A CFD-based empirical model for hazardous area extent prediction including wind effects

Nascimento, Claudemi A.; Luiz, Aurélio M.; Barros, Paloma L.; Neto, Antônio Tavernard Pereira; Alves, José Jaílson Nicácio

doi:10.1016/j.jlp.2021.104497

Cited by 5 publications

(1 citation statement)

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“…Gusti, I. Irhamah, and H. Kuswanto [24] uses Support Vector Regression (SVR) to forecast TCP throughput by using multivariate regression. Using supervised learning techniques such as random forest, Nascimento et al [25] developed a loss prediction model to estimate the likelihood of packet loss due to congestion. Predicting and reducing packet loss incidents, reducing the frequency of transmitting rate reductions, and increasing throughput are all benefits of this technology.…”

Section: Network Ecosystemmentioning

confidence: 99%

Application of Machine Learning Technologies for Transport layer Congestion Control

Dong

Yortsos

2022

JCNS

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Due to the advent of technology, humans now live in the modern age of information and data. In this form of world, different objects are interlinked to data sources, and every aspect of human’s lives are recorded in a digital form. For example, the present electronic globe has an abundance of distinct forms of data e.g., health data, social media fata, smartphone data, business data, smart city data, cybersecurity data and Internet of Things (IoT) data, including Covid-19 data. Data can be unstructured, semi-structured and structured, and this is increasing on a daily basis. Machine Learning (ML) is significantly employed in different aspects of real-life e.g., Congestion Control (CC). This paper provides an evaluation of the aspect ML employed in CC. CC has emerged as a fundamental viewpoint in communications system infrastructure in the recent years, since network operations, and network capacity have enhanced at a rapid rate.

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Section: Network Ecosystemmentioning

confidence: 99%