Publications

Books

• Espinosa-Anke, L., Martin-Vide, C. and Spasic, I. eds. 2020. Statistical language and speech processing: 8th International Conference, SLSP 2020, Cardiff, UK, October 14–16, 2020. Springer.

Articles

• Alsaedi, N., Burnap, P. and Rana, O. 2017. Can we predict a riot? Disruptive event detection using Twitter. ACM Transactions on Internet Technology 17(2), article number: 18. (10.1145/2996183)
• Arruda, E., Harper, P., England, T., Gartner, D., Aspland, E., Ourique, F. and Crosby, T. 2021. Resource optimisation for cancer pathways with aggregate diagnostic demand: a perishable inventory approach. IMA Journal of Management Mathematics 32(2), pp. 221-236. (10.1093/imaman/dpaa014)
• Aspland, E., Gartner, D. and Harper, P. 2021. Clinical pathway modelling: A literature review. Health Systems 10(1), pp. 1-23. (10.1080/20476965.2019.1652547)
• Aspland, E., Harper, P. R., Gartner, D., Webb, P. and Barrett-Lee, P. 2021. Modified Needleman-Wunsch algorithm for clinical pathway clustering. Journal of Biomedical Informatics 115, article number: 103668. (10.1016/j.jbi.2020.103668)
• Bai, X. et al. 2021. Integrated metabolomics and lipidomics analysis reveal remodeling of lipid metabolism and amino acid metabolism in glucagon receptor–deficient zebrafish. Frontiers in Cell and Developmental Biology 8, article number: 605979. (10.3389/fcell.2020.605979)
• Burnap, P., Cherdantseva, Y., Blyth, A., Eden, P., Jones, K., Soulsby, H. and Stoddart, K. 2017. Determining and sharing risk data in distributed interdependent systems. IEEE Computer 50(4), pp. 72-79. (10.1109/MC.2017.108)
• Burnap, P., Colombo, G., Amery, R., Hodorog, A. and Scourfield, J. 2017. Multi-class machine classification of suicide-related communication on Twitter. Online Social Networks and Media 2, pp. 32-44. (10.1016/j.osnem.2017.08.001)
• Burnap, P., French, R., Turner, F. and Jones, K. 2018. Malware classification using self organising feature maps and machine activity data. Computers and Security 73, pp. 399-410. (10.1016/j.cose.2017.11.016)
• Calzada, I. and Arranz, I. 2022. Western US Basque-American e-diaspora: action research in California, Idaho, and Nevada. Societies 12(6), article number: 153. (10.3390/soc12060153)
• Cao, Y., Li, Y., Coleman, S., Belatreche, A. and McGinnity, T. M. 2015. Adaptive hidden Markov model with anomaly states for price manipulation detection. IEEE Transactions on Neural Networks and Learning Systems 26(2), pp. 318-330. (10.1109/TNNLS.2014.2315042)
• Cao, Y., Li, Y., Coleman, S., Belatreche, A. and McGinnity, T. M. 2016. Detecting wash trade in financial market using digraphs and dynamic programming. IEEE Transactions on Neural Networks and Learning Systems 27(11), pp. 2351-2363. (10.1109/TNNLS.2015.2480959)
• Chard, K., Caton, S., Kugler, K., Rana, O. F. and Katz, D. S. 2017. A social content delivery network for e-science. Concurrency and Computation: Practice and Experience 29(4), article number: e3854. (10.1002/cpe.3854)
• Chopard, D., Treder, M., Corcoran, P., Johnson, C., Busse-Morris, M. and Spasic, I. 2021. Text mining of adverse events in clinical trials: Deep learning approach. JMIR Medical Informatics 9(12), article number: e28632. (10.2196/28632)
• Cooper, C. H. V., Harvey, I., Orford, S. and Chiaradia, A. J. F. 2021. Using multiple hybrid spatial design network analysis to predict longitudinal effect of a major city centre redevelopment on pedestrian flows. Transportation 48, pp. 643-672. (10.1007/s11116-019-10072-0)
• Corcoran, P., Palmer, G., Arman, L., Knight, D. and Spasic, I. 2021. Creating Welsh language word embeddings. Applied Sciences 11(15), article number: 6896. (10.3390/app11156896)
• Ding, X., Li, Y., Belatreche, A. and Maguire, L. P. 2015. Novelty detection using level set methods. IEEE Transactions on Neural Networks and Learning Systems 26(3), pp. 576-588. (10.1109/TNNLS.2014.2320293)
• Edenharter, G. et al. 2019. Delay of transfer from the intensive care unit: a prospective observational analysis on economic effects of delayed in-house transfer. European Journal of Medical Research 24(1), pp. -., article number: 30. (10.1186/s40001-019-0388-3)
• England, T., Harper, P., Crosby, T., Gartner, D., Arruda, E. F., Foley, K. and Williamson, I. J. 2021. Examining the diagnostic pathway for lung cancer patients in Wales using discrete event simulation. Translational Lung Cancer Research 10(3), pp. 1368-1382. (10.21037/tlcr-20-919)
