This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
UNODC, Estimating illicit financial flows resulting from drug trafficking and other transnational organized crimes. 2011, United Nations Office on Drugs and Crime: www.unodc.org.UNODC2011United Nations Office on Drugs and Crime: www.unodc.org.Search in Google Scholar
FATF, International Standards On Combating Money Laundering And The Financing Of Terrorism & Proliferation - FATF Recommendations, FATF, Editor. 2023, Financial Action Task Force: https://www.fatf-gafi.org/en/publications/Fatfrecommendations/Fatfrecommendations.html.FATFFATFEditor2023Financial Action Task Force: https://www.fatf-gafi.org/en/publications/Fatfrecommendations/Fatfrecommendations.html.Search in Google Scholar
BSA, The Bank Secrecy Act of 1970, in 12 U.S.C. 1829b, 12 U.S.C. 1951–1960, 31 U.S.C. 5311–5314, 5316–5336, G.o.U.S.o. America, Editor. 1970, U.S. Government Printing Office: https://www.fincen.gov/resources/statutes-and-regulations/bank-secrecy-act.BSAThe Bank Secrecy Act of 1970inG.o.U.S.o. AmericaEditor.1970U.S. Government Printing Office: https://www.fincen.gov/resources/statutes-and-regulations/bank-secrecy-act.Search in Google Scholar
AML/CTF, Anti-Money Laundering and Counter-Terrorism Financing Act 2006, in C2023C00383 (C56), O.o.P.C. Canberra, Editor. 2006, Government of Australia: https://www.legislation.gov.au/C2006A00169/latest/text.AML/CTFAnti-Money Laundering and Counter-Terrorism Financing Act 2006inO.o.P.C. CanberraEditor.2006Government of Australia: https://www.legislation.gov.au/C2006A00169/latest/text.Search in Google Scholar
PMLA, Prevention of Money Laundering Act, 2002, in Act No. 15, M.o.F. Department of Revenue, Government of India, Editor. 2002, https://enforcementdirectorate.gov.in/pmla.PMLAPrevention of Money Laundering Act, 2002inM.o.F. Department of Revenue, Government of IndiaEditor.2002https://enforcementdirectorate.gov.in/pmla.Search in Google Scholar
FinCEN. Financial Crimes Enforcement Network. 1990 7 March 2024]; An official website of the United States Government - Financial Crimes Enforcement Network]. Available from: https://www.fincen.gov/.FinCEN19907 March 2024]; An official website of the United States Government - Financial Crimes Enforcement Network]. Available from: https://www.fincen.gov/.Search in Google Scholar
AUSTRAC. Australian Transaction Reports and Analysis Centre. 1989 7 March 2024]; Available from: https://www.austrac.gov.au/.AUSTRAC19897 March 2024]; Available from: https://www.austrac.gov.au/.Search in Google Scholar
FIU-India. Financial Intelligence Unit - India. 2004 10 March 2024]; Available from: https://fiuindia.gov.in/index.html.FIU-India200410 March 2024]; Available from: https://fiuindia.gov.in/index.html.Search in Google Scholar
OPA, Binance and CEO Plead Guilty to Federal Charges in $4B Resolution, U.S.D.o.J. Office of Public Affairs, Editor. 2023: https://www.justice.gov/opa/pr/binance-and-ceo-plead-guilty-federal-charges-4b-resolution.OPAU.S.D.o.J. Office of Public AffairsEditor.2023https://www.justice.gov/opa/pr/binance-and-ceo-plead-guilty-federal-charges-4b-resolution.Search in Google Scholar
Justice, U.S.D.o., Danske Bank Pleads Guilty to Fraud on U.S. Banks in Multi-Billion Dollar Scheme to Access the U.S. Financial System, O.o.P.A. Department of Justice, Editor. 2022, United State’s Official Government Website: https://www.justice.gov/opa/pr/danske-bank-pleads-guilty-fraud-us-banks-multi-billion-dollar-scheme-access-us-financial.Justice, U.S.D.o.O.o.P.A. Department of JusticeEditor.2022United State’s Official Government Website: https://www.justice.gov/opa/pr/danske-bank-pleads-guilty-fraud-us-banks-multi-billion-dollar-scheme-access-us-financial.Search in Google Scholar
