Paul Fergus

Professor of Machine Learning

Liverpool John Moores University

United Kingdom

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About

Professor Paul Fergus is a faculty member at Liverpool John Moores University in the United Kingdom. He specializes in machine learning, with recent research focused on AI applications in wildlife conservation, real-time monitoring of ground-nesting birds, and healthcare technology using deep learning. His work includes innovative studies on automated biodiversity assessments and the detection of sleep apnea using advanced neural network techniques.

Articles (14)

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2025

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2022

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UNITED KINGDOM

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Paul Fergus

Research Interests

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Articles (14)

Detection and Geolocation of Peat Fires Using Thermal Infrared Cameras on Drones

AI-Driven Real-Time Monitoring of Ground-Nesting Birds: A Case Study on Curlew Detection Using YOLOv10

Detection of Obstructive Sleep Apnoea Using Features Extracted From Segmented Time-Series ECG Signals With a One Dimensional Convolutional Neural Network

Towards Context-Rich Automated Biodiversity Assessments: Deriving AI-Powered Insights from Camera Trap Data

Harnessing Artificial Intelligence for Wildlife Conservation

Empowering Wildlife Guardians: An Equitable Digital Stewardship and Reward System for Biodiversity Conservation Using Deep Learning and 3/4G Camera Traps

Removing Human Bottlenecks in Bird Classification Using Camera Trap Images and Deep Learning

Pressure Ulcer Categorization and Reporting in Domiciliary Settings Using Deep Learning and Mobile Devices: A Clinical Trial to Evaluate End-to-End Performance

Understanding External Influences on Target Detection and Classification Using Camera Trap Images and Machine Learning

Anomaly Detection Using Autoencoder Reconstruction upon Industrial Motors

Detecting Activities of Daily Living and Routine Behaviours in Dementia Patients Living Alone Using Smart Meter Load Disaggregation

The Categorical Data Conundrum: Heuristics for Classification Problems—A Case Study on Domestic Fire Injuries

Misper-Bayes: A Bayesian Network Model for Missing Person Investigations

Modelling Segmented Cardiotocography Time-Series Signals Using One-Dimensional Convolutional Neural Networks for the Early Detection of Abnormal Birth Outcomes

Collaborators (6)

Carl Chalmers

Serge A. Wich

Philip Stephens

Paulo Lisboa

Sandra Ortega-Martorell

Chris Sutherland

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