Genetic Algorithm Support Vector Machine Model Research Articles

Evaluation of Corporate Social Responsibility and Core Competitiveness Based on GA-SVM Model

Abstract The author proposes a model evaluation based on the GA-SVM model to better understand the evaluation of the company’s relationship and core competitiveness. The system index is reduced by relative gray analysis, the support vector machine model is optimized by a genetic algorithm, and the specific algorithm steps are introduced. Select models from the top 100 enterprises in China’s construction industry in 2020 published by China Construction Industry Market, using relative gray to reduce the measure, and then use the genetic algorithm support vector machine (GA-SVM) model for the training model to achieve the evaluation of the core competencies of the target construction technique business. The experimental results show that the relative error of prediction of the GA-SVM(Genetic algorithm-support vector machine) model for the evaluation of the competitive core of the business is not more than 5, which meets the should be made of accurate predictions. Therefore, choosing the Gaussian radial basis kernel function (RBF) as the kernel function of the GA-SVM intelligent evaluation model is good. It proves that the GA-SVM model can analyze the relationship between the role and the important competition.

Inner Dynamic Detection and Prediction of Water Quality Based on CEEMDAN and GA-SVM Models

Urban water quality is facing strongly adverse degradation in rapidly developing areas. However, there exists a huge challenge to estimating the inner features and predicting the variation of long-term water quality due to the lack of related monitoring data and the complexity of urban water systems. Fortunately, multi-remote sensing data, such as nighttime light and evapotranspiration (ET), provide scientific data support and reasonably reveal the variation mechanisms. Here, we develop an integrated decomposition-reclassification-prediction method for water quality by integrating the CEEMDN method, the RF method mothed, and the genetic algorithm-support vector machine model (GA-SVM). The degression of the long-term water quality was decomposed and reclassified into three different frequency terms, i.e., high-frequency, low-frequency, and trend terms, to reveal the inner mechanism and dynamics in the CEEMDAN method. The RF method was then used to identify the teleconnection and the significance of the selected driving factors. More importantly, the GA-SVM model was designed with two types of model schemes, which were the data-driven model (GA-SVMd) and the integrated CEEMDAN-GA-SVM model (defined as GA-SVMc model), in order to predict urban water quality. Results revealed that the high-frequency terms for NH3-N and TN had a major contribution to the water quality and were mainly dominated by hydrometeorological factors such as ET, rainfall, and the dynamics of the lake water table. The trend terms revealed that the water quality continuously deteriorated during the study period; the terms were mainly regulated by the land use and land cover (LULC), land metrics, population, and yearly rainfall. The predicting results confirmed that the integrated GA-SVMc model had better performance than single data-driven models (such as the GA-SVM model). Our study supports that the integrated method reveals variation rules in water quality and provides early warning and guidance for reducing the water pollutant concentration.

SVM Kernel and Genetic Feature Selection Based Automated Diagnosis of Breast Cancer

Background: Breast cancer is the development of a malignant tumor in the breast of human beings (especially females). If not detected at the initial stages, it can substantially lead to an inoperable construct. It is a reason for majority of cancer-related deaths throughout the world. Objectives: The main aim of our study is to diagnose the breast cancer at early stage so that required treatment can be provided for survival. The tumor is classified as malignant or benign accurately at early stage using a novel approach that includes an ensemble of Genetic Algorithm for feature selection and kernel selection for SVM-Classifier. Methods: The proposed GA-SVM (Genetic Algorithm – Support Vector Machine) algorithm in this paper optimally selects the most appropriate features for training with the SVM classifier. Genetic Programming is used to select the features and the kernel for the SVM classifier. Genetic Algorithm operates by exploring the optimal layout of features for breast cancer, thus, subjugating the problems faced in exponentially immense feature space. Results: The proposed approach accounts for a mean accuracy of 98.82% by using the Wisconsin Diagnostic Breast Cancer (WDBC) dataset available on UCI with the training and testing ratio being 50:50 respectively. Conclusion: The results prove that our proposed model outperforms the previously designed models for breast cancer diagnosis. The outcome assures that the GA-SVM model may be used as an effective tool in assisting the doctors for treating the patients. Alternatively, it may be utilized as an alternate opinion in their eventual diagnosis.

