Cardiac signals classification based on Extra Trees model

Dhananjay, B.; Venkatesh, N. Prasanna; Bhardwaj, Arya; Jayaraman, Srinivasan

doi:10.1109/spin52536.2021.9565992

Cited by 5 publications

(1 citation statement)

References 27 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Dhananjay et al [13] employed the ETC to classify cardiac abnormalities into three classes: Sinus Rhythm (SR), Sinus Tachycardia (ST) under physical stress, and Atrial Tachycardia (AT). Manually measuring these conditions, especially when they exhibit similar wave morphologies, is a challenging task.…”

Section: Introductionmentioning

confidence: 99%

Enhancing testing cell set efficiency: A machine learning approach on hard disk drive data

Rakcheep,

Laosakun,

Khaengkarn

et al. 2024

Manufacturing Rev.

View full text Add to dashboard Cite

Hard Disk Drive (HDD) products undergo meticulous testing procedures to ensure their functionality prior to customer distribution. Nevertheless, anomalies can arise within the testing environment due to various factors, such as an increased number of media discs, leading to heightened current consumption by the spindle motor, and the frequent insertion and removal of HDDs during testing. These factors can induce malfunctions within the testing cell, which are identified by the tester's program. This study leverages diverse data measurements collected from tester HDDs within the testing cell to predict the status of the testing cell itself. Five distinct algorithms—Linear Discriminant Analysis (LDA), Ridge Classifier CV (RCCV), Extra-Tree Classifier (ETC), Random Forest Classifier (RFC), and Extreme Gradient Boosting (XGBoost)—were assessed. The research underscores that the proposed methodology, particularly utilizing XGBoost, achieves a notable prediction accuracy of 87.9% when applied to real datasets.

show abstract

Section: Introductionmentioning

confidence: 99%