A simplified minimum enclosing ball based fast incremental support vector machine (SVM) algorithm for person detection and tracking

The needs of the community for freight forwarding are now starting to increase with the marketplace. User opinion about freight forwarding services is currently carried out by the public through many things one of them is social media Twitter. By sentiment analysis, the tendency of an opinion will be able to be seen whether it has a positive or negative tendency. The methods that can be applied to sentiment analysis are the Naive Bayes Algorithm and Support Vector Machine (SVM). This research will implement the two algorithms that are optimized using the PSO algorithms in sentiment analysis. Testing will be done by setting parameters on the PSO in each classifier algorithm. The results of the research that have been done can produce an increase in the accreditation of 15.11% on the optimization of the PSO-based Naive Bayes algorithm. Improved accuracy on the PSO-based SVM algorithm worth 1.74% in the sigmoid kernel.

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Pulmonary Nodule Recognition Based on Multiple Kernel Learning Support Vector Machine-PSO

Computational and Mathematical Methods in Medicine ◽

10.1155/2018/1461470 ◽

2018 ◽

Vol 2018 ◽

pp. 1-10 ◽

Cited By ~ 2

Author(s):

Yang Li ◽

Zhichuan Zhu ◽

Alin Hou ◽

Qingdong Zhao ◽

Liwei Liu ◽

...

Keyword(s):

Support Vector Machine ◽

Pulmonary Nodule ◽

Multiple Kernel Learning ◽

Pso Algorithm ◽

Support Vector ◽

Training Time ◽

Inertia Weight ◽

Fitness Value ◽

Svm Algorithm ◽

Fitness Curve

Pulmonary nodule recognition is the core module of lung CAD. The Support Vector Machine (SVM) algorithm has been widely used in pulmonary nodule recognition, and the algorithm of Multiple Kernel Learning Support Vector Machine (MKL-SVM) has achieved good results therein. Based on grid search, however, the MKL-SVM algorithm needs long optimization time in course of parameter optimization; also its identification accuracy depends on the fineness of grid. In the paper, swarm intelligence is introduced and the Particle Swarm Optimization (PSO) is combined with MKL-SVM algorithm to be MKL-SVM-PSO algorithm so as to realize global optimization of parameters rapidly. In order to obtain the global optimal solution, different inertia weights such as constant inertia weight, linear inertia weight, and nonlinear inertia weight are applied to pulmonary nodules recognition. The experimental results show that the model training time of the proposed MKL-SVM-PSO algorithm is only 1/7 of the training time of the MKL-SVM grid search algorithm, achieving better recognition effect. Moreover, Euclidean norm of normalized error vector is proposed to measure the proximity between the average fitness curve and the optimal fitness curve after convergence. Through statistical analysis of the average of 20 times operation results with different inertial weights, it can be seen that the dynamic inertial weight is superior to the constant inertia weight in the MKL-SVM-PSO algorithm. In the dynamic inertial weight algorithm, the parameter optimization time of nonlinear inertia weight is shorter; the average fitness value after convergence is much closer to the optimal fitness value, which is better than the linear inertial weight. Besides, a better nonlinear inertial weight is verified.

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Intercropping Classification From GF-1 and GF-2 Satellite Imagery Using a Rotation Forest Based on an SVM

ISPRS International Journal of Geo-Information ◽

10.3390/ijgi8020086 ◽

2019 ◽

Vol 8 (2) ◽

pp. 86 ◽

Cited By ~ 1

Author(s):

Ping Liu ◽

Xi Chen

Keyword(s):

Support Vector Machine ◽

Satellite Imagery ◽

Vegetation Cover ◽

Support Vector ◽

Sufficient Information ◽

Classification Result ◽

Rotation Forest ◽

Svm Algorithm ◽

Multi Temporal ◽

Occurrence Matrix

Remote sensing has been widely used in vegetation cover research but is rarely used for intercropping area monitoring. To investigate the efficiency of Chinese Gaofen satellite imagery, in this study the GF-1 and GF-2 of Moyu County south of the Tarim Basin were studied. Based on Chinese GF-1 and GF-2 satellite imagery features, this study has developed a comprehensive feature extraction and intercropping classification scheme. Textural features derived from a Gray level co-occurrence matrix (GLCM) and vegetation features derived from multi-temporal GF-1 and GF-2 satellites were introduced and combined into three different groups. The rotation forest method was then adopted based on a Support Vector Machine (RoF-SVM), which offers the advantage of using an SVM algorithm and that boosts the diversity of individual base classifiers by a rotation forest. The combined spectral-textural-multitemporal features achieved the best classification result. The results were compared with those of the maximum likelihood classifier, support vector machine and random forest method. It is shown that the RoF-SVM algorithm for the combined spectral-textural-multitemporal features can effectively classify an intercropping area (overall accuracy of 86.87% and kappa coefficient of 0.78), and the classification result effectively eliminated salt and pepper noise. Furthermore, the GF-1 and GF-2 satellite images combined with spectral, textural, and multi-temporal features can provide sufficient information on vegetation cover located in an extremely complex and diverse intercropping area.