AUSTRAC, AUSTRAC and Westpac agree to proposed $1.3bn penalty. 2020, Australian Transaction Reports and Analysis Centre Australia: https://www.austrac.gov.au/news-and-media/media-release/austrac-and-westpac-agree-penalty.AUSTRAC2020Australian Transaction Reports and Analysis Centre Australia: https://www.austrac.gov.au/news-and-media/media-release/austrac-and-westpac-agree-penalty.Search in Google Scholar
Monroe, B. Fincrime Briefing: AML fines in 2019 breach $8 billion, Treasury official pleads guilty to leaking, 2020 crypto compliance outlook, and more. 2020 7 March 2024]; Available from: https://www.acfcs.org/fincrime-briefing-aml-fines-in-2019-breach-8-billion-treasury-official-pleads-guilty-to-leaking-2020-crypto-compliance-outlook-and-more/#:~:text=Key%20observations%3A,25%20penalties%20totaling%20%242.29bn.MonroeB.20207 March 2024]; Available from: https://www.acfcs.org/fincrime-briefing-aml-fines-in-2019-breach-8-billion-treasury-official-pleads-guilty-to-leaking-2020-crypto-compliance-outlook-and-more/#:~:text=Key%20observations%3A,25%20penalties%20totaling%20%242.29bn.Search in Google Scholar
Refinitiv, Revealing the true cost of financial crime. 2018: https://www.refinitiv.com/.Refinitiv2018https://www.refinitiv.com/.Search in Google Scholar
McKinsey. Risk Transforming approaches to AML and financial crime. 2019 7 March 2024]; Available from: https://www.mckinsey.com/~/media/McKinsey/Business%20Functions/Risk/Our%20Insights/Transforming%20approaches%20to%20AML%20and%20financial%20crime/Transforming-approaches-to-AML-and-financial%20crime-vF.pdf.McKinsey20197 March 2024]; Available from: https://www.mckinsey.com/~/media/McKinsey/Business%20Functions/Risk/Our%20Insights/Transforming%20approaches%20to%20AML%20and%20financial%20crime/Transforming-approaches-to-AML-and-financial%20crime-vF.pdf.Search in Google Scholar
Al-Shabandar, R., et al., The Application of Artificial Intelligence in Financial Compliance Management, in ACM International Conference Proceeding Series. 2019, ACM. p. 1–6.Al-ShabandarR.inACM International Conference Proceeding Series2019ACM16Search in Google Scholar
PricewaterhouseCoopers. Explainable AI Driving business value through greater understanding. 2017 7 March 2024]; Available from: https://www.pwc.co.uk/services/risk-assurance/insights/explainable-ai.html.PricewaterhouseCoopers20177 March 2024]; Available from: https://www.pwc.co.uk/services/risk-assurance/insights/explainable-ai.html.Search in Google Scholar
PricewaterhouseCoopers, 22nd Annual Global CEO Survey. 2020: www.pwc.com.PricewaterhouseCoopers2020www.pwc.com.Search in Google Scholar
EU, General Data Protection Regulation (GDPR). 2016, Official Journal of the European Union.EU2016Official Journal of the European UnionSearch in Google Scholar
State of California, U., TITLE 1.81.5. California Consumer Privacy Act of 2018. 2018.State of California, U.2018Search in Google Scholar
Kute, D.V., et al., Deep Learning and Explainable Artificial Intelligence Techniques Applied for Detecting Money Laundering-A Critical Review. IEEE Access, 2021. 9: p. 82300–82317.KuteD.V.Deep Learning and Explainable Artificial Intelligence Techniques Applied for Detecting Money Laundering-A Critical Review202198230082317Search in Google Scholar
Kute, D.V., Explainable Deep Learning Approach for Detecting Money Laundering Transactions in Banking System, in Faculty of Engineering and Information Technology. 2022, University of Technology Sydney (UTS), Australia: OPUS. p. 166.KuteD.V.2022University of Technology Sydney (UTS)AustraliaOPUS166Search in Google Scholar
Cutler, A., D.R. Cutler, and J.R. Stevens, Random forests, in Ensemble machine learning. 2012, Springer. p. 157–175.CutlerA.CutlerD.R.StevensJ.R.Random forestsin2012Springer157175Search in Google Scholar
Chen, T., et al., Xgboost: extreme gradient boosting. R package version 0.4-2, 2015. 1(4): p. 1–4.ChenT.Xgboost: extreme gradient boosting20151414Search in Google Scholar
