A New Point-of-Interest Classification Model with an Extreme Learning Machine

2018 ◽  
Vol 10 (6) ◽  
pp. 951-964 ◽  
Author(s):  
Zhen Zhang ◽  
Xiangguo Zhao ◽  
Guoren Wang ◽  
Xin Bi
Keyword(s):  
Extreme Learning Machine ◽  
Classification Model ◽  
Point Of Interest ◽  
Learning Machine

An intelligent COVID-19 classification model using optimal grey-level co-occurrence matrix features with extreme learning machine

2021 ◽  
Vol 65 (4) ◽  
pp. 334
Author(s):  
Pavan Kumar Paruchuri ◽  
V. Gomathy ◽  
E. Anna Devi ◽  
Shweta Sankhwar ◽  
S.K. Lakshmanaprabu
Keyword(s):  
Extreme Learning Machine ◽  
Classification Model ◽  
Grey Level ◽  
Learning Machine ◽  
Occurrence Matrix

scholarly journals Spectral-Spatial Hyperspectral Image Classification with Superpixel Pattern and Extreme Learning Machine

2019 ◽  
Vol 11 (17) ◽  
pp. 1983 ◽  
Author(s):  
Yongshan Zhang ◽  
Xinwei Jiang ◽  
Xinxin Wang ◽  
Zhihua Cai
Keyword(s):  
Remote Sensing ◽  
Extreme Learning Machine ◽  
Spatial Patterns ◽  
Hyperspectral Image ◽  
Classification Model ◽  
Image Texture ◽  
Spatial Characteristic ◽  
Spectral Features ◽  
Sensing Applications ◽  
Learning Machine

Spectral-spatial classification of hyperspectral images (HSIs) has recently attracted great attention in the research domain of remote sensing. It is well-known that, in remote sensing applications, spectral features are the fundamental information and spatial patterns provide the complementary information. With both spectral features and spatial patterns, hyperspectral image (HSI) applications can be fully explored and the classification performance can be greatly improved. In reality, spatial patterns can be extracted to represent a line, a clustering of points or image texture, which denote the local or global spatial characteristic of HSIs. In this paper, we propose a spectral-spatial HSI classification model based on superpixel pattern (SP) and kernel based extreme learning machine (KELM), called SP-KELM, to identify the land covers of pixels in HSIs. In the proposed SP-KELM model, superpixel pattern features are extracted by an advanced principal component analysis (PCA), which is based on superpixel segmentation in HSIs and used to denote spatial information. The KELM method is then employed to be a classifier in the proposed spectral-spatial model with both the original spectral features and the extracted spatial pattern features. Experimental results on three publicly available HSI datasets verify the effectiveness of the proposed SP-KELM model, with the performance improvement of 10% over the spectral approaches.

An intelligent COVID-19 classification model using optimal grey-level co-occurrence matrix features with extreme learning machine

2021 ◽  
Vol 65 (4) ◽  
pp. 334
Author(s):  
S.K. Lakshmanaprabu ◽  
Shweta Sankhwar ◽  
E. Anna Devi ◽  
Pavan Kumar Paruchuri ◽  
V. Gomathy
Keyword(s):  
Extreme Learning Machine ◽  
Classification Model ◽  
Grey Level ◽  
Learning Machine ◽  
Occurrence Matrix

scholarly journals Predicting Coronary Atherosclerotic Heart Disease: An Extreme Learning Machine with Improved Salp Swarm Algorithm

Symmetry ◽  
2020 ◽  
Vol 12 (10) ◽  
pp. 1651
Author(s):  
Wenming He ◽  
Yanqing Xie ◽  
Haoxuan Lu ◽  
Mingjing Wang ◽  
Huiling Chen
Keyword(s):  
Heart Disease ◽  
Extreme Learning Machine ◽  
Classification Performance ◽  
Auxiliary Function ◽  
Classification Model ◽  
Diagnostic Model ◽  
Transformation Mechanism ◽  
Salp Swarm Algorithm ◽  
Swarm Algorithm ◽  
Learning Machine

To provide an available diagnostic model for diagnosing coronary atherosclerotic heart disease to provide an auxiliary function for doctors, we proposed a new evolutionary classification model in this paper. The core of the prediction model is a kernel extreme learning machine (KELM) optimized by an improved salp swarm algorithm (SSA). To get a better subset of parameters and features, the space transformation mechanism is introduced in the optimization core to improve SSA for obtaining an optimal KELM model. The KELM model for the diagnosis of coronary atherosclerotic heart disease (STSSA-KELM) is developed based on the optimal parameters and a subset of features. In the experiment, STSSA-KELM is compared with some widely adopted machine learning methods (MLM) in coronary atherosclerotic heart disease prediction. The experimental results show that STSSA-KELM can realize excellent classification performance and more robust stability under four indications. We also compare the convergence of STSSA-KELM with other MLM; the STSSA-KELM model has demonstrated a higher classification performance. Therefore, the STSSA-KELM model can effectively help doctors to diagnose coronary heart disease.

