Classification of Sugar Beets Based on Hyperspectral and Extreme Learning Machine Methods

2018 ◽  
Vol 34 (6) ◽  
pp. 891-897 ◽  
Author(s):  
Rongchao Yang ◽  
Haiqing Tian ◽  
Jiangming Kan
Keyword(s):  
Standard Deviation ◽  
Sugar Beet ◽  
Extreme Learning Machine ◽  
Signal To Noise Ratio ◽  
Recognition Performance ◽  
Pretreatment Method ◽  
Standard Normal Variate ◽  
Spectral Models ◽  
Input Variables ◽  
Learning Machine

Abstract. Sugar beet varieties were classified based on hyperspectral technology and the Extreme Learning Machine (ELM) algorithm. The influences of seven pretreatment methods, namely, Savitzky-Golay smoothing (SG), the first derivative (FD) method, SG smoothing combined with the FD method (SG-FD), logarithmic transformation (LT), LT combined with the FD method (LT-FD), the standard normal variate (SNV) method, and SNV combined with the FD method (SNV-FD), on the recognition performance of the ELM model were analyzed to select the best pretreatment method. To simplify the input variables, the standard deviation peak method was used to extract the feature bands for different preprocessed spectral data. The experimental results showed that for different pretreatment methods, the recognition rates of sugar beet varieties by ELM models were all over 80%. Additionally, the combination of different pretreatment methods and FD effectively improved the signal-to-noise ratio and enhanced the accuracy and stability of spectral models. Overall, the recognition accuracy of the ELM models established based on the feature bands was better than that established based on all bands, which suggests that the feature bands extracted by the standard deviation peak method are effective. Based on the SG-FD pretreatment method, the ELM models established using all bands and feature bands both achieved the highest recognition effect. Specifically, the recognition rates of the prediction sets were 93.94% and 95.45%, respectively. Keywords: Hyperspectral, Sugar beet variety, ELM, Different pretreatment methods, Standard deviation peak method.

scholarly journals A Novel Blind Digital Watermarking Based on SVD and Extreme Learning Machine

2017 ◽  
Vol 10 (1) ◽  
pp. 160-167
Author(s):  
Neelam Dabas ◽  
Rampal Singh ◽  
Vikash Chaudhary
Keyword(s):  
Digital Media ◽  
Extreme Learning Machine ◽  
Signal To Noise Ratio ◽  
Extraction Process ◽  
Digital Data ◽  
Learning Machine ◽  
Value Decomposition ◽  
Cropping Rotation ◽  
Integer Wavelet ◽  
Watermarking Scheme

Modification of media and illegal production is a big problem now a days because of free availability of digital media. Protection and securing the digital data is a challenge. An Integer Wavelet Transformation (IWT) domain based robust watermarking scheme with Singular Value Decomposition (SVD) and Extreme Learning Machine (ELM) have been proposed and tested on different images. In this proposed scheme, a watermark or logo is embedded in the IWT domain as ownership information with SVD and ELM is trained to learn the relationship between the original coefficient and the watermarked one. This trained ELM is used in the extraction process to extract the embedded logo from the image. Experimental results show that the proposed watermarking scheme is robust against various image attacks like Blurring, Noise, Cropping, Rotation, Sharpening etc. Performance analysis of proposed watermarking scheme is measured with Peak Signal to Noise Ratio (PSNR) and Bit Error Rate (BER)

scholarly journals Extreme Learning Machine-Based Model for Solubility Estimation of Hydrocarbon Gases in Electrolyte Solutions

2020 ◽  
Author(s):  
Narjes Nabipour ◽  
Amir Mosavi ◽  
Alireza Baghban ◽  
Shahaboddin Shamshirband ◽  
Imre Felde
Keyword(s):  
Extreme Learning Machine ◽  
Chemical Engineering ◽  
Electrolyte Solutions ◽  
Key Factors ◽  
Hydrocarbon Gases ◽  
Natural Gases ◽  
Solubility Model ◽  
Input Variables ◽  
Learning Machine ◽  
Solubility Estimation

Calculating hydrocarbon components solubility of natural gases is known as one of the important issues for operational works in petroleum and chemical engineering. In this work, a novel solubility estimation tool has been proposed for hydrocarbon gases including methane, ethane, propane and butane in aqueous electrolyte solutions based on extreme learning machine (ELM) algorithm. Comparing the ELM outputs with a comprehensive real databank which has 1175 solubility points concluded to R-squared values of 0.985 and 0.987 for training and testing phases respectively. Furthermore, the visual comparison of estimated and actual hydrocarbon solubility leaded to confirm the ability of proposed solubility model. Additionally, sensitivity analysis has been employed on the input variables of model to identify their impacts on hydrocarbon solubility. Such a comprehensive and reliable study can help engineers and scientists to successfully determine the important thermodynamic properties which are key factors in optimizing and designing different industrial units such as refineries and petrochemical plants.

scholarly journals Non-Tuned Machine Learning Approach for Predicting the Compressive Strength of High-Performance Concrete

Materials ◽  
2020 ◽  
Vol 13 (5) ◽  
pp. 1023
Author(s):  
Abobakr Khalil Al-Shamiri ◽  
Tian-Feng Yuan ◽  
Joong Hoon Kim
Keyword(s):  
Machine Learning ◽  
Compressive Strength ◽  
Prediction Model ◽  
Extreme Learning Machine ◽  
High Performance ◽  
High Performance Concrete ◽  
Fine Aggregate ◽  
Input Variables ◽  
Prediction Reliability ◽  
Learning Machine

