Ruairi O'Reilly

Ruairi O'Reilly
Munster Technological University

PhD in Computer Science

About

57
Publications
19,249
Reads
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147
Citations
Citations since 2017
44 Research Items
135 Citations
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Introduction
Dr Ruairí O'Reilly is an academic in the Department of Computer Science at Cork Institute of Technology. Dr O'Reilly's research focus is on distributed systems, artificial intelligence, pattern recognition and data analytics.
Additional affiliations
October 2015 - July 2021
Munster Technolgoical University
Position
  • Lecturer
January 2012 - December 2015
Griffith College Cork
Position
  • Lecturer
January 2012 - March 2014
University College Cork
Position
  • Research Officer
Education
October 2012 - May 2013
Griffith College Cork
Field of study
  • Training & Education
September 2008 - May 2015
University College Cork
Field of study
  • Computer Science
September 2004 - May 2008
University College Cork
Field of study
  • Computer Science

Publications

Publications (57)
Conference Paper
Full-text available
This article describes an exploratory study that aimed to analyse the relationship between personality traits and emotions. In particular , it investigates to what extent the sub-traits of the Five Factor Model has an empirically quantifiable correlation with the Basic Emotions (Anger, Anxiety, Disgust, Fear, Joy, Sadness, Surprise). If links betwe...
Conference Paper
Full-text available
In the healthcare domain, Artificial Intelligence (AI) based systems are being increasingly adopted with applications ranging from surgical robots to automated medical diagnostics. While a Machine Learning (ML) engineer might be interested in the parameters related to the performance and accuracy of these AI-based systems, it is postulated that a m...
Conference Paper
Full-text available
How does the relationship between personality traits and the basic emotions vary across the modalities of self-report and facial expression analysis? This article presents the results of an exploratory study that quantifies consistencies and differences in personality-emotion mappings across these two modalities. Twenty-four participants answered a...
Preprint
Full-text available
In biomedical image analysis, the applicability of deep learning methods is directly impacted by the quantity of image data available. This is due to deep learning models requiring large image datasets to provide high-level performance. Generative Adversarial Networks (GANs) have been widely utilized to address data limitations through the generati...
Preprint
Full-text available
Biomedical image datasets can be imbalanced due to the rarity of targeted diseases. Generative Adversarial Networks play a key role in addressing this imbalance by enabling the generation of synthetic images to augment and balance datasets. It is important to generate synthetic images that incorporate a diverse range of features such that they accu...
Preprint
Full-text available
Imbalanced image datasets are commonly available in the domain of biomedical image analysis. Biomedical images contain diversified features that are significant in predicting targeted diseases. Generative Adversarial Networks (GANs) are utilized to address the data limitation problem via the generation of synthetic images. Training challenges such...
Preprint
Full-text available
Advances in wearable technology have significantly increased the sensitivity and accuracy of devices for recording physiological signals. Commercial off-the-shelf wearable devices can gather large quantities of physiological data un-obtrusively. This enables momentary assessments of human physiology, which provide valuable insights into an individu...
Preprint
Full-text available
Publicly available data for the training of diabetic retinopathy classifiers is unbalanced. Generative adversarial networks can successfully synthesize retinal fundus imagery. In order for synthetic imagery to be of benefit, images need to be of high quality and diverse. Presently, several evaluation metrics are used to evaluate the quality and div...
Preprint
Full-text available
In healthcare, detecting stress and enabling individuals to monitor their mental health and wellbeing is challenging. Advancements in wearable technology now enable continuous physiological data collection. This data can provide insights into mental health and behavioural states through psychophysiological analysis. However, automated analysis is r...
Conference Paper
Biomedical image datasets can be imbalanced due to the rarity of targeted diseases. Generative Adversarial Networks play a key role in addressing this imbalance by enabling the generation of synthetic images to augment datasets. It is important to generate synthetic images that incorporate a diverse range of features to accurately represent the dis...
Conference Paper
Full-text available
In the domain of skin lesion classification using computer-aided diagnosis, machine learning approaches found in the literature are reported to be highly effective. However, state-of-the-art findings can prove challenging to reimplement due to inconsistencies and ambiguities in recorded methodologies. These ambiguities reduce the velocity at which...
Preprint
Full-text available
This work explores the effect of gender and linguistic-based vocal variations on the accuracy of emotive expression classification. Emotive expressions are considered from the perspective of spectral features in speech (Mel-frequency Cepstral Coefficient, Melspectrogram, Spectral Contrast). Emotions are considered from the perspective of Basic Emot...
Conference Paper
Full-text available
This work explores the effect of gender and linguistic-based vocal variations on the accuracy of emotive expression classification. Emotive expressions are considered from the perspective of spectral features in speech (Mel-frequency Cepstral Coefficient, Melspectrogram, Spectral Contrast). Emotions are considered from the perspective of Basic Emot...
