A5074269233- Vehicle emissions and performance
- Air Quality and Health Impacts
- Air Quality Monitoring and Forecasting
- Surgical Simulation and Training
- Stock Market Forecasting Methods
- Phonocardiography and Auscultation Techniques
- BIM and Construction Integration
- Cardiovascular Disease and Adiposity
- Medical Image Segmentation Techniques
- Cardiac Imaging and Diagnostics
- Hydrological Forecasting Using AI
- Occupational Health and Safety Research
- COVID-19 diagnosis using AI
- Infrastructure Maintenance and Monitoring
- Energy Load and Power Forecasting
- Digital Imaging in Medicine
- AI in cancer detection
- Coronary Interventions and Diagnostics
- Colorectal Cancer Screening and Detection
- Advanced X-ray and CT Imaging
University of the West of England
2020-2025
Rainfall forecasting has gained utmost research relevance in recent times due to its complexities and persistent applications such as flood monitoring of pollutant concentration levels, among others. Existing models use complex statistical that are often too costly, both computationally budgetary, or not applied downstream applications. Therefore, approaches Machine Learning algorithms conjunction with time-series data being explored an alternative overcome these drawbacks. To this end,...
Abstract Over the past few years, surgical data science has attracted substantial interest from machine learning (ML) community. Various studies have demonstrated efficacy of emerging ML techniques in analysing data, particularly recordings procedures, for digitising clinical and non-clinical functions like preoperative planning, context-aware decision-making, operating skill assessment. However, this field is still its infancy lacks representative, well-annotated datasets training robust...
The construction of intercity highways by the government has resulted in a progressive increase vehicle emissions and pollution from noise, dust, vibrations despite its recognition air menace. Efforts that have targeted roadside still do not accurately monitor deadly pollutants such as nitrogen oxides particulate matter. Reports on regional across country are based limited number fixed monitoring stations sometimes located far highway. These periodic coarse-grained measurements cause...
Traffic-related air pollution (TRAP) remains one of the main contributors to urban and its impact on climate change cannot be overemphasised. Experts in developed countries strive make optimal use traffic quality data gain valuable insights into effect public health. Over years, research community has advanced methods forecasting traffic-related using several machine learning albeit with persistent accuracy insufficient challenges. Despite potentials emerging techniques such as multi-target...
Traffic-related air pollution remains a major contributor to urban and its impact on climate change cannot be overemphasised.Experts in developed countries strive make optimal use of traffic quality data gain valuable insights into effect public health.Over the years,the research community has advanced methods forecasting traffic-related using machine learning albeit with persistent accuracy challenges.Despite potentials emerging techniques such as multi-target deep neural-network achieve...
In recent times, surgical data science has emerged as an important research discipline in interventional healthcare. There are many potential applications for analysing endoscopic videos using machine learning (ML) techniques such tool classification, action recognition, and tissue segmentation. However, the efficacy of ML algorithms to learn robust features drastically deteriorates when models trained on noise-affected [1]. Appropriate preprocessing is thus crucial ensure training. To this...
Over the past few years, surgical data science has attracted substantial interest from machine learning (ML) community. Various studies have demonstrated efficacy of emerging ML techniques in analysing data, particularly recordings procedures, for digitizing clinical and non-clinical functions like preoperative planning, context-aware decision-making, operating skill assessment. However, this field is still its infancy lacks representative, well-annotated datasets training robust models...
Coronary angiography analysis is a common clinical task performed by cardiologists to diagnose coronary artery disease (CAD) through an assessment of atherosclerotic plaque's accumulation. This study introduces end-to-end machine learning solution developed as part our for the MICCAI 2023 Automatic Region-based Artery Disease diagnostics using x-ray imagEs (ARCADE) challenge, which aims benchmark solutions multivessel segmentation and potential stenotic lesion localisation from X-ray...