Suthaharan, S., Support vector machine, in Machine learning models and algorithms for big data classification. 2016, Springer. p. 207–235.SuthaharanS.2016Springer207235Search in Google Scholar
Chen, Z., et al., Machine learning techniques for anti-money laundering (AML) solutions in suspicious transaction detection: a review. Knowledge and Information Systems, 2018. 57(2): p. 245–285.ChenZ.Machine learning techniques for anti-money laundering (AML) solutions in suspicious transaction detection: a review2018572245285Search in Google Scholar
Mark Weber, J.C., Toyotaro Suzumura, Aldo Pareja, Tengfei Ma, Hiroki Kanezashi, Tim Kaler, Charles E. Leiserson, Tao B. Schardl, Scalable Graph Learning for Anti-Money Laundering: A First Look. 2018.Mark WeberJ.C.SuzumuraToyotaroParejaAldoMaTengfeiKanezashiHirokiKalerTimLeisersonCharles E.SchardlTao B.2018Search in Google Scholar
Alarab, I., S. Prakoonwit, and M.I. Nacer. Competence of graph convolutional networks for anti-money laundering in bitcoin blockchain. In 5th International Conference on Machine Learning Technologies, ICMLT 2020. 2020. Association for Computing Machinery.AlarabI.PrakoonwitS.NacerM.I.In5th International Conference on Machine Learning Technologies, ICMLT 20202020Association for Computing MachinerySearch in Google Scholar
Han, J., et al. NextGen AML: Distributed deep learning based language technologies to augment anti money laundering investigation. In 56th Annual Meeting of the Association for Computational Linguistics, ACL 2018. 2015. Association for Computational Linguistics (ACL).HanJ.In56th Annual Meeting of the Association for Computational Linguistics, ACL 20182015Association for Computational Linguistics (ACL)Search in Google Scholar
Paula, E.L., et al. Deep learning anomaly detection as suppor fraud investigation in Brazilian exports and anti-money laundering. In 15th IEEE International Conference on Machine Learning and Applications, ICMLA 2016. 2017. Institute of Electrical and Electronics Engineers Inc.PaulaE.L.In15th IEEE International Conference on Machine Learning and Applications, ICMLA 20162017Institute of Electrical and Electronics Engineers Inc.Search in Google Scholar
Wei, T., et al. A Dynamic Graph Convolutional Network for Anti-money Laundering. In International Conference on Intelligent Computing. 2023. Springer.WeiT.InInternational Conference on Intelligent Computing2023SpringerSearch in Google Scholar
Jensen, R.I.T. and A. Iosifidis, Qualifying and raising anti-money laundering alarms with deep learning. Expert Systems with Applications, 2023. 214: p. 119037.JensenR.I.T.IosifidisA.Qualifying and raising anti-money laundering alarms with deep learning2023214119037Search in Google Scholar
Tatulli, M.P., et al. HAMLET: A Transformer Based Approach for Money Laundering Detection. In International Symposium on Cyber Security, Cryptology, and Machine Learning. 2023. Springer.TatulliM.P.InInternational Symposium on Cyber Security, Cryptology, and Machine Learning2023SpringerSearch in Google Scholar
Silva, Í.D.G., L.H.A. Correia, and E.G. Maziero. Graph Neural Networks Applied to Money Laundering Detection in Intelligent Information Systems. In Proceedings of the XIX Brazilian Symposium on Information Systems. 2023.SilvaÍ.D.G.CorreiaL.H.A.MazieroE.G.InProceedings of the XIX Brazilian Symposium on Information Systems2023Search in Google Scholar
Cheng, D., et al., Anti-Money Laundering by Group-Aware Deep Graph Learning. IEEE Transactions on Knowledge and Data Engineering, 2023.ChengD.IEEE Transactions on Knowledge and Data Engineering2023Search in Google Scholar
Song, J. and Y. Gu, HBTBD: A Heterogeneous Bitcoin Transaction Behavior Dataset for Anti-Money Laundering. Applied Sciences, 2023. 13(15): p. 8766.SongJ.GuY.HBTBD: A Heterogeneous Bitcoin Transaction Behavior Dataset for Anti-Money Laundering202313158766Search in Google Scholar