Mapping Mineral Prospectivity Using a Hybrid Genetic Algorithm–Support Vector Machine (GA–SVM) Model

Machine learning (ML) as a powerful data-driven method is widely used for mineral prospectivity mapping. This study employs a hybrid of the genetic algorithm (GA) and support vector machine (SVM) model to map prospective areas for Au deposits in Karamay, northwest China. In the proposed method, GA is used as an adaptive optimization search method to optimize the SVM parameters that result in the best fitness. After obtaining evidence layers from geological and geochemical data, GA–SVM models trained using different training datasets were applied to discriminate between prospective and non-prospective areas for Au deposits, and to produce prospectivity maps for mineral exploration. The F1 score and spatial efficiency of classification were calculated to objectively evaluate the performance of each prospectivity model. The best model predicted 95.83% of the known Au deposits within prospective areas, occupying 35.68% of the study area. The results demonstrate the effectiveness of the GA–SVM model as a tool for mapping mineral prospectivity.

Lane change strategy analysis and recognition for intelligent driving systems based on random forest

The development of intelligent connected technology provides a platform for multidimensional information interaction and makes the recognition of a specific lane change strategy become a reality. Intelligent driving systems cannot make human-like decisions by only recognizing left or right lane change behaviors; in fact, poor environmental cognitive competence is the main cause of accidents of intelligent driving systems during road tests. Therefore, the recognition of specific lane change strategies can provide support for safety performance improvement. This research aims to identify the different lane change strategies of a subject vehicle with a rear approaching vehicle in the target lane. For the purpose of acquiring different strategies, a total of 42 experienced drivers including 37 males and 5 females participated in the on-road vehicle experiments, and we divided lane change strategies into three categories: mandatory, yielding, and waiting for lane change. The lane change duration time, lane crossing time, and characteristic parameters including distance to lane line, steering wheel angle, relative distance, and relative speed under different lane change strategies were compared and analyzed. Then the random forest classifier was employed to construct a lane change strategy identification model. Results indicated that the maximum value of global recognition accuracy under three input parameters reached 88.78% when the time window was 1.2 s, which was higher than the maximum value of recognition accuracy under four input parameters, but the identification time was 0.1 s later than the model with four input parameters. In addition, the recognition performance of the random forest model, attention-bidirectional long short-term memory (Atten-BiLSTM) model and genetic algorithm-support vector machine (GA-SVM) model was compared. The results demonstrated that the global recognition accuracy and identification time of the proposed model were better than that of GA-SVM model under different input parameters, and the performance was comparable to the Atten-BiLSTM model. The findings provide the basis for the intelligent driving system to make more human-like decisions and improve safety performance.

Direct symbol decoding using GA-SVM in chaotic baseband wireless communication system

To retrieve the information from the serious distorted received signal is the key challenge of communication signal processing. The chaotic baseband communication promises theoretically to eliminate the inter-symbol interference (ISI), however, it needs complicated calculation, if it is not impossible. In this paper, a genetic algorithm support vector machine (GA-SVM) based symbol detection method is proposed for chaotic baseband wireless communication system (CBWCS), by this way, treating the problem from a different viewpoint, the symbol decoding process is converted to be a binary classification through GA-SVM model. A trained GA-SVM model is used to decode the symbols directly at the receiver, so as to improve the bit error rate (BER) performance of the CBWCS and simplify the symbol detection process by removing the channel identification and the threshold calculation process as compared to that using the calculated threshold to decode symbol in the traditional methods. The simulation results show that the proposed method has better BER performance in both the static and time-varying wireless channels. The experimental results, based on the wireless open-access research platform, indicate that the BER of the proposed GA-SVM based symbol detection approach is superior to the other counterparts under a practical wireless multipath channel.

Accurate identification of soluble solid content in citrus by indirect laser-induced breakdown spectroscopy with its leaves

The soluble solid content (SSC) is vital to distinguish citrus quality, and non-destructive detection on site in the identification of SSC for citrus is urgently demanded. In this paper, a new approach was proposed to accurately identify citrus’s SSC using indirect laser-induced breakdown spectroscopy (LIBS) with its leaves. Forty-five Ehime Kashi No.28 citruses with surrounding leaves picked at two different times were prepared for LIBS experiment. The genetic algorithm-support vector machine model had the best identification result with cross-validation set accuracy of 91.2% and the prediction set accuracy of 92.03%. Furthermore, the influence of element in citrus leaves on SSC identification was analyzed by one-way analysis of variance, which showed that magnesium in citrus leaves was notably related to SSC due to its participation in photosynthesis and carbohydrates transportation. All in all, the results demonstrated that the indirect LIBS with citrus leaves could be a non-destructive and reliable approach for accurate identification of citrus SSC on spot.

Susceptibility mapping and zoning of highway landslide disasters in China.