• Espinosa-Anke, L., Palmer, G., Filimonov, M., Corcoran, P., Spasic, I. and Knight, D. 2021. English–Welsh cross-lingual embeddings. Applied Sciences 11(14), article number: 6541. (10.3390/app11146541)
• Ferrão, J., Oliveira, M. D., Gartner, D., Janela, F. and Martins, H. 2021. Can structured EHR data support clinical coding? A data mining approach. Health Systems 10(2), pp. 138-161. (10.1080/20476965.2020.1729666)
• Filimonov, M., Chopard, D. and Spasic, I. 2022. Simulation and annotation of global acronyms. Bioinformatics 38(11), pp. 3136-3138., article number: btac298. (10.1093/bioinformatics/btac298)
• Foley, K. G. et al. 2018. Development and validation of a prognostic model incorporating texture analysis derived from standardised segmentation of PET in patients with oesophageal cancer. European Radiology 28, pp. 428-436. (10.1007/s00330-017-4973-y)
• Gartner, D., Zhang, Y. and Padman, R. 2019. Reducing clinical workload in the care prescription process: optimization of order sets. IMA Journal of Management Mathematics 30(3), pp. 305-321. (10.1093/imaman/dpy018)
• Hannah, C., Spasic, I. and Corcoran, P. 2018. A computational model of pedestrian road safety: the long way round is the safe way home. Accident Analysis & Prevention 121, pp. 347-357. (10.1016/j.aap.2018.06.004)
• Harper, P. R. 2020. Server behaviours in healthcare queueing systems. Journal of the Operational Research Society 71(7), pp. 1124-1136. (10.1080/01605682.2019.1567653)
• Harper, P. 2020. Healthy O.R. in Wales. Impact 2020(2), pp. 28-32. (10.1080/2058802X.2020.1768684)
• Hughes, C., Filimonov, M., Wray, A. and Spasic, I. 2021. Leaving no stone unturned: flexible retrieval of idiomatic expressions from a large text corpus. Machine Learning and Knowledge Extraction 3(1), pp. 263-283. (10.3390/make3010013)
• Javed, A., Burnap, P. and Rana, O. 2019. Prediction of drive-by download attacks on Twitter. Information Processing and Management 56(3), pp. 1133-1145. (10.1016/j.ipm.2018.02.003)
• Jha, S., Katz, D. S., Luckow, A., Chue Hong, N., Rana, O. and Simmhan, Y. 2017. Introducing distributed dynamic data-intensive (D3) science: Understanding applications and infrastructure. Concurrency and Computation: Practice and Experience 29(8), article number: e4032. (10.1002/cpe.4032)
• Kheiri, A., Lewis, R., Thompson, J. and Harper, P. 2021. Constructing operating theatre schedules using partitioned graph colouring techniques. Health Systems 10(4), pp. 286-297. (10.1080/20476965.2020.1796530)
• Leonenko, N. and Malyarenko, A. 2017. Matérn class tesor-valued random fields and beyond. Journal of Statistical Physics 168(6), pp. 1276-1301. (10.1007/s10955-017-1847-2)
• Leonenko, N. and Ruiz-Medina, M. 2017. Increasing domain asymptotics for the first Minkowski functional of spherical random fields. Theory of Probability and Mathematical Statistics 97, pp. 127-149. (10.1090/tpms/1053)
• Liu, J., Harkin, J., Li, Y. and Maguire, L. P. 2016. Fault-tolerant networks-on-chip routing with coarse and fine-grained look-ahead. IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems 35(2), pp. 260-273. (10.1109/TCAD.2015.2459050)
• Liu, Z. et al. 2019. Circular RNA hsa_circ_001783 regulates breast cancer progression via sponging miR-200c-3p. Cell Death and Disease 10, article number: 55. (10.1038/s41419-018-1287-1)
• Morgan, J., Harper, P., Knight, V., Artemiou, A., Carney, A. and Nelson, A. 2019. Determining patient outcomes from patient letters: A comparison of text analysis approaches. Journal of the Operational Research Society 70(9), pp. 1425-1439. (10.1080/01605682.2018.1506559)
• Mugabi, I. K. 2016. Microchips law in the UK and animal liability post-2016. SSRN: English & Commonwealth Law eJournal, pp. 1-14. (10.2139/ssrn.2723071)
• Muralidaran, V., Spasic, I. and Knight, D. 2021. A systematic review of unsupervised approaches to grammar induction. Natural Language Engineering 27(6), pp. 647-689. (10.1017/S1351324920000327)
• Ouro, P., Fraga Bugallo, B., Lopez Novoa, U. and Stoesser, T. 2019. Scalability of an Eulerian-Lagrangian large-eddy simulation solver with hybrid MPI/OpenMP parallelisation. Computers and Fluids 179, pp. 123-136. (10.1016/j.compfluid.2018.10.013)
