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Classification of In-House Managed Equipment by Listing Its Parts

Publication Type : Journal Article

Source : In Computer Networks and Inventive Communication Technologies (pp. 337-349). Springer, Singapore.

Url : https://link.springer.com/chapter/10.1007/978-981-15-9647-6_26

Campus : Coimbatore

School : School of Engineering

Department : Electronics and Communication

Verified : No

Year : 2021

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.

Cite this Research Publication : Aruna, S.V. and Karthika, R., 2021. Classification of In-House Managed Equipment by Listing Its Parts. In Computer Networks and Inventive Communication Technologies (pp. 337-349). Springer, Singapore.

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