scholarly journals Seeing It All: Evaluating Supervised Machine Learning Methods for the Classification of Diverse Otariid Behaviours

PLoS ONE ◽  
2016 ◽  
Vol 11 (12) ◽  
pp. e0166898 ◽  
Author(s):  
Monique A. Ladds ◽  
Adam P. Thompson ◽  
David J. Slip ◽  
David P. Hocking ◽  
Robert G. Harcourt
Keyword(s):  
Machine Learning ◽  
Supervised Machine Learning ◽  
Learning Methods ◽  
Machine Learning Methods

scholarly journals An evaluation of machine learning classifiers for next-generation, continuous-ethogram smart trackers

2021 ◽  
Vol 9 (1) ◽  
Author(s):  
Hui Yu ◽  
Jian Deng ◽  
Ran Nathan ◽  
Max Kröschel ◽  
Sasha Pekarsky ◽  
...  
Keyword(s):  
Machine Learning ◽  
Feature Reduction ◽  
Supervised Machine Learning ◽  
Support Vector ◽  
Learning Methods ◽  
Machine Learning Methods ◽  
Behavioural Research ◽  
Intermittent Sampling

Abstract Background Our understanding of movement patterns and behaviours of wildlife has advanced greatly through the use of improved tracking technologies, including application of accelerometry (ACC) across a wide range of taxa. However, most ACC studies either use intermittent sampling that hinders continuity or continuous data logging relying on tracker retrieval for data downloading which is not applicable for long term study. To allow long-term, fine-scale behavioural research, we evaluated a range of machine learning methods for their suitability for continuous on-board classification of ACC data into behaviour categories prior to data transmission. Methods We tested six supervised machine learning methods, including linear discriminant analysis (LDA), decision tree (DT), support vector machine (SVM), artificial neural network (ANN), random forest (RF) and extreme gradient boosting (XGBoost) to classify behaviour using ACC data from three bird species (white stork Ciconia ciconia, griffon vulture Gyps fulvus and common crane Grus grus) and two mammals (dairy cow Bos taurus and roe deer Capreolus capreolus). Results Using a range of quality criteria, SVM, ANN, RF and XGBoost performed well in determining behaviour from ACC data and their good performance appeared little affected when greatly reducing the number of input features for model training. On-board runtime and storage-requirement tests showed that notably ANN, RF and XGBoost would make suitable on-board classifiers. Conclusions Our identification of using feature reduction in combination with ANN, RF and XGBoost as suitable methods for on-board behavioural classification of continuous ACC data has considerable potential to benefit movement ecology and behavioural research, wildlife conservation and livestock husbandry.

scholarly journals TOWARDS AUTOMATED CLASSIFICATION OF PRODUCT DATA BASED ON MACHINE LEARNING

2020 ◽  
Vol 1 ◽  
pp. 375-382
Author(s):  
S. Schleibaum ◽  
S. Kehl ◽  
P. Stiefel ◽  
J. P. Müller
Keyword(s):  
Machine Learning ◽  
Product Lifecycle Management ◽  
Supervised Machine Learning ◽  
Automated Classification ◽  
Learning Methods ◽  
Industrial Case Study ◽  
Machine Learning Methods ◽  
Novel Approach ◽  
Lifecycle Management

AbstractModern machine learning methods have the potential to supply industrial product lifecycle management (PLM) with automated classification of product components. However, there is only little work in the literature on this topic. We propose to apply supervised machine learning on component meta-data. By analysing an industrial case study, we identify requirements and opportunities for automating classification, e.g. in part numbers and product structures. We validate our novel approach through a classification experiment comparing four machine learning methods on a realistic component dataset.