Poster
Full-text available
Activity trackers are becoming more advanced with the current generation being capable of deriving blood oxygen saturation (SpO2). SpO2 has been shown to be an important indicator for sleep apnea. Activity tracker data acquisition faces challenges, such as the sampling rate of data and noise associated with the signal. This work compares the perfor...
Poster
Full-text available
In the domain of skin lesion classification using computer-aided diagnosis, machine learning approaches found in the literature are highly effective. However, state-of-the-art findings can prove challenging to reimplement due to inconsistencies and ambiguities in recorded methodologies, reducing the velocity at which future research advancements ca...
Conference Paper
Full-text available
This paper presents an approach for enabling emotive expression classification through speech analysis combining affective prosody (Mel-frequency Cepstral Coefficient, Zero Crossing Rate, Chroma Energy Normalised) and semantic analysis (Bag-of-words model). Two machine learning (ML) classifiers, a convolutional neural network (CNN) and logistic reg...
Conference Paper
Full-text available
The applicability and utility of Artificial Intelligence (AI) based solutions has been demonstrated widely in the healthcare domain via the automated analysis of medical information. However, the adoption rate of AI-based healthcare systems is inhibited due to their complicated nature. Also, the hierarchical nature of the medical settings adds a la...
Preprint
Full-text available
This paper presents an approach for enabling emotive expression classification through speech analysis combining affective prosody (Mel-frequency Cepstral Coefficient, Zero Crossing Rate, Chroma Energy Normalised) and semantic analysis (Bag-of-words model). Two machine learning (ML) classifiers, a convolutional neural network (CNN) and logistic reg...
Preprint
Full-text available
The applicability and utility of Artificial Intelligence (AI) based solutions has been demonstrated widely in the healthcare domain via the automated analysis of medical information. However, the adoption rate of AI-based healthcare systems is inhibited due to their complicated nature. Also, the hierarchical nature of the medical settings adds a la...
Presentation
Full-text available
The Personality Emotion Model (PEM) is a workflow for generating quantifiable and bi-directional mappings between 15 personality traits and the basic emotions. PEM utilises Affective computing methodology to map this relationship across the modalities of self-report, facial expressions, semantic analysis, and affective prosody. The workflow is an e...
Conference Paper
Full-text available
The Personality Emotion Model (PEM) is a workflow for generating quantifiable and bi-directional mappings between 15 personality traits and the basic emotions. PEM utilises Affective computing methodology to map this relationship across the modalities of self-report, facial expressions, semantic analysis, and affective prosody. The workflow is an e...
Preprint
Full-text available
Personality enables the accurate prediction of one’s emotional experiences. The accuracy of these predictions is dependent on how accurate the relationships between personality and emotions are modelled. While such relationships are typically modelled using a linear statistical approach, recent research has suggested that a non-linear approach may...
Preprint
Full-text available
How does the relationship between personality traits and the basic emotions vary across the modalities of self-report and facial expression analysis? This article presents the results of an exploratory study that quantifies consistencies and differences in personality-emotion map-pings across these two modalities. Twenty-four participants answered...
Preprint
Full-text available
In the healthcare domain, Artificial Intelligence (AI) based systems are increasingly being adopted with applications ranging from surgical robots to automated medical diagnostics. While a machine learning (ML) engineer might be interested in the parameters related to the performance and accuracy of these AI-based systems. It is postulated that a m...
Preprint
Full-text available
The Personality Emotion Model (PEM) is a workflow for generating quantifiable and bi-directional mappings between 15 personality traits and the basic emotions. PEM utilises Affective computing methodology to map this relationship across the modalities of self-report, facial expressions, semantic analysis, and affective prosody. The workflow is an e...
Chapter
Full-text available
This article describes an exploratory study that aimed to analyse the relationship between personality traits and emotions. In particular, it investigates to what extent the sub-traits of the Five Factor Model has an empirically quantifiable correlation with the Basic Emotions (Anger, Anxiety, Disgust, Fear, Joy, Sadness, Surprise). If links betwee...
Conference Paper
Full-text available
Despite state of the art performance on object recognition and image classification problems, CNNs are considered to have two significant weaknesses. Firstly, their inability to cater for changes in object orientation, position or lighting. Secondly, their inability to deal with part-whole relationships between objects. Capsule Networks are an enha...
Conference Paper
Full-text available
The rapid emergence and spread of COVID-19 resulted in a surge in demand for laboratory-based testing globally. Currently, the gold standard diagnostic approach is large-scale molecular testing of biological samples which detect the SARS-CoV-2 virus RNA. Infrastructure limitations and supply shortages are limiting testing capacity with a growing de...
Conference Paper
Full-text available
Artificial Intelligence (AI) is an enabling technology that when integrated into healthcare applications and smart wearable devices such as Fitbits etc. can predict the occurrence of health conditions in users by capturing and analysing their health data. The integration of AI and smart wearable devices has a range of potential applications in the...
