SVM Fusion Research Articles

A Novel Intelligent Condition Monitoring Framework of Essential Service Water Pumps

Essential service water pumps are necessary safety devices responsible for discharging waste heat from containments through seawater; their condition monitoring is critical for the safe and stable operation of seaside nuclear power plants. However, it is difficult to directly apply existing intelligent methods to these pumps. Therefore, an intelligent condition monitoring framework is designed, including the parallel implementation of unsupervised anomaly detection and fault diagnosis. A model preselection algorithm based on the highest validation accuracy is proposed for anomaly detection and fault diagnosis model selection among existing models. A novel information integration algorithm is proposed to fuse the output of anomaly detection and fault diagnosis. According to the experimental results of modules, a kernel principal component analysis using mean fusion processing multi-channel data (AKPCA (fusion)) is selected, and a support vector machine using mean fusion processing multi-channel data (SVM (fusion)) is selected. The overall test accuracy and false negative rate of AKPCA (fusion) are 0.83 and 0.144, respectively, and the overall test accuracy and f1-score of SVM (fusion) are 0.966 and 1, respectively. The test results of AKPCA (fusion), SVM (fusion), and the proposed information integration algorithm show that the information integration algorithm successfully avoids a lack of abnormal status information and misdiagnosis. The proposed framework is a meaningful attempt to achieve the intelligent condition monitoring of complex equipment.

Toward Precise Long-Term Rockburst Forecasting: A Fusion of SVM and Cutting-Edge Meta-heuristic Algorithms

Toward Precise Long-Term Rockburst Forecasting: A Fusion of SVM and Cutting-Edge Meta-heuristic Algorithms

Research on Coal and Gas Outburst Risk Warning Based on Multiple Algorithm Fusion

To improve the accuracy of gas outburst early warning, this paper proposes a gas outburst risk warning model based on XGBoost–GR–stacking. The statistic is based on gas outburst data from 26 mines and establishes a data generation model based on XGBoost. The obtained virtual datasets are analyzed through visualization analysis and ROC curve analysis with respect to the original data. If the augmented data has an ROC area under the curve of 1, it indicates good predictive performance of the augmented data. Grey correlation analysis is used to calculate the grey correlation degrees between each indicator and the “gas emission”. The indicator groups with correlation degrees greater than 0.670 are selected as the main control factor groups based on the sorting of correlation degrees. In this study, SVM, RF, XGBoost, and GBDT are selected as the original models for stacking. The original data and virtual data with correlation degrees greater than 0.670 are used as inputs for SVM, RF, XGBoost, GBDT, and stacking fusion models. The results show that the stacking fusion model has an MAE, MSE, and R2 of 0.031, 0.031, and 0.981. Comparing the actual and predicted values for each model, the stacking fusion model achieves the highest accuracy in gas outburst prediction and the best model fitting effect.

Fast plug-in capacitors polarity detection with morphology and SVM fusion method in automatic optical inspection system

Defect detection is a critical element in the PCB manufacturing process. Different from surface mount PCB, the components on the plug-in PCB are usually installed manually, resulting in significant errors. We make contributions in the following two aspects: (1) a framework and measurement method of a light source and make a cheap and efficient lighting system; (2) a fusion algorithm based on machine learning and morphology for polarity detection of plug-in capacitors. The capacitor is detected using SVM and fused with the polar coordinate expansion method. The AOI system and the proposed fusion algorithm have been applied to the production line, with an accuracy of \(99.73\%\) and a missed detection rate \(0.12\%\).

Research on Intelligent Recognition and Classification Algorithm of Music Emotion in Complex System of Music Performance

In the complex system of music performance, there are differences in the expression of music emotions by listeners, so it is of great significance to study the classification of different emotions under different audio signals. In this paper, the research of human emotional intelligence recognition and classification algorithm in the complex system of music performance is proposed. Through the recognition of SVM, KNN, ANN, and ID3 classifiers, the accuracy of a single classifier is compared, and then the four classifiers are combined to compare the classification accuracy of audio signals before and after preprocessing. The results show that the accuracy of SVM and ANN fusion is the highest. Finally, recall and F1 are comprehensively compared in the fusion algorithm, and the fusion classification effect of SVM and ANN is better than that of the algorithm model.

