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The effects of measurement error and testing frequency on the fitness fatigue model applied to resistance training: a simulation approach. (2019)
Journal Article
HEMINGWAY, B.H.S., BURGESS, K.E., ELYAN, E. and SWINTON, P.A. [2019]. The effects of measurement error and testing frequency on the fitness fatigue model applied to resistance training: a simulation approach. International journal of sports science and coaching [online], (accepted).

This study investigated the effects of measurement error and testing frequency on prediction accuracy of the standard Fitness-Fatigue Model. A simulation-based approach was used to systematically assess measurement error and frequency inputs commonly... Read More

Data stream mining: methods and challenges for handling concept drift. (2019)
Journal Article
WARES, S., ISAACS, J. and ELYAN, E. [2019]. Data stream mining: methods and challenges for handling concept drift. Machine learning [online], (accepted). Available from: https://doi.org/10.1007/s42452-019-1433-0

Mining and analysing streaming data is crucial for many applications, and this area of research has gained extensive attention over the past decade. However, there are several inherent problems that continue to challenge the hardware and the state-of... Read More

Multiple fake classes GAN for data augmentation in face image dataset. (2019)
Conference Proceeding
ALI-GOMBE, A., ELYAN, E. and JAYNE, C. 2019. Multiple fake classes GAN for data augmentation in face image dataset. In Proceedings of the 2019 International joint conference on neural networks (IJCNN 2019), 14-19 July 2019, Budapest, Hungary. Piscataway: IEEE [online], article ID 8851953. Available from: https://doi.org/10.1109/IJCNN.2019.8851953

Class-imbalanced datasets often contain one or more class that are under-represented in a dataset. In such a situation, learning algorithms are often biased toward the majority class instances. Therefore, some modification to the learning algorithm o... Read More

Context-aware anomaly detector for monitoring cyber attacks on automotive CAN bus. (2019)
Conference Proceeding
KALUTARAGE, H.K., AL-KADRI, O., CHEAH, M. and MADZUDZO, G. 2019. Context-aware anomaly detector for monitoring cyber attacks on automotive CAN bus. In Proceedings of 2019 Computer science in cars symposium (CSCS 2019), 8 October 2019, Kaiserslautern, Germany. Available from: https://doi.org/10.1145/1122445.1122456

Automotive electronics is rapidly expanding. An average vehicle contains million lines of software codes, running on 100 of electronic control units (ECUs), in supporting number of safety, driver assistance and infotainment functions. These ECUs are... Read More

Neighbourhood-based undersampling approach for handling imbalanced and overlapped data. (2019)
Journal Article
VUTTIPITTAYAMONGKOL, P. and ELYAN, E. 2020. Neighbourhood-based undersampling approach for handling imbalanced and overlapped data. Information sciences [online], 509, pages 47-70. Available from: https://doi.org/10.1016/j.ins.2019.08.062

Class imbalanced datasets are common across different domains including health, security, banking and others. A typical supervised learning algorithm tends to be biased towards the majority class when dealing with imbalanced datasets. The learning ta... Read More

Developing a catalogue of explainability methods to support expert and non-expert users. (2019)
Conference Proceeding
MARTIN, K., LIRET, A., WIRATUNGA, N., OWUSU, G. and KERN, M. [2019]. Developing a catalogue of explainability methods to support expert and non-expert users. To be presented at the 39th British Computer Society's Specialist Group on Artificial Intelligence (SGAI) International annual conference (SGAI 2019), 17-19 December 2019, Cambridge, UK.

Organisations face growing legal requirements and ethical responsibilities to ensure that decisions made by their intelligent systems are explainable. However, provisioning of an explanation is often application dependent, causing an extended design... Read More

Learning to self-manage by intelligent monitoring, prediction and intervention. (2019)
Conference Proceeding
WIRATUNGA, N., CORSAR, D., MARTIN, K., WIJEKOON, A., ELYAN, E., COOPER, K., IBRAHIM, Z., CELIKTUTAN, O., DOBSON, R.J., MCKENNA, S., MORRIS, J., WALLER, A., ABD-ALHAMMED, R., QAHWAJI, R. and CHAUDHURI, R. 2019. Learning to self-manage by intelligent monitoring, prediction and intervention. In Wiratunga, N., Coenen, F. and Sani, S. (eds.) Proceedings of the 4th International workshop on knowledge discovery in healthcare data (KDH 2019), co-located with the 28th International joint conference on artificial intelligence (IJCAI-19), 10-11 August 2019, Macao, China. CEUR workshop proceedings, 2429. Aachen: CEUR-WS [online], pages 60-67. Available from: http://ceur-ws.org/Vol-2429/paper10.pdf

Despite the growing prevalence of multimorbidities, current digital self-management approaches still prioritise single conditions. The future of out-of-hospital care requires researchers to expand their horizons; integrated assistive technologies sho... Read More

Sensitivity analysis applied to fuzzy inference on the value of information in the oil and gas industry. (2019)
Journal Article
VILELA, M., OLUYEMI, G. and PETROVSKI, A. [2019]. Sensitivity analysis applied to fuzzy inference on the value of information in the oil and gas industry. International journal of applied decision sciences [online], (accepted).

