19 citations · 60 across the 14 of their papers we have counts for
16 papers
Removing Human Bottlenecks in Bird Classification Using Camera Trap Images and Deep Learning
Carl Chalmers, Paul Fergus, Serge Wich +7
Birds are important indicators for monitoring both biodiversity and habitat health; they also play a crucial role in ecosystem management. Decline in bird populations can result in…
Empowering Wildlife Guardians: An Equitable Digital Stewardship and Reward System for Biodiversity Conservation using Deep Learning and 3/4G Camera Traps
Paul Fergus, Carl Chalmers, Steven Longmore +7
The biodiversity of our planet is under threat, with approximately one million species expected to become extinct within decades. The reason; negative human actions, which include…
Pressure Ulcer Categorisation using Deep Learning: A Clinical Trial to Evaluate Model Performance
Paul Fergus, Carl Chalmers, William Henderson +2
Pressure ulcers are a challenge for patients and healthcare professionals. In the UK, 700,000 people are affected by pressure ulcers each year. Treating them costs the National Hea…
Choosing an Appropriate Platform and Workflow for Processing Camera Trap Data using Artificial Intelligence
Juliana Vélez, Paula J. Castiblanco-Camacho, Michael A. Tabak +3
Camera traps have transformed how ecologists study wildlife species distributions, activity patterns, and interspecific interactions. Although camera traps provide a cost-effective…
Real-Time Predictive Maintenance using Autoencoder Reconstruction and Anomaly Detection
Sean Givnan, Carl Chalmers, Paul Fergus +2
Rotary machine breakdown detection systems are outdated and dependent upon routine testing to discover faults. This is costly and often reactive in nature. Real-time monitoring off…
Modelling Animal Biodiversity Using Acoustic Monitoring and Deep Learning
C. Chalmers, P. Fergus, S. Wich +1
For centuries researchers have used sound to monitor and study wildlife. Traditionally, conservationists have identified species by ear; however, it is now common to deploy audio r…
Detection of Obstructive Sleep Apnoea Using Features Extracted from Segmented Time-Series ECG Signals Using a One Dimensional Convolutional Neural Network
Steven Thompson, Paul Fergus, Carl Chalmers +1
The study in this paper presents a one-dimensional convolutional neural network (1DCNN) model, designed for the automated detection of obstructive Sleep Apnoea (OSA) captured from…
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…
Extracting Epistatic Interactions in Type 2 Diabetes Genome-Wide Data Using Stacked Autoencoder
Basma Abdulaimma, Paul Fergus, Carl Chalmers
2 Diabetes is a leading worldwide public health concern, and its increasing prevalence has significant health and economic importance in all nations. The condition is a multifactor…
Collaborative Pressure Ulcer Prevention: An Automated Skin Damage and Pressure Ulcer Assessment Tool for Nursing Professionals, Patients, Family Members and Carers
Paul Fergus, Carl Chalmers, David Tully
This paper describes the Pressure Ulcers Online Website, which is a first step solution towards a new and innovative platform for helping people to detect, understand and manage pr…
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…