Machine learning for the extragalactic astronomy educational manual

2019 ◽  
Vol 15 (S367) ◽  
pp. 461-463
Author(s):  
Maksym Vasylenko ◽  
Daria Dobrycheva
Keyword(s):  
Machine Learning ◽  
Support Vector Machine ◽  
Programming Language ◽  
Supervised Machine Learning ◽  
Support Vector ◽  
Learning Methods ◽  
Machine Learning Methods ◽  
Python Programming Language ◽  
Python Programming

AbstractWe evaluated a new approach to the automated morphological classification of large galaxy samples based on the supervised machine learning techniques (Naive Bayes, Random Forest, Support Vector Machine, Logistic Regression, and k-Nearest Neighbours) and Deep Learning using the Python programming language. A representative sample of ∼315000 SDSS DR9 galaxies at z < 0.1 and stellar magnitudes r < 17.7m was considered as a target sample of galaxies with indeterminate morphological types. Classical machine learning methods were used to binary morphologically classification of galaxies into early and late types (96.4% with Support Vector Machine). Deep machine learning methods were used to classify images of galaxies into five visual types (completely rounded, rounded in-between, smooth cigar-shaped, edge-on, and spiral) with the Xception architecture (94% accuracy for four classes and 88% for cigar-like galaxies). These results created a basis for educational manual on the processing of large data sets in the Python programming language, which is intended for students of the Ukrainian universities.

scholarly journals Deep learning classification of lipid droplets in quantitative phase images

PLoS ONE ◽  
2021 ◽  
Vol 16 (4) ◽  
pp. e0249196
Author(s):  
Luke Sheneman ◽  
Gregory Stephanopoulos ◽  
Andreas E. Vasdekis
Keyword(s):  
Machine Learning ◽  
Lipid Droplets ◽  
Supervised Machine Learning ◽  
Label Free ◽  
Learning Methods ◽  
Method Performance ◽  
Quantitative Phase ◽  
Machine Learning Methods ◽  
Phase Images

We report the application of supervised machine learning to the automated classification of lipid droplets in label-free, quantitative-phase images. By comparing various machine learning methods commonly used in biomedical imaging and remote sensing, we found convolutional neural networks to outperform others, both quantitatively and qualitatively. We describe our imaging approach, all implemented machine learning methods, and their performance with respect to computational efficiency, required training resources, and relative method performance measured across multiple metrics. Overall, our results indicate that quantitative-phase imaging coupled to machine learning enables accurate lipid droplet classification in single living cells. As such, the present paradigm presents an excellent alternative of the more common fluorescent and Raman imaging modalities by enabling label-free, ultra-low phototoxicity, and deeper insight into the thermodynamics of metabolism of single cells.

scholarly journals FRI0046 PHARMACOGENOMICS-DRIVEN INDIVIDUALIZED PREDICTION OF TREATMENT RESPONSE TO METHOTREXATE IN PATIENTS WITH RHEUMATOID ARTHRITIS: A MACHINE LEARNING APPROACH

2020 ◽  
Vol 79 (Suppl 1) ◽  
pp. 598.2-598
Author(s):  
E. Myasoedova ◽  
A. Athreya ◽  
C. S. Crowson ◽  
R. Weinshilboum ◽  
L. Wang ◽  
...  
Keyword(s):  
Rheumatoid Arthritis ◽  
Machine Learning ◽  
Supervised Machine Learning ◽  
Research Support ◽  
Eular Response ◽  
Learning Methods ◽  
Machine Learning Methods ◽  
Early Ra ◽  
Genome Wide