Li, Z., et al., Transactional Network Analysis and Money Laundering Behavior Identification of Central Bank Digital Currency of China. Journal of Social Computing, 2022. 3(3): p. 219–230.LiZ.Transactional Network Analysis and Money Laundering Behavior Identification of Central Bank Digital Currency of China202233219230Search in Google Scholar
Liu, X., X. Wang, and S. Matwin. Interpretable Deep Convolutional Neural Networks via Meta-learning. In 2018 International Joint Conference on Neural Networks, IJCNN 2018. 2018. Institute of Electrical and Electronics Engineers Inc.LiuX.WangX.MatwinS.In2018 International Joint Conference on Neural Networks, IJCNN 20182018Institute of Electrical and Electronics Engineers Inc.Search in Google Scholar
Statistics, A.B.o., Statistics - Australian People and Economy. 2021, Australian Bureau of Statistics Australian government website.Statistics, A.B.o.2021Australian Bureau of Statistics Australian government websiteSearch in Google Scholar
LeCun, Y., Y. Bengio, and G. Hinton, Deep learning. nature, 2015. 521(7553): p. 436–444.LeCunY.BengioY.HintonG.Deep learning20155217553436444Search in Google Scholar
Sabokrou, M., et al., Deep-HR: Fast heart rate estimation from face video under realistic conditions. Expert Systems with Applications, 2021. 186.SabokrouM.Deep-HR: Fast heart rate estimation from face video under realistic conditions2021186Search in Google Scholar
Palraj, K. and V. Kalaivani, Predicting the abnormality of brain and compute the cognitive power of human using deep learning techniques using functional magnetic resonance images. Soft Computing, 2021. 25(23): p. 14461–14478.PalrajK.KalaivaniV.Predicting the abnormality of brain and compute the cognitive power of human using deep learning techniques using functional magnetic resonance images202125231446114478Search in Google Scholar
Li, D., et al., BLSTM and CNN Stacking Architecture for Speech Emotion Recognition. Neural Processing Letters, 2021. 53(6): p. 4097–4115.LiD.BLSTM and CNN Stacking Architecture for Speech Emotion Recognition202153640974115Search in Google Scholar
Roshanzamir, A., H. Aghajan, and M. Soleymani Baghshah, Transformer-based deep neural network language models for Alzheimer’s disease risk assessment from targeted speech. BMC Medical Informatics and Decision Making, 2021. 21(1).RoshanzamirA.AghajanH.Soleymani BaghshahM.Transformer-based deep neural network language models for Alzheimer’s disease risk assessment from targeted speech2021211Search in Google Scholar
Miyake, S. and K. Fukushima, A neural network model for the mechanism of feature-extraction - A self-organizing network with feedback inhibition. Biological cybernetics, 1984. 50(5): p. 377–384.MiyakeS.FukushimaK.A neural network model for the mechanism of feature-extraction - A self-organizing network with feedback inhibition1984505377384Search in Google Scholar
Chollet, F., Keras: The python deep learning library. Astrophysics source code library, 2018: p. ascl: 1806.022.CholletF.Astrophysics source code library2018p. ascl: 1806.022.Search in Google Scholar
Abadi, M., et al. {TensorFlow}: A System for {Large-Scale} Machine Learning. In 12th USENIX symposium on operating systems design and implementation (OSDI 16). 2016.AbadiM.In12th USENIX symposium on operating systems design and implementation (OSDI 16)2016Search in Google Scholar
Schmidhuber, J., Deep Learning in neural networks: An overview. Neural Networks, 2015. 61: p. 85–117.SchmidhuberJ.Deep Learning in neural networks: An overview20156185117Search in Google Scholar
Ioffe, S. and C. Szegedy. Batch normalization: Accelerating deep network training by reducing internal covariate shift. In 32nd International Conference on Machine Learning, ICML 2015. 2015. International Machine Learning Society (IMLS).IoffeS.SzegedyC.In32nd International Conference on Machine Learning, ICML 20152015International Machine Learning Society (IMLS)Search in Google Scholar
