| Assessment of various machine learning models for peach maturity prediction using non-destructive sensor data D Ljubobratović, M Vuković, M Brkić Bakarić, T Jemrić, M Matetić Sensors 22 (15), 5791, 2022 | 15 | 2022 |
| Using LMS activity logs to predict student failure with random forest algorithm D Ljubobratović, M Matetić The Future of Information Sciences 113, 2019 | 12 | 2019 |
| Predicting peach fruit ripeness using explainable machine learning D Ljubobratović, G Zhang, MB Bakarić, T Jemrić, M Matetić Annals of DAAAM & Proceedings 7 (1), 2020 | 5 | 2020 |
| Utilization of explainable machine learning algorithms for determination of important features in ‘Suncrest’peach maturity prediction D Ljubobratović, M Vuković, M Brkić Bakarić, T Jemrić, M Matetić Electronics 10 (24), 3115, 2021 | 4 | 2021 |
| Utjecaj kategorije zrelosti na kakvoću ploda breskve'Redhaven' M Vuković, D Ljubobratović, M Matetić, M Brkić Bakarić, T Jemrić Zbornik apstrakta/17. kongresa voćara i vinogradara Srbije, 224-225, 2024 | | 2024 |
| Agile development of software products M Velić, I Padavić, D Ljubobratović Infoteh Jahorina 10, 2011 | | 2011 |
| Agilni razvoj programskih proizvoda M Velić, I Padavić, D Ljubobratović INFOTEH-JAHORINA, 2011 | | 2011 |
| Predviđanje zrelosti breskvi korištenjem modela strojnog učenja D Ljubobratović Infcon 2022, 22, 0 | | |
| Interpretabilno strojno učenje D Ljubobratović | | |
Maja MateticUniversity of Rijeka, Faculty of Informatics and Digital Technologiesinf.uniri.hr의 이메일 확인됨
