Computer Engineering 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 …

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 …

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 …

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 …

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 …

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.

Performance Comparison Of Support Vector Machine, Random Forest, And Extreme Learning Machine For Intrusion Detection, Iftikhar Ahmad, Muhammad Javed Iqbal, Mohammad Basheri, Aneel Rahim

Articles

Intrusion detection is a fundamental part of security tools, such as adaptive security appliances, intrusion detection systems, intrusion prevention systems, and firewalls. Various intrusion detection techniques are used, but their performance is an issue. Intrusion detection performance depends on accuracy, which needs to improve to decrease false alarms and to increase the detection rate. To resolve concerns on performance, multilayer perceptron, support vector machine (SVM), and other techniques have been used in recent work. Such techniques indicate limitations and are not efficient for use in large data sets, such as system and network data. The intrusion detection system is used …

“Woodlands” - A Virtual Reality Serious Game Supporting Learning Of Practical Road Safety Skills., Krzysztof Szczurowski, Matt Smith

Conference Papers

In developed societies road safety skills are taught early and often practiced under the supervision of a parent, providing children with a combination of theoretical and practical knowledge. At some point children will attempt to cross a road unsupervised, at that point in time their safety depends on the effectiveness of their road safety education. To date, various attempts to supplement road safety education with technology were made. Most common approach focus on addressing declarative knowledge, by delivering road safety theory in an engaging fashion. Apart from expanding on text based resources to include instructional videos and animations, some stakeholders …

Using Regular Languages To Explore The Representational Capacity Of Recurrent Neural Architectures, Abhijit Mahalunkar, John D. Kelleher

Conference papers

The presence of Long Distance Dependencies (LDDs) in sequential data poses significant challenges for computational models. Various recurrent neural architectures have been designed to mitigate this issue. In order to test these state-of-the-art architectures, there is growing need for rich benchmarking datasets. However, one of the drawbacks of existing datasets is the lack of experimental control with regards to the presence and/or degree of LDDs. This lack of control limits the analysis of model performance in relation to the specific challenge posed by LDDs. One way to address this is to use synthetic data having the properties of subregular languages. …

Investigating The Impact Of Unsupervised Feature-Extraction From Multi-Wavelength Image Data For Photometric Classification Of Stars, Galaxies And Qsos, Annika Lindh

Conference papers

Accurate classification of astronomical objects currently relies on spectroscopic data. Acquiring this data is time-consuming and expensive compared to photometric data. Hence, improving the accuracy of photometric classification could lead to far better coverage and faster classification pipelines. This paper investigates the benefit of using unsupervised feature-extraction from multi-wavelength image data for photometric classification of stars, galaxies and QSOs. An unsupervised Deep Belief Network is used, giving the model a higher level of interpretability thanks to its generative nature and layer-wise training. A Random Forest classifier is used to measure the contribution of the novel features compared to a set …

Investigating The Impact Of Unsupervised Feature-Extraction From Multi-Wavelength Image Data For Photometric Classification Of Stars, Galaxies And Qsos, Annika Lindh

Dissertations

This thesis reviews the current state of photometric classification in Astronomy and identifies two main gaps: a dependence on handcrafted rules, and a lack of interpretability in the more successful classifiers. To address this, Deep Learning and Computer Vision were used to create a more interpretable model, using unsupervised training to reduce human bias.

The main contribution is the investigation into the impact of using unsupervised feature-extraction from multi-wavelength image data for the classification task. The feature-extraction is achieved by implementing an unsupervised Deep Belief Network to extract lower-dimensionality features from the multi-wavelength image data captured by the Sloan Digital …

Predicting Intake Of Applications For First Registration In The Property Registration Authority, Orlaith Mernagh

Dissertations

The motivation for this dissertation is rooted in a real business need. The Property Registration Authority is the state organisation tasked with maintaining a register of land ownership on the island of Ireland. The PRA currently faces a series of challenges; a high level of staff retiring and the inherent loss of knowledge associated with this trend, a lack of recruitment in recent years and a large increase in lodgement of applications for first registration as a result of legislation. The organisation therefore requires a reliable system for predicting future intake. Prior to this project, there has also been a …

The Role Of Cryptography In Security For Electronic Commerce, Ann Murphy, David Murphy

The ITB Journal

Many businesses and consumers are wary of conducting business over the Internet due to a perceived lack of security. Electronic business is subject to a variety of threats such as unauthorised access, misappropriation, alteration and destruction of both data and systems. This paper explores the major security concerns of businesses and users and describes the cryptographic techniques used to reduce such risks.

