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Risk factors analysis of COVID-19 patients with ARDS and prediction based on machine learning
Risk factors analysis of COVID-19 patients with ARDS and prediction based on machine learning
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Risk factors analysis of COVID-19 patients with ARDS and prediction based on machine learning

Xu, Wan ; Sun, Nan-Nan ; Gao, Hai-Nv ; Chen, Zhi-Yuan ; Yang, Ya ; Ju, Bin ; Tang, Ling-Ling

Scientific reports, 2021-02, Vol.11 (1), p.2933-12, Article 2933 [Periódico revisado por pares]

England: Nature Publishing Group

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2
The relevance of machining practices competencies through industrial needs
The relevance of machining practices competencies through industrial needs
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The relevance of machining practices competencies through industrial needs

Paryanto, P ; Munadi, S ; Wagiran ; Purnomo, E ; Ardiyanto, A ; Rahmawati, E D

Journal of physics. Conference series, 2020-12, Vol.1700 (1), p.12025 [Periódico revisado por pares]

IOP Publishing

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3
Howard’s algorithm for high-order approximations of American options under jump-diffusion models
Howard’s algorithm for high-order approximations of American options under jump-diffusion models
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Howard’s algorithm for high-order approximations of American options under jump-diffusion models

Thakoor, Nawdha ; Behera, Dhiren Kumar ; Tangman, Désiré Yannick ; Bhuruth, Muddun

International journal of data science and analytics, 2020-08, Vol.10 (2), p.193-203 [Periódico revisado por pares]

Cham: Springer International Publishing

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4
SZZ unleashed: an open implementation of the SZZ algorithm - featuring example usage in a study of just-in-time bug prediction for the Jenkins project
SZZ unleashed: an open implementation of the SZZ algorithm - featuring example usage in a study of just-in-time bug prediction for the Jenkins project
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SZZ unleashed: an open implementation of the SZZ algorithm - featuring example usage in a study of just-in-time bug prediction for the Jenkins project

Borg, Markus ; Svensson, Oscar ; Berg, Kristian ; Hansson, Daniel

Proceedings of the 3rd ACM SIGSOFT International Workshop on Machine Learning Techniques for Software Quality Evaluation, 2019, p.7-12

New York, NY, USA: ACM

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5
Task recognition from joint tracking data in an operational manufacturing cell
Task recognition from joint tracking data in an operational manufacturing cell
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Task recognition from joint tracking data in an operational manufacturing cell

Rude, Don J. ; Adams, Stephen ; Beling, Peter A.

Journal of intelligent manufacturing, 2018-08, Vol.29 (6), p.1203-1217 [Periódico revisado por pares]

New York: Springer US

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6
What Are They Talking About？ Analyzing Code Reviews in Pull-Based Development Model
What Are They Talking About？ Analyzing Code Reviews in Pull-Based Development Model
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What Are They Talking About？ Analyzing Code Reviews in Pull-Based Development Model

Li, Zhi-Xing ; Yu, Yue ; Yin, Gang ; Wang, Tao ; Wang, Huai-Min

Journal of computer science and technology, 2017-11, Vol.32 (6), p.1060-1075 [Periódico revisado por pares]

New York: Springer US

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7
A patent search strategy based on machine learning for the emerging field of service robotics
A patent search strategy based on machine learning for the emerging field of service robotics
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A patent search strategy based on machine learning for the emerging field of service robotics

Kreuchauff, Florian ; Korzinov, Vladimir

Scientometrics, 2017-05, Vol.111 (2), p.743-772 [Periódico revisado por pares]

Dordrecht: Springer Netherlands

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8
Behavioral analysis and classification of spammers distributing pornographic content in social media
Behavioral analysis and classification of spammers distributing pornographic content in social media
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Behavioral analysis and classification of spammers distributing pornographic content in social media

Singh, Monika ; Bansal, Divya ; Sofat, Sanjeev

Social network analysis and mining, 2016-12, Vol.6 (1), p.41, Article 41 [Periódico revisado por pares]

Vienna: Springer Vienna

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9
Demonstrating PlanetSense: gathering geo-spatial intelligence from crowd-sourced and social-media data
Demonstrating PlanetSense: gathering geo-spatial intelligence from crowd-sourced and social-media data
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Ata de Congresso 		Adicionar ao Meu Espaço

Demonstrating PlanetSense: gathering geo-spatial intelligence from crowd-sourced and social-media data

Thakur, Gautam S. ; Sparks, Kevin ; Li, Roger ; Stewart, Robert N. ; Urban, Marie L.

Proceedings of the 24th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, 2016, p.1-4

New York, NY, USA: ACM

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10
Identifying Earmarks in Congressional Bills
Identifying Earmarks in Congressional Bills
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Identifying Earmarks in Congressional Bills

Wulczyn, Ellery ; Khabsa, Madian ; Vora, Vrushank ; Heston, Matthew ; Walsh, Joe ; Berry, Christopher ; Ghani, Rayid

Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2016, p.303-311

New York, NY, USA: ACM

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