Srivastava, N., et al., Dropout: A simple way to prevent neural networks from overfitting. Journal of Machine Learning Research, 2014. 15: p. 1929–1958.SrivastavaN.Dropout: A simple way to prevent neural networks from overfitting20141519291958Search in Google Scholar
Huang, G., et al. Densely connected convolutional networks. In 30th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2017. 2017. Institute of Electrical and Electronics Engineers Inc.HuangG.In30th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 20172017Institute of Electrical and Electronics Engineers Inc.Search in Google Scholar
Nair, V. and G.E. Hinton. Rectified linear units improve Restricted Boltzmann machines. In 27th International Conference on Machine Learning, ICML 2010. 2010. Haifa.NairV.HintonG.E.In27th International Conference on Machine Learning, ICML 20102010HaifaSearch in Google Scholar
Han, J. and C. Moraga. The influence of the sigmoid function parameters on the speed of backpropagation learning. In International workshop on artificial neural networks. 1995. Springer.HanJ.MoragaC.InInternational workshop on artificial neural networks1995SpringerSearch in Google Scholar
Tjoa, E. and C. Guan, A Survey on Explainable Artificial Intelligence (XAI): Toward Medical XAI. IEEE Transactions on Neural Networks and Learning Systems, 2021. 32(11): p. 4793–4813.TjoaE.GuanC.A Survey on Explainable Artificial Intelligence (XAI): Toward Medical XAI2021321147934813Search in Google Scholar
Huynh, T.D., et al., Addressing Regulatory Requirements on Explanations for Automated Decisions with Provenance—A Case Study. Digital government (New York, N.Y. Online), 2021. 2(2): p. 1–14.HuynhT.D.Addressing Regulatory Requirements on Explanations for Automated Decisions with Provenance—A Case Study202122114Search in Google Scholar
Hall, P., N. Gill, and N. Schmidt, Proposed Guidelines for the Responsible Use of Explainable Machine Learning. 2019.HallP.GillN.SchmidtN.2019Search in Google Scholar
Hepenstal, S., et al., Developing Conversational Agents for Use in Criminal Investigations. ACM transactions on interactive intelligent systems, 2021. 11(3–4): p. 1–35.HepenstalS.Developing Conversational Agents for Use in Criminal Investigations2021113–4135Search in Google Scholar
Kuiper, O., et al., Exploring Explainable AI in the Financial Sector: Perspectives of Banks and Supervisory Authorities. 2021.KuiperO.2021Search in Google Scholar
Lundberg, S.M. and S.I. Lee. A unified approach to interpreting model predictions. 2017. Neural information processing systems foundation.LundbergS.M.LeeS.I.2017Neural information processing systems foundationSearch in Google Scholar
Ribeiro, M.T., S. Singh, and C. Guestrin. “Why should i trust you?” Explaining the predictions of any classifier. 2016. Association for Computing Machinery.RibeiroM.T.SinghS.GuestrinC.2016Association for Computing MachinerySearch in Google Scholar
Rudin, C., Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead. Nature Machine Intelligence, 2019. 1(5): p. 206–215.RudinC.Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead201915206215Search in Google Scholar
Shapley, L.S., A Value for N-person Games. Defense Technical Information Center.ShapleyL.S.Defense Technical Information CenterSearch in Google Scholar
Molnar, C., Interpretable machine learning. A Guide for Making Black Box Models Explainable. 2019, https://christophm.github.io/interpretable-ml-book/.MolnarC.2019https://christophm.github.io/interpretable-ml-book/.Search in Google Scholar
Shrikumar, A., P. Greenside, and A. Kundaje. Learning important features through propagating activation differences. In 34th International Conference on Machine Learning, ICML 2017. 2017. International Machine Learning Society (IMLS).ShrikumarA.GreensideP.KundajeA.In34th International Conference on Machine Learning, ICML 20172017International Machine Learning Society (IMLS)Search in Google Scholar