Conference Paper
Full-text available
Academic performance across Computer Science (CS) courses in the Republic of Ireland is underwhelming. CS undergraduates are statistically the most likely cohort in the country not to progress past year one of their studies. Insufficient motivation to pursue CS studies has been demonstrated to be a significant cause of poor CS academic performance....
Conference Paper
Full-text available
The analysis of physiological data plays a significant role in medical diagnostics. While state-of-the-art machine learning models demonstrate high levels of performance in classifying physiological data clinicians are slow to adopt them. A contributing factor to the slow rate of adoption is the "black-box" nature of the underlying model whereby th...
Conference Paper
Full-text available
Cervical cancer is a severe concern for women's health. Every year in the Republic of Ireland, approximately 300 women are diagnosed with cervical cancer, 30% for whom the diagnosis will prove fatal. It is the second most common cause of death due to cancer in women aged 25 to 39 years [14]. Recently there has been a series of controversies concern...
Conference Paper
Full-text available
The quantity and quality of data available to an organisation plays an increasingly important role in its operation. This data can relate to a variety of subjects, from the internal logistics to consumer sentiment towards a product in a specific market. This data provides increasingly optimal behaviour derived from its analysis, e.g. improved decis...
Conference Paper
Full-text available
Electrocardiography (ECG) is a form of physiological data used to record the electrical activity of the heart. Numerous researchers have proposed and developed methods to extract features from the ECG signal (for example, R-R segment, P-R segment). These features can be used to analyse and classify various forms of heart arrhythmia. In this work, a...
Conference Paper
Full-text available
This research paper investigates the running of object detection algorithms on low-end devices to detect individuals in images while leveraging cloud-based services to provide facial verification of the individuals detected. The performance of three computer vision object detection algorithms that utilize Convolutional Neural Networks (CNN) are com...
Conference Paper
Full-text available
Beats-Per-Minute (BPM) is a microservice-based platform that provides a monitoring solution for the continuous acquisition, analysis and visualisation of health related data. BPM combines Commercial Off-The-Self (COTS) Activity Trackers and a scalable cloud-based infrastructure. This paper demonstrates the efficacy, reliability and integrity of BPM...
Thesis
Full-text available
Systems for the monitoring and analysis of data, e.g. stock markets, smart grids and sensor networks, can be made up of massive numbers of individual components. These components can be geographically distributed yet may interact with one another via continuous data streams, which in turn may affect the state of the sender or receiver. This introdu...
Conference Paper
Full-text available
Seizures are both the most common neurological emergency afflicting neonates and the most difficult to detect clinically. Currently, the monitoring of a multi-channel electroencephalogram (EEG) is the gold standard for seizure detection. The accurate analysis of this physiological data requires a neurophysiologist with expertise in neonatal EEG. Th...
Presentation
Full-text available
The processing of a patient in a medical facility encompasses data acquisition, data analysis, diagnosis and medical reporting. The result is a program of treatment for the patient. It is postulated that the latter two stages can be facilitated by allowing acquisition locations to avail of data interpretation by multiple off-site analysts who inter...
Conference Paper
Full-text available
A system, constructed as a “proof of concept”, for providing an Irish tele-neurophysiology service is presented. It is based on a distributed architecture and capable of handling synchronous data streams from multiple Irish Neonatal Intensive Care Units. It provides Ireland with an infrastructure for overcoming factors affecting the diagnosis of ne...
Conference Paper
Full-text available
A system, constructed as a "proof of concept", for providing an Irish tele-neurophysiology service is presented. It is based on a distributed architecture and capable of handling synchronous data streams from multiple Irish Neonatal Intensive Care Units. It provides Ireland with an infrastructure for overcoming factors affecting the diagnosis of ne...
Presentation
Full-text available
A presentation based on the BabyLink Project delivered in SickKids Hospital, Toronto, Ontario
Presentation
Full-text available
Conference Paper
Full-text available
The processing of a patient in a medical facility encompasses data acquisition, data analysis, diagnosis and medical reporting. The result is a program of treatment for the patient. It is postulated that the latter two stages can be facilitated by allowing acquisition locations to avail of data interpretation by multiple off-site analysts who inter...
Conference Paper
Full-text available
An agent framework that assists with the interpretation of streaming physiological data is presented. The framework operates as a module within an existing remote monitoring system for streaming physiological data. Agents complement the remote monitoring system by enhancing the users' view of the signals through the addition of annotations and the...
Conference Paper
Full-text available
In a critical care setting, delays in the diagnosis of neurological conditions can have a significant impact on patient outcomes. Recording the electrical activity of the brain (EEG) is often used to diagnose and monitor neurological conditions. A trained neurophysiologist is then required to analyse these signals. However, in many cases this exper...

