A comparison of native and cross-platform frameworks for mobile applications P Nawrocki, K Wrona, M Marczak, B Sniezynski Computer 54 (3), 18-27, 2021 | 44 | 2021 |
VM reservation plan adaptation using machine learning in cloud computing B Sniezynski, P Nawrocki, M Wilk, M Jarzab, K Zielinski Journal of Grid Computing 17, 797-812, 2019 | 34 | 2019 |
Adapting everyday manipulation skills to varied scenarios P Gajewski, P Ferreira, G Bartels, C Wang, F Guerin, B Indurkhya, ... 2019 International Conference on Robotics and Automation (ICRA), 1345-1351, 2019 | 29 | 2019 |
Adaptable mobile cloud computing environment with code transfer based on machine learning P Nawrocki, B Sniezynski, H Slojewski Pervasive and Mobile Computing 57, 49-63, 2019 | 28 | 2019 |
Adaptive service management in mobile cloud computing by means of supervised and reinforcement learning P Nawrocki, B Sniezynski Journal of Network and Systems Management 26 (1), 1-22, 2018 | 23 | 2018 |
The use of LPR (logic of plausible reasoning) to obtain information on innovative casting technologies S Kluska-Nawarecka, B Śnieżyński, W Parada, M Lustofin, ... Archives of Civil and Mechanical Engineering 14 (1), 25-31, 2014 | 22 | 2014 |
Learning agent for a service-oriented context-aware recommender system in heterogeneous environment P Nawrocki, B Sniezynski Computing and Informatics 35 (5), 1005-1026, 2016 | 19 | 2016 |
Neural network modelling studies of steam oxidised kinetic behaviour of advanced steels and Ni-based alloys at 800° C for 3000 h T Dudziak, P Gajewski, B Śnieżyński, V Deodeshmukh, M Witkowska, ... Corrosion Science 133, 94-111, 2018 | 18 | 2018 |
WATCH: Wasserstein change point detection for high-dimensional time series data K Faber, R Corizzo, B Sniezynski, M Baron, N Japkowicz 2021 IEEE International Conference on Big Data (Big Data), 4450-4459, 2021 | 17 | 2021 |
Adaptive resource planning for cloud-based services using machine learning P Nawrocki, M Grzywacz, B Sniezynski Journal of Parallel and Distributed Computing 152, 88-97, 2021 | 17 | 2021 |
Adaptive context-aware energy optimization for services on mobile devices with use of machine learning P Nawrocki, B Sniezynski Wireless Personal Communications 115 (3), 1839-1867, 2020 | 15 | 2020 |
Autonomous context-based service optimization in mobile cloud computing P Nawrocki, B Sniezynski Journal of Grid computing 15, 343-356, 2017 | 15 | 2017 |
VLAD: Task-agnostic VAE-based lifelong anomaly detection K Faber, R Corizzo, B Sniezynski, N Japkowicz Neural Networks 165, 248-273, 2023 | 14 | 2023 |
Combining rule induction and reinforcement learning: An agent-based vehicle routing B Śniezyński, W Wójcik, JD Gehrke, J Wojtusiak 2010 ninth international conference on machine learning and applications …, 2010 | 14 | 2010 |
Knowledge visualization using optimized general logic diagrams B Śnieżyński, R Szymacha, RS Michalski Intelligent Information Processing and Web Mining: Proceedings of the …, 2005 | 14 | 2005 |
Autoencoder-based ids for cloud and mobile devices K Faber, L Faber, B Sniezynski 2021 IEEE/ACM 21st International Symposium on Cluster, Cloud and Internet …, 2021 | 13 | 2021 |
Adaptive context-aware energy optimization for services on mobile devices with use of machine learning considering security aspects P Nawrocki, B Sniezynski, J Kolodziej, P Szynkiewicz 2020 20th IEEE/ACM International Symposium on Cluster, Cloud and Internet …, 2020 | 13 | 2020 |
A strategy learning model for autonomous agents based on classification B SNIEZYNSKI Int. J. Appl. Math. Comput. Sci 25 (3), 471-482, 2015 | 13 | 2015 |
Agent-based adaptation system for service-oriented architectures using supervised learning B Śnieżyński Procedia Computer Science 29, 1057-1067, 2014 | 13 | 2014 |
Agent strategy generation by rule induction B Sniezynski Computing and Informatics 32, 1055-1078, 2013 | 13* | 2013 |