Background:Methotrexate (MTX) is the most common anchor drug for rheumatoid arthritis (RA), but the risk of missing the opportunity for early effective treatment with alternative medications is substantial given the delayed onset of MTX action and 30-40% inadequate response rate. There is a compelling need to accurately predicting MTX response prior to treatment initiation, which allows for effectively identifying patients at RA onset who are likely to respond to MTX.Objectives:To test the ability of machine learning approaches with clinical and genomic biomarkers to predict MTX response with replications in independent samples.Methods:Age, sex, clinical, serological and genome-wide association study (GWAS) data on patients with early RA of European ancestry from 647 patients (336 recruited in United Kingdom [UK]; 307 recruited across Europe; 70% female; 72% rheumatoid factor [RF] positive; mean age 54 years; mean baseline Disease Activity Score with 28-joint count [DAS28] 5.65) of the PhArmacogenetics of Methotrexate in RA (PAMERA) consortium was used in this study. The genomics data comprised 160 genome-wide significant single nucleotide polymorphisms (SNPs) with p<1×10-5 associated with risk of RA and MTX metabolism. DAS28 score was available at baseline and 3-month follow-up visit. Response to MTX monotherapy at the dose of ≥15 mg/week was defined as good or moderate by the EULAR response criteria at 3 months’ follow up visit. Supervised machine-learning methods were trained with 5-repeats and 10-fold cross-validation using data from PAMERA’s 336 UK patients. Class imbalance (higher % of MTX responders) in training was accounted by using simulated minority oversampling technique. Prediction performance was validated in PAMERA’s 307 European patients (not used in training).Results:Age, sex, RF positivity and baseline DAS28 data predicted MTX response with 58% accuracy of UK and European patients (p = 0.7). However, supervised machine-learning methods that combined demographics, RF positivity, baseline DAS28 and genomic SNPs predicted EULAR response at 3 months with area under the receiver operating curve (AUC) of 0.83 (p = 0.051) in UK patients, and achieved prediction accuracies (fraction of correctly predicted outcomes) of 76.2% (p = 0.054) in the European patients, with sensitivity of 72% and specificity of 77%. The addition of genomic data improved the predictive accuracies of MTX response by 19% and achieved cross-site replication. Baseline DAS28 scores and following SNPs rs12446816, rs13385025, rs113798271, and rs2372536 were among the top predictors of MTX response.Conclusion:Pharmacogenomic biomarkers combined with DAS28 scores predicted MTX response in patients with early RA more reliably than using demographics and DAS28 scores alone. Using pharmacogenomics biomarkers for identification of MTX responders at early stages of RA may help to guide effective RA treatment choices, including timely escalation of RA therapies. Further studies on personalized prediction of response to MTX and other anti-rheumatic treatments are warranted to optimize control of RA disease and improve outcomes in patients with RA.Disclosure of Interests:Elena Myasoedova: None declared, Arjun Athreya: None declared, Cynthia S. Crowson Grant/research support from: Pfizer research grant, Richard Weinshilboum Shareholder of: co-founder and stockholder in OneOme, Liewei Wang: None declared, Eric Matteson Grant/research support from: Pfizer, Consultant of: Boehringer Ingelheim, Gilead, TympoBio, Arena Pharmaceuticals, Speakers bureau: Simply Speaking

scholarly journals Identifying Spatiotemporal Patterns in Land Use and Cover Samples from Satellite Image Time Series

2021 ◽  
Vol 13 (5) ◽  
pp. 974
Author(s):  
Lorena Alves Santos ◽  
Karine Ferreira ◽  
Michelle Picoli ◽  
Gilberto Camara ◽  
Raul Zurita-Milla ◽  
...  
Keyword(s):  
Machine Learning ◽  
Land Use ◽  
Time Series ◽  
Satellite Image ◽  
Spatiotemporal Patterns ◽  
Supervised Machine Learning ◽  
Learning Methods ◽  
Self Organizing Maps ◽  
Machine Learning Methods ◽  
Land Use And Cover

The use of satellite image time series analysis and machine learning methods brings new opportunities and challenges for land use and cover changes (LUCC) mapping over large areas. One of these challenges is the need for samples that properly represent the high variability of land used and cover classes over large areas to train supervised machine learning methods and to produce accurate LUCC maps. This paper addresses this challenge and presents a method to identify spatiotemporal patterns in land use and cover samples to infer subclasses through the phenological and spectral information provided by satellite image time series. The proposed method uses self-organizing maps (SOMs) to reduce the data dimensionality creating primary clusters. From these primary clusters, it uses hierarchical clustering to create subclusters that recognize intra-class variability intrinsic to different regions and periods, mainly in large areas and multiple years. To show how the method works, we use MODIS image time series associated to samples of cropland and pasture classes over the Cerrado biome in Brazil. The results prove that the proposed method is suitable for identifying spatiotemporal patterns in land use and cover samples that can be used to infer subclasses, mainly for crop-types.

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