Lundberg, S., SHAP API Library. 2018: https://shap.readthedocs.io.LundbergS.2018https://shap.readthedocs.io.Search in Google Scholar
Sammut, C. and G.I. Webb, True Positive, in Encyclopedia of Machine Learning, C. Sammut and G.I. Webb, Editors. 2010, Springer US: Boston, MA. p. 999–999.SammutC.WebbG.I.True PositiveinSammutC.WebbG.I.Editors.2010Springer USBoston, MA999999Search in Google Scholar
Sammut, C. and G.I. Webb, True Negative, in Encyclopedia of Machine Learning, C. Sammut and G.I. Webb, Editors. 2010, Springer US: Boston, MA. p. 999–999.SammutC.WebbG.I.True NegativeinSammutC.WebbG.I.Editors.2010Springer USBoston, MA999999Search in Google Scholar
Sammut, C. and G.I. Webb, False Positive, in Encyclopedia of Machine Learning, C. Sammut and G.I. Webb, Editors. 2010, Springer US: Boston, MA. p. 397–397.SammutC.WebbG.I.False PositiveinSammutC.WebbG.I.Editors.2010Springer USBoston, MA397397Search in Google Scholar
Sammut, C. and G.I. Webb, False Negative, in Encyclopedia of Machine Learning, C. Sammut and G.I. Webb, Editors. 2010, Springer US: Boston, MA. p. 397–397.SammutC.WebbG.I.False NegativeinSammutC.WebbG.I.Editors.2010Springer USBoston, MA397397Search in Google Scholar
Sammut, C. and G.I. Webb, Accuracy, in Encyclopedia of Machine Learning, C. Sammut and G.I. Webb, Editors. 2010, Springer US: Boston, MA. p. 9–10.SammutC.WebbG.I.AccuracyinSammutC.WebbG.I.Editors.2010Springer USBoston, MA910Search in Google Scholar
Ting, K.M., Precision, in Encyclopedia of Machine Learning, C. Sammut and G.I. Webb, Editors. 2010, Springer US: Boston, MA. p. 780–780.TingK.M.PrecisioninSammutC.WebbG.I.Editors.2010Springer USBoston, MA780780Search in Google Scholar
Ting, K.M., Precision and Recall, in Encyclopedia of Machine Learning, C. Sammut and G.I. Webb, Editors. 2010, Springer US: Boston, MA. p. 781–781.TingK.M.Precision and RecallinSammutC.WebbG.I.Editors.2010Springer USBoston, MA781781Search in Google Scholar
Sammut, C. and G.I. Webb, Recall, in Encyclopedia of Machine Learning, C. Sammut and G.I. Webb, Editors. 2010, Springer US: Boston, MA. p. 829–829.SammutC.WebbG.I.RecallinSammutC.WebbG.I.Editors.2010Springer USBoston, MA829829Search in Google Scholar
Sammut, C. and G.I. Webb, F1-Measure, in Encyclopedia of Machine Learning, C. Sammut and G.I. Webb, Editors. 2010, Springer US: Boston, MA. p. 397–397.SammutC.WebbG.I.F1-MeasureinSammutC.WebbG.I.Editors.2010Springer USBoston, MA397397Search in Google Scholar
Sammut, C. and G.I. Webb, Area Under Curve, in Encyclopedia of Machine Learning, C. Sammut and G.I. Webb, Editors. 2010, Springer US: Boston, MA. p. 40–40.SammutC.WebbG.I.Area Under CurveinSammutC.WebbG.I.Editors.2010Springer USBoston, MA4040Search in Google Scholar
Flach, P.A., ROC Analysis, in Encyclopedia of Machine Learning, C. Sammut and G.I. Webb, Editors. 2010, Springer US: Boston, MA. p. 869–875.FlachP.A.ROC AnalysisinSammutC.WebbG.I.Editors.2010Springer USBoston, MA869875Search in Google Scholar
Kingma, D.P. and J. Ba, Adam: A Method for Stochastic Optimization. 2014.KingmaD.P.BaJ.2014Search in Google Scholar
Al-Shabandar, R., et al. The application of artificial intelligence in financial compliance management. In 2019 International Conference on Artificial Intelligence and Advanced Manufacturing, AIAM 2019. 2019. Association for Computing Machinery.Al-ShabandarR.In2019 International Conference on Artificial Intelligence and Advanced Manufacturing, AIAM 20192019Association for Computing MachinerySearch in Google Scholar
Abualigah, L., et al., Aquila Optimizer: A novel meta-heuristic optimization algorithm. Computers & Industrial Engineering, 2021. 157: p. 107250.AbualigahL.Aquila Optimizer: A novel meta-heuristic optimization algorithm2021157107250Search in Google Scholar
Vaswani, A., et al. Attention is All you Need. In Neural Information Processing Systems. 2017.VaswaniA.Attention is All you Need2017Search in Google Scholar