scholarly journals A Machine Learning and Data-Driven Prediction and Inversion of Reservoir Brittleness from Geophysical Logs and Seismic Signals: A Case Study in Southwest Pennsylvania, Central Appalachian Basin

2020 ◽  
Author(s):  
Tobi Micheal Ore
Keyword(s):  
Machine Learning ◽  
Appalachian Basin ◽  
Data Driven ◽  
Seismic Signals ◽  
Geophysical Logs ◽  
And Inversion ◽  
Southwest Pennsylvania

scholarly journals Data-Driven Machine Learning System for Optimization of Processes Supporting the Distribution of Goods and Services – a case study

2020 ◽  
Vol 44 ◽  
pp. 60-67
Author(s):  
Zbigniew Tarapata ◽  
Tadeusz Nowicki ◽  
Ryszard Antkiewicz ◽  
Jaroslaw Dudzinski ◽  
Konrad Janik
Keyword(s):  
Machine Learning ◽  
Learning System ◽  
Data Driven ◽  
Goods And Services

Machine Learning Based Interpretation of Microkinetic Data: A Fischer-Tropsch Synthesis Case Study

2021 ◽  
Author(s):  
Anoop Chakkingal ◽  
Pieter Janssens ◽  
Jeroen Poissonnier ◽  
Alan J Barrios ◽  
Mirella Virginie ◽  
...  
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Chemical Reactions ◽  
Tropsch Synthesis ◽  
Data Driven ◽  
Fischer Tropsch ◽  
Fischer Tropsch Synthesis ◽  
Artificial Neural

Machine-Learning (ML) methods, such as Artificial Neural Networks (ANN) bring the data-driven design of chemical reactions within reach. Simultaneously with the verification of the absence of any bias in the...

scholarly journals Combining an Expert-Based Medical Entity Recognizer to a Machine-Learning System: Methods and a Case Study

2013 ◽  
Vol 6s1 ◽  
pp. BII.S11770 ◽  
Author(s):  
Pierre Zweigenbaum ◽  
Thomas Lavergne ◽  
Natalia Grabar ◽  
Thierry Hamon ◽  
Sophie Rosset ◽  
...  
Keyword(s):  
Machine Learning ◽  
Linear Chain ◽  
Learning System ◽  
Entity Recognition ◽  
Supervised Machine Learning ◽  
Data Driven ◽  
Driven System ◽  
Medical Entity ◽  
F Measure

Medical entity recognition is currently generally performed by data-driven methods based on supervised machine learning. Expert-based systems, where linguistic and domain expertise are directly provided to the system are often combined with data-driven systems. We present here a case study where an existing expert-based medical entity recognition system, Ogmios, is combined with a data-driven system, Caramba, based on a linear-chain Conditional Random Field (CRF) classifier. Our case study specifically highlights the risk of overfitting incurred by an expert-based system. We observe that it prevents the combination of the 2 systems from obtaining improvements in precision, recall, or F-measure, and analyze the underlying mechanisms through a post-hoc feature-level analysis. Wrapping the expert-based system alone as attributes input to a CRF classifier does boost its F-measure from 0.603 to 0.710, bringing it on par with the data-driven system. The generalization of this method remains to be further investigated.

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