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2024 Journal Article Benchmarking the benchmark — Comparing synthetic and real-world Network IDS datasetsLayeghy, Siamak, Gallagher, Marcus and Portmann, Marius (2024). Benchmarking the benchmark — Comparing synthetic and real-world Network IDS datasets. Journal of Information Security and Applications, 80 103689, 1-18. doi: 10.1016/j.jisa.2023.103689 |
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2024 Journal Article Feature extraction for machine learning-based intrusion detection in IoT networksSarhan, Mohanad, Layeghy, Siamak, Moustafa, Nour, Gallagher, Marcus and Portmann, Marius (2024). Feature extraction for machine learning-based intrusion detection in IoT networks. Digital Communications and Networks, 10 (1), 205-216. doi: 10.1016/j.dcan.2022.08.012 |
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2023 Journal Article Guest editorial: special issue on evolutionary computation for gamesSchrum, Jacob, Liu, Jialin, Browne, Cameron, Ekárt, Anikó and Gallagher, Marcus (2023). Guest editorial: special issue on evolutionary computation for games. IEEE Transactions on Games, 15 (1), 1-4. doi: 10.1109/tg.2022.3225730 |
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2023 Journal Article From zero-shot machine learning to zero-day attack detectionSarhan, Mohanad, Layeghy, Siamak, Gallagher, Marcus and Portmann, Marius (2023). From zero-shot machine learning to zero-day attack detection. International Journal of Information Security, 22 (4), 947-959. doi: 10.1007/s10207-023-00676-0 |
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2023 Journal Article Opioid dispensing 2008–18: a Queensland perspectiveSuckling, Benita, Pattullo, Champika, Donovan, Peter, Gallagher, Marcus, Patanwala, Asad and Penm, Jonathan (2023). Opioid dispensing 2008–18: a Queensland perspective. Australian Health Review, 47 (2), 217-225. doi: 10.1071/ah22247 |
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2022 Journal Article An agile new research framework for hybrid human-AI teaming: trust, transparency, and transferabilityCaldwell, Sabrina, Sweetser, Penny, O'donnell, Nicholas, Knight, Matthew J., Aitchison, Matthew, Gedeon, Tom, Johnson, Daniel, Brereton, Margot, Gallagher, Marcus and Conroy, David (2022). An agile new research framework for hybrid human-AI teaming: trust, transparency, and transferability. ACM Transactions on Interactive Intelligent Systems, 12 (3) 17, 1-36. doi: 10.1145/3514257 |
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2021 Journal Article Using regression models for characterizing and comparing black box optimization problemsSaleem, Sobia and Gallagher, Marcus (2021). Using regression models for characterizing and comparing black box optimization problems. Swarm and Evolutionary Computation, 68 100981, 1-10. doi: 10.1016/j.swevo.2021.100981 |
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2020 Journal Article Considerations for selecting a machine learning technique for predicting deforestationMayfield, Helen J. , Smith, Carl , Gallagher, Marcus and Hockings, Marc (2020). Considerations for selecting a machine learning technique for predicting deforestation. Environmental Modelling and Software, 131 104741, 1-10. doi: 10.1016/j.envsoft.2020.104741 |
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2020 Journal Article Predicting alcohol dependence treatment outcomes: A prospective comparative study of clinical psychologists vs ‘trained’ machine learning modelsSymons, Martyn, Feeney, Gerald F. X., Gallagher, Marcus R., Young, Ross Mc D. and Connor, Jason P. (2020). Predicting alcohol dependence treatment outcomes: A prospective comparative study of clinical psychologists vs ‘trained’ machine learning models. Addiction, 115 (11) add.15038, 2164-2175. doi: 10.1111/add.15038 |
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2019 Journal Article Network analysis and visualisation of opioid prescribing dataHu, Xuelei, Gallagher, Marcus, Loveday, William, Dev, Abhilash and Connor, Jason P. (2019). Network analysis and visualisation of opioid prescribing data. IEEE Journal of Biomedical and Health Informatics, 24 (5) 8822723, 1-9. doi: 10.1109/jbhi.2019.2939028 |
