| Real-time maritime traffic anomaly detection based on sensors and history data embedding J Venskus, P Treigys, J Bernatavičienė, G Tamulevičius, V Medvedev Sensors 19 (17), 3782, 2019 | 31 | 2019 |
| Unsupervised marine vessel trajectory prediction using LSTM network and wild bootstrapping techniques J Venskus, P Treigys, J Markevičiūtė Nonlinear analysis: modelling and control 26 (4), 718-737, 2021 | 27 | 2021 |
| Integration of a self-organizing map and a virtual pheromone for real-time abnormal movement detection in marine traffic J Venskus, P Treigys, J Bernatavičienė, V Medvedev, M Voznak, ... Informatica 28 (2), 359-374, 2017 | 21 | 2017 |
| Attention-based and time series models for short-term forecasting of COVID-19 spread J Markevičiūtė, J Bernatavičienė, R Levulienė, V Medvedev, P Treigys, ... CMC-Computers, materials & continua 70 (1), 695-714, 2022 | 9 | 2022 |
| Self-learning adaptive algorithm for maritime traffic abnormal movement detection based on virtual pheromone method J Venskus, M Kurmis, A Andziulis, Ž Lukošius, M Voznak, D Bykovas 2015 International Symposium on Performance Evaluation of Computer and …, 2015 | 8 | 2015 |
| Investigation of recurrent neural network architectures for prediction of vessel trajectory R Jurkus, P Treigys, J Venskus Information and Software Technologies: 27th International Conference, ICIST …, 2021 | 5 | 2021 |
| Application of coordinate systems for vessel trajectory prediction improvement using a recurrent neural networks R Jurkus, J Venskus, P Treigys Engineering Applications of Artificial Intelligence 123, 106448, 2023 | 4 | 2023 |
| Impact of covid-19-related lockdown measures on economic and social outcomes in lithuania J Markevičiūtė, J Bernatavičienė, R Levulienė, V Medvedev, P Treigys, ... Mathematics 10 (15), 2734, 2022 | 2 | 2022 |
| Preparation of training data by filling in missing vessel type data using deep multi-stacked LSTM neural network for abnormal marine transport evaluation J Venskus, P Treigys ITISE 2019. Proceedings of papers. Vol 2, 2019 | 2 | 2019 |
| Detecting maritime traffic anomalies with long-short term memory recurrent neural network J Venskus, P Treigys, J Bernatavičienė, J Markevičiūtė 11th international workshop on data analysis methods for software systems …, 2019 | 2 | 2019 |
| Meteorological data influence on missing vessel type detection using deep multi-stacked LSTM neural network J Venskus, P Treigys Minsk: BSU, 2019 | 2 | 2019 |
| Usage in maritime traffic awareness evaluation using LSTM deep neural networks J Venskus, R Jurkus, P Treigys DAMSS: 14th conference on data analysis methods for software systems …, 2023 | | 2023 |
| Semi-supervised and Unsupervised Machine Learning Methods for Sea Traffic Anomaly Detection J Venskus Vilniaus universitetas, 2021 | | 2021 |
| Prediction of vessels trajectory using different coordinate systems R Jurkus, P Treigys, J Venskus DAMSS: 12th conference on data analysis methods for software systems …, 2021 | | 2021 |
| Dalinai prižiūrimų ir neprižiūrimų mašininio mokymosi metodų tyrimas jūrų eismo anomalijoms aptikti J Venskus Vilniaus universitetas, 2021 | | 2021 |
| Referees of INFORMATICA Volume 31 J Antuchevičiene, A Baginskas, T Baležentis, R Barauskas, R Baronas, ... Informatica 31 (4), 881-881, 2020 | | 2020 |
| Didžiaisiais duomenimis apsimokančio algoritmo ir posistemio neįprastam laivų eismui atpažinti jūrų uoste kūrimas J Venskus Klaipėdos universitetas., 2016 | | 2016 |
| Development of virtual instrument for piezoelectrical sensor signal transformation using the transfer function Z Lukosius, M Kurmis, A Andziulis, J Venskus | | 2014 |
| DOKTORANTO MOKSLINĖ ATASKAITA UŽ 2018/2019 METUS J Venskus | | |
| ASPECTS OF DATA COLLECTION FOR ABNORMAL MARINE TRANSPORT EVALUATION J VENSKUS, P TREIGYS, J BERNATAVIČIENĖ, V MEDVEDEV | | |
