| Using VADER sentiment and SVM for predicting customer response sentiment A Borg, M Boldt Expert Systems with Applications 162, 113746, 2020 | 254 | 2020 |
| Finding Influential Users in Social Media Using Association Rule Learning F Erlandsson, P Bródka, A Borg, H Johnson Entropy 18 (5), 164, 2016 | 130 | 2016 |
| Detecting serial residential burglaries using clustering A Borg, M Boldt, N Lavesson, U Melander, V Boeva Expert Systems with Applications 41 (11), 5252-5266, 2014 | 52 | 2014 |
| E-mail classification with machine learning and word embeddings for improved customer support A Borg, M Boldt, O Rosander, J Ahlstrand Neural Computing and Applications 33 (6), 1881-1902, 2021 | 33 | 2021 |
| Evaluating Temporal Analysis Methods Using Residential Burglary Data M Boldt, A Borg ISPRS International Journal of Geo-Information 5 (9), 148, 2016 | 30 | 2016 |
| Crime hotspots: An evaluation of the KDE spatial mapping technique E Johansson, C Gĺhlin, A Borg 2015 European Intelligence and Security Informatics Conference, 69-74, 2015 | 29 | 2015 |
| Seed selection for information cascade in multilayer networks F Erlandsson, P Bródka, A Borg Complex Networks & Their Applications VI: Proceedings of Complex Networks …, 2017 | 25 | 2017 |
| Comparison of clustering approaches for gene expression data A Borg, N Lavesson, V Boeva Twelfth Scandinavian Conference on Artificial Intelligence, 55-64, 2013 | 21 | 2013 |
| Clustering Residential Burglaries Using Modus Operandi and Spatiotemporal Information A Borg, M Boldt International Journal of Information Technology & Decision Making 15 (01), 23-42, 2016 | 20 | 2016 |
| Anomaly detection of event sequences using multiple temporal resolutions and Markov chains M Boldt, A Borg, S Ickin, J Gustafsson Knowledge and Information Systems 62 (2), 669-686, 2020 | 19 | 2020 |
| Predicting user participation in social media F Erlandsson, A Borg, H Johnson, P Bródka Advances in Network Science: 12th International Conference and School …, 2016 | 17 | 2016 |
| Alarm prediction in cellular base stations using data-driven methods M Boldt, S Ickin, A Borg, V Kulyk, J Gustafsson IEEE Transactions on Network and Service Management 18 (2), 1925-1933, 2021 | 16 | 2021 |
| Recent Developments in Low-Power AI Accelerators: A Survey C Ĺleskog, H Grahn, A Borg Algorithms 15 (11), 419, 2022 | 15 | 2022 |
| Predicting burglars’ risk exposure and level of pre-crime preparation using crime scene data M Boldt, A Borg, M Svensson, J Hildeby Intelligent Data Analysis 22 (1), 167-190, 2018 | 13 | 2018 |
| E-mail Classification using Social Network Information A Borg, N Lavesson Availability, Reliability and Security (ARES), 2012 Seventh International …, 2012 | 9 | 2012 |
| A Comparison of Unsupervised Learning Algorithms for Intrusion Detection in IEC 104 SCADA Protocol M Anwar, A Borg, L Lundberg 2021 International Conference on Machine Learning and Cybernetics (ICMLC), 1-8, 2021 | 7 | 2021 |
| Bibliometric Mining of Research Trends in Machine Learning L Lundberg, M Boldt, A Borg, H Grahn AI 5 (1), 208-236, 2024 | 6 | 2024 |
| An Interdisciplinary Web-based Framework for Data-driven Placement Analysis of CCTV Cameras K Lewenhagen, M Boldt, A Borg, M Gerell, J Dahlén 2021 Swedish Workshop on Data Science (SweDS), 1-6, 2021 | 5 | 2021 |
| A Statistical Method for Detecting Significant Temporal Hotspots using LISA Statistics M Boldt, A Borg 2017 European Intelligence and Security Informatics Conference (EISIC), 123-126, 2017 | 5 | 2017 |
| On the simulation of a software reputation system M Boldt, A Borg, B Carlsson 2010 International Conference on Availability, Reliability and Security, 333-340, 2010 | 5 | 2010 |
Martin BoldtAssociate professor, Computer science, Blekinge Institute of TechnologyVerified email at bth.se
