scholarly journals Study on Risk Classification of Goaf Based on RS-SVM

2019 ◽  
Vol 295 ◽  
pp. 02001
Author(s):  
Haiping Yuan ◽  
Biaohua Le ◽  
Yubo Wang ◽  
Chenghao Chen ◽  
Hanbing Bian
Keyword(s):  
Eastern China ◽  
Risk Classification ◽  
Classification Model ◽  
Good Effect ◽  
Risk Level ◽  
Support Vector ◽  
Treatment Rate ◽  
Empty Area ◽  
Maximum Exposure

According to the uncertainty and concealment of the risk of goaf, a risk classification model of goaf is constructed based on rough set (RS) knowledge and support vector machine (SVM) theory. In this paper, based on statistical analysis and measured data, nine parameters including mining method, empty area excavation depth, goaf height, maximum exposed area of empty area, maximum exposure height, maximum exposure span, pillar condition, empty volume and treatment rate are selected as the main influencing factors. The RS theory is used to reduce the sample, and SVM is compiled by Matlab. The one-to-one method is used to construct the binary classifier to realize the multi-class classification algorithm of goaf. Finally, a SVM model for evaluating the risk level of the goaf is obtained. The research shows that: based on RS theory, SVM has a good effect on the hazard classification of the goaf iron ore mine, and the difference with the actual situation is 13.3%. The research results have certain theoretical significance and guiding role for the safe mining of an iron mine in Eastern China.

scholarly journals A Spectral Feature Based Convolutional Neural Network for Classification of Sea Surface Oil Spill

2019 ◽  
Vol 8 (4) ◽  
pp. 160 ◽  
Author(s):  
Bingxin Liu ◽  
Ying Li ◽  
Guannan Li ◽  
Anling Liu
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Convolutional Neural Network ◽  
Convolutional Neural Networks ◽  
Machine Learning Algorithms ◽  
Classification Model ◽  
Support Vector ◽  
Oil Film ◽  
One Dimensional

Spectral characteristics play an important role in the classification of oil film, but the presence of too many bands can lead to information redundancy and reduced classification accuracy. In this study, a classification model that combines spectral indices-based band selection (SIs) and one-dimensional convolutional neural networks was proposed to realize automatic oil films classification using hyperspectral remote sensing images. Additionally, for comparison, the minimum Redundancy Maximum Relevance (mRMR) was tested for reducing the number of bands. The support vector machine (SVM), random forest (RF), and Hu’s convolutional neural networks (CNN) were trained and tested. The results show that the accuracy of classifications through the one dimensional convolutional neural network (1D CNN) models surpassed the accuracy of other machine learning algorithms such as SVM and RF. The model of SIs+1D CNN could produce a relatively higher accuracy oil film distribution map within less time than other models.

Biomimetic Pattern Recognition for Classification of Proteomic Profile

2013 ◽  
Vol 791-793 ◽  
pp. 1961-1964
Author(s):  
Xiao Li Yang ◽  
Qiong He
Keyword(s):  
Pattern Recognition ◽  
Cancer Diagnosis ◽  
Principal Component ◽  
Classification Model ◽  
Support Vector ◽  
Discrete Wavelet ◽  
Proteomic Profile ◽  
Satisfactory Model ◽  
Over Time

We propose a biomimetic pattern recognition (BPR) approach for classification of proteomic profile. The proposed approach preprocess profile using iterative minimum in adaptive setting window (IMASW) method for baseline correction, discrete wavelet transform (DWT) for fitting and smoothing, and average total ion normalization (ATIN) for remove the influence of vary amount of sample and degradation over time. Then principal component analysis (PCA) and BPR build classification model. With an optimization of the parameters involved in the modeling, we obtain a satisfactory model for cancer diagnosis in three proteomic profile datasets. The predicted results show that BPR technique is more reliable and efficient than support vector machine (SVM) method.

scholarly journals Machine Learning Techniques for Land Use/Land Cover Classification of Medium Resolution Optical Satellite Imagery Focusing on Temporary Inundated Areas

2020 ◽  
Vol 13 (1-2) ◽  
pp. 43-52
Author(s):  
Boudewijn van Leeuwen ◽  
Zalán Tobak ◽  
Ferenc Kovács
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Land Use ◽  
Land Cover ◽  
Classification Model ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Land Use Land Cover ◽  
Learning Techniques

