19 citations · 47 across the 5 of their papers we have counts for
7 papers
Conservation AI: Live Stream Analysis for the Detection of Endangered Species Using Convolutional Neural Networks and Drone Technology
C. Chalmers, P. Fergus, Serge Wich +1
Many different species are adversely affected by poaching. In response to this escalating crisis, efforts to stop poaching using hidden cameras, drones and DNA tracking have been i…
SAERMA: Stacked Autoencoder Rule Mining Algorithm for the Interpretation of Epistatic Interactions in GWAS for Extreme Obesity
Casimiro Aday Curbelo Montañez, Paul Fergus, Carl Chalmers +4
One of the most important challenges in the analysis of high-throughput genetic data is the development of efficient computational methods to identify statistically significant Sin…
Modelling Segmented Cardiotocography Time-Series Signals Using One-Dimensional Convolutional Neural Networks for the Early Detection of Abnormal Birth Outcomes
Paul Fergus, Carl Chalmers, Casimiro Curbelo Montanez +3
Gynaecologists and obstetricians visually interpret cardiotocography (CTG) traces using the International Federation of Gynaecology and Obstetrics (FIGO) guidelines to assess the w…
Detecting Activities of Daily Living and Routine Behaviours in Dementia Patients Living Alone Using Smart Meter Load Disaggregation
C. Chalmers, P. Fergus, C. Aday Curbelo Montanez +3
The emergence of an ageing population is a significant public health concern. This has led to an increase in the number of people living with progressive neurodegenerative disorder…
Analysis of Extremely Obese Individuals Using Deep Learning Stacked Autoencoders and Genome-Wide Genetic Data
Casimiro A. Curbelo Montañez, Paul Fergus, Carl Chalmers +1
The aetiology of polygenic obesity is multifactorial, which indicates that life-style and environmental factors may influence multiples genes to aggravate this disorder. Several lo…
Deep Learning Classification of Polygenic Obesity using Genome Wide Association Study SNPs
Casimiro Adays Curbelo Montañez, Paul Fergus, Almudena Curbelo Montañez +1
In this paper, association results from genome-wide association studies (GWAS) are combined with a deep learning framework to test the predictive capacity of statistically signific…
Utilising Deep Learning and Genome Wide Association Studies for Epistatic-Driven Preterm Birth Classification in African-American Women
Paul Fergus, Casimiro Curbelo Montanez, Basma Abdulaimma +2
Genome Wide Association Studies (GWAS) are used to identify statistically significant genetic variants in case-control studies. GWAS typically use a p-value threshold of 5 x 10-8 t…