Josep Escrig
Orcid: 0000-0002-0918-8148
According to our database1,
Josep Escrig
authored at least 15 papers
between 2020 and 2024.
Collaborative distances:
Collaborative distances:
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Bibliography
2024
Inter-Satellite Link Prediction with Supervised Learning Based on Kepler and SGP4 Orbits.
Int. J. Comput. Intell. Syst., December, 2024
Proceedings of the Artificial Intelligence Research and Development, 2024
Optimizing Energy Consumption of Kubernetes Clusters with Deep Reinforcement Learning.
Proceedings of the Artificial Intelligence Research and Development, 2024
2023
On the Application of Q-learning for Mobility Load Balancing in Realistic Vehicular Scenarios.
Proceedings of the 97th IEEE Vehicular Technology Conference, 2023
Proceedings of the 10th International Conference on Future Internet of Things and Cloud, 2023
Inter-Satellite Link Prediction for Non-Terrestrial Networks Using Supervised Learning.
Proceedings of the 2023 Joint European Conference on Networks and Communications & 6G Summit, 2023
Achieving High-Fidelity Explanations for Risk Exposition Assessment in the Cybersecurity Domain.
Proceedings of the APWG Symposium on Electronic Crime Research, 2023
Proceedings of the 10th IEEE International Conference on Data Science and Advanced Analytics, 2023
Inter-Satellite Link Prediction with Supervised Learning Based on Kepler and SGP4 Orbits.
Proceedings of the Artificial Intelligence Research and Development, 2023
Proceedings of the Artificial Intelligence Research and Development, 2023
2022
Proceedings of the 96th Vehicular Technology Conference, 2022
Analysis of Vehicular Scenarios and Mitigation of Cell Overload due to Traffic Congestions.
Proceedings of the 95th IEEE Vehicular Technology Conference, 2022
2021
CARAMEL: results on a secure architecture for connected and autonomous vehicles detecting GPS spoofing attacks.
EURASIP J. Wirel. Commun. Netw., 2021
2020
Intelligent Industrial Cleaning: A Multi-Sensor Approach Utilising Machine Learning-Based Regression.
Sensors, 2020
Considerations, challenges and opportunities when developing data-driven models for process manufacturing systems.
Comput. Chem. Eng., 2020