no code implementations • 3 May 2023 • Carl Chalmers, Paul Fergus, Serge Wich, Steven N Longmore, Naomi Davies Walsh, Philip Stephens, Chris Sutherland, Naomi Matthews, Jens Mudde, Amira Nuseibeh
In this paper, we outline an approach for overcoming these issues by utilising deep learning for real-time classi-fication of bird species and automated removal of false positives in camera trap data.
no code implementations • 25 Apr 2023 • Paul Fergus, Carl Chalmers, Steven Longmore, Serge Wich, Carmen Warmenhove, Jonathan Swart, Thuto Ngongwane, André Burger, Jonathan Ledgard, Erik Meijaard
Each time an animal was captured in a camera and successfully classified, 1 penny (an arbitrary amount - mechanisms still need to be developed to determine the real value of species) was transferred from the animal account to its associated guardian.
no code implementations • 7 Mar 2022 • Paul Fergus, Carl Chalmers, William Henderson, Danny Roberts, Atif Waraich
In this paper, we report the findings of a clinical trial conducted by Mersey Care NHS Foundation Trust that evaluated the performance of a faster region-based convolutional neural network and mobile platform that categorised and documented pressure ulcers.
no code implementations • 4 Feb 2022 • Juliana Vélez, Paula J. Castiblanco-Camacho, Michael A. Tabak, Carl Chalmers, Paul Fergus, John Fieberg
Camera traps have transformed how ecologists study wildlife species distributions, activity patterns, and interspecific interactions.
no code implementations • 1 Oct 2021 • Sean Givnan, Carl Chalmers, Paul Fergus, Sandra Ortega, Tom Whalley
To address this issue, we propose a Machine Learning (ML) approach to model normal working operation and detect anomalies.
no code implementations • 3 Feb 2020 • Steven Thompson, Paul Fergus, Carl Chalmers, Denis Reilly
Evaluation of the model is performed using a set of standard metrics which show the proposed model achieves high classification results in both training and validation using our windowing strategy, particularly W=500 (Sensitivity 0. 9705, Specificity 0. 9725, F1 Score 0. 9717, Kappa Score 0. 9430, Log Loss 0. 0836, ROCAUC 0. 9945).
no code implementations • 27 Aug 2019 • Casimiro Aday Curbelo Montañez, Paul Fergus, Carl Chalmers, Nurul Ahamed Hassain Malim, Basma Abdulaimma, Denis Reilly, Francesco Falciani
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 Single Nucleotide Polymorphisms (SNPs).
no code implementations • 6 Aug 2019 • Paul Fergus, Carl Chalmers, Casimiro Curbelo Montanez, Denis Reilly, Paulo Lisboa, Beth Pineles
Machine learning models, trained with FIGO and other user derived features extracted from CTG traces, have been shown to increase positive predictive capacity and minimise variability.
no code implementations • 28 Aug 2018 • Basma Abdulaimma, Paul Fergus, Carl Chalmers
Standard statistical tests for single and multi-locus analysis such as logistic regression, have demonstrated little effect in understanding the genetic architecture of complex human diseases.
no code implementations • 17 Aug 2018 • 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 pressure ulcers.
no code implementations • 16 Apr 2018 • Casimiro A. Curbelo Montañez, Paul Fergus, Carl Chalmers, Jade Hind
SNPs were filtered based on the effects associations have with BMI.
no code implementations • 9 Apr 2018 • Casimiro Adays Curbelo Montañez, Paul Fergus, Almudena Curbelo Montañez, Carl Chalmers
Using a deep learning classifier model and genetic variants with P-value < 1x10-2 (2465 SNPs) it was possible to obtain results (SE=0. 9604, SP=0. 9712, Gini=0. 9817, LogLoss=0. 1150, AUC=0. 9908 and MSE=0. 0300).