• Palmer, G. I., Knight, V. A., Harper, P. R. and Hawa, A. L. 2019. Ciw: An open-source discrete event simulation library. Journal of Simulation 13(1), pp. 68-82. (10.1080/17477778.2018.1473909)
• Radanliev, P., De Roure, D., Nicolescu, R., Huth, M., Mantilla, R., Cannady, S. and Burnap, P. 2018. Future developments in cyber risk assessment for the internet of things. Computers in Industry 102, pp. 14-22. (10.1016/j.compind.2018.08.002)
• Raza, H., Prasad, G. and Li, Y. 2015. EWMA model based shift-detection methods for detecting covariate shifts in non-stationary environments. Pattern Recognition 48(3), pp. 659-669. (10.1016/j.patcog.2014.07.028)
• Rogers, D., Preece, A., Innes, M. and Spasic, I. 2022. Real-time text classification of user-generated content on social media: Systematic review. IEEE Transactions on Computational Social Systems 9(4), pp. 1154-1166. (10.1109/TCSS.2021.3120138)
• Spasic, I. and Button, K. 2020. Patient triage by topic modelling of referral letters: Feasibility study. JMIR Medical Informatics 8(11), article number: e21252. (10.2196/21252)
• Spasic, I., Krzeminski, D., Corcoran, P. and Balinsky, A. 2019. Cohort selection for clinical trials from longitudinal patient records: text mining approach. JMIR Medical Informatics 7(4), article number: e15980. (10.2196/15980)
• Spasic, I. and Nenadic, G. 2020. Clinical text data in machine learning: Systematic review. JMIR Medical Informatics 8(3), article number: e17984. (10.2196/17984)
• Spasic, I., Owen, D., Smith, A. and Button, K. 2019. KLOSURE: Closing in on open–ended patient questionnaires with text mining. Journal of Biomedical Semantics 10(S1), article number: 24. (10.1186/s13326-019-0215-3)
• Spasic, I., Uzuner, ?. and Zhou, L. 2020. Emerging clinical applications of text analytics. International Journal of Medical Informatics 134, article number: 103974. (10.1016/j.ijmedinf.2019.103974)
• Taherkhani, A., Belatreche, A., Li, Y. and Maguire, L. P. 2018. A supervised learning algorithm for learning precise timing of multiple spikes in multilayer spiking neural networks. IEEE Transactions on Neural Networks and Learning Systems 29(11), pp. 5394-5407. (10.1109/TNNLS.2018.2797801)
• Tolosana-Calasanz, R., Diaz-Montes, J., Rana, O. F. and Parashar, M. 2017. Feedback-control & queueing theory-based resource management for streaming applications. IEEE Transactions on Parallel and Distributed Systems 28(4), pp. 1061-1075. (10.1109/TPDS.2016.2603510)
• Webb, H. et al. 2016. Digital wildfires: propagation, verification, regulation, and responsible innovation. ACM Transactions on Information Systems 34(3), article number: 15. (10.1145/2893478)
• Williams, L., Arribas-Ayllon, M., Artemiou, A. and Spasic, I. 2019. Comparing the utility of different classification schemes for emotive language analysis. Journal of Classification 36(3), pp. 619-648. (10.1007/s00357-019-9307-0)
• Yaseen, M. U., Anjum, A., Rana, O. and Hill, R. 2018. Cloud-based scalable object detection and classification in video streams. Future Generation Computer Systems 80, pp. 286-298. (10.1016/j.future.2017.02.003)
• Zunic, A., Corcoran, P. and Spasic, I. 2020. Sentiment analysis in health and wellbeing: A systematic review. JMIR Medical Informatics 8(1), article number: e16023. (10.2196/16023)
• Zunic, A., Corcoran, P. and Spasic, I. 2021. Aspect-based sentiment analysis with graph convolution over syntactic dependencies. Artificial Intelligence in Medicine 119, article number: 102138. (10.1016/j.artmed.2021.102138)
• Zunic, A., Corcoran, P. and Spasic, I. 2022. The case of aspect in sentiment analysis: seeking attention or co-dependency?. Machine Learning and Knowledge Extraction 4(2), pp. 474-487. (10.3390/make4020021)

Conferences

• Alsaedi, N., Burnap, P. and Rana, O. F. 2015. Identifying disruptive events from social media to enhance situational awareness. Presented at: IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM), Paris, France, 24-27 August 2015ASONAM '15 Proceedings of the 2015 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining. ACM pp. 934-941., (10.1145/2808797.2808879)
• Alsaedi, N., Burnap, P. and Rana, O. F. 2016. Automatic summarization of real world events using Twitter. Presented at: International AAAI Conference on Web and Social Media (ICWSM), Cologne, Germany, 17-20 May 2016Proceedings of the Tenth International AAAI Confe. AAAI pp. 511-514.
