Videos of my machine learning course(s) are also available at: https://www.youtube.com/playlist?list=PLS8J_PRPtGfdnPf9QqT7Itnn2rAHsoWqY
See my Google Scholar publication list by clicking here
The following is an automatically retrieved incomplete list of publications. Please click here for a more complete list.
Journal publications registered in DiVA
Rebeen Ali Hamad, Alberto Salguero Hidalgo, Mohamed-Rafik Bouguelia, Macarena Espinilla Estevez, Javier Medina Quero (2020). Efficient Activity Recognition in Smart Homes Using Delayed Fuzzy Temporal Windows on Binary Sensors. IEEE journal of biomedical and health informatics. 24(2), pp. 387-395
Shiraz Farouq, Stefan Byttner, Mohamed-Rafik Bouguelia, Natasa Nord, Henrik Gadd (2020). Large-scale monitoring of operationally diverse district heating substations : A reference-group based approach. Engineering applications of artificial intelligence.
Mohamed-Rafik Bouguelia, Sławomir Nowaczyk, Amir H. Payberah (2018). An adaptive algorithm for anomaly and novelty detection in evolving data streams. Data mining and knowledge discovery. 32(6), pp. 1597-1633
Mohamed-Rafik Bouguelia, Sławomir Nowaczyk, K. C. Santosh, Antanas Verikas (2017). Agreeing to disagree : active learning with noisy labels without crowdsourcing. International Journal of Machine Learning and Cybernetics.
Mohamed-Rafik Bouguelia, Ramon Gonzalez, Karl Iagnemma, Stefan Byttner (2017). Unsupervised classification of slip events for planetary exploration rovers. Journal of terramechanics. 73C, pp. 95-106
Conference publications registered in DiVA
Anders Holst, Alexander Karlsson, Juhee Bae, Mohamed-Rafik Bouguelia (2019). Interactive clustering for exploring multiple data streams at different time scales and granularity. 1st Workshop on Interactive Data Mining, WIDM 2019, co-located with 12th ACM International Conference on Web Search and Data Mining, WSDM 2019, 15 February 2019.
Shiraz Farouq, Stefan Byttner, Mohamed-Rafik Bouguelia (2018). On monitoring heat-pumps with a group-based conformal anomaly detection approach. 2018 Internal Conference on Data Science (ICDATA’18), Las Vegas, NV, USA.