On The Applications Of Deterministic Chaos For Encrypting Data On The Cloud, Jonathan Blackledge, Nikolai Ptitsyn

Conference papers

Cloud computing is expected to grow considerably in the future because it has so many advantages with regard to sale and cost, change management, next generation architectures, choice and agility. However, one of the principal concerns for users of the Cloud is lack of control and above all, data security. This paper considers an approach to encrypting information before it is ‘placed’ on the Cloud where each user has access to their own encryption algorithm, an algorithm that is based on a set of iterated function systems that outputs a chaotic number stream, designed to produce a cryptographically secure cipher. …

Cloud Computing:Strategies For Cloud Computing Adoption, Faith Shimba

Dissertations

The advent of cloud computing in recent years has sparked an interest from different organisations, institutions and users to take advantage of web applications. This is a result of the new economic model for the Information Technology (IT) department that cloud computing promises. The model promises a shift from an organisation required to invest heavily for limited IT resources that are internally managed, to a model where the organisation can buy or rent resources that are managed by a cloud provider, and pay per use. Cloud computing also promises scalability of resources and on-demand availability of resources.

Although, the adoption …

Authentication Of Biometric Features Using Texture Coding For Id Cards, Jonathan Blackledge, Eugene Coyle

Conference papers

The use of image based information exchange has grown rapidly over the years in terms of both e-to-e image storage and transmission and in terms of maintaining paper documents in electronic form. Further, with the dramatic improvements in the quality of COTS (Commercial-Off-The-Shelf) printing and scanning devices, the ability to counterfeit electronic and printed documents has become a widespread problem. Consequently, there has been an increasing demand to develop digital watermarking techniques which can be applied to both electronic and printed images (and documents) that can be authenticated, prevent unauthorized copying of their content and, in the case of printed …

On The Applications Of Deterministic Chaos For Encrypting Data On The Cloud, Jonathan Blackledge, Nikolai Ptitsyn

Conference papers

Cloud computing is expected to grow considerably in the future because it has so many advantages with regard to sale and cost, change management, next generation architectures, choice and agility. However, one of the principal concerns for users of the Cloud is lack of control and above all, data security. This paper considers an approach to encrypting information before it is ‘place’ on the Cloud where each user has access to their own encryption algorithm, an algorithm that is based on a set of Iterative Function Systems that outputs a chaotic number stream, designed to produce a cryptographically secure cipher. …

Self-Authentication Of Audio Signals By Chirp Coding, Jonathan Blackledge, Eugene Coyle

Conference papers

This paper discusses a new approach to ‘watermarking’ digital signals using linear frequency modulated or ‘chirp’ coding. The principles underlying this approach are based on the use of a matched filter to provide a reconstruction of a chirped code that is uniquely robust in the case of signals with very low signal-to-noise ratios. Chirp coding for authenticating data is generic in the sense that it can be used for a range of data types and applications (the authentication of speech and audio signals, for example). The theoretical and computational aspects of the matched filter and the properties of a chirp …

A Mobile Ecg Monitoring System With Context Collection, Jin Peng Li, Damon Berry, Richard Hayes

Conference Papers

Preventative health management represents a shift from the traditional approach of reactive treatment-based healthcare towards a proactive wellness-management approach where patients are encouraged to stay healthy with expert support when they need it, at any location and any time. This work represents a step along the road towards proactive, preventative healthcare for cardiac patients. It seeks to develop a smart mobile ECG monitoring system that requests and records context information about what is happening around the subject when an arrhythmia event occurs. Context information about the subject’s activities of daily living will, it is hoped, provide an enriched data set …