Ruairi O'Reilly

Munster Technological University | MTU

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...

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...

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...

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...

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...

Affective computing research aims to accurately detect users' emotional states to enhance human-computer interaction. While existing studies demonstrate the ability of affective computing models to accurately detect posed expressions of emotions, there is limited investigation into their validity in detecting natural or spontaneous emotional expres...

Generative Adversarial Networks (GANs) have high computational costs to train their complex architectures. Throughout the training process, GANs' output is analyzed qualitatively based on the loss and synthetic images' diversity and quality. Based on this qualitative analysis, training is manually halted once the desired synthetic images are genera...

Objective To investigate relationships between the Big Five, their sub-traits, and basic emotional states.Background Most research has focused on how emotional attributes relate to Extraversion and Neuroticism. However, the potential for discrete emotional states to enable a richer understanding of the emotive nature of all Big Five traits has been...

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...

Over the years, several frameworks have been proposed in the domain of Explainable AI (XAI), however their practical applicability and utility need to be clarified. The neighbourhood contexts are shown to significantly impact the explanations generated by XAI frameworks, thus directly affecting their utility in addressing specific questions, or “ex...

Publicly available diabetic retinopathy (DR) datasets are imbalanced, containing limited numbers of images with DR. This imbalance contributes to overfitting when training machine learning classifiers. The impact of this imbalance is exacerbated as the severity of the DR stage increases, affecting the classifiers’ diagnostic capacity. The imbalance...

The diverse nature of emotion models presents challenges in psychophysiological analysis, notably affecting the comparability and reliability of research findings across studies. This exploratory work aims to address the diversity of emotion models by abstracting them into a higher-order perspective while preserving the specificity of the original...

In biomedical image analysis, data imbalance is common across several imaging modalities. Data augmentation is one of the key solutions in addressing this limitation. Generative Adversarial Networks (GANs) are increasingly being relied upon for data augmentation tasks. Biomedical image features are sensitive to evaluating the efficacy of synthetic...

This open access book constitutes selected papers presented during the 30th Irish Conference on Artificial Intelligence and Cognitive Science, held in Munster, Ireland, in December 2022. The 41 presented papers were thoroughly reviewed and selected from the 102 submissions. They are organized in topical sections on machine learning, deep learning...

Psychophysiology investigates the causal relationship of physiological changes resulting from psychological states. There are significant challenges with machine learning-based momentary assessments of physiology due to varying data collection methods, physiological differences , data availability and the requirement for expertly annotated data. Ad...

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...

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...

Publicly available diabetic retinopathy (DR) datasets are imbalanced, containing limited numbers of images with DR. This imbalance contributes to overfitting when training machine learning classifiers. The impact of this imbalance is exacerbated as the severity of the DR stage increases, affecting the classifiers' diagnostic capacity. The imbalance...

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...

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...

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...

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...

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...

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...

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...

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...

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...

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...

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...

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...

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...

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...

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...

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...

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...

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...

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...

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...

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....

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...

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...

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...

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...

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...

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...

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...

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...

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...

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...

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...

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...

A presentation based on the BabyLink Project delivered in SickKids Hospital, Toronto, Ontario

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...

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...

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...