Professor of Data Science and Intelligent Systems
Thuemmler, C. (2014). Shock Index demonstrator
This is an outcome from the FI-STAR project and a contribution to the FIWARE catalogue.
Thuemmler, C. (2017). Health and Sport Committee: Technology and Innovation in Health and Social Care. Edinburgh: Scottish Government
No abstract available
Smales, A., Buchanan, W. J., & Thuemmler, C. (2015, January). Review of e-Frailty evaluation frameworks. Paper presented at HIS 2015, Edinburgh
This paper outlines some of the key methods used to evaluate frailty and provide important metrics for the implementation of an e-Frailty framework. Frailty is an emergent pro...
Scottish Funding Council
The project has successfully defined the long-term requirements of the solution and also produced a prototype implementing on of these requirements
Investigation of Big Data applied into Health and Social Care
Weight management is a key health issue within Scotland. The focus of this project is to develop an intelligent online platform for weight management. The project will use artificial intelligence met...
Lo, O., Fan, L., Buchanan, W. J., Thuemmler, C., & Lawson, A. (2012, June). Towards simulation of patient data for evaluation of E-health platform and services. Paper presented at 13th Annual Post Graduate Symposium on the Convergence of Telecommunications, Networking and Broadcasting, Liverpool
This paper presents the design and implementation of the Patient Simulator, a software application used for the simulation of patient data. The simulator aims to evaluate e- H...
Buchanan, W. J., Fan, L., Ekonomou, E., Lo, O., Uthmani, O., & Thuemmler, C. (2012, May). Integrating assisted living with primary and secondary health care. Paper presented at Data Handling in Health and Social Care: Striking the balance between confidentiality, security and information sharing, Edinburgh
This presentation outlines the Cloud4Health platform.
Buchanan, W. J., Fan, L., Ekonomou, E., Lo, O., & Thuemmler, C. (2012, February). Case Study: moving towards an e-health platform to store NHS patient Information in the cloud. Paper presented at Cloud Computing in the Public Sector: The Way Forward, London
Case Study: Moving Towards an e-health Platform to Store NHS Patient Information in the Cloud The NHS pilot scheme to store patient information in the Cloud How can the health...
Savaglio, C., Pace, P., Aloi, G., Liotta, A., & Fortino, G. (2019). Lightweight Reinforcement Learning for Energy Efficient Communications in Wireless Sensor Networks. IEEE Access, 7, 29355-29364. https://doi.org/10.1109/access.2019.2902371
High-density communications in wireless sensor networks (WSNs) demand for new approaches to meet stringent energy and spectrum requirements. We turn to reinforcement learning,...
Erhan, L., Ndubuaku, M., Ferrara, E., Richardson, M., Sheffield, D., Ferguson, F. J., …Liotta, A. (2019). Analyzing Objective and Subjective Data in Social Sciences: Implications for Smart Cities. IEEE Access, 7, 19890-19906. https://doi.org/10.1109/access.2019.2897217
The ease of deployment of digital technologies and the Internet of Things gives us the opportunity to carry out large-scale social studies and to collect vast amounts of data ...
van der Lee, T., Liotta, A., & Exarchakos, G. (2019). Interference graphs to monitor and control schedules in low-power WPAN. Future Generation Computer Systems, 93, 111-120. https://doi.org/10.1016/j.future.2018.10.014
• This study presents the complete and slotted interference graph model.
• The service uses the complete interference graph to evaluate the network.
• Slotted int...
Mocanu, D. C., Mocanu, E., Stone, P., Nguyen, P. H., Gibescu, M., & Liotta, A. (2018). Scalable training of artificial neural networks with adaptive sparse connectivity inspired by network science. Nature Communications, 9(1), 1-12. https://doi.org/10.1038/s41467-018-04316-3
Through the success of deep learning in various domains, artificial neural networks are currently among the most used artificial intelligence methods. Taking inspiration from ...
Pace, P., Aloi, G., Gravina, R., Caliciuri, G., Fortino, G., & Liotta, A. (2019). An Edge-Based Architecture to Support Efficient Applications for Healthcare Industry 4.0. IEEE Transactions on Industrial Informatics, 15(1), 481-489. https://doi.org/10.1109/tii.2018.2843169
Edge computing paradigm has attracted many interests in the last few years as a valid alternative to the standard cloud-based approaches to reduce the interaction timing and t...
Mocanu, E., Mocanu, D. C., Nguyen, P. H., Liotta, A., Webber, M. E., Gibescu, M., & Slootweg, J. G. (2019). On-Line Building Energy Optimization Using Deep Reinforcement Learning. IEEE Transactions on Smart Grid, 10(4), 3698-3708. https://doi.org/10.1109/tsg.2018.2834219
Unprecedented high volumes of data are becoming available with the growth of the advanced metering infrastructure. These are expected to benefit planning and operation of the ...
Torres Vega, M., Perra, C., De Turck, F., & Liotta, A. (2018). A Review of Predictive Quality of Experience Management in Video Streaming Services. IEEE Transactions on Broadcasting, 64(2), 432-445. https://doi.org/10.1109/tbc.2018.2822869
Satisfying the requirements of devices and users of online video streaming services is a challenging task. It requires not only managing the network quality of service but als...
Chincoli, M., & Liotta, A. (2018). Self-Learning Power Control in Wireless Sensor Networks. Sensors, 18(2), 1-29. https://doi.org/10.3390/s18020375
Current trends in interconnecting myriad smart objects to monetize on Internet of Things applications have led to high-density communications in wireless sensor networks. This...
Mocanu, D. C., Exarchakos, G., & Liotta, A. (2018). Decentralized dynamic understanding of hidden relations in complex networks. Scientific Reports, 8(1), https://doi.org/10.1038/s41598-018-19356-4
Almost all the natural or human made systems can be understood and controlled using complex networks. This is a difficult problem due to the very large number of elements in s...
New ideas are on the way to make healthcare more accurate, more affordable and matching the needs of our changing societies. Demographic changes, progress in technology and in medicine offer options t...
Royal Society of Edinburgh
This SE/RSE Enterprise Fellowship aimed to commercialise intellectual property owned by Edinburgh Napier University related to e-Health data management into an end-user product. The project paved the ...
IIDI is working with Patient Reminders Limited supported the SFC Innovation Voucher scheme.
Patient Reminders Limited provides patient reminder products and solutions for use in clinical studies, ph...
Engineering and Physical Sciences Research Council
This project extends the e-Health Cloud-based Platform, and integrates with assisted living. The project integrates Edinburgh Napier University, Microsoft and HoIP, and has created a novel governance ...
This project relates to the research collaboration between Edinburgh Napier University, CipherLab, Chelsea and Westminster Hospital, GS1 UK, Imperial College, and Kodit, and is funded through a resear...
Queen Margaret University
This IIDI project led by Alistair Lawson investigates the feasibility of developing a prototype Flash-based version of PEPS-C (Profiling Elements of Prosody in Speech-Communication). This is a test w...
The "Frailty Framework" is the development of a next generation health assessment and predictive analysis system. It will...