scholarly journals KELM-KPCA Method for COVID-19-induced Pneumonia Detection

2021 ◽  
Vol 17 ◽  
pp. 166-171
Author(s):  
Bacha Sawssen ◽  
Taouali Okba ◽  
Liouane Noureeddine
Keyword(s):  
Principal Component Analysis ◽  
Extreme Learning Machine ◽  
Principal Component ◽  
Component Analysis ◽  
Chest Radiographs ◽  
Kernel Principal Component Analysis ◽  
The World ◽  
Kernel Extreme Learning Machine ◽  
Learning Machine ◽  
One Year

The new corona virus 2019 (COVID-19) has become the most pressing issue facing mankind. Like a wildfire burning through the world, the COVID-19 disease has changed the global landscape in only one year. In this mini-review, a novel image classifier based on Kernel Extreme Learning Machine (KELM) and Kernel Principal Component Analysis (KPCA) is presented. The proposed algorithm called KELM-KPCA, aims to detect COVID-19 disease in chest radiographs, using a constrained dataset.

Watermelon Ripeness Detection via Extreme Learning Machine with Kernel Principal Component Analysis Based on Acoustic Signals

2019 ◽  
Vol 33 (08) ◽  
pp. 1951002 ◽  
Author(s):  
Yinghao Zhang ◽  
Xiaoyan Deng ◽  
Zhou Xu ◽  
Peipei Yuan
Keyword(s):  
Principal Component Analysis ◽  
Linear Combination ◽  
Extreme Learning Machine ◽  
Principal Component ◽  
Acoustic Signals ◽  
Component Analysis ◽  
Training Sample ◽  
Training Data ◽  
Kernel Principal Component Analysis ◽  
Learning Machine

Many investigations have proved that the acoustics method is intuitive and effective for determining watermelon ripeness. The objective of this work is to drive a new robust acoustics classification scheme KPCA-ELM, which is based on the kernel principal component analysis (KPCA) and extreme learning machine (ELM). Acoustic signals are sampled by a microphone from unripe, ripe and over-ripe watermelon samples, which are randomly divided into two sample sets for training and testing. A set of basic signals is first obtained via KPCA of the training sample. Thus, any given signal can be represented as a linear combination of basis signals, and the coefficients of linear combination are extracted as the features of a signal. Corresponding to the unripe, ripe and over-ripe watermelons, a three-class ELM identification model is constructed based on the training data. The scheme presented in this paper is tested with the testing sample and an accuracy of 92% is achieved. To further evaluate the scheme performance, a comparison of ELM and SVM is conducted in terms of the classification results. The results reveal that the proposed scheme can classify faster than SVM, while ELM is better than SVM in accuracy.

scholarly journals A Hybrid Model Based on Principal Component Analysis, Wavelet Transform, and Extreme Learning Machine Optimized by Bat Algorithm for Daily Solar Radiation Forecasting

2019 ◽  
Vol 11 (15) ◽  
pp. 4138 ◽  
Author(s):  
Zhang ◽  
Wei
Keyword(s):  
Principal Component Analysis ◽  
Wavelet Transform ◽  
Solar Radiation ◽  
Extreme Learning Machine ◽  
Hybrid Model ◽  
Principal Component ◽  
Bat Algorithm ◽  
Component Analysis ◽  
Meteorological Variables ◽  
Learning Machine

Precise solar radiation forecasting is of great importance for solar energy utilization and its integration into the grid, but because of the daily solar radiation’s intrinsic non-stationary and nonlinearity, which is influenced by a lot of elements, single predicting models may have difficulty obtaining results with high accuracy. Therefore, this paper innovatively puts forward an original hybrid model that predicts solar radiation through extreme learning machine (ELM) optimized by the bat algorithm (BA) based on wavelet transform (WT) and principal component analysis (PCA). First, choose the meteorological variables on the basis of Pearson coefficient test, and WT will decompose historical solar radiation into two time series, which are de-noised signal and noise signal. In the approximate series, the lag phase of historical radiation is obtained by partial autocorrelation function (PACF). After that, use PCA to reduce the dimensions of the influencing factors, including meteorological variables and historical radiation. Finally, ELM is established to predict daily solar radiation, whose input weight and deviation thresholds gained optimization by BA, thus it is called BA-ELM henceforth. In view of the four distinct solar radiation series obtained by NASA, the empirical simulation explained the hybrid model’s validity and effectiveness compared to other primary methods.

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