Outputs (15)

Machine learning and deep learning prediction models for time-series: a comparative analytical study for the use case of the UK short-term electricity price prediction (2024)
Journal Article
Mishra, B. K., Preniqi, V., Thakker, D., & Feigl, E. (2024). Machine learning and deep learning prediction models for time-series: a comparative analytical study for the use case of the UK short-term electricity price prediction. Discover Internet of Things, 4(1), Article 24. https://doi.org/10.1007/s43926-024-00075-4

Electricity price prediction has an imperative role in the UK energy market among energy trading organisations. The price prediction directly impacts organisational policy for profitable electricity trading, better bidding plans, and the optimisation... Read More about Machine learning and deep learning prediction models for time-series: a comparative analytical study for the use case of the UK short-term electricity price prediction.

Measuring AI Fairness in a Continuum Maintaining Nuances: A Robustness Case Study (2024)
Journal Article
Paxton, K., Aslansefat, K., Thakker, D., & Papadopoulos, Y. (2024). Measuring AI Fairness in a Continuum Maintaining Nuances: A Robustness Case Study. IEEE Internet Computing, 28(5), 11-19. https://doi.org/10.1109/MIC.2024.3450815

As machine learning is increasingly making decisions about hiring or healthcare, we want AI to treat ethnic and socioeconomic groups fairly. Fairness is currently measured by comparing the average accuracy of reasoning across groups. We argue that im... Read More about Measuring AI Fairness in a Continuum Maintaining Nuances: A Robustness Case Study.

Tailored risk assessment and forecasting in intermittent claudication (2024)
Journal Article
Ravindhran, B., Prosser, J., Lim, A., Lathan, R., Mishra, B., Hitchman, L., Smith, G. E., Carradice, D., Thakker, D., Chetter, I. C., & Pymer, S. (2024). Tailored risk assessment and forecasting in intermittent claudication. BJS Open, 8(1), Article zrad166. https://doi.org/10.1093/bjsopen/zrad166

Background: Guidelines recommend cardiovascular risk reduction and supervised exercise therapy as the first line of treatment in intermittent claudication, but implementation challenges and poor patient compliance lead to significant variation in man... Read More about Tailored risk assessment and forecasting in intermittent claudication.

Ontology Development for Detecting Complex Events in Stream Processing: Use Case of Air Quality Monitoring (2023)
Journal Article
Yemson, R., Kabir, S., Thakker, D., & Konur, S. (2023). Ontology Development for Detecting Complex Events in Stream Processing: Use Case of Air Quality Monitoring. Computers, 12(11), Article 238. https://doi.org/10.3390/computers12110238

With the increasing amount of data collected by IoT devices, detecting complex events in real-time has become a challenging task. To overcome this challenge, we propose the utilisation of semantic web technologies to create ontologies that structure... Read More about Ontology Development for Detecting Complex Events in Stream Processing: Use Case of Air Quality Monitoring.

Towards achieving net zero by 2050 in the UK – Stakeholder perspectives in integrated urban planning (2023)
Journal Article
Mazumdar, S., Thakker, D., Hayes, J., Matos, N., & Bate, P. (2023). Towards achieving net zero by 2050 in the UK – Stakeholder perspectives in integrated urban planning. Futures: for the interdisciplinary study of futures, visioning, anticipation and foresight, 152, Article 103197. https://doi.org/10.1016/j.futures.2023.103197

In light of severe risks of extreme conditions arising out of global warming, the push towards sustainable development and management of our resources has been a topic of immense interest globally. The UK's recent promise of reducing all greenhouse g... Read More about Towards achieving net zero by 2050 in the UK – Stakeholder perspectives in integrated urban planning.

From Raising Awareness to a Behavioural Change: A Case Study of Indoor Air Quality Improvement Using IoT and COM-B Model (2023)
Journal Article
Kureshi, R. R., Thakker, D., Mishra, B. K., & Barnes, J. (2023). From Raising Awareness to a Behavioural Change: A Case Study of Indoor Air Quality Improvement Using IoT and COM-B Model. Sensors, 23(7), Article 3613. https://doi.org/10.3390/s23073613

The topic of indoor air pollution has yet to receive the same level of attention as ambient pollution. We spend considerable time indoors, and poorer indoor air quality affects most of us, particularly people with respiratory and other health conditi... Read More about From Raising Awareness to a Behavioural Change: A Case Study of Indoor Air Quality Improvement Using IoT and COM-B Model.

Understanding Urban Planning Outcomes in the UK: Practitioner Perspectives in Outcome Assessment (2023)
Journal Article
Mazumdar, S., Qi, J., Thakker, D., & Goodchild, B. (2023). Understanding Urban Planning Outcomes in the UK: Practitioner Perspectives in Outcome Assessment. International Journal of E-Planning Research, 12(1), 1-40. https://doi.org/10.4018/IJEPR.326126

The planning process in the UK is a highly complex system, developed over many decades, and is in the process of rapid transitions into digital planning. Among these transformations is a desire to move from an outputs-based assessment to an outcomes-... Read More about Understanding Urban Planning Outcomes in the UK: Practitioner Perspectives in Outcome Assessment.

FedMSA: A Model Selection and Adaptation System for Federated Learning (2022)
Journal Article
Sun, R., Li, Y., Shah, T., Sham, R. W., Szydlo, T., Qian, B., Thakker, D., & Ranjan, R. (2022). FedMSA: A Model Selection and Adaptation System for Federated Learning. Sensors, 22(19), Article 7244. https://doi.org/10.3390/s22197244

Federated Learning (FL) enables multiple clients to train a shared model collaboratively without sharing any personal data. However, selecting a model and adapting it quickly to meet user expectations in a large-scale FL application with heterogeneou... Read More about FedMSA: A Model Selection and Adaptation System for Federated Learning.

Dynamic Data Streams for Time-Critical IoT Systems in Energy-Aware IoT Devices Using Reinforcement Learning (2022)
Journal Article
Habeeb, F., Szydlo, T., Kowalski, L., Noor, A., Thakker, D., Morgan, G., & Ranjan, R. (2022). Dynamic Data Streams for Time-Critical IoT Systems in Energy-Aware IoT Devices Using Reinforcement Learning. Sensors, 22, Article 2375. https://doi.org/10.3390/s22062375

Thousands of energy-aware sensors have been placed for monitoring in a variety of scenarios, such as manufacturing, control systems, disaster management, flood control and so on, requiring time-critical energy-efficient solutions to extend their life... Read More about Dynamic Data Streams for Time-Critical IoT Systems in Energy-Aware IoT Devices Using Reinforcement Learning.

Data-Driven Techniques for Low-Cost Sensor Selection and Calibration for the Use Case of Air Quality Monitoring (2022)
Journal Article
Kureshi, R., Mishra, B., Thakker, D., John, R., Walker, A., Simpson, S., Thakkar, N., & Wante, A. (2022). Data-Driven Techniques for Low-Cost Sensor Selection and Calibration for the Use Case of Air Quality Monitoring. Sensors, 22(3), Article 1093. https://doi.org/10.3390/s22031093

With the emergence of Low-Cost Sensor (LCS) devices, measuring real-time data on a large scale has become a feasible alternative approach to more costly devices. Over the years, sensor technologies have evolved which has provided the opportunity to h... Read More about Data-Driven Techniques for Low-Cost Sensor Selection and Calibration for the Use Case of Air Quality Monitoring.