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Cristiano M. Silva

Researcher at Universidade Federal de São João del-Rei

Publications -  72
Citations -  853

Cristiano M. Silva is an academic researcher from Universidade Federal de São João del-Rei. The author has contributed to research in topics: Vehicular ad hoc network & Vehicular communication systems. The author has an hindex of 14, co-authored 66 publications receiving 586 citations. Previous affiliations of Cristiano M. Silva include Universidade Federal de Ouro Preto & Universidade Federal de Minas Gerais.

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A Survey on Infrastructure-Based Vehicular Networks

TL;DR: This paper presents an in-depth survey of more than ten years of research on infrastructures, wireless access technologies and techniques, and deployment that make vehicular connectivity available, and identifies the limitations and challenges associated with such infrastructure-based vehicular communications.
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Broadening Understanding on Managing the Communication Infrastructure in Vehicular Networks: Customizing the Coverage Using the Delta Network

TL;DR: This article proposes three deployment strategies based on a general heuristic based on the definition of scores to identify the areas of the road network that should receive coverage and shows how small changes in the score computation can generate very distinct patterns of coverage.
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On the analysis of mortality risk factors for hospitalized COVID-19 patients: A data-driven study using the major Brazilian database.

TL;DR: In this paper, the authors presented a comprehensive overview of the hospitalized Brazilian COVID-19 patients profile and the mortality risk factors, showing that the disease outcome is influenced by multiple factors, as unequally affects different segments of population.
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Deployment of roadside units based on partial mobility information

TL;DR: This work presents an algorithm for deployment of roadside units based on partial mobility information that uses the partition of the road network into same size urban cells, and the migration ratios between adjacent urban cells in order to infer the better locations for the deployment of the roadside units.
Posted ContentDOI

Predicting the disease outcome in COVID-19 positive patients through Machine Learning: a retrospective cohort study with Brazilian data

TL;DR: Machine learning techniques fed with demographic and clinical data along with comorbidities of the patients can assist in the prognostic prediction and physician decision-making, allowing a faster response and contributing to the non-overload of healthcare systems.