• Alsaedi, N., Burnap, P. and Rana, O. F. 2016. Sensing real-world events using Arabic Twitter posts. Presented at: International AAAI Conference on Web and Social Media (ICWSM), Cologne, Germany, 17-20 May 2016Proceedings of the Tenth International AAAI Conference on Web and Social Media (ICWSM 2016). AAAI pp. 515-518.
• Alsaedi, N., Burnap, P. and Rana, O. F. 2017. Temporal TF-IDF: a high performance approach for event summarization in Twitter. Presented at: IEEE/WIC/ACM International Conference on Web Intelligence, Omaha, Nebraska, USA, 13-16 October 20162016 IEEE/WIC/ACM International Conference on Web Intelligence. IEEE pp. 515-521., (10.1109/WI.2016.0087)
• Bent, G., Simpkin, C., Li, Y. and Preece, A. 2022. Hyperdimensional computing using time-to-spike neuromorphic circuits. Presented at: 2022 IEEE World Congress on Computational Intelligence (WCCI), Padova, Italy, 18-23 July 2022. IEEE
• Bent, G., Simpkin, C., Li, Y. and Preece, A. 2022. Energy efficient spiking neural network neuromorphic processing to enable decentralised service workflow composition in support of multi-domain operations. Presented at: SPIE Defense + Commercial Sensing 2022, Orlando, Florida, United States, 3 April - 13 June 2022 Presented at Pham, T. and Solomon, L. eds.Artificial Intelligence and Machine Learning for Multi-Domain Operations Applications, Vol. 12113. SPIE pp. 121131M., (10.1117/12.2617362)
• Burnap, P., Colombo, G. and Scourfield, J. B. 2015. Machine classification and analysis of suicide-related communication on Twitter. Presented at: 26th ACM Conference on Hypertext & Social Media, Cyprus, 1-4 September 2015HT '15- Proceedings of the 26th ACM Conference on Hypertext & Social Media. Association for Computing Machinery pp. 75-84.
• Burnap, P., Javed, A., Rana, O. F. and Awan, M. 2015. Real-time classification of malicious URLs on Twitter using Machine Activity Data. Presented at: IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM), Paris, France, 25-27 August 2015. ACM
• Burnap, P. and Williams, M. L. 2016. Computational human and cyber security analytics for government and policy. Presented at: Data Science and Government Conference, Oxford, UK, 22 June 2016.
• Burnap, P. and Williams, M. L. 2017. Classifying and modeling cyber hate speech: research and opportunities for practical intervention. Presented at: 1st International Conference on Cyber Deviance Detection (CyberDD) in conjunction with 10th ACM International Conference on Web Search and Data Mining (WSDM 2017), Cambridge, UK, 10 Feb 2017.
• Chopard, D. and Spasic, I. 2019. A deep learning approach to self-expansion of abbreviations based on morphology and context distance. Presented at: SLSP 2019: 7th International Conference on Statistical Language and Speech Processing, Ljubljana, Slovenia, 14-16 October 2019 Presented at Martín-Vide, C., Purver, M. and Pollak, S. eds.Statistical Language and Speech Processing: 7th International Conference, SLSP 2019, Ljubljana, Slovenia, October 14–16, 2019, Proceedings, Vol. 11816. Springer pp. 71-82., (10.1007/978-3-030-31372-2_6)
• Cooper, C., Harvey, I. and Orford, S. 2018. Testing the ability of multivariate hybrid spatial network analysis to predict the effect of a major urban redevelopment on pedestrian flows. Presented at: GISRUK 2018: 26th GIScience Research UK Conference, University of Leicester, 17-20 April 2018.
