, The Concept of a Lawful Interference with Fundamental Rights, Understanding Human Rights Principles

S. Wu and K. L. Lo, Non-Intrusive Monitoring Algorithm for Resident Loads with Similar Electrical Characteristic, Processes, vol.8, issue.11, p.1385, 2020.

D. Wulsin, J. Blanco, R. Mani, and B. Litt, Semi-Supervised Anomaly Detection for EEG Waveforms Using Deep Belief Nets, 2010 Ninth International Conference on Machine Learning and Applications, 2010.

L. Nhien-an, M. Khac, and . Kechadi, Application of Data Mining for Anti-Money Laundering Detection: A Case Study, 2010 IEEE International Conference on Data Mining Workshops, 2010.

L. Ebberth, M. Paula, R. N. Ladeira, T. Carvalho, and . Marzagao, Deep Learning Anomaly Detection as Support Fraud Investigation in Brazilian Exports and Anti-Money Laundering, 15th IEEE International Conference on Machine Learning and Applications (ICMLA), 2016.

R. Liu, X. Qian, S. Mao, and S. Zhu, Research on anti-money laundering based on core decision tree algorithm, 2011 Chinese Control and Decision Conference (CCDC), 2011.

D. Van-puyvelde, S. Coulthart, and M. S. Hossain, Beyond the buzzword: big data and national security decision-making, International Affairs, vol.93, issue.6, pp.1397-1416, 2017.

M. Weber, J. Chen, T. Suzumura, A. Pareja, T. Ma et al., Scalable Graph Learning for Anti-Money Laundering: A First Look', 2018.