Automatic Detection of Invasive Ductal Carcinoma Based on the Fusion of Multi-Scale Residual Convolutional Neural Network and SVM

Invasive ductal carcinoma (IDC) is the most common type of breast cancer which is the leading cause of cancer-related deaths in middle-aged women. Pathological analysis of biopsy is the gold standard for diagnosis of breast cancer, and early detection, diagnosis, and treatment can significantly increase the survival rate. This paper proposes a method for the automatic detection of IDC based on the fusion of multi-scale residual convolutional neural network (MSRCNN) and SVM. First, the patches from whole slide images (WSI) were preprocessed by data enhancement and normalization and then input into the MSRCNN for features extraction. Second, the extracted features were input to the SVM and are classified into two categories: healthy and diseased patches. Finally, it is restored to the WSI according to the coordinate information of the patches, therefore the IDC and healthy tissue regions were built. Experimental results show that after 5-fold cross-validation, our method obtained an average accuracy of 87.45±0.81%, an average balance accuracy of 85.7±0.95%, and an average F1 score of 79.89±1.11%. Consequently, it has important practical value and scientific research significance.

An algorithm with LightGBM + SVM fusion model for the assessment of dynamic security region

With the development of energy transition, the complexity of power systems’ structure, planning and operation is continuously increasing. As to quickly and accurately assess the dynamic security region of power system, there are prominent problems with traditional manual analysis method, i.e. the rules’ roughness and a low calculation efficiency while data mining approach could provide a new way to get off such problems. Considering that the performance of SVM algorithm depends on feature selection and the LightGBM, a fast and efficient classification algorithm, can be used for feature selection, this paper proposes a new algorithm based on a fusion model. With the CEPRI-36 bus power system, the results of different algorithms are compared and the proposed algorithm verified.

Fusion of Classification with Hybrid Optimization Technique to Predict Diabetes

The main objective of this paper is to predict diabetes which is growing like an epidemic in India. The key focus is to envisage diabetic and normal patient using classification approach .Fusion of SVM enhanced with hybrid optimization of PSO-BAT algorithm is proposed. Classification techniques used namely Multilayer Perceptron (MLP), Sequential Minimal Optimization (SMO), Random Forest (RF) are compared with our novel approach SVM enhanced with hybrid optimization of PSO-BAT algorithm. The accuracy is increased using the combination technique. The benchmark diabetic dataset, PIMA Indian Diabetes Dataset from UCI machine learning repository is utilized for the research. To improve the efficiency more, classifiers such as Precision, Recall and f-measure is used.

Research and Implementation of SVM and Bootstrapping Fusion Algorithm in Emotion Analysis of Stock Review Texts

Research and Implementation of SVM and Bootstrapping Fusion Algorithm in Emotion Analysis of Stock Review Texts

Research and Implementation of SVM and Bootstrapping Fusion Algorithm in Emotion Analysis of Stock Review Texts

Research and Implementation of SVM and Bootstrapping Fusion Algorithm in Emotion Analysis of Stock Review Texts

An optimized feature selection technique based on incremental feature analysis for bio-metric gait data classification

The classification of humanoid locomotion is a troublesome exercise because of non-linearity associate with gait signals. The classification using the different machine learning technique leads for over fitting and under fitting. To select the optimized feature is a difficult task. The high dimension feature vector requires a high computational cost. The hand craft feature selection machine learning techniques performed poor. We have used the incremental feature selection strategy for feature selection. In this paper we first selected the feature and identify the principle feature then we classify gait data using different machine learning technique (KNN, ANN, SVM, DNN and classifier fusion) and shown the performance comparison. Experimental result on real time datasets propose method is better than previous method as far as humanoid locomotion classification is concerned and the generalization accuracy provided by new feature selection method i.e. incremental feature selection (IFS) with analysis of variance (ANOVA) (Zhang et al., 20). During the feature extraction, 17 features were selected from the existing literatures (Wang et al. in IEEE Trans Circ Syst Video Technol 14(2):149–158, 15). Using all the features could lead to over fitting, information redundancy and dimension disaster. Thus, a system with optimal features was selected using ANOVA combined with IFA. These selected features were then fed as an input to the ANN, SVM, KNN and DNN model. These individual classifiers were combined to produce classifier fusion model. The 5-fold cross-validation was used to evaluate the performance of the proposed model. Based on the empirical results it may be concluded that classifier fusion provides satisfactory results (92.23 %) compared to other individual classifiers. One-way analysis of variance test, Friedman’s test and Kruskal-Wallis test has also been conducted to validate the statistical significance of the results. The proposed system can be used as recommender system based on behavioral gait pattern about the performance of player of Indian cricket team, Biometric and help to diagnosis Parkinson disease.

