Resultados de búsqueda - "Machine Learning"

Ransomware Detection System Based on Machine Learning por Omar Ahmed, Omar Al-Dabbagh

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An integration engineering framework for machine learning in healthcare por Azadeh Assadi, Azadeh Assadi, Peter C. Laussen, Peter C. Laussen, Andrew J. Goodwin, Andrew J. Goodwin, Sebastian Goodfellow, William Dixon, Robert W. Greer, Anusha Jegatheeswaran, Devin Singh, Devin Singh, Melissa McCradden, Melissa McCradden, Melissa McCradden, Sara N. Gallant, Anna Goldenberg, Anna Goldenberg, Anna Goldenberg, Anna Goldenberg, Danny Eytan, Danny Eytan, Danny Eytan, Mjaye L. Mazwi, Mjaye L. Mazwi, Mjaye L. Mazwi

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Histological stain evaluation for machine learning applications por Jimmy C Azar, Christer Busch, Ingrid B Carlbom

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Application of machine learning techniques in the diagnosis of endometriosis por Ningning Zhao, Ting Hao, Fengge Zhang, Qin Ni, Dan Zhu, Yanan Wang, Yali Shi, Xin Mi

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Applying Machine Learning Techniques to Implementation Science por Nathalie Huguet, Jinying Chen, Ravi B Parikh, Miguel Marino, Susan A Flocke, Sonja Likumahuwa-Ackman, Justin Bekelman, Jennifer E DeVoe

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Machine learning and deep learning in medicine and neuroimaging por Iván Sánchez Fernández, Jurriaan M. Peters

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Commentary: Machine learning in clinical decision-making por Amanda C. Filiberto, Daniel A. Donoho, Ira L. Leeds, Tyler J. Loftus

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Editorial: Machine Learning in Clinical Decision-Making por Amanda C. Filiberto, Ira L. Leeds, Tyler J. Loftus

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Machine learning enables update to pediatric neurorehabilitation por Yunfang He, Simian Cai, Tingting Peng, Yan Qiao, Naiqi Wu, Kaishou Xu

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Machine Learning and Health Science Research: Tutorial por Hunyong Cho, Jane She, Daniel De Marchi, Helal El-Zaatari, Edward L Barnes, Anna R Kahkoska, Michael R Kosorok, Arti V Virkud

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Enhancing adaptive learning with Moodle's machine learning por Liliia O. Fadieieva

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Morphological Neuron Classification Using Machine Learning por Xavier Vasques, Xavier Vasques, Laurent Vanel, Guillaume Vilette, LAURA CIF

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Machine learning in the loop for tuberculosis diagnosis support por Alvaro D. Orjuela-Cañón, Andrés L. Jutinico, Carlos Awad, Erika Vergara, Angélica Palencia

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Chapter Machine Learning in Volcanology: A Review por Carniel, Roberto

Materias: “…machine learning, volcano seismology, volcano geophysics, volcano geochemistry, volcano geology, data reduction, feature vectors…”
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Chapter Machine Learning Models for Industrial Applications por Enislay, Ramentol

Materias: “…machine learning, prediction, regression methods, maintenance, degradation prediction…”
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Machine Learning and Its Application to Reacting Flows ML and Combustion

Materias: “…Machine learning bicssc…”
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The Price of Explainability in Machine Learning Models for 100-Day Readmission Prediction in Heart Failure: Retrospective, Comparative, Machine Learning Study por Amira Soliman, Björn Agvall, Kobra Etminani, Omar Hamed, Markus Lingman

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Integrating machine learning with pharmacokinetic models: Benefits of scientific machine learning in adding neural networks components to existing PK models por Diego Valderrama, Ana Victoria Ponce‐Bobadilla, Sven Mensing, Holger Fröhlich, Sven Stodtmann

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

Tabla de Contenidos: “…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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Computer Vision and Machine Learning for Intelligent Sensing Systems

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