To switch or not to switch – a machine learning approach for ferroelectricity

The introduced two-dimensional representation of two-parameter signal dependence allows for clear interpretation and classification of the measured signal upon using machine learning methods.

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Classifying Non-Sentential Utterances in Dialogue: A Machine Learning Approach

Computational Linguistics ◽

10.1162/coli.2007.33.3.397 ◽

2007 ◽

Vol 33 (3) ◽

pp. 397-427 ◽

Cited By ~ 17

Author(s):

Raquel Fernández ◽

Jonathan Ginzburg ◽

Shalom Lappin

Keyword(s):

Machine Learning ◽

Pilot Study ◽

Full Range ◽

Learning Approach ◽

Learning Methods ◽

Fine Grained ◽

Machine Learning Methods ◽

Machine Learning Approach ◽

The Right

In this article we use well-known machine learning methods to tackle a novel task, namely the classification of non-sentential utterances (NSUs) in dialogue. We introduce a fine-grained taxonomy of NSU classes based on corpus work, and then report on the results of several machine learning experiments. First, we present a pilot study focused on one of the NSU classes in the taxonomy—bare wh-phrases or “sluices”—and explore the task of disambiguating between the different readings that sluices can convey. We then extend the approach to classify the full range of NSU classes, obtaining results of around an 87% weighted F-score. Thus our experiments show that, for the taxonomy adopted, the task of identifying the right NSU class can be successfully learned, and hence provide a very encouraging basis for the more general enterprise of fully processing NSUs.

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A Machine Learning Approach to Study Glycosidase Activities from Bifidobacterium

Microorganisms ◽

10.3390/microorganisms9051034 ◽

2021 ◽

Vol 9 (5) ◽

pp. 1034

Author(s):

Carlos Sabater ◽

Lorena Ruiz ◽

Abelardo Margolles

Keyword(s):

Machine Learning ◽

Supervised Classification ◽

Machine Learning Algorithms ◽

Learning Approach ◽

Human Milk Oligosaccharides ◽

Future Studies ◽

High Fiber ◽

Machine Learning Approach ◽

Prebiotic Oligosaccharides

This study aimed to recover metagenome-assembled genomes (MAGs) from human fecal samples to characterize the glycosidase profiles of Bifidobacterium species exposed to different prebiotic oligosaccharides (galacto-oligosaccharides, fructo-oligosaccharides and human milk oligosaccharides, HMOs) as well as high-fiber diets. A total of 1806 MAGs were recovered from 487 infant and adult metagenomes. Unsupervised and supervised classification of glycosidases codified in MAGs using machine-learning algorithms allowed establishing characteristic hydrolytic profiles for B. adolescentis, B. bifidum, B. breve, B. longum and B. pseudocatenulatum, yielding classification rates above 90%. Glycosidase families GH5 44, GH32, and GH110 were characteristic of B. bifidum. The presence or absence of GH1, GH2, GH5 and GH20 was characteristic of B. adolescentis, B. breve and B. pseudocatenulatum, while families GH1 and GH30 were relevant in MAGs from B. longum. These characteristic profiles allowed discriminating bifidobacteria regardless of prebiotic exposure. Correlation analysis of glycosidase activities suggests strong associations between glycosidase families comprising HMOs-degrading enzymes, which are often found in MAGs from the same species. Mathematical models here proposed may contribute to a better understanding of the carbohydrate metabolism of some common bifidobacteria species and could be extrapolated to other microorganisms of interest in future studies.

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A machine-learning approach for automatic classification of volcanic seismicity at La Soufrière Volcano, Guadeloupe

Journal of Volcanology and Geothermal Research ◽

10.1016/j.jvolgeores.2020.107151 ◽

2020 ◽

pp. 107151

Author(s):

Alexis Falcin ◽

Jean-Philippe Métaxian ◽

Jérôme Mars ◽

Éléonore Stutzmann ◽

Jean-Christophe Komorowski ◽

...

Keyword(s):

Machine Learning ◽

Automatic Classification ◽

Learning Approach ◽

Machine Learning Approach ◽

Volcanic Seismicity

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How can machine-learning methods assist in virtual screening for hyperuricemia? A healthcare machine-learning approach

Journal of Biomedical Informatics ◽

10.1016/j.jbi.2016.09.012 ◽

2016 ◽

Vol 64 ◽

pp. 20-24 ◽

Cited By ~ 26

Author(s):

Daisuke Ichikawa ◽

Toki Saito ◽

Waka Ujita ◽

Hiroshi Oyama

Keyword(s):

Machine Learning ◽

Virtual Screening ◽

Learning Approach ◽

Learning Methods ◽

Machine Learning Methods ◽

Machine Learning Approach

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The Classification of Skateboarding Tricks : A Transfer Learning and Machine Learning Approach

Mekatronika ◽

10.15282/mekatronika.v2i2.6683 ◽

2020 ◽

Vol 2 (2) ◽

pp. 1-12

Author(s):

Muhammad Nur Aiman Shapiee ◽

Muhammad Ar Rahim Ibrahim ◽

Muhammad Amirul Abdullah ◽

Rabiu Muazu Musa ◽

Noor Azuan Abu Osman ◽

...

Keyword(s):

Machine Learning ◽

Classification Accuracy ◽

Nearest Neighbor ◽

Olympic Games ◽

Learning Approach ◽

K Nearest Neighbor ◽

Test Dataset ◽

Machine Learning Approach ◽

Competitive Games

The skateboarding scene has arrived at new statures, particularly with its first appearance at the now delayed Tokyo Summer Olympic Games. Hence, attributable to the size of the game in such competitive games, progressed creative appraisal approaches have progressively increased due consideration by pertinent partners, particularly with the enthusiasm of a more goal-based assessment. This study purposes for classifying skateboarding tricks, specifically Frontside 180, Kickflip, Ollie, Nollie Front Shove-it, and Pop Shove-it over the integration of image processing, Trasnfer Learning (TL) to feature extraction enhanced with tradisional Machine Learning (ML) classifier. A male skateboarder performed five tricks every sort of trick consistently and the YI Action camera captured the movement by a range of 1.26 m. Then, the image dataset were features built and extricated by means of three TL models, and afterward in this manner arranged to utilize by k-Nearest Neighbor (k-NN) classifier. The perception via the initial experiments showed, the MobileNet, NASNetMobile, and NASNetLarge coupled with optimized k-NN classifiers attain a classification accuracy (CA) of 95%, 92% and 90%, respectively on the test dataset. Besides, the result evident from the robustness evaluation showed the MobileNet+k-NN pipeline is more robust as it could provide a decent average CA than other pipelines. It would be demonstrated that the suggested study could characterize the skateboard tricks sufficiently and could, over the long haul, uphold judges decided for giving progressively objective-based decision.

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