Classification of different skarn deposits based on the compositional variability of associated grandite garnets: a data science and Machine Learning approach

Mapping Intimacies ◽

10.5194/egusphere-egu21-10537 ◽

2021 ◽

Author(s):

Urmi Ghosh ◽

Tuhin Chakraborty

Keyword(s):

Machine Learning ◽

Trace Element ◽

Data Science ◽

Training Dataset ◽

Support Vector ◽

Learning Approach ◽

Machine Learning Approach ◽

Skarn Deposits

<p>Rapid technological improvements made in in-situ analysis techniques, including LA-ICPMS, have transformed the field of analytical geochemistry. This has a far-reaching impact for different petrogenetic and ore-genetic studies where minute major and trace element compositional changes between different mineral zones within a single crystal can now be demarcated. Minerals such as garnet although robust are quite sensitive to the changing P-T and fluid conditions during their formation. These minerals have become powerful tools to characterize mineralization types. Previously, Meinert (1992) has used in-situ major element EPMA analysis results to classify different skarn deposit based on the end-member composition of hydrothermal garnets. Alternatively, Tian et al. (2019) used the garnet trace element composition for the similar purpose. However, these discrimination plots/ classification schemes show major overlap in different skarn deposits, such as Fe, Cu, Zn, and Au. The present study is an attempt to use machine learning approach on available garnet data to found a more potent classification scheme for skarn deposits, thus reaffirming garnet as a faithful indicator for hydrothermal ore deposits. We have meticulously collected major and trace element data of Ca-rich garnets, associated with different skarn deposits worldwide from 40 publications. This collected data is then used to train a model for fingerprinting the skarn deposits. Stratified random sampling method has been used on the dataset with 80% of the samples as test set and the rest 20 % as training dataset. We have used K-nearest neighbour (KNN), Support Vector Machine (SVM) and Random Forest algorithms on the data by using Python as a platform. These ML classification algorithm performs better than the earlier existing models available for classification of ore types based on garnet composition in skarn system. Factor importance is calculated that shows which elements play a pivotal role in classification of the ore type. Our results depict that multiple garnet forming elements taken together can reliably be used to discriminate between different ore formation settings.</p>

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A Fusion-Based Machine Learning Approach for the Prediction of the Onset of Diabetes

Healthcare ◽

10.3390/healthcare9101393 ◽

2021 ◽

Vol 9 (10) ◽

pp. 1393

Author(s):

Muhammad Waqas Nadeem ◽

Hock Guan Goh ◽

Vasaki Ponnusamy ◽

Ivan Andonovic ◽

Muhammad Adnan Khan ◽

...

Keyword(s):

Machine Learning ◽

Support Vector ◽

Learning Approach ◽

Health Providers ◽

Machine Learning Approach ◽

Patients With Diabetes ◽

Diagnosis And Classification ◽

Onset Of Diabetes ◽

The Cost

A growing portfolio of research has been reported on the use of machine learning-based architectures and models in the domain of healthcare. The development of data-driven applications and services for the diagnosis and classification of key illness conditions is challenging owing to issues of low volume, low-quality contextual data for the training, and validation of algorithms, which, in turn, compromises the accuracy of the resultant models. Here, a fusion machine learning approach is presented reporting an improvement in the accuracy of the identification of diabetes and the prediction of the onset of critical events for patients with diabetes (PwD). Globally, the cost of treating diabetes, a prevalent chronic illness condition characterized by high levels of sugar in the bloodstream over long periods, is placing severe demands on health providers and the proposed solution has the potential to support an increase in the rates of survival of PwD through informing on the optimum treatment on an individual patient basis. At the core of the proposed architecture is a fusion of machine learning classifiers (Support Vector Machine and Artificial Neural Network). Results indicate a classification accuracy of 94.67%, exceeding the performance of reported machine learning models for diabetes by ~1.8% over the best reported to date.

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Compositions and classification of fractionated boninite series melts from the Izu-Bonin-Mariana arc: A machine learning approach

Journal of Petrology ◽

10.1093/petrology/egab013 ◽

2021 ◽

Author(s):

Matthew J Valetich ◽

Charles Le Losq ◽

Richard J Arculus ◽

Susumu Umino ◽

John Mavrogenes

Keyword(s):

Machine Learning ◽

Major Elements ◽

Learning Approach ◽

New Classification ◽

Machine Learning Approach ◽

Geochemical Indicators ◽

Mariana Arc ◽

Classification Tool

Abstract Much of the boninite magmatism in the Izu-Bonin-Mariana (IBM) arc is preserved as evolved boninite series compositions wherein extensive fractional crystallisation of pyroxene and spinel have obscured the diagnostic geochemical indicators of boninite parentage, such as high-Mg and low-Ti at intermediate silica contents. As a result, the usual geochemical discriminants used for the classification of the broad range of parental boninites are inapplicable to such highly fractionated melts. These issues are compounded by the mixing of demonstrably different whole-rock and glass analyses in classification schemes and petrological interpretations based thereon. Whole-rock compositions are compromised by entrainment of variable proportions of crystalline phases resulting in inconsistent differences with corresponding in-situ glass analyses, which arguably better reflect prior melt compositions. To circumvent such issues, we herein present a robust method for the classification of highly fractionated boninite series glasses. This new classification leverages the analysis of trace elements, much more sensitive to evolutionary processes than major elements, and benefits from the use of unsupervised machine learning as a classification tool. The results show the most fractionated boninite series melts preserve geochemical indicators of their parentage, and highlight the pitfalls of interpreting whole rock and glass analyses interchangeably.

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Multivariate neuroanatomical classification of cognitive subtypes in schizophrenia: A support vector machine learning approach

NeuroImage Clinical ◽

10.1016/j.nicl.2014.09.009 ◽

2014 ◽

Vol 6 ◽

pp. 229-236 ◽

Cited By ~ 42

Author(s):

Ian C. Gould ◽

Alana M. Shepherd ◽

Kristin R. Laurens ◽

Murray J. Cairns ◽

Vaughan J. Carr ◽

...

Keyword(s):

Machine Learning ◽

Support Vector Machine ◽

Support Vector ◽

Learning Approach ◽

Machine Learning Approach

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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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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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