| 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 | 56 | 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 | 37 | 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 | 30 | 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 | 29 | 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 | 25 | 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 | 24 | 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 | 23 | 2021 |
| 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 | 22 | 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 |
| VLAD: Task-agnostic VAE-based lifelong anomaly detection K Faber, R Corizzo, B Sniezynski, N Japkowicz Neural Networks 165, 248-273, 2023 | 21 | 2023 |
| 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 |
| 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 | 18 | 2020 |
| 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 | 16 | 2021 |
| Autonomous context-based service optimization in mobile cloud computing P Nawrocki, B Sniezynski Journal of Grid computing 15, 343-356, 2017 | 15 | 2017 |
| Active lifelong anomaly detection with experience replay K Faber, R Corizzo, B Sniezynski, N Japkowicz 2022 IEEE 9th international conference on data science and advanced …, 2022 | 14 | 2022 |
| Multi-Platform Intelligent System for Multimodal Human-Computer Interaction. M Jarosz, P Nawrocki, B Sniezynski, B Indurkhya Computing & Informatics 40 (1), 2021 | 14 | 2021 |
| Gaze aversion in conversational settings: An investigation based on mock job interview C Acarturk, B Indurkya, P Nawrocki, B Sniezynski, M Jarosz, KA Usal Journal of Eye Movement Research 14 (1), 2021 | 14 | 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 | 14 | 2020 |
| A strategy learning model for autonomous agents based on classification B SNIEZYNSKI Int. J. Appl. Math. Comput. Sci 25 (3), 471-482, 2015 | 14 | 2015 |
| 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 |
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