Josep Escrig

Orcid: 0000-0002-0918-8148

According to our database1, Josep Escrig authored at least 12 papers between 2020 and 2023.

Collaborative distances:
  • Dijkstra number2 of four.
  • Erdős number3 of four.

Timeline

Legend:

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PhD thesis 
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Links

On csauthors.net:

Bibliography

2023
On the Application of Q-learning for Mobility Load Balancing in Realistic Vehicular Scenarios.
Proceedings of the 97th IEEE Vehicular Technology Conference, 2023

Towards IoT Ambient Intelligence for Industry 4.0.
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

A Data-driven Approach for Risk Exposure Analysis in Enterprise Security.
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

A Real-World Dataset for Benchmarking False Alarm Rate in Keyword Spotting.
Proceedings of the Artificial Intelligence Research and Development, 2023

2022
On Alleviating Cell Overload in Vehicular Scenarios.
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


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