Multimodal Web Aesthetics Assessment Based on Structural SVM and Multitask Fusion Learning

The overall visual attributes (e.g., aesthetics) of Web pages significantly influence user experience. A beautiful and well laid out Web page greatly facilitates user access and enhances the browsing experience. In this paper, a new method is proposed to learn an assessment model for the (visual) aesthetics of Web pages. First, multimodal features (structural, local visual, global visual, and functional) of a Web page that are known to significantly affect the aesthetics of a Web page are extracted to construct a feature vector. Second, the interuser disagreement of aesthetics is analyzed and novel aesthetic representations are obtained from the multiuser ratings of a page. A structural learning algorithm is proposed for the new aesthetic representations. Third, as a Web page's functional purpose also affects the perceived aesthetics, we divide Web pages into different types using functional features, and a soft multitask fusion learning strategy is introduced to train assessment models for pages with functional purposes. Experimental results show the effectiveness of our method: 1) the combination of structural, local, and global visual features outperforms existing state-of-the-art Web aesthetic features; 2) the proposed structural learning algorithm achieves good results for the new aesthetic representations; and 3) the proposed soft multitask fusion learning strategy improves the performances of aesthetics assessment models.

English

Diagnosis techniques based on the dissolved gas analysis (DGA) have been developed to detect incipient faults in power transformers. Various methods exist based on DGA such as IEC, Roger, Dornenburg, and etc. However, these methods have been applied to different problems with different standards. Furthermore, it is difficult to achieve an accurate diagnosis by DGA without experienced experts. In order to resolve these drawbacks, this paper proposes a novel diagnosis method using Area Under Receiver Operating Characteristics Curve (AUROCC)-based Genetic Fuzzy SVM fusion model. Recently, the use of Receiver Operating Characteristic (ROC) Curve and the area under the ROC Curve called Area Under Receiver Operating Characteristics Curve (AUROCC) has been receiving much attention as a measure of the performance of machine learning algorithms. In this paper, we propose a SVM classifier fusion model using genetic fuzzy system. Genetic algorithms are applied to tune the optimal fuzzy membership functions. The performance of SVM classifiers are evaluated by their AUROCCs. Our experiments show that AUROCC-based genetic fuzzy SVM fusion model produces not only better AUROCC but also better accuracy than individual SVM classifiers

Segmentation and Classification of Skin Lesions for Disease Diagnosis

In this paper, a novel approach for automatic segmentation and classification of skin lesions is proposed. Initially, skin images are filtered to remove unwanted hairs and noise and then the segmentation process is carried out to extract lesion areas. For segmentation, a region growing method is applied by automatic initialization of seed points. The segmentation performance is measured with different well known measures and the results are appreciable. Subsequently, the extracted lesion areas are represented by color and texture features. SVM and k-NN classifiers are used along with their fusion for the classification using the extracted features. The performance of the system is tested on our own dataset of 726 samples from 141 images consisting of 5 different classes of diseases. The results are very promising with 46.71% and 34% of F-measure using SVM and k-NN classifier respectively and with 61% of F-measure for fusion of SVM and k-NN.

Evidence-based SVM fusion for 3D model retrieval

Many existing 3D model retrieval use KNN (k-nearest neighbor) method for similarity search, but it is inefficient in high-dimension space search. In this paper, the classification tools are integrated for supporting more effective 3D model search in the high-dimensional feature space. Our proposed algorithm used multiple SVM classifiers to predict 3D models for a given query and D-S Evidence theory is used to fuse all the prediction results. Experimental results show that our proposed 3D model retrieval algorithm can improve the accuracy significantly compared with the traditional kNN method.

Adaptive SVM Fusion for Robust Multi-Biometrics Verification with Missing Data

Adaptive SVM Fusion for Robust Multi-Biometrics Verification with Missing Data

Classification of High Spatial Resolution Remotely Sensed Imagery Based Upon Fusion of Multiscale Features and SVM

Classification of High Spatial Resolution Remotely Sensed Imagery Based Upon Fusion of Multiscale Features and SVM