​​Utilizing Process Mining and Modelling Techniques for Sensor-Based Patient Activity Recognition within the Smart Home Environment

These scholarships will cover full-time PhD tuition fees for three years (subject to satisfactory academic performance) and will provide a £900 per annum research training support grant (RTSG) to help support the PhD researcher.

Applicants who already hold a doctoral degree or who have been registered on a programme of research leading to the award of a doctoral degree on a full-time basis for more than one year (or part-time equivalent) are NOT eligible to apply for an award.

Please note: you will automatically be entered into the competition for the Full Award, unless you state otherwise in your application.

The scholarship will cover tuition fees at the Home rate and a maintenance allowance of £19,000 (tbc) per annum for three years (subject to satisfactory academic performance).

This scholarship also comes with £900 per annum for three years as a research training support grant (RTSG) allocation to help support the PhD researcher.

​​​Tariq, Z., Khan, N., Charles, D., McClean, S., McChesney, I. and Taylor, P., 2020. Understanding contrail business processes through hierarchical clustering: A multi-stage framework. Algorithms, 13(10), p.244.

​​Tariq, Z., Charles, D., McClean, S., McChesney, I. and Taylor, P., 2021, August. An Event-Level Clustering Framework for Process Mining Using Common Sequential Rules. In International Conference for Emerging Technologies in Computing (pp. 147-160). Springer, Cham.

​​Shuai Zhang, Sally I. McClean, Bryan W. Scotney (2012): Probabilistic Learning From Incomplete Data for Recognition of Activities of Daily Living in Smart Homes, IEEE Transactions on Information Technology in Biomedicine, 16(3): 454-462.

​​Zhang, S., McClean, S.I., Scotney, B.W., Hong, X., Nugent, C.D., & Mulvenna, M.D. (2010). An Intervention Mechanism for Assistive Living in Smart Homes. Journal of Ambient Intelligence and Smart Environments (Smart Home Thematic Issue), Volume 2,  Issue 3, pp. 233-252.

​​​Munoz-Gama, J., Martin, N., Fernandez-Llatas, C., Johnson, O.A., Sepúlveda, M., Helm, E., Galvez-Yanjari, V., Rojas, E., Martinez-Millana, A., Aloini, D. and Amantea, I.A., 2022. Process mining for healthcare: Characteristics and challenges. Journal of Biomedical Informatics, 127, p.103994.​

​​L Yang, S McClean, M Donnelly, K Burke, K Khan (2022).  A multi-components approach to monitoring process structure and customer behaviour concept drift. Expert Systems with Applications​