• Dai, X., Spasic, I., Chapman, S. and Meyer, B. 2020. The state of the art in implementing machine learning for mobile apps: A survey. Presented at: IEEE SoutheastCon, Raleigh, USA, 12-15 Mar 2020. pp. -.
• Dai, X., Spasic, I., Meyer, B., Chapman, S. and Andres, F. 2019. Machine learning on mobile: An on-device inference app for skin cancer detection. Presented at: 2019 4th International Conference on Fog and Mobile Edge Computing, Rome, Italy, 10-13 June 20192019 Fourth International Conference on Fog and Mobile Edge Computing (FMEC). IEEE pp. 301-305., (10.1109/FMEC.2019.8795362)
• Lopez, U., Morgan, J., Jones, K., Rana, O., Edwards, T. and Grigoletto, F. 2019. Enabling citizen science in rural environments with IoT and mobile technologies. Presented at: IoT ’19, Bilbao, Spain, 22-25 Oct 2019. pp. -.
• Medel, V., Rana, O. F., Bañares, J. A. and Arronategui, U. 2016. Modelling performance & resource management in kubernetes. Presented at: 9th International Conference on Utility and Cloud Computing, Shanghai, China, 6-9 December 2016UCC '16: Proceedings of the 9th International Conference on Utility and Cloud Computing. UCC Proceedings New York, NY: Association for Computing Machinery pp. 257-262., (10.1145/2996890.3007869)
• Perez Almendros, C., Espinosa-Anke, L. and Schockaert, S. 2020. Don't patronize me! an annotated dataset with patronizing and condescending language towards vulnerable communities. Presented at: The 28th International Conference on Computational Linguistics (COLING 2020), Virtual, 8-13 December 2020Proceedings of the 28th International Conference on Computational Linguistics. Barcelona, Spain: International Committee on Computational Linguistics pp. 5891–5902., (10.18653/v1/2020.coling-main.518)
• Sidorowicz, T., Peres, P. and Li, Y. 2022. A novel approach for cross-selling insurance products using positive unlabelled learning. Presented at: International Joint Conference on Neural Networks, Padua - Italy, 18-23 July 20222022 International Joint Conference on Neural Networks (IJCNN). IEEE, (10.1109/IJCNN55064.2022.9892762)
• Spasic, I., Owen, D., Knight, D. and Artemiou, A. 2019. Unsupervised multi-word term recognition in Welsh. Presented at: Celtic Language Technology Workshop 2019, Dublin, Ireland, 19 August 2019 Presented at Lynn, T. et al. eds.Proceedings of the Celtic Language Technology Workshop. European Association for Machine Translation
• Whitaker, R. M., Turner, L. and Colombo, G. 2017. Intra-group tension under inter-group conflict: a generative model using group social norms and identity. Presented at: 8th International Conference on Applied Human Factors and Ergonomics, Los Angeles, CA, USA, 17-21 July 2017.
• Williams, M. L. and Burnap, P. 2017. Data science solutions for detecting and monitoring Brexit related online hate speech. Presented at: UK Government Data Science Community Interest Workshop, ONS Data Science Campus, Newport, Wales, UK, 4 September 2017.
• Zou, M., Zamani, A. R., Diaz-Montes, J., Petri, I., Rana, O. F. and Parashar, M. 2016. Leveraging in-transit computational capabilities in federated ecosystems. Presented at: 2016 IEEE Symposium on Service-Oriented System Engineering (SOSE), Oxford, UK, March 29 -April 2 20162016 IEEE Symposium on Service-Oriented System Engineering (SOSE). IEEE pp. 99-108., (10.1109/SOSE.2016.15)
• Zunic, A., Corcoran, P. and Spasic, I. 2020. Improving the performance of sentiment analysis in health and wellbeing using domain knowledge. Presented at: the Third UK Healthcare Text Analytics Conference (HealTAC), London, UK, 22-24 April 2020. pp. -.

Monographs

• Setchi, R. and Spasic, I. 2020. AI-assisted patent prior art searching - feasibility study. The Intellectual Property Office. Available at: https://www.gov.uk/government/publications/ai-assisted-patent-prior-art-searching-feasibility-study