Value of information is a widely accepted methodology for evaluating the need to acquire new data in the oil and gas industry. In the conventional approach to estimating the value of information, the outcomes of a project assessment relate to the dec... Read More

The use of machine learning algorithms for detecting advanced persistent threats. (2019)
Conference Proceeding
EKE, H.N., PETROVSKI, A. and AHRIZ, H. 2019. The use of machine learning algorithms for detecting advanced persistent threats. Presented at the 12th International on security of information and networks conference 2019 (SINCONF 2019), 12-15 September 2019, Sochi, Russia. New York: ACM [online], (accepted). Availalbe from: https://doi.org/10.1145/3357613.3357618

Advanced Persistent Threats (APTs) have been a major challenge in securing both Information Technology (IT) and Operational Technology (OT) systems. Due to their capability to navigates around defenses and to evade detection for a prolonged period of... Read More

Human activity recognition with deep metric learners. (2019)
Conference Proceeding
MARTIN, K., WIJEKOON, A. and WIRATUNGA, N. [2019]. Human activity recognition with deep metric learners. In Workshop proceedings of the 27th International conference on case-based reasoning (ICCBR 2019), 8-12 September 2019, Otzenhausen, Germany. CEUR workshop proceedings: CEUR-WS [online], (accepted).

Establishing a strong foundation for similarity-based return is a top priority in Case-Based Reasoning (CBR) systems. Deep Metric Learners (DMLs) are a group of neural network architectures which learn to optimise case representations for similarity-... Read More

Introducing the dynamic customer location-allocation problem. (2019)
Conference Proceeding
ANKRAH, R., LACROIX, B., MCCALL, J., HARDWICK, A. and CONWAY, A. 2019. Introducing the dynamic customer location-allocation problem. In Proceedings of the 2019 Institute of Electrical and Electronics Engineers (IEEE) Congress on evolutionary computation (IEEE CEC 2019), 10-13 June 2019, Wellington, NZ. Piscataway: IEEE [online], pages 3157-3164. Available from: https://doi.org/10.1109/CEC.2019.8790150.

In this paper, we introduce a new stochastic Location-Allocation Problem which assumes the movement of customers over time. We call this new problem Dynamic Customer Location-Allocation Problem (DC-LAP). The problem is based on the idea that customer... Read More

Comparative analysis of two asynchronous parallelization variants for a multi-objective coevolutionary solver. (2019)
Conference Proceeding
ZAVOIANU, A.-C., SAMINGER-PLATZ, S. and AMRHEIN, W. 2019. Comparative analysis of two asynchronous parallelization variants for a multi-objective coevolutionary solver. In Proceedings of the 2019 Institute of Electrical and Electronics Engineers (IEEE) congress on evolutionary computation (IEEE CEC 2019), 10-13 June 2019, Wellington, New Zealand. Piscataway: IEEE [online], article number 8790133, pages 3078-3085. Available from: https://doi.org/10.1109/CEC.2019.8790133

We describe and compare two steady state asynchronous parallelization variants for DECMO2++, a recently proposed multi-objective coevolutionary solver that generally displays a robust run-time convergence behavior. The two asynchronous variants were... Read More

An online variational inference and ensemble based multi-label classifier for data streams. (2019)
Conference Proceeding
NGUYEN, T.T.T., NGUYEN, T.T., LIEW, A.W.-C., WANG, S.-L., LIANG, T. and HU, Y. 2019. An online variational inference and ensemble based multi-label classifier for data streams. In Proceedings of 11th International conference on advanced computational intelligence (ICACI 2019), 7-9 June 2019, Guilin, China. Piscataway: IEEE [online], pages 302-307. Available from: https://doi.org/10.1109/icaci.2019.8778594

Recently, multi-label classification algorithms have been increasingly required by a diversity of applications, such as text categorization, web, and social media mining. In particular, these applications often have streams of data coming continuousl... Read More

