All Outputs (6)

On the Classification of SSVEP-Based Dry-EEG Signals via Convolutional Neural Networks (2018)
Presentation / Conference Contribution
Aznan, N., Bonner, S., Connolly, J., Al Moubayed, N., & Breckon, T. (2018, October). On the Classification of SSVEP-Based Dry-EEG Signals via Convolutional Neural Networks. Presented at 2018 IEEE International Conference on Systems, Man, and Cybernetics (SMC2018), Miyazaki, Japan

Electroencephalography (EEG) is a common signal acquisition approach employed for Brain-Computer Interface (BCI) research. Nevertheless, the majority of EEG acquisition devices rely on the cumbersome application of conductive gel (so-called wet-EEG)... Read More about On the Classification of SSVEP-Based Dry-EEG Signals via Convolutional Neural Networks.

Deep Topology Classification: A New Approach for Massive Graph Classification (2017)
Presentation / Conference Contribution
Bonner, S., Brennan, J., Theodoropoulos, G., McGough, S., Kureshi, I., Joshi, J., Karypis, G., Liu, L., Hu, X., Ak, R., Xia, Y., Xu, W., Sato, A.-H., Rachuri, S., Ungar, L., Yu, P. S., Govindaraju, R., & Suzumura, T. (2016, February). Deep Topology Classification: A New Approach for Massive Graph Classification. Presented at IEEE International Conference on Big Data, Washington D.C

The classification of graphs is a key challenge within many scientific fields using graphs to represent data and is an active area of research. Graph classification can be critical in identifying and labelling unknown graphs within a dataset and has... Read More about Deep Topology Classification: A New Approach for Massive Graph Classification.

Efficient Comparison of Massive Graphs Through The Use Of 'Graph Fingerprints' (2016)
Presentation / Conference Contribution
Bonner, S., Brennan, J., Theodoropoulos, G., Kureshi, I., & McGough, A. (2016, August). Efficient Comparison of Massive Graphs Through The Use Of 'Graph Fingerprints'. Presented at Twelfth Workshop on Mining and Learning with Graphs (MLG) at KDD'16., San Francisco, USA

The problem of how to compare empirical graphs is an area of great interest within the field of network science. The ability to accurately but efficiently compare graphs has a significant impact in such areas as temporal graph evolution, anomaly dete... Read More about Efficient Comparison of Massive Graphs Through The Use Of 'Graph Fingerprints'.

Data Quality Assessment and Anomaly Detection Via Map / Reduce and Linked Data: A Case Study in the Medical Domain (2015)
Presentation / Conference Contribution
Bonner, S., McGough, S., Kureshi, I., Brennan, J., Theodoropoulos, G., Moss, L., Corsar, D., & Antoniou, G. (2023, October). Data Quality Assessment and Anomaly Detection Via Map / Reduce and Linked Data: A Case Study in the Medical Domain. Presented at IEEE International Conference on Big Data, Santa Clara

Recent technological advances in modern healthcare have lead to the ability to collect a vast wealth of patient monitoring data. This data can be utilised for patient diagnosis but it also holds the potential for use within medical research. However,... Read More about Data Quality Assessment and Anomaly Detection Via Map / Reduce and Linked Data: A Case Study in the Medical Domain.

PBStoHTCondor system for campus grids (2015)
Presentation / Conference Contribution
Brennan, J., Holmes, V., Bonner, S., & Kureshi, I. (2015, July). PBStoHTCondor system for campus grids. Presented at 2015 Science and Information Conference (SAI)., London, UK

The campus grid architectures currently available are considered to be overly complex. We have focused on High Throughput Condor HTCondor as one of the most popular middlewares among UK universities, and are proposing a new system for unifying campus... Read More about PBStoHTCondor system for campus grids.

Using Hadoop To Implement a Semantic Method Of Assessing The Quality Of Research Medical Datasets (2014)
Presentation / Conference Contribution
Bonner, S., Antoniou, G., Moss, L., Kureshi, I., Corsair, D., Tachmazidis, I., Chin, A., Xu, W., & Wang, F. (2014, August). Using Hadoop To Implement a Semantic Method Of Assessing The Quality Of Research Medical Datasets. Presented at The 2014 International Conference on Big Data Science and Computing - BigDataScience '14., Beijing, China

In this paper a system for storing and querying medical RDF data using Hadoop is developed. This approach enables us to create an inherently parallel framework that will scale the workload across a cluster. Unlike existing solutions, our framework us... Read More about Using Hadoop To Implement a Semantic Method Of Assessing The Quality Of Research Medical Datasets.