AbstractClassification of multispectral optical satellite data using machine learning techniques to derive land use/land cover thematic data is important for many applications. Comparing the latest algorithms, our research aims to determine the best option to classify land use/land cover with special focus on temporary inundated land in a flat area in the south of Hungary. These inundations disrupt agricultural practices and can cause large financial loss. Sentinel 2 data with a high temporal and medium spatial resolution is classified using open source implementations of a random forest, support vector machine and an artificial neural network. Each classification model is applied to the same data set and the results are compared qualitatively and quantitatively. The accuracy of the results is high for all methods and does not show large overall differences. A quantitative spatial comparison demonstrates that the neural network gives the best results, but that all models are strongly influenced by atmospheric disturbances in the image.

scholarly journals An Accurate Tool for Uncovering Cancer Subtypes by Fast Kernel Learning Method to Integrate Multiple Profile Data

2021 ◽  
Vol 9 ◽  
Author(s):  
Hongyu Zhang ◽  
Limin Jiang ◽  
Jijun Tang ◽  
Yijie Ding
Keyword(s):  
Lung Cancer ◽  
Feature Representation ◽  
The Cancer Genome Atlas ◽  
Classification Model ◽  
Kernel Learning ◽  
Support Vector ◽  
Cancer Subtypes ◽  
Operation Speed ◽  
Kernel Fusion

In recent years, cancer has become a severe threat to human health. If we can accurately identify the subtypes of cancer, it will be of great significance to the research of anti-cancer drugs, the development of personalized treatment methods, and finally conquer cancer. In this paper, we obtain three feature representation datasets (gene expression profile, isoform expression and DNA methylation data) on lung cancer and renal cancer from the Broad GDAC, which collects the standardized data extracted from The Cancer Genome Atlas (TCGA). Since the feature dimension is too large, Principal Component Analysis (PCA) is used to reduce the feature vector, thus eliminating the redundant features and speeding up the operation speed of the classification model. By multiple kernel learning (MKL), we use Kernel target alignment (KTA), fast kernel learning (FKL), Hilbert-Schmidt Independence Criterion (HSIC), Mean to calculate the weight of kernel fusion. Finally, we put the combined kernel function into the support vector machine (SVM) and get excellent results. Among them, in the classification of renal cell carcinoma subtypes, the maximum accuracy can reach 0.978 by using the method of MKL (HSIC calculation weight), while in the classification of lung cancer subtypes, the accuracy can even reach 0.990 with the same method (FKL calculation weight).

scholarly journals Research on Automatic Classification Method of Ethnic Music Emotion Based on Machine Learning

2022 ◽  
Vol 2022 ◽  
pp. 1-11
Author(s):  
Zijin Wu
Keyword(s):  
Folk Music ◽  
Classification Accuracy ◽  
Fine Tuning ◽  
Classification Model ◽  
Classification Method ◽  
Support Vector ◽  
Ethnic Music ◽  
Huge Impact ◽  
Speed Up

With the development of the country’s economy, there is a flourishing situation in the field of culture and art. However, the diversification of artistic expressions has not brought development to folk music. On the contrary, it brought a huge impact, and some national music even fell into the dilemma of being lost. This article is mainly aimed at the recognition and classification of folk music emotions and finds the model that can make the classification accuracy rate as high as possible. The classification model used in this article is mainly after determining the use of Support Vector Machine (SVM) classification method, a variety of attempts have been made to feature extraction, and good results have been achieved. Explore the Deep Belief Network (DBN) pretraining and reverse fine-tuning process, using DBN to learn the fusion characteristics of music. According to the abstract characteristics learned by them, the recognition and classification of folk music emotions are carried out. The DBN is improved by adding “Dropout” to each Restricted Boltzmann Machine (RBM) and adjusting the increase standard of weight and bias. The improved network can avoid the overfitting problem and speed up the training of the network. Through experiments, it is found that using the fusion features proposed in this paper, through classification, the classification accuracy has been improved.

scholarly journals Classification of Rice and Starch Flours by Using Multiple Hyperspectral Imaging Systems and Chemometric Methods