MFC-GAN: class-imbalanced dataset classification using multiple fake class generative adversarial network. (2019)
Journal Article
ALI-GOMBE, A. and ELYAN, E. 2019. MFC-GAN: class-imbalanced dataset classification using multiple fake class generative adversarial network. Neurocomputing [online], 361, pages 212-221. Available from: https://doi.org/10.1016/j.neucom.2019.06.043

Class-imbalanced datasets are common across different domains such as health, banking, security and others. With such datasets, the learning algorithms are often biased toward the majority class-instances. Data Augmentation is a common approach tha... Read More

Challenges of delivering a graduate apprenticeship. (2019)
Presentation / Conference
YOUNG, T. and ZARB, M. 2019. Challenges of delivering a graduate apprenticeship. In Proceedings of the 24th Innovation and technology in computer science education annual conference (ITiCSE 2019), 15-17 July 2019, Aberdeen, UK. New York: ACM Press [online], page 327. Available from: https://doi.org/10.1145/3304221.3325566

Graduate Apprenticeship degree programmes look to overcome the segregation of learning and working, by integrating traditional education into the context of the work environment. This poster showcases a number of actions that were put into place to m... Read More

Incorporating on-campus days in a graduate apprenticeship. (2019)
Presentation / Conference
YOUNG, T. and ZARB, M. 2019. Incorporating on-campus days in a graduate apprenticeship. In Proceedings of the 24th Innovation and technology in computer science education annual conference (ITiCSE 2019), 15-17 July 2019, Aberdeen, UK. New York: ACM Press [online], page 328. Available from: https://doi.org/10.1145/3304221.3325567

Graduate Apprenticeships look to overcome the segregation of learning and working by integrating traditional education into the context of the work environment. This poster looks to highlight the importance of peer interactions, specifically for Onli... Read More

Integrating real-world clients in a project management module. (2019)
Presentation / Conference
ZARB, M., YOUNG, T. and BALLEW, W. 2019. Integrating real-world clients in a project management module. In Proceedings of the 24th Innovation and technology in computer science education annual conference 2019 (ITiCSE 2019), 15-17 July 2019, Aberdeen, UK. New York: ACM Press [online], pages 329. Available from: https://doi.org/10.1145/3304221.3325559

Graduates in CS are required to have a background in project management, but often this is taught as a theoretical module, with little "real-world" relevance. A Project Management module has been redeveloped around the idea of having a real client in... Read More

Simultaneous meta-data and meta-classifier selection in multiple classifier system. (2019)
Conference Proceeding
NGUYEN, T.T., LUONG, A.V., NGUYEN, T.M.V., HA, T.S., LIEW, A.W.-C. and MCCALL, J. 2019. Simultaneous meta-data and meta-classifier selection in multiple classifier system. In López-Ibáñez, M. (ed.) Proceedings of the 2019 Genetic and evolutionary computation conference (GECCO ’19), 13-17 July 2019, Prague, Czech Republic. New York: ACM [online], pages 39-46. Available from: https://doi.org/10.1145/3321707.3321770

In ensemble systems, the predictions of base classifiers are aggregated by a combining algorithm (meta-classifier) to achieve better classification accuracy than using a single classifier. Experiments show that the performance of ensembles significan... Read More

Limitations of benchmark sets and landscape features for algorithm selection and performance prediction. (2019)
Conference Proceeding
LACROIX, B. and MCCALL, J. 2019. Limitations of benchmark sets and landscape features for algorithm selection and performance prediction. In López-Ibáñe, M. (ed.) Proceedings of the 2019 Genetic and evolutionary computation conference (GECCO 2019) companion, 13-17 July 2019, Prague, Czech Republic. New York: Association for Computing Machinery [online], pages 261-262. Available from: https://doi.org/10.1145/3319619.3322051

Benchmark sets and landscape features are used to test algorithms and to train models to perform algorithm selection or configuration. These approaches are based on the assumption that algorithms have similar performances on problems with similar fea... Read More

On the definition of dynamic permutation problems under landscape rotation. (2019)
Conference Proceeding
ALZA, J., BARTLETT, M., CEBERIO, J. and MCCALL, J. 2019. On the definition of dynamic permutation problems under landscape rotation. In López-Ibáñez, M. (ed.) Proceedings of the 2019 Genetic and evolutionary computation conference companion (GECCO 2019), 13-17 July 2019, Prague, Czech Republic. New York: ACM [online], pages 1518-1526. Available from: https://doi.org/10.1145/3319619.3326840

Dynamic optimisation problems (DOPs) are optimisation problems that change over time. Typically, DOPs have been defined as a sequence of static problems, and the dynamism has been inserted into existing static problems using different techniques. In... Read More