Detection and classification of power quality disturbances in wind-grid integrated system using fast time-time transform and small residual-extreme learning machine

2018 ◽  
Vol 28 (4) ◽  
pp. e2519 ◽  
Author(s):  
Manish Kumar Saini ◽  
Rajender Kumar Beniwal
Keyword(s):  
Power Quality ◽  
Extreme Learning Machine ◽  
Integrated System ◽  
Fast Time ◽  
Power Quality Disturbances ◽  
Learning Machine

Efficient Kernel Extreme Learning Machine and Neutrosophic C-means-based Attribute Weighting Method for Medical Data Classification

2020 ◽  
Vol 29 (16) ◽  
pp. 2050260 ◽  
Author(s):  
D. Shiny Irene ◽  
T. Sethukarasi
Keyword(s):  
Extreme Learning Machine ◽  
Data Classification ◽  
Medical Data ◽  
Integrated System ◽  
Weighting Method ◽  
Attribute Weighting ◽  
Kernel Extreme Learning Machine ◽  
Medical Data Classification ◽  
Learning Machine

This paper proposes an integrated system neutrosophic C-means-based attribute weighting-kernel extreme learning machine (NCMAW-KELM) for medical data classification using NCM clustering and KELM. To do that, NCMAW is developed, and then combined with classification method in classification of medical data. The proposed approach contains two steps. In the first step, input attributes are weighted using NCMAW method. The purpose of the weighting method is twofold: (i) to improve the classification performance in the classification of the medical data, (ii) to transform from nonlinearly separable dataset to linearly separable dataset. Finally, KELM algorithm is used for medical data classification purpose. In KELM algorithm, four types of kernels, such as Polynomial, Sigmoid, Radial basis function and Linear, are used. The simulation result on our three datasets demonstrates that the sigmoid kernel is outperformed to ELM in most cases. From the results, NCMAW-KELM approach may be a promising method in medical data classification problem.

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