Fabric wrinkle level classification via online sequential extreme learning machine based on improved sine cosine algorithm

2020 ◽  
Vol 90 (17-18) ◽  
pp. 2007-2021 ◽  
Author(s):  
Zhiyu Zhou ◽  
Ruoxi Zhang ◽  
Jianxin Zhang ◽  
Yaming Wang ◽  
Zefei Zhu ◽  
...  
Keyword(s):  
Extreme Learning Machine ◽  
Optimal Parameter ◽  
Classification Model ◽  
Initial Population ◽  
Local Optimum ◽  
Search Spaces ◽  
Proposed Model ◽  
Sine Cosine Algorithm ◽  
Learning Machine ◽  
Hidden Layer

Because it is difficulty to classify level of fabric wrinkle, this paper proposes a fabric winkle level classification model via online sequential extreme learning machine based on improved sine cosine algorithm (SCA). The SCA has excellent global optimization ability, can explore different search spaces, and effectively avoid falling into local optimum. Because the initial population of SCA will have an impact on its optimization speed and quality, the SCA is initialized by differential evolution (DE) to avoid local optimization, and then the output weight and hidden layer bias are optimized; that is, the improved SCA is used to select the optimal parameters of the online sequential extreme learning machine (OSELM) to improve the generalization performance of the algorithm. To verify the performance of the proposed model DE-SCA-OSELM, it will be compared with other algorithms using a fabric wrinkles dataset collected under standard conditions. The experimental results indicate that the proposed model can effectively find the optimal parameter value of OSELM. The average classification accuracy increased by 6.95%, 3.62%, 6.67%, and 3.34%, respectively, compared with the partial algorithms OSELM, SCAELM, RVFL and PSOSVM, which meets expectations.

Lithofacies Recognition Based on Extreme Learning Machine

2012 ◽  
Vol 241-244 ◽  
pp. 1762-1767 ◽  
Author(s):  
Ya Juan Tian ◽  
Hua Xian Pan ◽  
Xuan Chao Liu ◽  
Guo Jian Cheng
Keyword(s):  
Extreme Learning Machine ◽  
Learning Algorithm ◽  
Selection Process ◽  
Feedforward Neural Networks ◽  
Parameter Selection ◽  
Classification Model ◽  
Support Vector ◽  
New Learning ◽  
Learning Machine ◽  
Hidden Layer

To overcome the problem of lower training speed and difficulty parameter selection in traditional support vector machine (SVM), a method based on extreme learning machine (ELM) for lithofacies recognition is presented in this paper. ELM is a new learning algorithm with single-hidden layer feedforward neural networks (SLFNN). Not only it can simplify the parameter selection process, but also improve the training speed of the network learning. By determining the optimal parameters, the lithofacies classification model is established, and the classification result of ELM is also compared to traditional SVM. The experimental results show that, ELM with less number of neurons has similar classification accuracy compared to SVM, and it is easier to select the parameters which significantly reduce the training speed. The feasibility of ELM for lithofacies recognition and the availability of the algorithm are verified and validated

scholarly journals An Effective Framework Using Spatial Correlation and Extreme Learning Machine for Moving Cast Shadow Detection

2019 ◽  
Vol 9 (23) ◽  
pp. 5042 ◽  
Author(s):  
Yugen Yi ◽  
Jiangyan Dai ◽  
Chengduan Wang ◽  
Jinkui Hou ◽  
Huihui Zhang ◽  
...  
Keyword(s):  
Spatial Correlation ◽  
Extreme Learning Machine ◽  
Moving Objects ◽  
Shadow Detection ◽  
Classification Model ◽  
Cast Shadow ◽  
Different Color ◽  
Learning Machine ◽  
Tracking Object ◽  
High Level

Moving cast shadows of moving objects significantly degrade the performance of many high-level computer vision applications such as object tracking, object classification, behavior recognition and scene interpretation. Because they possess similar motion characteristics with their objects, moving cast shadow detection is still challenging. In this paper, we present a novel moving cast-shadow detection framework based on the extreme learning machine (ELM) to efficiently distinguish shadow points from the foreground object. First, according to the physical model of shadows, pixel-level features of different channels in different color spaces and region-level features derived from the spatial correlation of neighboring pixels are extracted from the foreground. Second, an ELM-based classification model is developed by labelled shadow and un-shadow points, which is able to rapidly distinguish the points in the new input whether they belong to shadows or not. Finally, to guarantee the integrity of shadows and objects for further image processing, a simple post-processing procedure is designed to refine the results, which also drastically improves the accuracy of moving shadow detection. Extensive experiments on two publicly common datasets including 13 different scenes demonstrate that the performance of the proposed framework is superior to representative state-of-the-art methods.

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