Search Results - "artificial neural networks"

Development of Unconventional Reservoirs 2020

Subjects: “…artificial neural network…”
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Environment-Friendly Construction Materials by Wu, Shaopeng

Subjects: “…artificial neural network…”
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Environment-Friendly Construction Materials by Wu, Shaopeng

Subjects: “…artificial neural network…”
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Environment-Friendly Construction Materials by Wu, Shaopeng

Subjects: “…artificial neural network…”
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Testing of Materials and Elements in Civil Engineering (2nd Edition) Volume I

Subjects: “…artificial neural networks…”
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Testing of Materials and Elements in Civil Engineering (2nd Edition) Volume II

Subjects: “…artificial neural networks…”
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Advances in Agricultural Engineering Technologies and Application Volume I

Subjects: “…artificial neural network…”
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Advances in Agricultural Engineering Technologies and Application Volume II

Subjects: “…artificial neural network…”
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Sensors for Gait, Posture, and Health Monitoring Volume 1

Subjects: “…artificial neural network…”
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Sensors for Gait, Posture, and Health Monitoring Volume 2

Subjects: “…artificial neural network…”
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Sensors for Gait, Posture, and Health Monitoring Volume 3

Subjects: “…artificial neural network…”
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Paediatric Medicinal Formulation Development: Utilising Human Taste Panels and Incorporating Their Data into Machine Learning Training by Okhee Yoo, Britta S. von Ungern-Sternberg, Lee Yong Lim

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A Theoretical Exploration of Artificial Intelligence’s Impact on Feto-Maternal Health from Conception to Delivery by Yaseen I, Rather RA

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Modelling for Prediction vs. Modelling for Understanding by Michael Schneider, Peter Edelsbrunner

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Modelling for understanding AND for prediction/classification - the power of neural networks in research by Eduardo Cascallar, Mariel F. Musso, Eva Kyndt, Filip Dochy

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Multivariate Statistical Machine Learning Methods for Genomic Prediction by Montesinos López, Osval Antonio, Montesinos López, Abelardo, Crossa, José

Table of Contents: “…Preface -- Chapter 1 -- General elements of genomic selection and statistical learning -- Chapter. 2 -- Preprocessing tools for data preparation -- Chapter. 3 -- Elements for building supervised statistical machine learning models -- Chapter. 4 -- Overfitting, model tuning and evaluation of prediction performance -- Chapter. 5 -- Linear Mixed Models -- Chapter. 6 -- Bayesian Genomic Linear Regression -- Chapter. 7 -- Bayesian and classical prediction models for categorical and count data -- Chapter. 8 -- Reproducing Kernel Hilbert Spaces Regression and Classification Methods -- Chapter. 9 -- Support vector machines and support vector regression -- Chapter. 10 -- Fundamentals of artificial neural networks and deep learning -- Chapter. 11 -- Artificial neural networks and deep learning for genomic prediction of continuous outcomes -- Chapter. 12 -- Artificial neural networks and deep learning for genomic prediction of binary, ordinal and mixed outcomes -- Chapter. 13 -- Convolutional neural networks -- Chapter. 14 -- Functional regression -- Chapter. 15 -- Random forest for genomic prediction.…”
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In Silico Prediction of Toxicological and Pharmacokinetic Characteristics of Medicinal Compounds by P. M. Vassiliev, A. V. Golubeva, A. R. Koroleva, M. A. Perfilev, A. N. Kochetkov

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Review of machine learning algorithms' application in pharmaceutical technology by Đuriš Jelena, Kurćubić Ivana, Ibrić Svetlana

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Using soft computing techniques to diagnose Glaucoma disease by Mousa Al-Akhras, Ala' Barakat, Mohammed Alawairdhi, Mohamed Habib

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Knowledge tracing for adaptive learning in a metacognitive tutor by Carlon May Kristine Jonson, Cross Jeffrey S.

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