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Support vector machine classification for large data sets via minimum enclosing ball clustering

Neurocomputing ◽

10.1016/j.neucom.2007.07.028 ◽

2008 ◽

Vol 71 (4-6) ◽

pp. 611-619 ◽

Cited By ~ 59

Author(s):

Jair Cervantes ◽

Xiaoou Li ◽

Wen Yu ◽

Kang Li

Keyword(s):

Support Vector Machine ◽

Large Data ◽

Large Data Sets ◽

Support Vector ◽

Data Sets ◽

Support Vector Machine Classification ◽

Minimum Enclosing Ball

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Analysis of HCV’s Linearity Using Support Vector Machine (SVM)

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.548-549.1265 ◽

2014 ◽

Vol 548-549 ◽

pp. 1265-1269

Author(s):

Yun Sik Hwang ◽

Byeong Joo Jun ◽

Tae Seon Yoon

Keyword(s):

Neural Network ◽

Support Vector Machine ◽

Hepatitis C Virus ◽

Data Analysis ◽

Hepatitis C ◽

Amino Acid ◽

Support Vector ◽

Svm Algorithm ◽

Genetic Patterns

As the stage of bioinformatics has been upgraded, classification of certain pathogen has been improved into a new manner. The main topic of this research is genetic singularity of HCV (Hepatitis C Virus) and our objective is to assay features of the HCV's amino acid under usage of Support Vector Machine (SVM) algorithm. HCV data used in our experiment has 10 kinds of sequences and 257 kinds of data. According to data analysis, some peculiar genetic patterns of HCV’s linearity that discord pre-existing neural network and C5.0 were found.

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Modeling of Flexible Structures by Means of Least Square Support Vector Machine

Volume 2: Diagnostics and Detection; Drilling; Dynamics and Control of Wind Energy Systems; Energy Harvesting; Estimation and Identification; Flexible and Smart Structure Control; Fuels Cells/Energy Storage; Human Robot Interaction; HVAC Building Energy Management; Industrial Applications; Intelligent Transportation Systems; Manufacturing; Mechatronics; Modelling and Validation; Motion and Vibration Control Applications ◽

10.1115/dscc2015-9673 ◽

2015 ◽

Author(s):

Hammam Tamimi ◽

Dirk Söffker

Keyword(s):

Support Vector Machine ◽

Least Squares ◽

Flexible Structures ◽

Operating Conditions ◽

Least Square ◽

Support Vector ◽

Suitable Model ◽

Support Vector Machine Algorithm ◽

Training Time ◽

Svm Algorithm

This paper investigates modeling of flexible structures by means of the least squares support vector machine (LS-SVM) algorithm. Modeling is the first step to obtain a suitable model-based controller for any given system. Accurate modeling of a flexible structure based on experimental data using LS-SVM algorithm requires less knowledge about the physical system. Least squares support vector machine algorithm can achieve global and unique solution when compared with other soft computing algorithms. Also, LS-SVM algorithm requires less training time. In this paper, the successful use of support vector machine algorithm to model the flexible cantilever is demonstrated. The acquired model is able to provide accurate prediction of the system output under different operating conditions. Experimental results demonstrate the efficiency and high precision of the proposed approach.

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Scale-Adaptive Face Detection and Tracking in Real Time with SSR Filters and Support Vector Machine

IEICE Transactions on Information and Systems ◽

10.1093/ietisy/e88-d.12.2857 ◽

2005 ◽

Vol E88-D (12) ◽

pp. 2857-2863 ◽

Cited By ~ 14

Author(s):

S. KAWATO

Keyword(s):

Support Vector Machine ◽

Real Time ◽

Face Detection ◽

Support Vector ◽

Detection And Tracking

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The Prediction of Human Abdominal Adiposity Based on the Combination of a Particle Swarm Algorithm and Support Vector Machine

International Journal of Environmental Research and Public Health ◽

10.3390/ijerph17031117 ◽

2020 ◽

Vol 17 (3) ◽

pp. 1117

Author(s):

Xiue Gao ◽

Wenxue Xie ◽

Shifeng Chen ◽

Junjie Yang ◽

Bo Chen

Keyword(s):