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2019 Journal Article Machine learning vs addiction therapists: a pilot study predicting alcohol dependence treatment outcome from patient data in behavior therapy with adjunctive medicationSymons, Martyn, Feeney, Gerald F.X., Gallagher, Marcus R., Young, Ross McD. and Connor, Jason P. (2019). Machine learning vs addiction therapists: a pilot study predicting alcohol dependence treatment outcome from patient data in behavior therapy with adjunctive medication. Journal of Substance Abuse Treatment, 99, 156-162. doi: 10.1016/j.jsat.2019.01.020 |
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2019 Journal Article Quantitative measure of nonconvexity for black-box continuous functionsTamura, Kenichi and Gallagher, Marcus (2019). Quantitative measure of nonconvexity for black-box continuous functions. Information Sciences, 476, 64-82. doi: 10.1016/j.ins.2018.10.009 |
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2018 Journal Article Direct feature evaluation in black-box optimization using problem transformationsSaleem, Sobia, Gallagher, Marcus and Wood, Ian (2018). Direct feature evaluation in black-box optimization using problem transformations. Evolutionary Computation, 27 (1), 75-98. doi: 10.1162/evco_a_00247 |
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2017 Journal Article Parallel evolutionary algorithm for single and multi-objective optimisation: Differential evolution and constraints handlingPedroso, Dorival M., Bonyadi, Mohammad Reza and Gallagher, Marcus (2017). Parallel evolutionary algorithm for single and multi-objective optimisation: Differential evolution and constraints handling. Applied Soft Computing, 61, 995-1012. doi: 10.1016/j.asoc.2017.09.006 |
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2017 Journal Article Multiple community energy storage planning in distribution networks using a cost-benefit analysisSardi, Junainah, Mithulananthan, N., Gallagher, M. and Hung, Duong Quoc (2017). Multiple community energy storage planning in distribution networks using a cost-benefit analysis. Applied Energy, 190, 453-463. doi: 10.1016/j.apenergy.2016.12.144 |
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2017 Journal Article Use of freely available datasets and machine learning methods in predicting deforestationMayfield, Helen, Smith, Carl, Gallagher, Marcus and Hockings, Marc (2017). Use of freely available datasets and machine learning methods in predicting deforestation. Environmental Modelling and Software, 87, 17-28. doi: 10.1016/j.envsoft.2016.10.006 |
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2016 Journal Article The importance of implementation details and parameter settings in black-box optimization: a case study on Gaussian estimation-of-distribution algorithms and circles-in-a-square packing problemsBosman, Peter A. N. and Gallagher, Marcus (2016). The importance of implementation details and parameter settings in black-box optimization: a case study on Gaussian estimation-of-distribution algorithms and circles-in-a-square packing problems. Soft Computing, 22 (4), 1-15. doi: 10.1007/s00500-016-2408-3 |
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2016 Journal Article Towards improved benchmarking of black-box optimization algorithms using clustering problemsGallagher, Marcus (2016). Towards improved benchmarking of black-box optimization algorithms using clustering problems. Soft Computing, 20 (10), 1-15. doi: 10.1007/s00500-016-2094-1 |
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2015 Journal Article Analysing and characterising optimization problems using length scaleMorgan, Rachel and Gallagher, Marcus (2015). Analysing and characterising optimization problems using length scale. Soft Computing, 21 (7), 1735-1752. doi: 10.1007/s00500-015-1878-z |
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2014 Journal Article Detecting contaminated birthdates using generalized additive modelsLuo, Wei, Gallagher, Marcus, Loveday Bill, Ballantyne, Susan, Connor, Jason P. and Wiles, Janet (2014). Detecting contaminated birthdates using generalized additive models. BMC Bioinformatics, 15 (1) 185. doi: 10.1186/1471-2105-15-185 |
