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1
Inpatient stroke rehabilitation: prediction of clinical outcomes using a machine-learning approach
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Inpatient stroke rehabilitation: prediction of clinical outcomes using a machine-learning approach

Harari, Yaar ; O'Brien, Megan K ; Lieber, Richard L ; Jayaraman, Arun

Journal of neuroengineering and rehabilitation, 2020-06, Vol.17 (1), p.71-71, Article 71 [Periódico revisado por pares]

England: BioMed Central Ltd

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2
Predicting clinically significant motor function improvement after contemporary task-oriented interventions using machine learning approaches
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Predicting clinically significant motor function improvement after contemporary task-oriented interventions using machine learning approaches

Thakkar, Hiren Kumar ; Liao, Wan-Wen ; Wu, Ching-Yi ; Hsieh, Yu-Wei ; Lee, Tsong-Hai

Journal of neuroengineering and rehabilitation, 2020-09, Vol.17 (1), p.131-131, Article 131 [Periódico revisado por pares]

England: BioMed Central Ltd

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3
Forecasting wind power in the Mai Liao Wind Farm based on the multi-layer perceptron artificial neural network model with improved simplified swarm optimization
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Forecasting wind power in the Mai Liao Wind Farm based on the multi-layer perceptron artificial neural network model with improved simplified swarm optimization

Yeh, Wei-Chang ; Yeh, Yuan-Ming ; Chang, Po-Chun ; Ke, Yun-Chin ; Chung, Vera

International journal of electrical power & energy systems, 2014-02, Vol.55, p.741-748 [Periódico revisado por pares]

Oxford: Elsevier Ltd

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4
Predictive repair scheduling of wind turbine drive‐train components based on machine learning
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Predictive repair scheduling of wind turbine drive‐train components based on machine learning

Colone, Lorenzo ; Dimitrov, Nikolay ; Straub, Daniel

Wind energy (Chichester, England), 2019-09, Vol.22 (9), p.1230-1242 [Periódico revisado por pares]

Bognor Regis: John Wiley & Sons, Inc

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5
Temporal Causal Inference in Wind Turbine SCADA Data Using Deep Learning for Explainable AI
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Temporal Causal Inference in Wind Turbine SCADA Data Using Deep Learning for Explainable AI

Chatterjee, Joyjit ; Dethlefs, Nina

Journal of physics. Conference series, 2020-09, Vol.1618 (2), p.22022 [Periódico revisado por pares]

Bristol: IOP Publishing

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6
Predictive Maintenance using Machine Learning Based Classification Models
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Predictive Maintenance using Machine Learning Based Classification Models

Chazhoor, Anisha ; Mounika, Y ; Vergin Raja Sarobin, M ; Sanjana, M V ; Yasashvini, R

IOP conference series. Materials Science and Engineering, 2020-10, Vol.954 (1), p.12001 [Periódico revisado por pares]

IOP Publishing

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7
Development of Wind Power Prediction Models for Pawan Danavi Wind Farm in Sri Lanka
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Development of Wind Power Prediction Models for Pawan Danavi Wind Farm in Sri Lanka

Ekanayake, Piyal ; Peiris, Amila T. ; Jayasinghe, J. M. Jeevani W. ; Rathnayake, Upaka Jauregui-Correa, Juan C. ; Juan C Jauregui-Correa

Mathematical problems in engineering, 2021, Vol.2021, p.1-13 [Periódico revisado por pares]

New York: Hindawi

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8
Data Insights from an Offshore Wind Turbine Gearbox Replacement
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Data Insights from an Offshore Wind Turbine Gearbox Replacement

Koltsidopoulos Papatzimos, Alexios ; Dawood, Tariq ; Thies, Philipp R.

Journal of physics. Conference series, 2018-10, Vol.1104 (1), p.12003 [Periódico revisado por pares]

Bristol: IOP Publishing

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9
Using improved support vector regression to predict the transmitted energy consumption data by distributed wireless sensor network
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Using improved support vector regression to predict the transmitted energy consumption data by distributed wireless sensor network

Guo, Ni ; Gui, Weifeng ; Chen, Wei ; Tian, Xin ; Qiu, Weiguo ; Tian, Zijian ; Zhang, Xiangyang

EURASIP journal on wireless communications and networking, 2020-06, Vol.2020 (1), p.1-16, Article 120 [Periódico revisado por pares]

Cham: Springer International Publishing

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10
Machine learning approach as an alternative tool to build a bankruptcy prediction model in banking industry
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Machine learning approach as an alternative tool to build a bankruptcy prediction model in banking industry

Siswoyo, B ; Herman, N S ; Dewi, D A

IOP conference series. Materials Science and Engineering, 2020-04, Vol.830 (2), p.22083 [Periódico revisado por pares]

Bristol: IOP Publishing

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