scholarly journals Classification of Mt Etna (Italy) Volcanic Activity by Machine Learning Approaches

2019 ◽  
Vol 61 (Vol 61 (2018)) ◽  
Author(s):  
Alireza Hajian ◽  
Flavio Cannavò ◽  
Filippo Greco ◽  
Giuseppe Nunnari
Keyword(s):  
Machine Learning ◽  
Volcanic Activity ◽  
Learning Approaches ◽  
Mt Etna

scholarly journals Clustering activity at Mt Etna based on volcanic tremor: A case study

2021 ◽  
Author(s):  
Giuseppe Nunnari
Keyword(s):  
Machine Learning ◽  
Volcanic Activity ◽  
Roc Curve ◽  
Volcanic Tremor ◽  
Machine Learning Algorithms ◽  
Etna Volcano ◽  
Mt Etna ◽  
Geophysical Signal

AbstractThis paper deals with the classification of volcanic activity into three classes, referred to as Quite, Strombolian and Paroxysm. The main purpose is to give a measure of the reliability with which such a classification, typically carried out by experts, can be performed by Machine Learning algorithms, by using the volcanic tremor as a feature. Both supervised and unsupervised methods are considered. It is experimentally shown that at least the Paroxysm activity can be reliably classified. Performances are rigorously assessed, in comparison with the classification made by expert volcanologists, in terms of popular indices such as the f1-score and the Area under the ROC curve (AuC). The work is basically a case study carried out on a dataset recorded in the area of the Mt Etna volcano. However, as volcanic tremor is a geophysical signal widely available, considered methods and strategies can be easily applied to similar volcanic areas.

scholarly journals Classification of Oil Palm Female Inflorescences Anthesis Stages Using Machine Learning Approaches

2020 ◽  
Author(s):  
Mamehgol Yousefi ◽  
Azmin Shakrine ◽  
Samsuzana bt. Abd Aziz ◽  
Syaril Azrad ◽  
Mohamed Mazmira ◽  
...  
Keyword(s):  
Machine Learning ◽  
Oil Palm ◽  
Learning Approaches

scholarly journals Machine learning approaches to predict the Plant-associated phenotype of Xanthomonas strains

BMC Genomics ◽  
2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Dennie te Molder ◽  
Wasin Poncheewin ◽  
Peter J. Schaap ◽  
Jasper J. Koehorst
Keyword(s):  
Machine Learning ◽  
Plant Pathogens ◽  
De Novo ◽  
Classification Algorithms ◽  
Learning Approaches ◽  
Enabling Factors ◽  
Essential Step ◽  
The World ◽  
Genome Content

Abstract Background The genus Xanthomonas has long been considered to consist predominantly of plant pathogens, but over the last decade there has been an increasing number of reports on non-pathogenic and endophytic members. As Xanthomonas species are prevalent pathogens on a wide variety of important crops around the world, there is a need to distinguish between these plant-associated phenotypes. To date a large number of Xanthomonas genomes have been sequenced, which enables the application of machine learning (ML) approaches on the genome content to predict this phenotype. Until now such approaches to the pathogenomics of Xanthomonas strains have been hampered by the fragmentation of information regarding pathogenicity of individual strains over many studies. Unification of this information into a single resource was therefore considered to be an essential step. Results Mining of 39 papers considering both plant-associated phenotypes, allowed for a phenotypic classification of 578 Xanthomonas strains. For 65 plant-pathogenic and 53 non-pathogenic strains the corresponding genomes were available and de novo annotated for the presence of Pfam protein domains used as features to train and compare three ML classification algorithms; CART, Lasso and Random Forest. Conclusion The literature resource in combination with recursive feature extraction used in the ML classification algorithms provided further insights into the virulence enabling factors, but also highlighted domains linked to traits not present in pathogenic strains.

Classification of Neurotransmitter Response in Dynamic PET Data Using Machine Learning Approaches

2020 ◽  
Vol 4 (6) ◽  
pp. 708-719 ◽  
Author(s):  
Oliver K. Fuller ◽  
Georgios I. Angelis ◽  
Steven R. Meikle
Keyword(s):  
Machine Learning ◽  
Learning Approaches ◽  
Dynamic Pet

A Comparison of Machine Learning Approaches for the Automated Classification of Dementia

2002 ◽  
pp. 721-722 ◽  
Author(s):  
Herbert Jelinek ◽  
David Cornforth ◽  
Patricia Waley ◽  
Eduardo Fernandez ◽  
Wayne Robinson
Keyword(s):  
Machine Learning ◽  
Learning Approaches ◽  
Automated Classification
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