Color difference classification of dyed fabrics via a kernel extreme learning machine based on an improved grasshopper optimization algorithm

2020 ◽  
Author(s):  
Jianqiang Li ◽  
Weimin Shi ◽  
Donghe Yang
Keyword(s):  
Extreme Learning Machine ◽  
Optimization Algorithm ◽  
Color Difference ◽  
Grasshopper Optimization Algorithm ◽  
Kernel Extreme Learning Machine ◽  
Grasshopper Optimization ◽  
Dyed Fabrics ◽  
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.

Genus and Species-Level Classification of Wrasse Fishes Using Multidomain Features and Extreme Learning Machine Classifier

2020 ◽  
Vol 34 (11) ◽  
pp. 2050028 ◽  
Author(s):  
Jisha Anu Jose ◽  
C. Sathish Kumar
Keyword(s):  
Extreme Learning Machine ◽  
Species Level ◽  
Feature Reduction ◽  
Polynomial Kernel ◽  
Automated Recognition ◽  
Background Elimination ◽  
Frequency Domains ◽  
Kernel Extreme Learning Machine ◽  
Learning Machine

Automated recognition and classification of fishes are useful for studies dealing with counting of fishes for population assessments, discovering association between fishes and ecosystem, and monitoring of the ecosystem. This paper proposes a model which classifies the fishes belonging to the family Labridae in the genus and the species level. Features computed in the spatial and frequency domains are used in this work. All the images are preprocessed before feature extraction. Preprocessing step involves image segmentation for background elimination, de-noising and image enhancement. A combination of color, local binary pattern (LBP), histogram of oriented gradients (HOG), and wavelet features forms the feature vector. An ensemble feature reduction technique is used to reduce the attribute size. Performances of the system using combined as well as reduced feature sets are evaluated using seven popular classifiers. Among the classifiers, wavelet kernel extreme learning machine (ELM) showed higher classification accuracy of 96.65% in genus level and polynomial kernel ELM showed an accuracy of 92.42% in species level with the reduced feature set.

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