| Distance and density clustering for time series data R Ma, R Angryk 2017 IEEE international conference on data mining workshops (ICDMW), 25-32, 2017 | 47 | 2017 |
| Machinery fault diagnosis based on deep learning for time series analysis and knowledge graphs H Liu, R Ma, D Li, L Yan, Z Ma journal of signal processing systems 93, 1433-1455, 2021 | 35 | 2021 |
| SPARQL queries on RDF with fuzzy constraints and preferences R Ma, X Jia, J Cheng, RA Angryk Journal of Intelligent & Fuzzy Systems 30 (1), 183-195, 2016 | 31 | 2016 |
| Solar flare prediction using multivariate time series decision trees R Ma, SF Boubrahimi, SM Hamdi, RA Angryk 2017 IEEE International Conference on Big Data (Big Data), 2569-2578, 2017 | 29 | 2017 |
| A time series classification-based approach for solar flare prediction SM Hamdi, D Kempton, R Ma, SF Boubrahimi, RA Angryk 2017 IEEE International Conference on Big Data (Big Data), 2543-2551, 2017 | 26 | 2017 |
| Two-stage prediction of machinery fault trend based on deep learning for time series analysis H Xu, R Ma, L Yan, Z Ma Digital Signal Processing 117, 103150, 2021 | 25 | 2021 |
| Coronal mass ejection data clustering and visualization of decision trees R Ma, RA Angryk, P Riley, SF Boubrahimi The Astrophysical Journal Supplement Series 236 (1), 14, 2018 | 24 | 2018 |
| Developing machine learning algorithm literacy with novel plugged and unplugged approaches R Ma, IT Sanusi, V Mahipal, JE Gonzales, FG Martin Proceedings of the 54th ACM Technical Symposium on Computer Science …, 2023 | 21 | 2023 |
| Spatiotemporal interpolation methods for solar event trajectories SF Boubrahimi, B Aydin, MA Schuh, D Kempton, RA Angryk, R Ma The Astrophysical Journal Supplement Series 236 (1), 23, 2018 | 16 | 2018 |
| RDF approximate queries based on semantic similarity L Yan, R Ma, D Li, J Cheng Computing 99, 481-491, 2017 | 15 | 2017 |
| Efficient processing of twig query with compound predicates in fuzzy XML J Liu, ZM Ma, R Ma Fuzzy sets and systems 229, 33-53, 2013 | 14 | 2013 |
| Segmentation of time series in improving dynamic time warping R Ma, A Ahmadzadeh, SF Boubrahimi, RA Angryk 2018 IEEE International Conference on Big Data (Big Data), 3756-3761, 2018 | 12 | 2018 |
| Scalable knn search approximation for time series data SF Boubrahimi, R Ma, B Aydin, SM Hamdi, R Angryk 2018 24th International Conference on Pattern Recognition (ICPR), 970-975, 2018 | 12 | 2018 |
| DoodleIt: A novel tool and approach for teaching how CNNs perform image recognition V Mahipal, S Ghosh, IT Sanusi, R Ma, JE Gonzales, FG Martin Proceedings of the 25th australasian computing education conference, 31-38, 2023 | 10 | 2023 |
| On the mining of the minimal set of time series data shapelets SF Boubrahimi, SM Hamdi, R Ma, R Angryk 2020 IEEE International Conference on Big Data (Big Data), 493-502, 2020 | 8 | 2020 |
| Machine learning in heliophysics and space weather forecasting: a white paper of findings and recommendations G Nita, M Georgoulis, I Kitiashvili, V Sadykov, E Camporeale, ... arXiv preprint arXiv:2006.12224, 2020 | 8 | 2020 |
| A data-driven analysis of interplanetary coronal mass ejecta and magnetic flux ropes R Ma, R Angryk, P Riley 2016 IEEE international conference on big data (Big Data), 3177-3186, 2016 | 8 | 2016 |
| Modeling and querying temporal RDF knowledge graphs with relational databases R Ma, X Han, L Yan, N Khan, Z Ma Journal of Intelligent Information Systems 61 (2), 569-609, 2023 | 6 | 2023 |
| TT-graph: A new model for building social network graphs from texts with time series W Jia, R Ma, L Yan, W Niu, Z Ma Expert Systems With Applications 192, 116405, 2022 | 6 | 2022 |
| A scalable segmented dynamic time warping for time series classification R Ma, A Ahmadzadeh, SF Boubrahimi, RA Angryk Artificial Intelligence and Soft Computing: 18th International Conference …, 2019 | 6 | 2019 |
Rafal AngrykDistinguished Professor of Computer Science, Georgia State UniversityVerified email at cs.montana.edu
