Modeling color fading ozonation of reactive-dyed cotton using the Extreme Learning Machine, Support Vector Regression and Random Forest

2019 ◽  
Vol 90 (7-8) ◽  
pp. 896-908 ◽  
Author(s):  
Zhenglei He ◽  
Kim-Phuc Tran ◽  
Sébastien Thomassey ◽  
Xianyi Zeng ◽  
Jie Xu ◽  
...  
Keyword(s):  
Random Forest ◽  
Support Vector Regression ◽  
Extreme Learning Machine ◽  
Support Vector ◽  
Training Time ◽  
Single Output ◽  
Predicting Performance ◽  
Color Fading ◽  
Learning Machine ◽  
Temperature Water

Textile products with a faded effect achieved via ozonation are increasingly popular nowadays. In order to better understand and apply this process, the complex factors and effects of color fading ozonation are investigated via process modeling in terms of pH, temperature, water pick-up, time (of process) and original color (of textile) affecting the color performance ( K/ S, L*, a*, b* values) of reactive-dyed cotton using the Extreme Learning Machine (ELM), Support Vector Regression (SVR) and Random Forest (RF), respectively. It is found that the RF and SVR perform better than the ELM as the latter were very unstable in the case of predicting a certain single output. Both the RF and SVR are potentially applicable, but SVR would be more recommended to be used in the real application due to its balancer predicting performance and lower training time cost.

scholarly journals Nonlinear models for recognition of concentration of mixture

2021 ◽  
Vol 47 ◽  
Author(s):  
Feliksas Ivanauskas ◽  
Robertas Paulauskas ◽  
Pranas Vaitkus
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Support Vector Regression ◽  
Extreme Learning Machine ◽  
Nonlinear Models ◽  
Support Vector ◽  
Artificial Neural ◽  
Learning Machine

In this paper extreme learning machine and support vector regression are used for biosensors response to mixtures of compounds classification. The results are compared with the results obtained using artificial neural networks and others.

scholarly journals Barium Titanate Semiconductor Band Gap Characterization through Gravitationally Optimized Support Vector Regression and Extreme Learning Machine Computational Methods

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Sunday O. Olatunji ◽  
Taoreed O. Owolabi
Keyword(s):  
Barium Titanate ◽  
Band Gap ◽  
Support Vector Regression ◽  
Extreme Learning Machine ◽  
Energy Gap ◽  
Gravitational Search Algorithm ◽  
High Rate ◽  
Percentage Error ◽  
Support Vector ◽  
Learning Machine

Barium titanate (BaTiO3) is a class of ceramic multifunctional materials with unique thermal stability, prominent piezoelectricity constant, excellent dielectric constant, environmental friendliness, and excellent photocatalytic activities. These features have rendered barium titanate indispensable in many areas of applications such as electromechanical devices, thermistors, multilayer capacitors, and electrooptical devices. The photocatalytic activity of barium titanate semiconductor is hindered by its large band gap and high rate of charge recombination. Doping of the parent barium titanate compound for band gap tuning is challenging and consumes appreciable time and other valuable resources. This present work relates the influence of foreign material incorporation into the parent barium titanate with the corresponding energy band gap by developing extreme learning machine- (ELM-) based models and hybridization of support vector regression (SVR) with gravitational search algorithm (GSA) using the structural lattice distortion that emanated from doping as model descriptors. The developed gravitationally optimized SVR (GSVR) is characterized with a low value of mean absolute error (MAE), mean absolute percentage error (MAPE), and root mean square error (RMSE) of 0.036 ev, 1.145 ev, and 0.122 ev, respectively. The developed GSVR model outperforms ELM-Sine and ELM-Sig models using various performance evaluators. The developed GSVR model investigates the significance of iodine and samarium incorporation on the band gap of the parent barium titanate and the attained energy gaps conform excellently to the experimentally reported values. The demonstrated precision of the developed GSVR as measured from the closeness of its estimates with the measured values provides a quick and accurate method of energy gap characterization with circumvention of experimental stress and conservation of valuable time as well as other resources.

Face Recognition Based on LBP and Extreme Learning Machine

2013 ◽  
Vol 380-384 ◽  
pp. 3526-3529 ◽  
Author(s):  
Jing Hui Wu ◽  
Lu Han ◽  
Jia Tong Li ◽  
Yi Xiao Zhao ◽  
Lin Bo Tang ◽  
...  
Keyword(s):  
Face Recognition ◽  
Extreme Learning Machine ◽  
Texture Features ◽  
Recognition Algorithm ◽  
Point Of View ◽  
Support Vector ◽  
Training Time ◽  
Optimization Constraints ◽  
The Face ◽  
Learning Machine

This paper proposes a face recognition algorithm based on the combination of local binary pattern (LBP) texture features and extreme learning machine (ELM). The face image is divided into several regions, and the LBP features are extracted from these regions and combined together to form a feature vector which will be the input data of ELM. It shows that ELM performs well in classification applications, and ELM and support vector machine (SVM) are equivalent from the optimization point of view. But ELM has milder optimization constraints and much less training time. Our experiments are carried out on two well-known face databases, and the results show that compared with compared to PCA+NN, PCA+SVM and PCA+ELM the proposed method can achieve higher recognition rates.

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