A Review on Machine Learning Application in Biodiesel Production Studies by Yuanzhi Xing, Zile Zheng, Yike Sun, Masoome Agha Alikhani

“…Machine learning applications in the production phase include quality optimization and estimation, process conditions, and quantity. …”
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Machine Learning of the Reactor Core Loading Pattern Critical Parameters by Krešimir Trontl, Dubravko Pevec, Tomislav Šmuc

“…In this paper, we investigate the applicability of a machine learning model which could be used for fast loading pattern evaluation. …”
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Machine Learning-Based Multitarget Tracking of Motion in Sports Video by Xueliang Zhang, Fu-Qiang Yang

“…In this paper, we track the motion of multiple targets in sports videos by a machine learning algorithm and study its tracking technique in depth. …”
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Reduction of monoclonal antibody viscosity using interpretable machine learning by Emily K. Makowski, Hsin-Ting Chen, Tiexin Wang, Lina Wu, Jie Huang, Marissa Mock, Patrick Underhill, Emma Pelegri-O’Day, Erick Maglalang, Dwight Winters, Peter M. Tessier

“…Here, we report an interpretable machine learning model for predicting antibody (IgG1) variants with low viscosity using only the sequences of their variable (Fv) regions. …”
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Predicting conversion to psychosis using machine learning: response to Cannon by Jason Smucny, Tyrone D. Cannon, Tyrone D. Cannon, Carrie E. Bearden, Carrie E. Bearden, Jean Addington, Kristen S. Cadenhead, Barbara A. Cornblatt, Matcheri Keshavan, Daniel H. Mathalon, Diana O. Perkins, William Stone, Elaine F. Walker, Scott W. Woods, Scott W. Woods, Ian Davidson, Cameron S. Carter

“…In this report we tested for model generalization using the previous iteration of NAPLS-3, the NAPLS-2, using the identical machine learning algorithms employed in our previous study.MethodStandard machine learning algorithms were trained to predict conversion to psychosis in clinical high risk individuals on the NAPLS-3 dataset and tested on the NAPLS-2 dataset.ResultsNAPLS-2 and -3 individuals significantly differed on most features used in machine learning models. …”
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Machine learning approach to student performance prediction of online learning. by Jing Wang, Yun Yu

“…Here, this paper proposes a machine learning method for the prediction of student performance based on online learning. …”
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Fault Diagnosis of Batch Reactor Using Machine Learning Methods by Sujatha Subramanian, Fathima Ghouse, Pappa Natarajan

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SURVEY OF MACHINE LEARNING APPLICATIONS IN MEDICAL IMAGING. ALGORITHMS AND TECHNOLOGIES by Marius Eremia Vlaicu POPA, Mihai CRUCERU, Bogdan DIACONU

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Machine Learning Applications to Dust Storms: A Meta-Analysis by Reem K. Alshammari, Omer Alrwais, Mehmet Sabih Aksoy

Subjects: “…Machine learning…”
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Analysis and prediction of atmospheric ozone concentrations using machine learning by Stephan Räss, Stephan Räss, Markus C. Leuenberger, Markus C. Leuenberger

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Breath Detection from a Microphone Using Machine Learning by Tomasz Sankowski, Piotr Sulewski, Jan Walczak, Aleksandra Bruska

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iMESc – an interactive machine learning app for environmental sciences by Danilo Cândido Vieira, Danilo Cândido Vieira, Fabiana S. Paula, Luciana Erika Yaginuma, Gustavo Fonseca

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Modified Hybrid Discriminant Analysis Methods and Their Applications in Machine Learning by Liwen Huang

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Study of machine learning techniques for outcome assessment of leptospirosis patients by Andreia Ferreira da Silva, Karla Figueiredo, Igor W. S. Falcão, Fernando A. R. Costa, Marcos César da Rocha Seruffo, Carla Cristina Guimarães de Moraes

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Applications of Machine Learning Methods in Complex Economics and Financial Networks by Benjamin M. Tabak, Thiago C. Silva, Liang Zhao, Ahmet Sensoy

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SuperKEKB positron beam tuning using machine learning by Natsui Takuya

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Machine learning approach to student performance prediction of online learning by Jing Wang, Yun Yu

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Task relevant autoencoding enhances machine learning for human neuroscience by Seyedmehdi Orouji, Vincent Taschereau-Dumouchel, Aurelio Cortese, Brian Odegaard, Cody Cushing, Mouslim Cherkaoui, Mitsuo Kawato, Hakwan Lau, Megan A. K. Peters

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Machine-learning crystal size distribution for volcanic stratigraphy correlation by Martin Jutzeler, Rebecca J. Carey, Yasin Dagasan, Andrew McNeill, Ray A. F. Cas

“…Here we present a novel technique based on machine learning identification of crystal size distribution to quantitatively fingerprint lavas, shallow intrusions and coarse lava breccias. …”
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Novel Machine Learning-Based Brain Attention Detection Systems by Junbo Wang, Song-Kyoo Kim

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