UAV Aided Data Collection for Wildlife Monitoring using Cache-enabled Mobile Ad-hoc Wireless Sensor Nodes

Chaudhry, Umair B.; Phillips, Chris

doi:10.14569/ijacsa.2021.0121002

Cited by 5 publications

(1 citation statement)

References 34 publications

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“…In this regard, in [29] it is pointed out that the Bagged Trees algorithm forms different trees when there is a change in the starting point of the training data that results in a decrease in stability. This technique or algorithm is also suitable to be used in the search for optimal models for large data, since the classification becomes easier [30], [31].…”

Section: Introductionmentioning

confidence: 99%

Machine Learning Model through Ensemble Bagged Trees in Predictive Analysis of University Teaching Performance

Chamorro-Atalaya¹,

Chávez-Herrera²,

Santos³

et al. 2021

IJACSA

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The objective of this study is to analyze and discuss the metrics of the Machine Learning model through the Ensemble Bagged Trees algorithm, which will be applied to data on satisfaction with teaching performance in the virtual environment. Initially the classification analysis through the Matlab R2021a software, identified an Accuracy of 81.3%, for the Ensemble Bagged Trees algorithm. When performing the validation of the collected data, and proceeding with the obtaining of the predictive model, for the 4 classes (satisfaction levels), total precision values of 82.21%, Sensitivity of 73.40%, Specificity of 91.02% and of 90.63% Accuracy. In turn, the highest level of the area under the curve (AUC) by means of the Receiver operating characteristic (ROC) is 0.93, thus considering a sensitivity of the predictive model of 93%. The validation of these results will allow the directors of the higher institution to have a database, to be used in the process of improving the quality of the educational service in relation to teaching performance.

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Section: Introductionmentioning

confidence: 99%