scholarly journals A Multi-Feature Motion Posture Recognition Model Based on Genetic Algorithm

2021 ◽  
Vol 38 (3) ◽  
pp. 599-605
Author(s):  
Yuanguo Liu ◽  
Ying Wu
Keyword(s):  
Genetic Algorithm ◽  
Feature Fusion ◽  
Fitness Function ◽  
Feature Model ◽  
Support Vector ◽  
Visual Background ◽  
Feature Information ◽  
Posture Recognition ◽  
The Mean ◽  
Mean Variance

The effect of motion posture recognition hinges on the accurate description of motion postures with effective feature information. This study introduces Wronskian function to improve the denoising ability of visual background extractor (ViBe) algorithm, and thus acquires relatively clear motion targets. Then, a multi-feature fusion motion posture feature model was developed based on genetic algorithm (GA). Specifically, GA was called to optimize and fuse the extracted feature information, while a fitness function was constructed based on the mean variance ratio, and used to select the feature information with high inter-class discriminability. Taking support vector machine (SVM) as the classifier, a multi-class classifier was designed by one-to-one method for the classification and recognition of motion postures. Through experiments, our model was proved highly accurate in motion posture recognition.

Parameters Selection and Application of LS-SVM Based on Chaotic Ant Swarm Algorithm

2011 ◽  
Vol 204-210 ◽  
pp. 423-426
Author(s):  
Chun Li Xie ◽  
Dan Dan Zhao ◽  
Juan Wang ◽  
Cheng Shao
Keyword(s):  
Support Vector Machines ◽  
Nonlinear Systems ◽  
Fitness Function ◽  
Selection Method ◽  
Support Vector ◽  
Mean Square ◽  
Parameters Selection ◽  
Vector Machines ◽  
The Mean ◽  
Simulation Results

Parameters selection plays an important role for the performance of least squares support vector machines (LS-SVM). In this paper, a novel parameters selection method for LS-SVM is presented based on chaotic ant swarm (CAS) algorithm. Using this method, the optimization model is established, within which the fitness function is the mean square error (MSE) index, and the constraints are the ranges of the designing parameters. The proposed method is used in the identification for inverse model of the nonlinear systems, and simulation results are given to show the efficiency.

scholarly journals A Composite Hybrid Feature Selection Learning-Based Optimization of Genetic Algorithm For Breast Cancer Detection

2020 ◽  
Author(s):  
Ahmed Abdullah Farid ◽  
Gamal Selim ◽  
Hatem Khater
Keyword(s):  
Breast Cancer ◽  
Genetic Algorithm ◽  
Feature Selection ◽  
Early Stage ◽  
Fitness Function ◽  
Support Vector ◽  
Initial Population ◽  
Tree Classifier ◽  
Selection Approach ◽  
Feature Selection Approach

Breast cancer is a significant health issue across the world. Breast cancer is the most widely-diagnosed cancer in women; early-stage diagnosis of disease and therapies increase patient safety. This paper proposes a synthetic model set of features focused on the optimization of the genetic algorithm (CHFS-BOGA) to forecast breast cancer. This hybrid feature selection approach combines the advantages of three filter feature selection approaches with an optimize Genetic Algorithm (OGA) to select the best features to improve the performance of the classification process and scalability. We propose OGA by improving the initial population generating and genetic operators using the results of filter approaches as some prior information with using the C4.5 decision tree classifier as a fitness function instead of probability and random selection. The authors collected available updated data from Wisconsin UCI machine learning with a total of 569 rows and 32 columns. The dataset evaluated using an explorer set of weka data mining open-source software for the analysis purpose. The results show that the proposed hybrid feature selection approach significantly outperforms the single filter approaches and principal component analysis (PCA) for optimum feature selection. These characteristics are good indicators for the return prediction. The highest accuracy achieved with the proposed system before (CHFS-BOGA) using the support vector machine (SVM) classifiers was 97.3%. The highest accuracy after (CHFS-BOGA-SVM) was 98.25% on split 70.0% train, remainder test, and 100% on the full training set. Moreover, the receiver operating characteristic (ROC) curve was equal to 1.0. The results showed that the proposed (CHFS-BOGA-SVM) system was able to accurately classify the type of breast tumor, whether malignant or benign.

