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11
Influence of Data Splitting on Performance of Machine Learning Models in Prediction of Shear Strength of Soil
Influence of Data Splitting on Performance of Machine Learning Models in Prediction of Shear Strength of Soil
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Influence of Data Splitting on Performance of Machine Learning Models in Prediction of Shear Strength of Soil

Nguyen, Quang Hung ; Ly, Hai-Bang ; Ho, Lanh Si ; Al-Ansari, Nadhir ; Le, Hiep Van ; Tran, Van Quan ; Prakash, Indra ; Pham, Binh Thai Shen, Yu-Sheng ; Yu-Sheng Shen

Mathematical problems in engineering, 2021, Vol.2021, p.1-15 [Periódico revisado por pares]

New York: Hindawi

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12
Application of artificial intelligence models and optimization algorithms in plant cell and tissue culture
Application of artificial intelligence models and optimization algorithms in plant cell and tissue culture
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Application of artificial intelligence models and optimization algorithms in plant cell and tissue culture

Hesami, Mohsen ; Jones, Andrew Maxwell Phineas

Applied microbiology and biotechnology, 2020-11, Vol.104 (22), p.9449-9485 [Periódico revisado por pares]

Berlin/Heidelberg: Springer Berlin Heidelberg

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13
A systematic review of data mining and machine learning for air pollution epidemiology
A systematic review of data mining and machine learning for air pollution epidemiology
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A systematic review of data mining and machine learning for air pollution epidemiology

Bellinger, Colin ; Mohomed Jabbar, Mohomed Shazan ; Zaïane, Osmar ; Osornio-Vargas, Alvaro

BMC public health, 2017-11, Vol.17 (1), p.907-907, Article 907 [Periódico revisado por pares]

England: BioMed Central Ltd

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14
Pre-trained convolutional neural networks as feature extractors toward improved malaria parasite detection in thin blood smear images
Pre-trained convolutional neural networks as feature extractors toward improved malaria parasite detection in thin blood smear images
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Pre-trained convolutional neural networks as feature extractors toward improved malaria parasite detection in thin blood smear images

Rajaraman, Sivaramakrishnan ; Antani, Sameer K ; Poostchi, Mahdieh ; Silamut, Kamolrat ; Hossain, Md A ; Maude, Richard J ; Jaeger, Stefan ; Thoma, George R

PeerJ (San Francisco, CA), 2018-04, Vol.6, p.e4568-e4568, Article e4568 [Periódico revisado por pares]

United States: PeerJ. Ltd

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15
Hybrid quantum-classical convolutional neural networks
Hybrid quantum-classical convolutional neural networks
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Hybrid quantum-classical convolutional neural networks

Liu, Junhua ; Lim, Kwan Hui ; Wood, Kristin L. ; Huang, Wei ; Guo, Chu ; Huang, He-Liang

Science China. Physics, mechanics & astronomy, 2021-09, Vol.64 (9), p.290311, Article 290311 [Periódico revisado por pares]

Beijing: Science China Press

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16
Assessment of supervised machine learning methods for fluid flows
Assessment of supervised machine learning methods for fluid flows
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Assessment of supervised machine learning methods for fluid flows

Fukami, Kai ; Fukagata, Koji ; Taira, Kunihiko

Theoretical and computational fluid dynamics, 2020-08, Vol.34 (4), p.497-519 [Periódico revisado por pares]

Berlin/Heidelberg: Springer Berlin Heidelberg

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17
Machine Learning in Network Centrality Measures: Tutorial and Outlook
Machine Learning in Network Centrality Measures: Tutorial and Outlook
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Machine Learning in Network Centrality Measures: Tutorial and Outlook

Grando, Felipe ; Granville, Lisandro Z. ; Lamb, Luis C.

ACM computing surveys, 2019-01, Vol.51 (5), p.1-32, Article 102 [Periódico revisado por pares]

New York, NY, USA: ACM

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18
Machine Learning Applications in Internet-of-Drones: Systematic Review, Recent Deployments, and Open Issues
Machine Learning Applications in Internet-of-Drones: Systematic Review, Recent Deployments, and Open Issues
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Machine Learning Applications in Internet-of-Drones: Systematic Review, Recent Deployments, and Open Issues

Heidari, Arash ; Jafari Navimipour, Nima ; Unal, Mehmet ; Zhang, Guodao

ACM computing surveys, 2023-12, Vol.55 (12), p.1-45, Article 247 [Periódico revisado por pares]

New York, NY: ACM

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19
Deep-learning: investigating deep neural networks hyper-parameters and comparison of performance to shallow methods for modeling bioactivity data
Deep-learning: investigating deep neural networks hyper-parameters and comparison of performance to shallow methods for modeling bioactivity data
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Deep-learning: investigating deep neural networks hyper-parameters and comparison of performance to shallow methods for modeling bioactivity data

Koutsoukas, Alexios ; Monaghan, Keith J. ; Li, Xiaoli ; Huan, Jun

Journal of cheminformatics, 2017-06, Vol.9 (1), p.42-42, Article 42 [Periódico revisado por pares]

Cham: Springer International Publishing

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20
Comparative Analysis of Modeling Algorithms for Forest Aboveground Biomass Estimation in a Subtropical Region
Comparative Analysis of Modeling Algorithms for Forest Aboveground Biomass Estimation in a Subtropical Region
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Comparative Analysis of Modeling Algorithms for Forest Aboveground Biomass Estimation in a Subtropical Region

Gao, Yukun ; Lu, Dengsheng ; Li, Guiying ; Wang, Guangxing ; Chen, Qi ; Liu, Lijuan ; Li, Dengqiu

Remote sensing (Basel, Switzerland), 2018-04, Vol.10 (4), p.627 [Periódico revisado por pares]

Basel: MDPI AG

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