scholarly journals Applications of machine learning methods to identifying and predicting building retrofit opportunities

2016 ◽  
Vol 128 ◽  
pp. 431-441 ◽  
Author(s):  
Daniel E. Marasco ◽  
Constantine E. Kontokosta
Keyword(s):  
Machine Learning ◽  
Learning Methods ◽  
Building Retrofit ◽  
Machine Learning Methods ◽  
Applications Of Machine Learning

PRACTICAL APPLICATIONS OF MACHINE LEARNING METHODS IN WELL LOGGING

2021 ◽  
Author(s):  
A. Petrov ◽  
◽  
K. Danilovskiy ◽  
K. Sukhorukova ◽  
A. Leonenko ◽  
...  
Keyword(s):  
Machine Learning ◽  
Well Logging ◽  
Learning Methods ◽  
Practical Applications ◽  
Machine Learning Methods ◽  
Applications Of Machine Learning

Machine Learning Methods in Precision Medicine Targeting Epigenetic Diseases

2019 ◽  
Vol 24 (34) ◽  
pp. 3998-4006
Author(s):  
Shijie Fan ◽  
Yu Chen ◽  
Cheng Luo ◽  
Fanwang Meng
Keyword(s):  
Machine Learning ◽  
Big Data ◽  
Precision Medicine ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Learning Methods ◽  
Advantages And Disadvantages ◽  
Machine Learning Methods ◽  
Accelerated Studies ◽  
Applications Of Machine Learning

Background: On a tide of big data, machine learning is coming to its day. Referring to huge amounts of epigenetic data coming from biological experiments and clinic, machine learning can help in detecting epigenetic features in genome, finding correlations between phenotypes and modifications in histone or genes, accelerating the screen of lead compounds targeting epigenetics diseases and many other aspects around the study on epigenetics, which consequently realizes the hope of precision medicine. Methods: In this minireview, we will focus on reviewing the fundamentals and applications of machine learning methods which are regularly used in epigenetics filed and explain their features. Their advantages and disadvantages will also be discussed. Results: Machine learning algorithms have accelerated studies in precision medicine targeting epigenetics diseases. Conclusion: In order to make full use of machine learning algorithms, one should get familiar with the pros and cons of them, which will benefit from big data by choosing the most suitable method(s).

scholarly journals Recent Advances on the Design Automation for Performance-Optimized Fiber Reinforced Polymer Composite Components

2020 ◽  
Vol 4 (2) ◽  
pp. 61
Author(s):  
Yi Di Boon ◽  
Sunil Chandrakant Joshi ◽  
Somen Kumar Bhudolia ◽  
Goram Gohel
Keyword(s):  
Machine Learning ◽  
Polymer Composite ◽  
Design Automation ◽  
Material Design ◽  
Fiber Reinforced Polymer ◽  
Fiber Reinforced ◽  
Learning Methods ◽  
Machine Learning Methods ◽  
Reinforced Polymer ◽  
Applications Of Machine Learning

Advanced manufacturing techniques, such as automated fiber placement and additive manufacturing enables the fabrication of fiber-reinforced polymer composite components with customized material and structural configurations. In order to take advantage of this customizability, the design process for fiber-reinforced polymer composite components needs to be improved. Machine learning methods have been identified as potential techniques capable of handling the complexity of the design problem. In this review, the applications of machine learning methods in various aspects of structural component design are discussed. They include studies on microstructure-based material design, applications of machine learning models in stress analysis, and topology optimization of fiber-reinforced polymer composites. A design automation framework for performance-optimized fiber-reinforced polymer composite components is also proposed. The proposed framework aims to provide a comprehensive and efficient approach for the design and optimization of fiber-reinforced polymer composite components. The challenges in building the models required for the proposed framework are also discussed briefly.

Applications of Machine Learning Methods in Retrospective Studies on Hearing

2018 ◽  
Author(s):  
Francois Charih ◽  
Ashlynn Steeves ◽  
Matthew Bromwich ◽  
Amy E. Mark ◽  
Renee Lefrancois ◽  
...  
Keyword(s):  
Machine Learning ◽  
Retrospective Studies ◽  
Learning Methods ◽  
Machine Learning Methods ◽  
Applications Of Machine Learning

scholarly journals Applications of Machine Learning Methods to Genomic Selection in Breeding Wheat for Rust Resistance

2018 ◽  
Vol 11 (2) ◽  
pp. 170104 ◽  
Author(s):  
Juan Manuel González‐Camacho ◽  
Leonardo Ornella ◽  
Paulino Pérez‐Rodríguez ◽  
Daniel Gianola ◽  
Susanne Dreisigacker ◽  
...  
Keyword(s):  
Machine Learning ◽  
Genomic Selection ◽  
Rust Resistance ◽  
Learning Methods ◽  
Machine Learning Methods ◽  
Applications Of Machine Learning

scholarly journals A Study on machine learning methods and applications in genetics and genomics

2018 ◽  
Vol 7 (1.7) ◽  
pp. 201
Author(s):  
K Jayanthi ◽  
C Mahesh
Keyword(s):  
Machine Learning ◽  
Gene Prediction ◽  
Genomic Data ◽  
Data Sets ◽  
Complex Data ◽  
Learning Methods ◽  
Machine Learning Methods ◽  
Complex Data Sets ◽  
Machine Learning Applications ◽  
Applications Of Machine Learning

Machine learning enables computers to help humans in analysing knowledge from large, complex data sets. One of the complex data is genetics and genomic data which needs to analyse various set of functions automatically by the computers. Hope this machine learning methods can provide more useful for making these data for further usage like gene prediction, gene expression, gene ontology, gene finding, gene editing and etc. The purpose of this study is to explore some machine learning applications and algorithms to genetic and genomic data. At the end of this study we conclude the following topics classifications of machine learning problems: supervised, unsupervised and semi supervised, which type of method is suitable for various problems in genomics, applications of machine learning and future views of machine learning in genomics.

scholarly journals The Prediction-Explanation Fallacy: A Pervasive Problem in Scientific Applications of Machine Learning

2021 ◽  
Author(s):  
Marco Del Giudice
Keyword(s):  
Machine Learning ◽  
Predictive Models ◽  
Complex Structure ◽  
Multiple Models ◽  
Similar Data ◽  
Scientific Applications ◽  
Learning Methods ◽  
Machine Learning Methods ◽  
Mitigating Factors ◽  
Applications Of Machine Learning

In this paper, I highlight a problem that has become ubiquitous in scientific applications of machine learning methods, and can lead to seriously distorted inferences about the phenomena under study. I call it the prediction-explanation fallacy. The fallacy occurs when researchers use prediction-optimized models for explanatory purposes, without considering the tradeoffs between explanation and prediction. This is a problem for at least two reasons. First, prediction-optimized models are often deliberately biased and unrealistic in order to prevent overfitting, and hence fail to accurately explain the phenomenon of interest. In other cases, they have an exceedingly complex structure that is hard or impossible to interpret, which greatly limits their explanatory value. Second, different predictive models trained on the same or similar data can be biased in different ways, so that multiple models may predict equally well but suggest conflicting explanations of the underlying phenomenon. In this note I introduce the tradeoffs between prediction and explanation in a non-technical fashion, present some illustrative examples from neuroscience, and end by discussing some mitigating factors and methods that can be used to limit or circumvent the problem.

Sign in / Sign up

Export Citation Format

Share Document