Research on intelligent fault diagnosis of gears using EMD, spectral features and data mining techniques

Sagar, Md. Samiul Islam; Vivekkumar, G; Reddy, D. Mallikarjuna; Devendiran, S.; Amarnath, M.

doi:10.1088/1757-899x/263/6/062047

Cited by 2 publications

(1 citation statement)

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“…In the field of medicine, DM plays a significant role in computing applications [8], [9], [10]. The applications and methods of DM are demonstrated in the areas of healthcare administrations, patient care, management, and intensive care systems.…”

Section: Introductionmentioning

confidence: 99%

A Comparative Analysis and Predicting for Breast Cancer Detection Based on Data Mining Models

Khorshid

Abdulazeez

Sallow

2021

AJRCoS

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Breast cancer is one of the most common diseases among women, accounting for many deaths each year. Even though cancer can be treated and cured in its early stages, many patients are diagnosed at a late stage. Data mining is the method of finding or extracting information from massive databases or datasets, and it is a field of computer science with a lot of potentials. It covers a wide range of areas, one of which is classification. Classification may also be accomplished using a variety of methods or algorithms. With the aid of MATLAB, five classification algorithms were compared. This paper presents a performance comparison among the classifiers: Support Vector Machine (SVM), Logistics Regression (LR), K-Nearest Neighbors (K-NN), Weighted K-Nearest Neighbors (Weighted K-NN), and Gaussian Naïve Bayes (Gaussian NB). The data set was taken from UCI Machine learning Repository. The main objective of this study is to classify breast cancer women using the application of machine learning algorithms based on their accuracy. The results have revealed that Weighted K-NN (96.7%) has the highest accuracy among all the classifiers.

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Section: Introductionmentioning

confidence: 99%