Lagree, A., Mohebpour, M., Meti, N., Saednia, K., Lu, F.-.I., Slodkowska, E., ... Tran, W. (2021). A review and comparison of breast tumor cell nuclei segmentation performances using deep convolutional neural networks. Scientific Reports, 11 (1), 8025. http://doi.org/10.1038/s41598-021-87496-1

Shenfield, A., & Howarth, M. (2020). A Novel Deep Learning Model for the Detection and Identification of Rolling Element-Bearing Faults. Sensors, 20, 5112.

Faust, O., Barika, R., Shenfield, A., Ciaccio, E.J., & Acharya, U.R. (2020). Accurate detection of sleep apnea with long short-term memory network based on RR interval signals. Knowledge-Based Systems, 106591. http://doi.org/10.1016/j.knosys.2020.106591

Faust, O., Kareem, M., Shenfield, A., Ali, A., & Acharya, U.R. (2020). Validating the robustness of an internet of things based atrial fibrillation detection system. Pattern Recognition Letters, 133, 55-61. http://doi.org/10.1016/j.patrec.2020.02.005

Liao, H., Milanovic, J.V., Rodrigues, M., & Shenfield, A. (2018). Voltage sag estimation in sparsely monitored power systems based on deep learning and system area mapping. IEEE Transactions on Power Delivery. http://doi.org/10.1109/TPWRD.2018.2865906

Faust, O., Shenfield, A., Kareem, M., San, T.R., Fujita, H., & Acharya, U.R. (2018). Automated detection of atrial fibrillation using long short-term memory network with RR interval signals. Computers in Biology and Medicine. http://doi.org/10.1016/j.compbiomed.2018.07.001

Shenfield, A., Day, D., & Ayesh, A. (2018). Intelligent intrusion detection systems using artificial neural networks. ICT Express. http://doi.org/10.1016/j.icte.2018.04.003

Shenfield, A., & Rostami, S. (2015). A multi-objective approach to evolving artificial neural networks for coronary heart disease classification. 2015 IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB), 1-8. http://doi.org/10.1109/CIBCB.2015.7300294

Rostami, S., O'Reilly, D., Shenfield, A., & Bowring, N. (2015). A novel preference articulation operator for the Evolutionary Multi-Objective Optimisation of classifiers in concealed weapons detection. Information Sciences, 295, 494-520. http://doi.org/10.1016/j.ins.2014.10.031

Journal articles

Tenzer, M., Pistilli, G., Bransden, A., & Shenfield, A. (2024). Debating AI in Archaeology: applications, implications, and ethical considerations. Internet Archaeology, (67). http://doi.org/10.11141/ia.67.8

Popoola, O., Rodrigues, M., Marchang, J., Shenfield, A., Ikpehai, A., & Popoola, J. (2023). A critical literature review of security and privacy in smart home healthcare schemes adopting IoT & blockchain: problems, challenges and solutions. Blockchain: Research and Applications. http://doi.org/10.1016/j.bcra.2023.100178

Madrigal-Garcia, M.I., Archer, D., Singer, M., Rodrigues, M., Shenfield, A., & Moreno-Cuesta, J. (2020). Do temporal changes in facial expressions help identify patients at risk of deterioration in hospital wards? A post hoc analysis of the Visual Early Warning Score study. Critical Care Explorations, 2 (5), e0115. http://doi.org/10.1097/CCE.0000000000000115

Shenfield, A., & Wainwright, R. (2018). Human Activity Recognition Making Use of Long Short-Term Memory Techniques. .

Madrigal-Garcia, M., Rodrigues, M., Shenfield, A., Singer, M., & Moreno-Cuesta, J. (2018). What faces reveal : a novel method to identify patients at risk of deterioration using facial expressions. Critical Care Medicine. http://doi.org/10.1097/CCM.0000000000003128

Rostami, S., & Shenfield, A. (2016). A multi-tier adaptive grid algorithm for the evolutionary multi-objective optimisation of complex problems. Soft Computing - A Fusion of Foundations, Methodologies and Applications, 21 (17), 4963-4979. http://doi.org/10.1007/s00500-016-2227-6

Shenfield, A., Rodrigues, M., Valentine, D., Liu, D., & Moreno-Cuesta, J. (2015). An improved classifier for mortality prediction in adult critical care admissions. Journal of the Intensive Care Society, 16 (4), 118. http://doi.org/10.1177/1751143715615287

Twigg, P., Sigurnjak, S., Southall, D., & Shenfield, A. (2014). Exploration of the effect of EEG Levels in experiencedarchers. Measurement and Control, 47 (6), 185-190. http://doi.org/10.1177/0020294014539281

Shenfield, A., & Fleming, P. (2014). Multi-objective evolutionary design of robust controllers on the grid. Engineering Applications of Artificial Intelligence, 27, 17-27. http://doi.org/10.1016/j.engappai.2013.09.015

Shenfield, A., & Fleming, P.J. (2013). A Novel Workload Allocation Strategy for Batch Jobs. International Journal of Computing and Network Technology, 1 (1), 1-17. http://doi.org/10.12785/IJCNT/010102

Shenfield, A., & Rodenburg, J. (2011). Evolutionary determination of experimental parameters for ptychographical imaging. Journal of Applied Physics, 109 (12), 124510. http://doi.org/10.1063/1.3600235

Shenfield, A., Fleming, P., Kadirkamanathan, V., & Allan, J. (2010). Optimisation of maintenance scheduling strategies on the grid. Annals of Operations Research, 180 (1), 213-231. http://doi.org/10.1007/s10479-008-0496-x

