activity
20182021
most citedConservation AI: Live Stream Analysis for the Detection of Endangered Species Using Convolutional Neural Networks and Drone Technology

19 citations · 34 across the 6 of their papers we have counts for

collaborators

11 papers

cs.SD20211 cited

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…

cs.LG2020

BMI: A Behavior Measurement Indicator for Fuel Poverty Using Aggregated Load Readings from Smart Meters

P. Fergus, C. Chalmers

Fuel poverty affects between 50 and 125 million households in Europe and is a significant issue for both developed and developing countries globally. This means that fuel poor resi…

eess.SP2020

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…

cs.CV201919 cited

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…

q-bio.GN2019

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…

cs.LG2019

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…