Support Vector Machine ◽

Sample Size ◽

Size Dependence ◽

Prediction Models ◽

Test Group ◽

Support Vector ◽

Particle Swarm Algorithm ◽

Abdominal Adiposity ◽

Modeling Group ◽

Svm Algorithm

Background: Abdominal adiposity is an important risk factor of chronic cardiovascular diseases, thus the prediction of abdominal adiposity and obesity can reduce the risks of contracting such diseases. However, the current prediction models display low accuracy and high sample size dependence. The purpose of this study is to put forward a new prediction method based on an improved support vector machine (SVM) to solve these problems. Methods: A total of 200 individuals participated in this study and were further divided into a modeling group and a test group. Their physiological parameters (height, weight, age, the four parameters of abdominal impedance and body fat mass) were measured using the body composition tester (the universal INBODY measurement device) based on BIA. Intelligent algorithms were used in the modeling group to build predictive models and the test group was used in model performance evaluation. Firstly, the optimal boundary C and parameter gamma were optimized by the particle swarm algorithm. We then developed an algorithm to classify human abdominal adiposity according to the parameter setup of the SVM algorithm and constructed the prediction model using this algorithm. Finally, we designed experiments to compare the performances of the proposed method and the other methods. Results: There are different abdominal obesity prediction models in the 1 KHz and 250 KHz frequency bands. The experimental data demonstrates that for the frequency band of 250 KHz, the proposed method can reduce the false classification rate by 10.7%, 15%, and 33% in relation to the sole SVM algorithm, the regression model, and the waistline measurement model, respectively. For the frequency band of 1 KHz, the proposed model is still more accurate. (4) Conclusions: The proposed method effectively improves the prediction accuracy and reduces the sample size dependence of the algorithm, which can provide a reference for abdominal obesity.

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Comparison of Support Vector Machine-Based Techniques for Detection of Bearing Faults

Shock and Vibration ◽

10.1155/2018/8174860 ◽

2018 ◽

Vol 2018 ◽

pp. 1-13

Author(s):

Lijun Wang ◽

Shengfei Ji ◽

Nanyang Ji

Keyword(s):

Support Vector Machine ◽

Global Optimization ◽

Fault Diagnosis ◽

Model Simulation ◽

Rolling Bearing ◽

Support Vector ◽

Denoising Method ◽

Svm Algorithm ◽

Svm Model ◽

Shuffled Frog Leaping

This paper presents a method that combines Shuffled Frog Leaping Algorithm (SFLA) with Support Vector Machine (SVM) method in order to identify the fault types of rolling bearing in the gearbox. The proposed method improves the accuracy of fault diagnosis identification after processing the collected vibration signals through wavelet threshold denoising. The global optimization and high computational efficiency of SFLA are applied to the SVM model. Simulation results show that the SFLA-SVM algorithm is effective in fault diagnosis. Compared with SVM and Particle Swarm Optimization SVM (PSO-SVM) algorithms, it is demonstrated that the SFLA-SVM algorithm has the advantages of better global optimization, higher accuracy, and better reliability of diagnosis. Its accuracy is further improved through the integration of the wavelet threshold denoising method.

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Comparison of Support Vector Machine, Bayesian Logistic Regression, and Alternating Decision Tree Algorithms for Shallow Landslide Susceptibility Mapping along a Mountainous Road in the West of Iran

Applied Sciences ◽

10.3390/app10155047 ◽

2020 ◽

Vol 10 (15) ◽

pp. 5047 ◽

Cited By ~ 7

Author(s):

Viet-Ha Nhu ◽

Danesh Zandi ◽

Himan Shahabi ◽

Kamran Chapi ◽

Ataollah Shirzadi ◽

...

Keyword(s):

Machine Learning ◽

Support Vector Machine ◽

Logistic Regression ◽

Decision Tree ◽

Shallow Landslide ◽

Machine Learning Algorithms ◽

Support Vector ◽

Svm Algorithm ◽

Alternating Decision Tree ◽

Bayesian Logistic Regression

This paper aims to apply and compare the performance of the three machine learning algorithms–support vector machine (SVM), bayesian logistic regression (BLR), and alternating decision tree (ADTree)–to map landslide susceptibility along the mountainous road of the Salavat Abad saddle, Kurdistan province, Iran. We identified 66 shallow landslide locations, based on field surveys, by recording the locations of the landslides by a global position System (GPS), Google Earth imagery and black-and-white aerial photographs (scale 1: 20,000) and 19 landslide conditioning factors, then tested these factors using the information gain ratio (IGR) technique. We checked the validity of the models using statistical metrics, including sensitivity, specificity, accuracy, kappa, root mean square error (RMSE), and area under the receiver operating characteristic curve (AUC). We found that, although all three machine learning algorithms yielded excellent performance, the SVM algorithm (AUC = 0.984) slightly outperformed the BLR (AUC = 0.980), and ADTree (AUC = 0.977) algorithms. We observed that not only all three algorithms are useful and effective tools for identifying shallow landslide-prone areas but also the BLR algorithm can be used such as the SVM algorithm as a soft computing benchmark algorithm to check the performance of the models in future.

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