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2021 | OriginalPaper | Chapter

Classification of In-House Managed Equipment by Listing Its Parts

Authors : S. V. Aruna, R. Karthika

Published in: Computer Networks and Inventive Communication Technologies

Publisher: Springer Nature Singapore

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Abstract

This paper proposes a novel research work, where the manual data are collected from various parts of the devices such as patient monitoring and syringe pump, and then the collected data are calibrated by self-organizing maps (SOM) using k-nearest neighbors (KNN), Naive Bayes, and support vector machine (SVM) classifiers. Sensitivity and specificity are used as the evaluation metrics. Comparing the accuracy and sensitivity parameters in naïve bayes and KNN classifier, the naïve bayes is performed better than KNN algorithm.

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Title: Classification of In-House Managed Equipment by Listing Its Parts
Authors: S. V. Aruna
R. Karthika
Publisher: Springer Nature Singapore
Book: Computer Networks and Inventive Communication Technologies
Print ISBN: 978-981-15-9646-9

Electronic ISBN: 978-981-15-9647-6

Copyright Year: 2021
DOI: https://doi.org/10.1007/978-981-15-9647-6_26

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