Prominent regional differentiations of highway landslide disasters (HLDs) bring great difficulties in highway planning, designing and disaster mitigation, therefore, a comprehensive understanding of HLDs from the spatial perspective is a basis for reducing damages. Statistical prediction methods and machine learning methods have some defects in landslide susceptibility mapping (LSM), meanwhile, hybrid methods have been developed by combining the statistical prediction methods with machine learning methods in recent years, and some of them were reported to perform better than conventional methods. In view of this, the principal component analysis (PCA) method was used to extract the susceptibility evaluation indexes of HLDs; the particle swarm optimization-support vector machine (PSO-SVM) model and genetic algorithm-support vector machine (GA-SVM) model were implemented to the susceptibility mapping and zoning of HLDs in China. The research results show that the accumulative contribution rate of the four principal components is 92.050%; evaluation results of the PSO-SVM model are better than those of the GA-SVM model; micro dangerous areas, moderate dangerous areas, severe dangerous areas and extreme dangerous areas account for 24.24%, 19.49%, 36.53% and 19.74% of the total areas of China; among the 1543 disaster points in the HLDs inventory, there are 134, 182, 421 and 806 located in the above areas respectively.

Integrating Support Vector Regression with Genetic Algorithm for Hydrate Formation Condition Prediction

To predict the natural gas hydrate formation conditions quickly and accurately, a novel hybrid genetic algorithm–support vector machine (GA-SVM) model was developed. The input variables of the model are the relative molecular weight of the natural gas (M) and the hydrate formation pressure (P). The output variable is the hydrate formation temperature (T). Among 10 gas samples, 457 of 688 data points were used for training to identify the optimal support vector machine (SVM) model structure. The remaining 231 data points were used to evaluate the generalisation capability of the best trained SVM model. Comparisons with nine other models and analysis of the outlier detection revealed that the GA-SVM model had the smallest average absolute relative deviation (0.04%). Additionally, the proposed GA-SVM model had the smallest amount of outlier data and the best stability in predicting the gas hydrate formation conditions in the gas relative molecular weight range of 15.64–28.97 g/mol and the natural gas pressure range of 367.65–33,948.90 kPa. The present study provides a new approach for accurately predicting the gas hydrate formation conditions.

Establishment of Landslide Groundwater Level Prediction Model Based on GA-SVM and Influencing Factor Analysis.

The monitoring and prediction of the landslide groundwater level is a crucial part of landslide early warning systems. In this study, Tangjiao landslide in the Three Gorges Reservoir area (TGRA) in China was taken as a case study. Three groundwater level monitoring sensors were installed in different locations of the landslide. The monitoring data indicated that the fluctuation of groundwater level is significantly consistent with rainfall and reservoir level in time, but there is a lag. In addition, there is a spatial difference in the impact of reservoir levels on the landslide groundwater level. The data of two monitoring locations were selected for establishing the prediction model of groundwater. Combined with the qualitative and quantitative analysis, the influencing factors were selected, respectively, to establish the hybrid Genetic Algorithm-Support Vector Machine (GA-SVM) prediction model. The single-factor GA-SVM without considering influencing factors and the backpropagation neural network (BPNN) model were adopted to make comparisons. The results showed that the multi-factor GA-SVM performed the best, followed by multi-factor BPNN and single-factor GA-SVM. We found that the prediction accuracy can be improved by considering the influencing factor. The proposed GA-SVM model combines the advantages of each algorithm; it can effectively construct the response relationship between groundwater level fluctuations and influencing factors. Above all, the multi-factor GA-SVM is an effective method for the prediction of landslides groundwater in the TGRA.

Estimating the rainfall threshold of a deep-seated landslide by integrating models for predicting the groundwater level and stability analysis of the slope

Rainfall thresholds of landslides often are determined by empirical meteorological thresholds, but the reliability of this approach sometimes is limited by the lack of information about the hydrological processes that trigger landslides. Groundwater plays a critical role in triggering deep-seated landslides. In this study, we propose a methodology to estimate the rainfall threshold for a deep-seated landslide based on an integrated model that combines a model for predicting the level of the groundwater with a finite-element, strength-reduction model. First, in order to obtain more accurate results when predicting the level of the groundwater, a method is proposed to estimate the groundwater level fluctuation caused by rainfall (GLFR). Then, two different machine learning methods, i.e., the genetic algorithm back-propagation neural network (GA-BPNN) method and the genetic algorithm support vector machine (GA-SVM) method, are proposed for predicting the GLFR of the Duxiantou landslide located in Zhejiang Province, China. The results of the predictions showed that the performance of the GA-SVM model was better than that of the GA-BPNN model. Then, a series of numerical simulations was conducted to investigate the factor of safety (Fs) of the slope at different groundwater levels. At last, the probabilities of the occurrences of Duxiantou landslides for different return periods of rainfall intensity were evaluated to determine the rainfall threshold.