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Projects

Projects (7)
Archived project
This centred on the design and development of a remote monitoring framework for the streaming and analysis of real-time EEG data. This enabled the acquisition of live EEG data from the Neonatal Intensive Care Unit. This provided remote medical facilities with clinical expertise that would otherwise be unavailable in assessing neonatal seizure. ScrutiniseIT: Cloud-based search and signal processing was incorporated. The work focused on the integration of an agent-based framework with a cloud service provider to enable the processing of physiological data at scale. The terms “Scan” and “Scrutinise” were used to refer to a search-based approach that quickly identified pieces of a signal that were of interest.
Project
The rapid emergence and spread of COVID-19 have seen a surge in demand for laboratory-based testing globally. Infrastructure limitations and supply shortages are limiting testing capacity with the growing demand for COVID-19 diagnostics across the EU. X-ray and CT images are essential in establishing the severity of a multitude of diseases and monitoring responses of patients in the hospital setting [2, 3] and as such, presents an ideal opportunity to integrate additional screening measures into a pre-existing workflow. The IBPMM is a platform to assist radiologists with the monitoring and detection of COVID-19. The platform uses Artificial Intelligence (AI) and machine learning to automatically analyse X-ray and Chest CT images. The platform will enable the automated analysis of X-ray and CT images, providing a classification with an associated probability for the presence of COVID-19.
Project
Consumer technology is changing the healthcare paradigm from a reactive treatment-based approach to a proactive preventative based approach. Big tech such as Google, Apple and Amazon are investing heavily in making healthcare services accessible and affordable a range of smart devices such as with the help of devices such as Fitbit and the Apple Watch. While previously smart healthcare was limited to hospitals using localised infrastructure, the smart healthcare ecosystem is evolving into one in which participants are encouraged to be in control of their well being using real-time information of their health on a fitness app or device. The role of Artificial Intelligence (AI) is set to be significant but still faces many challenges. The broad aim of this research is on the incorporation of AI into personalised healthcare. The focus is on achieving an increased utility of outcome in an explainable and interpretable manner.