O prezentare generală a rezultatelor proiectului.
Publicații
Publicații din cadrul proiectului
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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. (2020 IF=2.679, Q2).
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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 (2020 IF=2.258, Q1).
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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).
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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).
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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, (2020 IF=2.524, Q2).
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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, (2020 IF=1.025, Q4).
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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, (2020 IF=6.954, Q1).
Cristian-Lucian Grecu, Sateliții Meteorologici, TODAY SOFTWARE MAGAZINE, Nr. 116, February 2022, pp. 32-36. Abstract
Cristian-Lucian Grecu, Sateliții Meteorologici (II) - Produsele satelitare RGB, TODAY SOFTWARE MAGAZINE, Nr. 117, March 2022, pp. 18-21. Abstract
Udo Reckerth, Introducere în Meteorologia RADAR, TODAY SOFTWARE MAGAZINE, Nr. 118, April 2022, pp. 28-31. Abstract
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Albu, Alexandra-Ioana, Protein-Protein Interaction Prediction using Supervised Autoencoders, 26th International Conference on Knowledge-Based and Intelligent Information & Engineering Systems (KES2022), accepted for publication (B-ranked according to CORE classification, indexed WoS)
Publicații anterioare
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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
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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
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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 -
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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
Teze de doctorat
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2021, Andrei Mihai, Modele de învățare automată pentru prognoza pe termen scurt a vremii
Seminarii și workshop-uri
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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.
Prezentări
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2021, workshop WeADL, Eugen Mihuleț (ANM): Technology in weather forecasting
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2021, workshop WeADL, Sorin Burcea (ANM): Weather radars basic principles and application in nowcasting in Romania
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2021, workshop WeADL, Ivar Seierstad & Thomas Nipen (MET): Nowcasting on Yr - opportunities and challenges
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2021, workshop WeADL, Arild Burud (MET): A brief introduction to the netCDF format and THREDDS data server
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2021, workshop WeADL, Istvan Czibula (UBB): Computational models for nowcasting
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2021, workshop WeADL, Andrei Mihai (UBB): Supervised and unsupervised machine learning for nowcasting, applied on radar data from central Transylvania region
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2021, workshop WeADL, Alexandra Albu (UBB): Deep learning models for composite reflectivity prediction
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2021, workshop WeADL, Vlad Ionescu (UBB): Deep neural network models for nowcasting using satellite data
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2021, workshop WeADL, George Ciubotariu (UBB): Enhancing the performance of indoor-outdoor image classifications using features extracted from depth-maps
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2021, workshop WeADL, Alexandru-Marian Adăscăliței (UBB): Review and analysis of grayscale photography colorization using CNNs
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2021, workshop WeADL, Maria-Mădălina Mircea (UBB): A machine learning approach for data protection in VR therapy applications
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2021, workshop WeADL, Andrei Bratu (UBB): Augmenting time series datasets via latent space sampling with applications in algorithmic trading
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2021, workshop WeADL, Iulia-Monica Szuhai (UBB): DNA classification using supervised deep learning
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2021, workshop intern ANM, Sorin Burcea (ANM): Proiect WeaMyL
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2021, Cluj IT Days, Andrei Mihai (UBB): Enhancing the performance of weather nowcasting by use of machine learning techniques
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10-12 Noiembrie 2021, Sesiunea științifică anuală a Administrației Naționale de Meteorologie, Eugen Mihuleț (ANM): Analiza performanței modelului NowDeepN pentru predicția valorilor produselor radarului meteorologic WSR-98D Bobohalma
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18 Ianuarie 2022, workshop intern MET, Oslo, Norvegia: Overview of machine learning activities at MET Norway
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18 Martie 2022, conferința internațională Air and Water – Components of the Environment, Eugen Mihuleț (ANM), Analysis of Applying a Deep Learning Model for Prediction of WSR-98D Weather Radar Product Values
Video
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2021, workshop WeADL, Eugen Mihuleț (NMA): Technology in weather forecasting
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2021, workshop WeADL, Sorin Burcea (NMA): Weather radars - basic principles and application in nowcasting in Romania
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2021, workshop WeADL, Thomas Nipen (MET): Nowcasting on Yr - opportunities and challenges
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2021, workshop WeADL, Arild Burud (MET): A brief introduction to the netCDF format and THREDDS data server
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2021, workshop WeADL, Istvan Czibula (UBB): Computational models for nowcasting
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2021, workshop WeADL, Andrei Mihai (UBB): Supervised and unsupervised machine learning for nowcasting, applied on radar data from central Transylvania region
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2021, workshop WeADL, Vlad Ionescu (UBB): Deep neural network models for nowcasting using satellite data
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2021, workshop WeADL, Alexandra Albu (UBB): Deep learning models for composite reflectivity prediction
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2021, workshop WeADL, George Ciubotariu (UBB): Enhancing the performance of indoor-outdoor image classifications using depth-map features
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2021, workshop WeADL, Alexandru-Marian Adăscăliței (UBB): Review and analysis of grayscale photography colorization using CNNs
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2021, workshop WeADL, Maria-Mădălina Mircea (UBB): A machine learning approach for data protection in VR therapy applications
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2021, workshop WeADL, Andrei Bratu (UBB): Training data augmentation for RL based trading algorithms using adversarial techniques
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2021, workshop WeADL, Iulia-Monica Szuhai (UBB): DNA classification using supervised deep learning
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2021, conferința KES, Vlad-Sebastian Ionescu (UBB), Gabriela Czibula (UBB), Eugen Mihuleț (ANM): DeePSat: A deep learning model for prediction of satellite images for nowcasting purposes
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2021, Cluj IT Days, Andrei Mihai (UBB): Enhancing the performance of weather nowcasting by use of machine learning techniques