scholarly journals A Green Computing Supportive Allocation Scheme Utilizing Genetic Algorithm and Support Vector Machine

2019 ◽  
Vol 8 (9S) ◽  
pp. 760-766
Keyword(s):  
Genetic Algorithm ◽  
Support Vector Machine ◽  
Fitness Function ◽  
Research Work ◽  
Service Level Agreement ◽  
Service Level ◽  
Support Vector ◽  
Allocation Policy ◽  
Physical Machine ◽  
Best Fit

When a Physical Machine gets a job from user, it intends to complete it at any cost. Virtual Machine (VM) helps to attain maximum completion ratio. The Host to VM ratio increases with the increase in the workload over the system. The allocation policy of VM has ambiguities with leads to an overloaded Physical Machine (PM). This paper aims to reduce the overhead of the PMs. For the allocation, Modified Best Fit Decreasing (MBFD) algorithm is used to check the resources availability. For the allocation, Modified Best Fit Decreasing (MBFD) algorithm is used to check the resources availability. Genetic Algorithm (GA) has been used to optimize the MBFD performance by fitness function. For the cross-validation Polynomial Support Vector Machine (P-SVM) is used. It has been utilized for training and classification and accordingly, parameters, viz. (Service Level Agreement) SLA and Job Completion Ratio (JCR) are evaluated. A comparative analysis has been drawn in this article to depict the research work effectiveness and an improvement of 70% is perceived.

scholarly journals Hybrid Model Based on Genetic Algorithms and SVM Applied to Variable Selection within Fruit Juice Classification

2013 ◽  
Vol 2013 ◽  
pp. 1-13 ◽  
Author(s):  
C. Fernandez-Lozano ◽  
C. Canto ◽  
M. Gestal ◽  
J. M. Andrade-Garda ◽  
J. R. Rabuñal ◽  
...  
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Support Vector Machines ◽  
Hybrid Model ◽  
Apple Juice ◽  
Fruit Juice ◽  
Fitness Function ◽  
Classification Problem ◽  
Support Vector ◽  
Vector Machines

Given the background of the use of Neural Networks in problems of apple juice classification, this paper aim at implementing a newly developed method in the field of machine learning: the Support Vector Machines (SVM). Therefore, a hybrid model that combines genetic algorithms and support vector machines is suggested in such a way that, when using SVM as a fitness function of the Genetic Algorithm (GA), the most representative variables for a specific classification problem can be selected.

A New Approach for Coronary Artery Diseases Diagnosis Based on Genetic Algorithm

2014 ◽  
Vol 6 (4) ◽  
pp. 1-15 ◽  
Author(s):  
Sidahmed Mokeddem ◽  
Baghdad Atmani ◽  
Mostéfa Mokaddem
Keyword(s):  
Genetic Algorithm ◽  
Coronary Artery ◽  
Classification Accuracy ◽  
Selection Procedure ◽  
Machine Learning Algorithms ◽  
Feature Model ◽  
Support Vector ◽  
Coronary Artery Diseases ◽  
C4.5 Decision Tree ◽  
Pertinent Feature