2020 ◽  
Vol 10 (19) ◽  
pp. 6724
Author(s):  
Youngwook Seo ◽  
Ahyeong Lee ◽  
Balgeum Kim ◽  
Jongguk Lim
Keyword(s):  
Discriminant Analysis ◽  
Hyperspectral Imaging ◽  
Partial Least Square ◽  
Hyperspectral Images ◽  
Classification Model ◽  
Support Vector ◽  
Chemometric Methods ◽  
Infrared Wavelength ◽  
Linear Discriminant

(1) Background: The general use of food-processing facilities in the agro-food industry has increased the risk of unexpected material contamination. For instance, grain flours have similar colors and shapes, making their detection and isolation from each other difficult. Therefore, this study is aimed at verifying the feasibility of detecting and isolating grain flours by using hyperspectral imaging technology and developing a classification model of grain flours. (2) Methods: Multiple hyperspectral images were acquired through line scanning methods from reflectance of visible and near-infrared wavelength (400–1000 nm), reflectance of shortwave infrared wavelength (900–1700 nm), and fluorescence (400–700 nm) by 365 nm ultraviolet (UV) excitation. Eight varieties of grain flours were prepared (rice: 4, starch: 4), and the particle size and starch damage content were measured. To develop the classification model, four multivariate analysis methods (linear discriminant analysis (LDA), partial least-square discriminant analysis, support vector machine, and classification and regression tree) were implemented with several pre-processing methods, and their classification results were compared with respect to accuracy and Cohen’s kappa coefficient obtained from confusion matrices. (3) Results: The highest accuracy was achieved as 97.43% through short-wavelength infrared with normalization in the spectral domain. The submission of the developed classification model to the hyperspectral images showed that the fluorescence method achieves the highest accuracy of 81% using LDA. (4) Conclusions: In this study, the potential of non-destructive classification of rice and starch flours using multiple hyperspectral modalities and chemometric methods were demonstrated.

Quality Assessment in Vocational Education Based on a Hierarchical Classification Technique

2011 ◽  
Vol 403-408 ◽  
pp. 3724-3728
Author(s):  
Chantima Ekwong ◽  
Sageemas Na Wichain ◽  
Choochart Haruechaiyasak
Keyword(s):  
Quality Assessment ◽  
Vocational Education ◽  
Hierarchical Classification ◽  
Unstructured Data ◽  
Classification Model ◽  
Support Vector ◽  
Education Standards ◽  
National Education ◽  
Vector Machines

According to the laws of education in Thailand, the Office for National Education Standards and Quality Assessment is responsible for assessing the external educational institutes in order to develop the quality and educational standards. The external quality assessment reports are represented in both structured and unstructured data. In this paper, we focus on the analysis of unstructured data, i.e., to automatically classify strength and weakness points. We propose and evaluate two different classification models: Flat Classification and Hierarchical Classification. Three algorithms, Naive Bayes, Support Vector Machines (SVM) and Decision Tree, were used in the experiments. The results showed that classification viathe Hierarchical Classification model by using the SVM yielded the best performance. The classification of strength and weakness points yielded the F-measure equal to 0.843 and 0.893, respectively. The proposed approach can be applied as a decision support function for quality assessment in vocational education.

scholarly journals Modelos de aprendizaje automático para el apoyo en la clasificación de tipos de cáncer a partir de datos estructurados y no estructurados de expedientes clínicos

2020 ◽  
Author(s):  
◽  
Erick Esteven Montelongo González
Keyword(s):  
Unstructured Data ◽  
Classification Model ◽  
Support Vector ◽  
Classification Models ◽  
Multi Layer Perceptron ◽  
Health Area ◽  
Vector Machines ◽  
Clinical Records ◽  
Unstructured Information

The existence of large volumes of data generated by the health area presents an important opportunity for analysis. This can obtain information to support physicians in the decisionmaking process for the diagnosis or treatment of diseases, such as cancer. The present work shows a methodology for the classification of patients with liver, lung and breast cancer, through machine learning models, to obtain the model that performs best in the classification. The methodology considers three classification models: Support Vector Machines (SVM), Multi-Layer Perceptron (MLP) and AdaBoost using both structured and unstructured information from the patient's clinical records. Results show that the best classification model is MLP using only unstructured data, obtaining 89% of precision, showing the usefulness of this type of data in the classification of cancer patients.

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