O prezentare generală a rezultatelor proiectului.

  1. Czibula, G., Andrei, M., Mihuleț, E., NowDeepN: An ensemble of deep learning models for weather nowcasting based on radar products' values prediction, Applied Sciences, 2021, 11(1), 125; https://doi.org/10.3390/app11010125. (2021 IF=2.838, Q2).

  2. Czibula G., Mihai, A., Albu, A.-I., Czibula, I.G., Burcea, S. Mezghani, A., AutoNowP: An approach using deep autoencoders for precipitation nowcasting based on weather radar reflectivity prediction, Mathematics, 9(14):1653. https://doi.org/10.3390/math9141653 Special Issue on Computational Optimizations for Machine Learning. 2021 (2021 IF=2.592, Q1).

  3. Vlad-Sebastian Ionescu, Gabriela Czibula, Eugen Mihuleț, DeePSat: A deep learning model for prediction of satellite images for nowcasting purposes, 25thInternational Conference on Knowledge-Based and Intelligent Information & Engineering Systems (KES2021), Procedia Computer Science Volume 192, 2021, Pages 622-631, (B-ranked according to CORE classification, indexed WoS).

  4. Albu, Alexandra-Ioana: Towards learning transferable embeddings for protein conformations using Variational Autoencoders, 25th International Conference on Knowledge-Based and Intelligent Information & Engineering Systems (KES2021), Procedia Computer Science Volume 192, 2021, Pages 10-19 (B-ranked according to CORE classification, indexed WoS).

  5. Gabriela Czibula, Alexandra Albu, Maria Iuliana Bocicor, Camelia Chira, AutoPPI: An ensemble of deep autoencoders for protein-protein interaction prediction, Entropy, Special issue on Computational Methods and Algorithms for Bioinformatics, 23(6), 643, 2021, (2021 IF=2.738, Q2).

  6. Bratu A., Czibula G., DAuGAN: An approach for augmenting time series imbalanced datasets via latent space sampling using adversarial techniques, Scientific Programming, Special Issue on Theory, Algorithms, and Applications for the Multiclass Classification Problem, Vol. 2021, Article ID 7877590, (2021 IF=1.672, Q3).

  7. Nistor, S.C., Czibula, G., IntelliSwAS: Optimizing Deep Neural Network Architectures using a Particle Swarm-based Approach, Expert systems with Applications, Volume 187, ID 11594 January 2022. Disponibil online: 23 septembrie 2021 (2021 IF=8.665, Q1).

  8. Cristian-Lucian Grecu, Sateliții Meteorologici, TODAY SOFTWARE MAGAZINE, Nr. 116, Februarie 2022, pp. 32-36. Abstract

  9. Cristian-Lucian Grecu, Sateliții Meteorologici (II) - Produsele satelitare RGB, TODAY SOFTWARE MAGAZINE, Nr. 117, Martie 2022, pp. 18-21. Abstract

  10. Udo Reckerth, Introducere în Meteorologia RADAR, TODAY SOFTWARE MAGAZINE, Nr. 118, Aprilie 2022, pp. 28-31. Abstract

  11. Albu, Alexandra-Ioana, An Approach for Predicting Protein-Protein Interactions using Supervised Autoencoders, 26th International Conference on Knowledge-Based and Intelligent Information & Engineering Systems (KES2022), Volume 207, 2022, Pages 2023-2032 (B-ranked according to CORE classification, indexed WoS)

  12. Meda Andrei, Prognoza Vremii și Modelele Numerice, TODAY SOFTWARE MAGAZINE, Nr. 119, Mai 2022, pp. 27-30. Abstract

  13. Albu, A.-I., Czibula G., Mihai, A., Czibula, I.G., Burcea, S. Mezghani, A., NeXtNow: A Convolutional Deep Learning Model for the Prediction of Weather Radar Data for Nowcasting Purposes, Remote Sensing, Special Issue "Artificial Intelligence-Based Learning Approaches for Remote Sensing", 2022, 14(16), 3890 (2021 IF=5.349, Q1)

  14. Narcisa Milian, Cristina Blaga, Avertizările Meteorologice de tipul nowcasting, TODAY SOFTWARE MAGAZINE, Nr. 121, Iunie 2022, pp. 25-29. Abstract

  15. Gabriela-Victoria Harpa și Adela-Mariana Mitea, Date, măsurători și rețeaua meteorologică, TODAY SOFTWARE MAGAZINE, Nr. 122, Iulie 2022, pp. 22-27. Abstract

  16. Ciubotariu, G., Czibula, G., MBMT-Net: A multitask learning based convolutional neural network architecture for dense tasks performance improvement, IEEE Access, 2022, Volume 10, pages 125600 - 125615 (2021 IF=3.476, Q2)

  17. Alexandra-Ioana Albu, Maria-Iuliana Bocicor, Gabriela Czibula, MM-PPI: A New Deep Multimodal Approach for Protein-Protein Interaction Prediction, Computers in Biology and Medicine,Vol. 153, 106526, 2023 (2021 IF=6.698, Q1)

  18. Mihuleţ, Eugen, Sorin Burcea, Andrei Mihai, and Gabriela Czibula, Enhancing the Performance of Quantitative Precipitation Estimation Using Ensemble of Machine Learning Models Applied on Weather Radar Data, Atmosphere 14, no. 1: 182, 2023 (2021 IF=3.110, Q2)

  1. Czibula, G., Mihai, A., Mihuleț, E., Teodorovici, D., Using self-organizing maps for unsupervised analysis of radar data for nowcasting purposes, 23nd International Conference on Knowledge-Based and Intelligent Information & Engineering Systems (KES2019), Procedia Computer Science Vol 159, (2019) pp. 48-57

  2. Mihai, A., Czibula, G., Mihuleț, E., Analyzing Meteorological Data Using Unsupervised Learning Techniques, ICCP 2019: Proceedings of the IEEE International Conference on Intelligent Computer Communication and Processing, 2019, Cluj-Napoca, Romania, IEEE Computer Society Press, pp. 529 – 536

  3. Socaci, I. A., Czibula, G., Ionescu, V. S., Mihai, A., XNow: A deep learning technique for nowcasting based on radar products’ values prediction, IEEE 14th International Symposium on Applied Computational Intelligence and Informatics, SACI 2020, Timișoara, IEEE Computer Society, pp. 117-122 -

  4. Czibula, G., Mihai, A., Czibula, I.G., RadRAR: A relational association rule mining approach for nowcasting based on predicting radar products' values, 24nd International Conference on Knowledge-Based and Intelligent Information & Engineering Systems (KES2020), Procedia Computer Science, Vol. 176, pp. 300-309

  • 24 mai 2021, Primul workshop de Applied Deep Learning a fost organizat de echipa de cercetare în Machine Learning de la Facultatea de Matematică și Informatică a Universității „Babeș-Bolyai”, împreună cu Administrația Națională de Meteorologie și Institutul Meteorologic Norvegian.

  • 3 iunie 2022, Al doilea workshop de Applied Deep Learning a fost organizat de echipa de cercetare în Machine Learning de la Facultatea de Matematică și Informatică a Universității „Babeș-Bolyai”, împreună cu Administrația Națională de Meteorologie și Institutul Meteorologic Norvegian.