Hybrid feature selection approach for power transformer fault diagnosis based on support vector machine and genetic algorithm

To further improve fault diagnosis accuracy, a new hybrid feature selection approach combined with a genetic algorithm (GA) and support vector machine (SVM) is presented in this study. Adaptive synthetic technique and arctangent transformation method are adopted to improve the statistical property of the training set (IEC TC10 dataset). Five filter methods based on different evaluation metrics are employed to rank 48 input features derived from dissolved gas analysis (DGA). Then, feature combination methods are applied to aggregate feature ranks and form a lower-dimension candidate feature subset. The GA–SVM model is implemented to optimise parameters and select optimal feature subsets. 5-fold cross-validation accuracy of the GA-SVM is used to evaluate fault diagnosis capability of feature subsets and finally, a novel subset is determined as the optimal feature subset. Accuracy comparison manifests the superiority of the optimal feature subsets over that of conventional approaches. Besides, generalisation and robustness of the optimal subset are validated by testing DGA samples from the local power utility. Results indicate that the optimal feature subset obtained by the proposed method can significantly improve the accuracies of power transformer fault diagnosis.

Estimation of vessel collision risk index based on support vector machine

Collision risk index is important for assessing vessel collision risk and is one of the key problems in the research field of vessel collision avoidance. With accurate collision risk index obtained through vessel movement parameters and encounter situation analysis, the pilot can adopt correct avoidance action. In this article, a collision risk index estimation model based on support vector machine is proposed. The proposed method comprises two units, that is, support vector machine–based unit for predicting the collision risk index and the genetic algorithm–based unit for optimizing the parameters of support vector machine. The model and algorithm are illustrated in the empirical analysis phase, and the comparison results show that genetic algorithm-support vector machine model can generally provide a better performance for collision risk index estimation. Meanwhile, the result also indicates that the model may be not so good when we take a higher value of collision risk index. So, the distinguishing threshold of collision risk level should be adjusted according to actual situation when applying this model in practical application.

Estuary salinity prediction using a coupled GA-SVM model: a case study of the Min River Estuary, China

Estuary salinity predictions can help to improve water safety in coastal areas. Coupled genetic algorithm-support vector machine (GA-SVM) models, which adopt a GA to optimize the SVM parameters, have been successfully applied in some research fields. In light of previous research findings, an application of a GA-SVM model for tidal estuary salinity prediction is proposed in this paper. The corresponding model is developed to predict the salinity of the Min River Estuary (MRE). By conducting an analysis of the time series of daily salinity and the results of simulation experiments, the high-tide level, runoff and previous salinity are considered as the major factors that influence salinity variation. The prediction accuracy of the GA-SVM model is satisfactory, with coefficient of determination (R2) of 0.85, Nash–Sutcliffe efficiency of 0.84 and root mean square error of 119 (μS/cm). The proposed model performs significantly better than the traditional SVM model in terms of prediction accuracy and computing time. It can be concluded that the proposed model can successfully predict the salinity of MRE based on the high-tide level, runoff and previous salinity.

A combination of chemometrics methods and GC–MS for the classification of edible vegetable oils

The authenticity of edible vegetable oils is a very important issue due to consumer health and commercial reasons. Gas chromatography–mass spectrometry (GC–MS) was applied to analyze the fatty acid composition of sixty six samples from six different kinds of edible vegetable oils. The fatty acid profiles of these edible vegetable oils were used to classify the type of edible oils. For improving the classification accuracy of vegetable oils with respect to type, the support vector machine (SVM) technique, optimized using the genetic algorithm (GA), was employed to construct the classification model. The effectiveness of the GA–SVM combination in classification was compared with that of other well-known strategies for classification, such as minimum distance classification (MDC) and linear discriminant analysis (LDA). In addition, the Kennard–Stone algorithm was used to select the representative training samples and compared with the random sampling method. The misclassification rates were 8.48% and 3.03% for training and test set, respectively, by the GA–SVM model using the linear kernel. Only one or two samples will be misclassified in the process of GA–SVM classification. The classification task based on fatty acid data can be successfully achieved by the GA–SVM technique combined with the Kennard–Stone algorithm. The results reveal that this strategy is of great promise in flexible and accurate classification of edible vegetable oils.