Feature Selection (FS) has become the motivation of much research on decision support systems areas for which datasets with large number of features are analyzed. This paper presents a new method for the diagnosis of Coronary Artery Diseases (CAD) founded on Genetic Algorithm (GA) wrapper Bayes Naïve (BN). Initially, thirteen attributes were involved in predicting CAD. In GA–BN algorithm, GA produces in each iteration a subset of attributes that will be evaluated using the BN in the second step of the selection procedure. The final result set of attribute holds the most pertinent feature model that increases the accuracy. The accuracy results showed that the algorithm produces 85.50% classification accuracy in the diagnosis of CAD. Therefore, the strength of the Algorithm is then compared with other machine learning algorithms such as Support Vector Machine (SVM), Multi-Layer Perceptron (MLP) and C4.5 decision tree Algorithm. The result of classification accuracy for those algorithms are respectively 83.5%, 83.16% and 80.85%. Then, the GA wrapper BN Algorithm is similarly compared with other FS algorithms. The Obtained results have shown very favorable outcomes for the diagnosis of CAD.

Genetic Optimization of Energy Consumption of Pellet Shaft Furnace Combustor Based on Support Vector Machine (SVM)

2014 ◽  
Vol 12 (1) ◽  
pp. 205-214 ◽  
Author(s):  
Xi Chen ◽  
Wenqi Zhong ◽  
Tiancai Wang ◽  
Fei Liu ◽  
Zhi Zhang
Keyword(s):  
Genetic Algorithm ◽  
Support Vector Machine ◽  
Energy Consumption ◽  
Shaft Furnace ◽  
Fitness Function ◽  
Optimization Method ◽  
Support Vector ◽  
Svm Classifier ◽  
Genetic Optimization ◽  
Gas Consumption

Abstract Investigation on optimization of pellet shaft furnace based on the combination of genetic algorithm and support vector machine (SVM) is carried out. A SVM classifier model is developed to map the complex nonlinear relationship between operating parameters and the quality indexes of fired pellet, and a genetic algorithm is adapted in the energy optimization with the fitness function based on the SVM classifier model. This method can reduce the energy consumption while maintaining the fired pellet quality stable. The results show that the accuracy of the SVM classifier model is satisfied and the gas consumption can be reduced by 4% per ton of green pellets with this optimization method.

Improving Genetic Algorithm Based Parameter Optimizing LSSVM Model for Tyre Regression

2011 ◽  
Vol 55-57 ◽  
pp. 1839-1844
Author(s):  
Xiao Long Zhang ◽  
Liang Li ◽  
Jian Song ◽  
Dan Dan Sheng
Keyword(s):  
Genetic Algorithm ◽  
Real Time ◽  
Test Data ◽  
Fitness Function ◽  
Bench Test ◽  
Support Vector ◽  
Adaptive Genetic Algorithm ◽  
Risk Minimization ◽  
Simulation And Control ◽  
And Control

According to the requirement of vehicle dynamics' accurate simulation and control, the paper studies the tyre regression algorithm based on the tyre bench test data. Due to the tyre test's characters of few data and big discreteness, the method of least squares support vector regression (LSSVM), which has the superiority of structural risk minimization, was selected to find the nonlinear mapping between input and output variables of tyre test data. Removing data gross error and improving the sparsification measures were taken to increase the calculation real time of standard LSSVM algorithm. An adaptive genetic algorithm (AGA) with global searching ability was chosen to determine the kernel function and regularization parameters of LSSVM. Test data set’s regression root mean square error (RMSE) was taken as the fitness function of AGA. Finally, the tyre test data under steady state sideslip condition was provided to simulate and verify the effectiveness of tyre regression algorithm, according to the precision and real time requirements of vehicle dynamic simulation and control.

RANDOM FOREST BASED CLASSIFICATION OF MEDICAL X-RAY IMAGES USING A GENETIC ALGORITHM FOR FEATURE SELECTION

2015 ◽  
Vol 15 (02) ◽  
pp. 1540025 ◽  
Author(s):  
IMANE NEDJAR ◽  
MOSTAFA EL HABIB DAHO ◽  
NESMA SETTOUTI ◽  
SAÏD MAHMOUDI ◽  
MOHAMED AMINE CHIKH
Keyword(s):  
Genetic Algorithm ◽  
Feature Selection ◽  
Random Forest ◽  
Fitness Function ◽  
Classification Error ◽  
Support Vector ◽  
Second Phase ◽  
X Ray ◽  
Combinatorial Set

