| Systematic review of financial distress identification using artificial intelligence methods D Kuizinienė, T Krilavičius, R Damaševičius, R Maskeliūnas Applied Artificial Intelligence 36 (1), 2138124, 2022 | 22 | 2022 |
| Research on Factors Identification in FinTech Acceptance: Lithuania Context. S Graužinienė, D Kuizinienė Applied Economics: Systematic Research 14 (1), 2020 | 6 | 2020 |
| Cryptocurrencies short-term forecast: application of ARIMA, GARCH and SVR models D Kuizinienė, A Varoneckienė, T Krilavičius CEUR Workshop proceedings [electronic resource]: IVUS 2019, International …, 2019 | 6 | 2019 |
| Deep Learning Methods Application in Finance: A Review of State of Art D Kuizinieṅea, T Krilavičiusa algorithms 18, 19, 2020 | 1 | 2020 |
| Deep learning for credit scoring” D Kuizinienė, T Krilavičius International Journal of Design, Analysis and Tools for Integrated Circuits …, 2019 | 1 | 2019 |
| Skaitmeninių technologijų taikymo versle teorinių modelių ir jo poveikio ekonomikai tyrimų analizė D Kuizinienė KONFERENCIJŲ DARBAI, 123, 2013 | 1 | 2013 |
| A comparative study of feature selection and feature extraction methods for financial distress identification D Kuizinienė, P Savickas, R Kunickaitė, R Juozaitienė, R Damaševičius, ... PeerJ Computer Science 10, e1956, 2024 | | 2024 |
| Balancing Techniques for Advanced Financial Distress Detection Using Artificial Intelligence D Kuizinienė, T Krilavičius Electronics 13 (8), 1596, 2024 | | 2024 |
| Autoencoder as Feature Extraction Technique for Financial Distress Classification D Kuizinienė, P Savickas, T Krilavičius International Conference on Information and Software Technologies, 71-86, 2023 | | 2023 |
| Balancing techniques influence in financial distress detection D Kuizinienė, T Krilavičius DAMSS-2023: Data analysis methods for software systems: 14th conference …, 2023 | | 2023 |
| Feature extraction for bankruptcy prediction using autoencoder D Kuizinienė, P Savickas, T Krilavičius ICCS 2023: 23rd international conference on computational science, 1-1, 2023 | | 2023 |
| Dimensionality reduction for financial distress detection D Kuizinienė, T Krilavičius Vilnius: Vilnius University Press, 2022 | | 2022 |
| Can merged datasets help in training money laundering detection models? P Savickas, D Kuiziniene, M Bugarevicius, Z Kybartas, T Krilavicius IVUS, 111-116, 2022 | | 2022 |
| Application of artificial intelligence for automatic lending decision making using transactions data D Kuizinienė, P Savickas, T Krilavičius DAMSS-2021: Data analysis methods for software systems: 12th conference …, 2021 | | 2021 |
| Artificial intelligence financial distress barometer (companies’ case) D Kuizinienė, T Krilavičius DAMSS-2021: Data analysis methods for software systems: 12th conference …, 2021 | | 2021 |
| WHY FINANCIAL DISTRESS IS TOPIC FOR MACHINE LEARNING EXPERTS? D Kuizinienė, T Krilavičius ARTIFICIALITY AND SUSTAINABILITY IN ENTREPRENEURSHIP, 49, 2020 | | 2020 |
| Sentimentų klasifikavimas virtualių valiutų kontekste D Kuizinienė Lietuvos magistrantų informatikos ir IT tyrimai, 15, 2019 | | 2019 |
| Neuron networks suitability for crypto-currency short-term forecasting D Kuizinienė Applied Economics: Systematic Research, 29-44, 2018 | | 2018 |
| Neuroninio tinklo metodo tinkamumo trumpo laikotarpio virtualiųjų valiutų kursams prognozuoti tyrimas D Kuizinienė Taikomoji ekonomika: sisteminiai tyrimai 12 (2), 29-44, 2018 | | 2018 |
| Elektroninės komercijos poveikis valstybės ekonomikai D Stungytė KONFERENCIJŲ DARBAI, 155, 2012 | | 2012 |