Compressive strength is considered as one of the most important parameters in concrete design. Time and cost can be reduced if the compressive strength of concrete is accurately estimated. In this paper, a new prediction model for compressive strength of high-performance concrete (HPC) was developed using a non-tuned machine learning technique, namely, a regularized extreme learning machine (RELM). The RELM prediction model was developed using a comprehensive dataset obtained from previously published studies. The input variables of the model include cement, blast furnace slag, fly ash, water, superplasticizer, coarse aggregate, fine aggregate, and age of specimens. k-fold cross-validation was used to assess the prediction reliability of the developed RELM model. The prediction results of the RELM model were evaluated using various error measures and compared with that of the standard extreme learning machine (ELM) and other methods presented in the literature. The findings of this research indicate that the compressive strength of HPC can be accurately estimated using the proposed RELM model.

scholarly journals Aircraft Type Recognition in Remote Sensing Images: Bilinear Discriminative Extreme Learning Machine Framework

Electronics ◽  
2021 ◽  
Vol 10 (17) ◽  
pp. 2046
Author(s):  
Baojun Zhao ◽  
Wei Tang ◽  
Yu Pan ◽  
Yuqi Han ◽  
Wenzheng Wang
Keyword(s):  
Remote Sensing ◽  
Extreme Learning Machine ◽  
Feature Fusion ◽  
Recognition Performance ◽  
Optimization Method ◽  
Layer By Layer ◽  
Aircraft Model ◽  
Aircraft Type ◽  
Learning Machine ◽  
Model Recognition

Small inter-class and massive intra-class changes are important challenges in aircraft model recognition in the field of remote sensing. Although the aircraft model recognition algorithm based on the convolutional neural network (CNN) has excellent recognition performance, it is limited by sample sets and computing resources. To solve the above problems, we propose the bilinear discriminative extreme learning machine (ELM) network (BD-ELMNet), which integrates the advantages of the CNN, autoencoder (AE), and ELM. Specifically, the BD-ELMNet first executes the convolution and pooling operations to form a convolutional ELM (ELMConvNet) to extract shallow features. Furthermore, the manifold regularized ELM-AE (MRELM-AE), which can simultaneously consider the geometrical structure and discriminative information of aircraft data, is developed to extract discriminative features. The bilinear pooling model uses the feature association information for feature fusion to enhance the substantial distinction of features. Compared with the backpropagation (BP) optimization method, BD-ELMNet adopts a layer-by-layer training method without repeated adjustments to effectively learn discriminant features. Experiments involving the application of several methods, including the proposed method, to the MTARSI benchmark demonstrate that the proposed aircraft type recognition method outperforms the state-of-the-art methods.

scholarly journals Extreme Learning Machine-Based Model for Solubility Estimation of Hydrocarbon Gases in Electrolyte Solutions

Processes ◽  
2020 ◽  
Vol 8 (1) ◽  
pp. 92 ◽  
Author(s):  
Narjes Nabipour ◽  
Amir Mosavi ◽  
Alireza Baghban ◽  
Shahaboddin Shamshirband ◽  
Imre Felde
Keyword(s):  
Extreme Learning Machine ◽  
Chemical Engineering ◽  
Electrolyte Solutions ◽  
Key Factors ◽  
Hydrocarbon Gases ◽  
Natural Gases ◽  
Solubility Model ◽  
Input Variables ◽  
Learning Machine ◽  
Solubility Estimation

Calculating hydrocarbon components solubility of natural gases is known as one of the important issues for operational works in petroleum and chemical engineering. In this work, a novel solubility estimation tool has been proposed for hydrocarbon gases—including methane, ethane, propane, and butane—in aqueous electrolyte solutions based on extreme learning machine (ELM) algorithm. Comparing the ELM outputs with a comprehensive real databank which has 1175 solubility points yielded R-squared values of 0.985 and 0.987 for training and testing phases respectively. Furthermore, the visual comparison of estimated and actual hydrocarbon solubility led to confirm the ability of proposed solubility model. Additionally, sensitivity analysis has been employed on the input variables of model to identify their impacts on hydrocarbon solubility. Such a comprehensive and reliable study can help engineers and scientists to successfully determine the important thermodynamic properties, which are key factors in optimizing and designing different industrial units such as refineries and petrochemical plants.

scholarly journals Improved Emotion Recognition Using Gaussian Mixture Model and Extreme Learning Machine in Speech and Glottal Signals

2015 ◽  
Vol 2015 ◽  
pp. 1-13 ◽  
Author(s):  
Hariharan Muthusamy ◽  
Kemal Polat ◽  
Sazali Yaacob
Keyword(s):  
Emotion Recognition ◽  
Extreme Learning Machine ◽  
Gaussian Mixture Model ◽  
Mixture Model ◽  
Nearest Neighbor ◽  
Recognition Performance ◽  
Gaussian Mixture ◽  
Speech Emotion Recognition ◽  
New Feature ◽  
Learning Machine

Recently, researchers have paid escalating attention to studying the emotional state of an individual from his/her speech signals as the speech signal is the fastest and the most natural method of communication between individuals. In this work, new feature enhancement using Gaussian mixture model (GMM) was proposed to enhance the discriminatory power of the features extracted from speech and glottal signals. Three different emotional speech databases were utilized to gauge the proposed methods. Extreme learning machine (ELM) andk-nearest neighbor (kNN) classifier were employed to classify the different types of emotions. Several experiments were conducted and results show that the proposed methods significantly improved the speech emotion recognition performance compared to research works published in the literature.

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