Shenfield, A., Fleming, P., & Alkarouri, M. (2007). Computational steering of a multi-objective evolutionary algorithm for engineering design. Engineering Applications of Artificial Intelligence, 20, 1047-1057. http://doi.org/10.1016/j.engappai.2007.01.005

Conference papers

Kasturi, S., Shenfield, A., Roast, C., Le Page, D., & Broome, A. (2024). Object Detection in Heritage Archives using a Human-in-Loop Concept. In Naik, N., Jenkins, P., Grace, P., Yang, L., & Prajapat, S. (Eds.) The 22nd UK Workshop on Computational Intelligence, 6 September 2023 - 8 September 2023 (pp. 170-181). Cham: Springer: http://doi.org/10.1007/978-3-031-47508-5_14

Reji, A., Pranggono, B., Marchang, J., & Shenfield, A. (2023). Anomaly detection for the internet-of-medical-things. In IEEE International Conference on Communications - 5th International Workshop on IoT Enabling Technologies in Healthcare (IoT-Health 2023), Rome, Italy, 28 May 2023 - 1 June 2023 (pp. 1944-1949). IEEE: http://doi.org/10.1109/ICCWorkshops57953.2023.10283523

Wainwright, R., & Shenfield, A. (2023). Machine learning for mortality risk prediction with changing patient demographics. In 20th IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology, Eindhoven, The Netherlands, 29 August 2023 - 2023. IEEE: http://doi.org/10.1109/CIBCB56990.2023.10264891

Shenfield, A., Kasturi, S., & Tran, W. (2022). Accurate nuclei segmentation in breast cancer tumour biopsies. In 19th IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology, Ottawa, ON, Canada, 15 August 2022 - 17 August 2022. IEEE: http://doi.org/10.1109/CIBCB55180.2022.9863023

Barika, R., Shenfield, A., Razaghi, H., & Faust, O. (2021). A smart sleep apnea detection service. 17th International Conference on Condition Monitoring and Asset Management, CM 2021. https://www.bindt.org/shopbindt/cd-roms/proceedings-of-cm-2021.html#.YWQX39rMIuV

Tran, W., Lu, F., Tabbarah, S., Lagree, A., Dodington, D., Jerzak, K., ... Shenfield, A. (2020). Quantitative Digital Pathology Biomarkers of Neoadjuvant Therapy Response in Breast Cancer (Abstract only). Radiotherapy & Oncology, 152 (S1), S277-S278. https://www.thegreenjournal.com/issue/S0167-8140(20)X0011-9?pageStart=9

Shenfield, A., Khan, Z., & Ahmadi, H. (2020). Deep Learning Meets Cognitive Radio: Predicting Future Steps. In IEEE 91st Vehicular Technology Conference: VTC2020-Spring, 25 May 2020 - 28 May 2020. IEEE: http://doi.org/10.1109/VTC2020-Spring48590.2020.9129042

Shenfield, A., & Rostami, S. (2017). Multi-objective evolution of artificial neural networks in multi-class medical diagnosis problems with class imbalance. IEEE International Conference on Computational Intelligence in Bioinformatics and Computational Biology. http://cibcb2017.org/

Shenfield, A., Rodrigues, M., Moreno-Cuesta, J., & Nooreldeen, H. (2017). A novel hybrid differential evolution strategy applied to classifier design for mortality prediction in adult critical care admissions. IEEE International Conference on Computational Intelligence in Bioinformatics and Computational Biology. http://cibcb2017.org/

Rostami, S., Shenfield, A., Sigurnjak, S., & Fakorede, O. (2015). Evaluation of mental workload and familiarity in human computer interaction with integrated development environments using single-channel EEG. Proceeding of PPIG 2015 - 26th Annual Workshop. http://www.ppig.org/library/paper/evaluation-mental-workload-and-familiarity-human-computer-interaction-integrated

Rostami, S., & Shenfield, A. (2012). CMA-PAES : Pareto archived evolution strategy using covariance matrix adaptation for Multi-Objective Optimisation. 12th UK Workshop on Computational Intelligence (UKCI), 2012, 1-8. http://doi.org/10.1109/UKCI.2012.6335782

Delves, P., Manning, W., & Shenfield, A. (2012). A torque vectoring approach to post incident control. Proceedings of AVEC 2012.

Shenfield, A., & Fleming, P. (2011). Multi-objective evolutionary design of robust controllers on the grid. Proceedings of the 18th IFAC World Congress, 2011, 18/1 (18/1), 14711-14716. http://doi.org/10.3182/20110828-6-IT-1002.01384

Shenfield, A., Fleming, P., Kadirkamanathan, V., & Allan, J. (2007). Optimisation of maintenance scheduling strategies on the grid. In IEEE Symposium Series on Computational Intelligence (SSCI) 2007, Honolulu, Hawaii, 1 April 2007 - 5 April 2007.

Shenfield, A., & Fleming, P. (2005). A service oriented architecture for decision making in engineering design. Advances in Grid Computing - EGC 2005 Lecture Notes in Computer Science, 3470 (3470). http://link.springer.com/chapter/10.1007%2F11508380_35

Theses / Dissertations

Wainwright, R. (2023). Supporting medical decision-making using machinelearning. (Doctoral thesis). Supervised by Shenfield, A. http://doi.org/10.7190/shu-thesis-00542

Musameh, M.F.K.H. (2020). Power management strategy for the electric recreational vehicle. (Doctoral thesis). Supervised by Shenfield, A. http://doi.org/10.7190/shu-thesis-00288

Posters

Moreno-Cuesta, J., Madrigal, M., Shenfield, A., & Rodrigues, M. (2016). A novel method for identification of patients at risk of deterioration using FACS. Presented at: ICSSOA-2016 Intensive Care Society State of Art Meeting, London, 2016