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Refinado por: Base de dados/Biblioteca: ANTE: Abstracts in New Technology & Engineering remover Base de dados/Biblioteca: ScienceDirect Journals remover nível superior: Revistas revisadas por pares remover
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Machine learning as a tool to design glasses with controlled dissolution for healthcare applications
Machine learning as a tool to design glasses with controlled dissolution for healthcare applications
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Machine learning as a tool to design glasses with controlled dissolution for healthcare applications

Han, Taihao ; Stone-Weiss, Nicholas ; Huang, Jie ; Goel, Ashutosh ; Kumar, Aditya

Acta biomaterialia, 2020-04, Vol.107, p.286-298 [Periódico revisado por pares]

England: Elsevier Ltd

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Detection and diagnosis of dental caries using a deep learning-based convolutional neural network algorithm
Detection and diagnosis of dental caries using a deep learning-based convolutional neural network algorithm
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Detection and diagnosis of dental caries using a deep learning-based convolutional neural network algorithm

Lee, Jae-Hong ; Kim, Do-Hyung ; Jeong, Seong-Nyum ; Choi, Seong-Ho

Journal of dentistry, 2018-10, Vol.77, p.106-111 [Periódico revisado por pares]

England: Elsevier Ltd

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3
The use of artificial intelligence (AI) methods in the prediction of thermal comfort in buildings: energy implications of AI-based thermal comfort controls
The use of artificial intelligence (AI) methods in the prediction of thermal comfort in buildings: energy implications of AI-based thermal comfort controls
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The use of artificial intelligence (AI) methods in the prediction of thermal comfort in buildings: energy implications of AI-based thermal comfort controls

Ngarambe, Jack ; Yun, Geun Young ; Santamouris, Mat

Energy and buildings, 2020-03, Vol.211, p.109807, Article 109807 [Periódico revisado por pares]

Lausanne: Elsevier B.V

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4
What's in the box?! Towards explainable machine learning applied to non-residential building smart meter classification
What's in the box?! Towards explainable machine learning applied to non-residential building smart meter classification
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What's in the box?! Towards explainable machine learning applied to non-residential building smart meter classification

Miller, Clayton

Energy and buildings, 2019-09, Vol.199, p.523-536 [Periódico revisado por pares]

Lausanne: Elsevier B.V

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5
Crop nitrogen monitoring: Recent progress and principal developments in the context of imaging spectroscopy missions
Crop nitrogen monitoring: Recent progress and principal developments in the context of imaging spectroscopy missions
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Crop nitrogen monitoring: Recent progress and principal developments in the context of imaging spectroscopy missions

Berger, Katja ; Verrelst, Jochem ; Féret, Jean-Baptiste ; Wang, Zhihui ; Wocher, Matthias ; Strathmann, Markus ; Danner, Martin ; Mauser, Wolfram ; Hank, Tobias

Remote sensing of environment, 2020-06, Vol.242, p.111758-111758, Article 111758 [Periódico revisado por pares]

New York: Elsevier Inc

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6
Multi-step ahead forecasting of heat load in district heating systems using machine learning algorithms
Multi-step ahead forecasting of heat load in district heating systems using machine learning algorithms
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Multi-step ahead forecasting of heat load in district heating systems using machine learning algorithms

Xue, Puning ; Jiang, Yi ; Zhou, Zhigang ; Chen, Xin ; Fang, Xiumu ; Liu, Jing

Energy (Oxford), 2019-12, Vol.188, p.116085, Article 116085 [Periódico revisado por pares]

Oxford: Elsevier Ltd

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7
Comparison and evaluation of advanced machine learning methods for performance and emissions prediction of a gasoline Wankel rotary engine
Comparison and evaluation of advanced machine learning methods for performance and emissions prediction of a gasoline Wankel rotary engine
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Comparison and evaluation of advanced machine learning methods for performance and emissions prediction of a gasoline Wankel rotary engine

Wang, Huaiyu ; Ji, Changwei ; Shi, Cheng ; Ge, Yunshan ; Meng, Hao ; Yang, Jinxin ; Chang, Ke ; Wang, Shuofeng

Energy (Oxford), 2022-06, Vol.248, p.123611, Article 123611 [Periódico revisado por pares]

Oxford: Elsevier Ltd

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8
Predicting the impacts of land use/land cover changes on seasonal urban thermal characteristics using machine learning algorithms
Predicting the impacts of land use/land cover changes on seasonal urban thermal characteristics using machine learning algorithms
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Predicting the impacts of land use/land cover changes on seasonal urban thermal characteristics using machine learning algorithms

Kafy, Abdulla - Al ; Saha, Milan ; Faisal, Abdullah-Al ; Rahaman, Zullyadini A. ; Rahman, Muhammad Tauhidur ; Liu, Desheng ; Fattah, Md. Abdul ; Al Rakib, Abdullah ; AlDousari, Ahmad E. ; Rahaman, Sk Nafiz ; Hasan, Md Zakaria ; Ahasan, Md Ahasanul Karim

Building and environment, 2022-06, Vol.217, p.109066, Article 109066 [Periódico revisado por pares]

Oxford: Elsevier Ltd

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9
Estimating crop primary productivity with Sentinel-2 and Landsat 8 using machine learning methods trained with radiative transfer simulations
Estimating crop primary productivity with Sentinel-2 and Landsat 8 using machine learning methods trained with radiative transfer simulations
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Estimating crop primary productivity with Sentinel-2 and Landsat 8 using machine learning methods trained with radiative transfer simulations

Wolanin, Aleksandra ; Camps-Valls, Gustau ; Gómez-Chova, Luis ; Mateo-García, Gonzalo ; van der Tol, Christiaan ; Zhang, Yongguang ; Guanter, Luis

Remote sensing of environment, 2019-05, Vol.225, p.441-457 [Periódico revisado por pares]

New York: Elsevier Inc

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10
Estimation of root zone soil moisture from ground and remotely sensed soil information with multisensor data fusion and automated machine learning
Estimation of root zone soil moisture from ground and remotely sensed soil information with multisensor data fusion and automated machine learning
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Estimation of root zone soil moisture from ground and remotely sensed soil information with multisensor data fusion and automated machine learning

Babaeian, Ebrahim ; Paheding, Sidike ; Siddique, Nahian ; Devabhaktuni, Vijay K. ; Tuller, Markus

Remote sensing of environment, 2021-07, Vol.260, p.112434, Article 112434 [Periódico revisado por pares]

New York: Elsevier Inc

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