Automated classification of medical images is an increasingly important tool for physicians in their daily activities. However, due to its computational complexity, this task is one of the major current challenges in the field of content-based image retrieval (CBIR). In this paper, a medical image classification approach is proposed. This method is composed of two main phases. The first step consists of a pre-processing, where a texture and shape based features vector is extracted. Also, a feature selection approach was applied by using a Genetic Algorithm (GA). The proposed GA uses a kNN based classification error as fitness function, which enables the GA to obtain a combinatorial set of feature giving rise to optimal accuracy. In the second phase, a classification process is achieved by using random Forest classifier and a supervised multi-class classifier based on the support vector machine (SVM) for classifying X-ray images.

A New Approach for Coronary Artery Diseases Diagnosis Based on Genetic Algorithm

2019 ◽  
pp. 44-59
Author(s):  
Sidahmed Mokeddem ◽  
Baghdad Atmani ◽  
Mostéfa Mokaddem
Keyword(s):  
Genetic Algorithm ◽  
Coronary Artery ◽  
Classification Accuracy ◽  
Selection Procedure ◽  
Machine Learning Algorithms ◽  
Feature Model ◽  
Support Vector ◽  
Coronary Artery Diseases ◽  
C4.5 Decision Tree ◽  
Pertinent Feature

Feature Selection (FS) has become the motivation of much research on decision support systems areas for which datasets with large number of features are analyzed. This paper presents a new method for the diagnosis of Coronary Artery Diseases (CAD) founded on Genetic Algorithm (GA) wrapper Bayes Naïve (BN). Initially, thirteen attributes were involved in predicting CAD. In GA–BN algorithm, GA produces in each iteration a subset of attributes that will be evaluated using the BN in the second step of the selection procedure. The final result set of attribute holds the most pertinent feature model that increases the accuracy. The accuracy results showed that the algorithm produces 85.50% classification accuracy in the diagnosis of CAD. Therefore, the strength of the Algorithm is then compared with other machine learning algorithms such as Support Vector Machine (SVM), Multi-Layer Perceptron (MLP) and C4.5 decision tree Algorithm. The result of classification accuracy for those algorithms are respectively 83.5%, 83.16% and 80.85%. Then, the GA wrapper BN Algorithm is similarly compared with other FS algorithms. The Obtained results have shown very favorable outcomes for the diagnosis of CAD.

Gait and Posture Analysis Method Based on Genetic Algorithm and Support Vector Machines with Acceleration Data

2016 ◽  
Vol 28 (3) ◽  
pp. 418-424 ◽  
Author(s):  
Huan Gou ◽  
◽  
Tengda Shi ◽  
Lei Yan ◽  
Jiang Xiao
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Parameters Optimization ◽  
Support Vector ◽  
Analysis Method ◽  
Posture Analysis ◽  
Acceleration Data ◽  
Vector Machines ◽  
Posture Recognition ◽  
Simulation Results

[abstFig src='/00280003/18.jpg' width=""300"" text='The result of parameters optimization by GA' ] The support vector machine (SVM) we propose for automated gait and posture recognition is based on acceleration. Acceleration data are obtained from four accelerators attached to the human thigh and lower leg. In the experiment, volunteers take part in four gaits and postures, i.e., sitting, standing, walking and ascending stairs. Acceleration data that are preprocessed include normalization, a wavelet filter and dimension reduction. We used the SVM and a neural network to analyze the data processed. Simulation results indicate that SVM parametersCandgselected by a genetic algorithm (GA) are more effective for gait and posture analysis when compared to the parameterCandgselected by a grid search. The overall classification precision of the four gaits and postures exceeds 90.0%, and neural network simulation results indicate that the SVM using the GA is preferable for use in analysis.

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