words model, inspired by Bag-of Words model in document classification, was used to present images with the local descriptors for image classification, and also it performs well in some fields. This research provides the empirical evidence to prove that the BoVW model outperforms the traditional …

A Javascript Framework Comparison Based On Benchmarking Software Metrics And Environment Configuration, Jefferson Ferreira

Dissertations

JavaScript is a client-side programming language that can be used in multi-platform applications. It controls HTML and CSS to manipulate page behaviours and is widely used in most websites over the internet. JavaScript frameworks are structures made to help web developers build web applications faster by offering features that enhance the user interaction with the web page. An increasing number of JavaScript frameworks have been released in recent years in the market to help front-end developers build applications in a shorter space of time. Decision makers in software companies have been struggling to determine which frameworks are best suited for …

Classification Using Association Rules, Colin Kane

Dissertations

This research investigates the use of an unsupervised learning technique, association rules, to make class predictions. The use of association rules to make class predictions is a growing area of focus within data mining research. The research to date has focused predominately on balanced datasets or synthetized imbalanced datasets. There have been concerns raised that the algorithms using association rules to make classifications do not perform well on imbalanced datasets. This research comprehensively evaluates the accuracy of a number of association rule classifiers in predicting home loan sales in an Irish retail banking context. The experiments designed test three associative …

Using Machine Learning Techniques To Predict A Risk Score For New Members Of A Chit Fund Group, Sinead Aherne

Dissertations

Predicting the risk score of new and potential customers is used across the financial industry. By implementing the prediction of risk scores for their customers a chit fund company can improve the knowledge and customer understanding without relying on human knowledge. Data is collected on each customer before they have taken out credit and during the time they contribute to a chit fund. Having collected the necessary data, the company can then decide whether modelling customer risk would benefit them. As the data is available historically, one aspect of risk score prediction will be the focus of this thesis, supervised …

Comparing The Effectiveness Of Support Vector Machines And Convolutional Neural Networks For Determining User Intent In Conversational Agents, Kieran O Sullivan

Dissertations

Over the last fifty years, conversational agent systems have evolved in their ability to understand natural language input. In recent years Natural Language Processing (NLP) and Machine Learning (ML) have allowed computer systems to make great strides in the area of natural language understanding. However, little research has been carried out in these areas within the context of conversational systems. This paper identifies Convolutional Neural Network (CNN) and Support Vector Machine (SVM) as the two ML algorithms with the best record of performance in ex isting NLP literature, with CNN indicated as generating the better results of the two. A …

Investigation Into The Predictive Power Of Artificial Neural Networks And Logistic Regression For Predicting Default In Chit Funds, Ciara Kerrigan

Dissertations

This study evaluated the performance of an artificial neural network (ANN) multi-layer perceptron model and a logistic regression logitboost (LR) model to predict default in chit funds. The two types of default investigated were late payment of 30 days and late payment of 90 days. The dataset was broken up into training and validation datasets using random sampling and K folds cross validation was used on the training dataset to assess performance of the tuning parameters. The validation dataset was used to compare performance of both algorithms. Principle component analysis (PCA) was used to reduce the feature set while still …

Predicting Happiness - Comparison Of Supervised Machine Learning Techniques Performance On A Multiclass Classification Problem, Dorota Nieciecka

Dissertations

In the modern world, especially in contemporary economies and politics, a population's subjective well-being is a frequent subject of the public debate. As comparisons of happiness levels in different countries are published, different circumstances and their effect on the value of the subjective well-being reported by people are also analysed. However, a significant amount of the research related to subjective well-being and its determinants is still based upon survey answers and employing conventional statistical methods providing details regarding correlations and causality between different factors and subjective well-being. Application of Supervised Machine Learning techniques for prediction of subjective well-being may provide …

Clicking Into Mortgage Arrears: A Study Into Arrears Prediction With Clickstream Data, Gavin O'Brien

Dissertations

This research project investigates the predictive capability of clickstream data when used for the purpose of mortgage arrears prediction. With an ever growing number of people switching to digital channels to handle their daily banking requirements, there is a wealth of ever increasing online usage data, otherwise known as clickstream data. If leveraged correctly, this clickstream data can be a powerful data source for organisations as it provides detailed information about how their customers are interacting with their digital channels. Much of the current literature associated with clickstream data relates to organisations employing it within their customer relationship management mechanisms …

Through The Net: Investigating How User Characteristics Influence Susceptibility To Phishing, Charlie Marriott

Dissertations

In the past 25 years, the internet has grown and evolved from a niche networking technology, used almost exclusively by researchers and enthusiasts, into the driving force of modern economies. Fraud has evolved too, with rates of cybercrime on the increase as criminals become increasingly sophisticated in using technology to deceive their victims. The world is an online place, and data is the new oil. Phishing is a form of social engineering that is not that different from traditional fraud. Phishing attackers try to trick their victims into revealing valuable private information, usually for financial gain, by posing as a …

Automation Of Authorisation Vulnerability Detection In Authenticated Web Applications, Niall Caffrey

Dissertations

In the beginning the World Wide Web, also known as the Internet, consisted mainly of websites. These were essentially information depositories containing static pages, with the flow of information mostly one directional, from the server to the user’s browser. Most of these websites didn’t authenticate users, instead, each user was treated the same, and presented with the same information. A malicious party that gained access to the web server hosting these websites would usually not gain access to confidential information as most of the information on the web server would already be accessible to the public. Instead, the malicious party …

A Demographic Analysis To Determine User Vulnerability Among Several Categories Of Phishing Attacks., Robert Griffin

Dissertations

Phishing attacks have been on a meteoric rise in the last number of years, with 2016 seeing a 65% increase. The attacks range from targeting individuals with personalised messages to spam attacks from bot accounts. With the chances of being targeted by a phishing attack increasing, it is important to identify who is most at risk in order to help alleviate this threat. The aim of this study is to examine members from several demographics and their vulnerability to three types of phishing using data collected from a survey (n = 198). The survey tested the participant’s ability to recognise …