Study on the Quantitative Method of Oversaturated Intersection

Oversaturation in the modern urban traffic often happens. In order to describe the degree of oversaturation, the indexes of intersection oversaturation degree are put forward include dissipation time, stranded queue, overflow queue and travel speed. On the basis of selected indexes, the genetic algorithm support vector machine (GA-SVM) model was proposed to quantify the degree of oversaturation. In this method the genetic algorithm is used to select the model parameters. The GA-SVM model built is used to quantify the degree of oversaturation. Combining with the volume of intersections in Guangzhou city the method is calculated and simulated through programming. The simulation results show that GA-SVM method is effective and the accuracy of GA-SVM is higher than support vector machine (SVM).This method provides a theoretical basis for the analysis of traffic system under over-saturated traffic conditions.

Traffic Flow Prediction Model for Large-Scale Road Network Based on Cloud Computing

To increase the efficiency and precision of large-scale road network traffic flow prediction, a genetic algorithm-support vector machine (GA-SVM) model based on cloud computing is proposed in this paper, which is based on the analysis of the characteristics and defects of genetic algorithm and support vector machine. In cloud computing environment, firstly, SVM parameters are optimized by the parallel genetic algorithm, and then this optimized parallel SVM model is used to predict traffic flow. On the basis of the traffic flow data of Haizhu District in Guangzhou City, the proposed model was verified and compared with the serial GA-SVM model and parallel GA-SVM model based on MPI (message passing interface). The results demonstrate that the parallel GA-SVM model based on cloud computing has higher prediction accuracy, shorter running time, and higher speedup.

Prediction of P2Y 12 antagonists using a novel genetic algorithm-support vector machine coupled approach

Presently, a genetic algorithm (GA)-support vector machine (SVM) coupled approach is proposed for optimizing the 2D molecular descriptor subset generated for series of P2Y 12 (members of the G-protein-coupled receptor family) antagonists, with the statistical performance and efficiency of the model being simultaneously enhanced by SVM kernel-based nonlinear projection. As we know, this is the first QSAR study for prediction of P2Y 12 inhibition activity based on an unusually large dataset of 364 P2Y 12 antagonists with diversity of structures. In addition, three other widely used approaches, i.e., partial least squares (PLS), random forest (RF), and Gaussian process (GP) routines combined with GA (namely, GA–PLS, GA–RF, GA–GP, respectively) are also employed and compared with the GA–SVM method in terms of several rigorous evaluation criteria. The obtained results indicate that the GA–SVM model is a powerful tool for prediction of P2Y 12 antagonists, producing a conventional correlation coefficient R 2 of 0.976 and R cv 2 (cross-validation) of 0.829 for the training set as well as R pred 2 of 0.811 for the test set, which significantly outperforms the other three methods with the average R 2 = 0.894, R cv 2 = 0.741 , R pred 2 = 0.693 . The proposed model with excellent prediction capacity from both the internal to external quality should be helpful for screening and optimization of potential P2Y 12 antagonists prior to chemical synthesis in drug development.

Parameter optimisation using genetic algorithm for support vector machine-based price-forecasting model in National electricity market

The parameter selection is very important for successful modelling of input–output relationship in a function approximation model. In this study, support vector machine (SVM) has been used as a function approximation tool for a price series and genetic algorithm (GA) has been utilised for optimisation of the parameters of the SVM model. Instead of using single time series, separate time series for each trading interval has been employed to model each day's price profile, and SVM parameters of these separate series have been optimised using GA. The developed model has been applied to two large power systems from National electricity market (NEM) of Australia. The forecasting performance of the proposed model has been compared with a heuristic technique, a linear regression model and the other reported works in the literature. Effect of price volatility on the performance of the models has also been analysed. Testing results show that the proposed GA–SVM model has better forecasting ability than the other forecasting techniques.

A novel QSAR model for prediction of apoptosis-inducing activity of 4-aryl-4-H-chromenes based on support vector machine

In this work some chemometrics methods were applied for modeling and prediction of the induction of apoptosis by 4-aryl-4-H-chromenes with descriptors calculated from the molecular structure alone. The genetic algorithm (GA) and stepwise multiple linear regression methods were used to select descriptors which are responsible for the apoptosis-inducing activity of these compounds. Then support vector machine (SVM), artificial neural network (ANN), and multiple linear regression (MLR) were utilized to construct the nonlinear and linear quantitative structure–activity relationship models. The obtained results using SVM were compared with ANN and MLR; it revealed that the GA–SVM model was much better than other models. The root-mean-square errors of the training set and the test set for GA–SVM model are 0.181, 0.241 and the correlation coefficients were 0.950, 0.924, respectively, and the obtained statistical parameters of cross validation test on GA–SVM model were Q 2 = 0.71 and SRESS = 0.345 which revealed the reliability of this model. The results were also compared with previous published model and indicate the superiority of the present GA–SVM model.