Zobrazeno 1 - 10
of 4 662
pro vyhledávání: '"Hernández, Jose A."'
Autor:
Sánchez-Macián, Alfonso, Koneva, Nataliia, Quagliotti, Marco, Rivas-Moscoso, José M., Arpanaei, Farhad, Hernández, José Alberto, Fernández-Palacios, Juan P., Zhang, Li, Riccardi, Emilio
Model networks and their underlying topologies have been used as a reference for techno-economic studies for several decades. Existing reference topologies for optical networks may cover different network segments such as backbone, metro core, metro
Externí odkaz:
http://arxiv.org/abs/2408.01721
Lexicase selection is a successful parent selection method in genetic programming that has outperformed other methods across multiple benchmark suites. Unlike other selection methods that require explicit parameters to function, such as tournament si
Externí odkaz:
http://arxiv.org/abs/2407.15056
Autor:
Navarro, Alejandro L. García, Koneva, Nataliia, Sánchez-Macián, Alfonso, Hernández, José Alberto
Python has gained widespread popularity in the fields of machine learning, artificial intelligence, and data engineering due to its effectiveness and extensive libraries. R, on its side, remains a dominant language for statistical analysis and visual
Externí odkaz:
http://arxiv.org/abs/2407.14695
Autor:
Koneva, Nataliia, Sánchez-Macián, Alfonso, Hernández, José Alberto, Arpanaei, Farhad, de Dios, Óscar González
Accurate estimation of queuing delays is crucial for designing and optimizing communication networks, particularly in the context of Deterministic Networking (DetNet) scenarios. This study investigates the approximation of Internet queuing delays usi
Externí odkaz:
http://arxiv.org/abs/2406.16452
Peter Higgs was a British theoretical physicist, famous for his work published in 1964, where he proposed a mechanism that can generate masses for elementary particles, while respecting gauge invariance. Half a century later, two experiments at CERN
Externí odkaz:
http://arxiv.org/abs/2406.15706
Autor:
Navarro, A. L. García, Koneva, Nataliia, Sánchez-Macián, Alfonso, Hernández, José Alberto, de Dios, Óscar González, Rivas-Moscoso, J. M.
Publikováno v:
The 28th International Conference on Optical Network Design and Modelling (ONDM 2024)
This article provides a methodology and open-source implementation of Reinforcement Learning algorithms for finding optimal routes in a packet-optical network scenario. The algorithm uses measurements provided by the physical layer (pre-FEC bit error
Externí odkaz:
http://arxiv.org/abs/2406.12602
Publikováno v:
The 28th International Conference on Optical Network Design and Modelling (ONDM 2024)
The power of Machine Learning and Artificial Intelligence algorithms based on collected datasets, along with the programmability and flexibility provided by Software Defined Networking can provide the building blocks for constructing the so-called Ze
Externí odkaz:
http://arxiv.org/abs/2406.12594
Autor:
Hernández, José A., Scano, Davide, Cugini, Filippo, Martínez, Gonzalo, Koneva, Natalia, Sánchez-Macián, Alvaro, de Dios, Óscar González
Publikováno v:
The 28th International Conference on Optical Network Design and Modelling (ONDM 2024)
Monitoring streams of packets at 100~Gb/s and beyond requires using compact and efficient hashing-techniques like HyperLogLog (HLL) or Count-Min Sketch (CMS). In this work, we evaluate the uses and applications of Count-Min Sketch for Metro Networks
Externí odkaz:
http://arxiv.org/abs/2406.12586
Automated machine learning streamlines the task of finding effective machine learning pipelines by automating model training, evaluation, and selection. Traditional evaluation strategies, like cross-validation (CV), generate one value that averages t
Externí odkaz:
http://arxiv.org/abs/2406.12006
Autor:
Garcia-Sosa, Alejandro, Quintana-Hernandez, Jose J., Ballester, Miguel A. Ferrer, Carmona-Duarte, Cristina
Publikováno v:
IGS2023, 2023, 116-120
Sensors and Artificial Intelligence (AI) have revolutionized the analysis of human movement, but the scarcity of specific samples presents a significant challenge in training intelligent systems, particularly in the context of diagnosing neurodegener
Externí odkaz:
http://arxiv.org/abs/2405.04241