Support Vector Machine-based Algorithm Research Articles

Constructing a Consciousness Meter Based on the Combination of Non-Linear Measurements and Genetic Algorithm-Based Support Vector Machine.

Constructing a framework to evaluate consciousness is an important issue in neuroscience research and clinical practice. However, there is still no systematic framework for quantifying altered consciousness along the dimensions of both level and content. This study builds a framework to differentiate the following states: coma, general anesthesia, minimally conscious state (MCS), and normal wakefulness. This study analyzed electroencephalography (EEG) recorded from frontal channels in patients with disorders of consciousness (either coma or MCS), patients under general anesthesia, and healthy participants in normal waking consciousness (NWC). Four non-linear methods-permutation entropy (PE), sample entropy (SampEn), permutation Lempel-Ziv complexity (PLZC), and detrended fluctuation analysis (DFA)-as well as relative power (RP), extracted features from the EEG recordings. A genetic algorithm-based support vector machine (GA-SVM) classified the states of consciousness based on the extracted features. A multivariable linear regression model then built EEG indices for level and content of consciousness. The PE differentiated all four states of consciousness (p<0.001). Altered contents of consciousness for NWC, MCS, coma, and general anesthesia were best differentiated by the SampEn, and PLZC. In contrast, the levels of consciousness for these four states were best differentiated by RP of Gamma and PE. A multi-dimensional index, combined with the GA-SVM, showed that the integration of PE, PLZC, SampEn, and DFA had the highest classification accuracy (92.3%). The GA-SVM was better than random forest and neural networks at differentiating these four states. The 'coordinate value' in the dimensions of level and content were constructed by the multivariable linear regression model and the non-linear measures PE, PLZC, SampEn, and DFA. Multi-dimensional measurements, especially the PE, SampEn, PLZC, and DFA, when combined with GA-SVM, are promising methods for constructing a framework to quantify consciousness.

Ensemble of Rule Learner and Sequential Minimum Optimization Algorithm for Intrusion Detection System

An intrusion detection system is a process which automates analyzing activities in network or a computer system. It is used to detect nasty code, hateful activities, intruders and uninvited communications over the Internet. The general intrusion detection system is struggling with some problems like false positive rate, false negative rate, low classification accuracy and slow speed. Now-a-days, this has turned an attention of many researchers to handle these issues. Recently, ensemble of different base classifier is widely used to implement intrusion detection system. In ensemble method of machine learning, the proper selection of base classifier is a challenging task. In this paper, machine learning ensemble have designed and implemented for the intrusion detection system. The ensemble of Partial Decision Tree and Sequential Minimum optimization algorithm to train support vector machine have used for intrusion detection system. Partial Decision Tree rule learner is simplicity and it generates rules fast. Sequential Minimum optimization algorithm is easy to use and is better scaling with training set size with less computational time. Due to these advantages of both classifiers, they jointly used with different methods of ensemble. We make use of all types of methods of ensemble. The performances of base classifiers have evaluated in term of false positive, accuracy and true positive. Performance results display that proposed majority voting method of ensemble using Partial Decision Tree rule learner and Sequential Minimum optimization algorithm based Support Vector Machine offers highest classification among different ensemble classifiers on training dataset. This method of ensemble exhibits highest true positive and lowest false positive rates. It is also observed that stacking of both PART and SMO exhibits lowest and same classification accuracy on test dataset

Stability analysis of GMAW based on multi-scale entropy and genetic optimized support vector machine

The gas metal arc welding is a complex chaotic dynamic process. To study the relationship between arc electrical signal and welding stability, the multi-scale entropy method was introduced to analyze the current signals under different welding process parameters. Under the short-circuiting droplet transition mode, the larger shielding gas flow rate led to, the more stable welding and the smaller amplitude of multi-scale entropy curves. When the welding current parameter increased gradually, the droplet transition mode changed, and the amplitude of multi-scale entropy curves increased. As the welding voltage rose, the droplet transfer frequency decreased and the multi-scale entropy increased. Furthermore, the four-class prediction of welding forming quality was studied by combining with the genetic algorithm-based support vector machine (GA-SVM). The multi-scale entropy distribution was closely related to the type and stability of short-circuiting transfer in the welding process.

A memetic algorithm using emperor penguin and social engineering optimization for medical data classification

Gene selection and classification of microarray data play an important role in cancer diagnosis and treatment. One of the most popular and faster classification model is support vector machine (SVM). However, the major challenge in SVM lies in the selection of its two parameters, namely, regularization parameter C and kernel parameter γ. Attempts have been made to improve the performance of SVM by tuning these two parameters with the help of metaheuristics. Although existing metaheuristics can search the promising regions of the search space, they are unable to explore the global optimum efficiently. In this paper, a memetic algorithm-based SVM (M-SVM) is presented for simultaneous feature selection and optimization of SVM parameters. The memetic algorithm is a fusion of local search strategy using social engineering optimizer (SEO) and global optimization framework using emperor penguin optimizer (EPO). The idea of embedding SEO in EPO is to enhance the exploitation capability of EPO. The performance of our algorithm is evaluated on seven standard benchmark datasets. To prove the efficacy of the method, it is compared with particle swarm optimization based SVM (PSO-SVM), genetic algorithm based SVM (GA-SVM), and fifteen other state-of-the-art methods. The experimental results confirm that the proposed method significantly outperforms other existing techniques in terms of accuracy and number of selected genes. The proposed method is validated using a statistical analysis, namely, ANOVA.

An efficient intrusion detection technique based on support vector machine and improved binary gravitational search algorithm

‘Curse of Dimensionality’ and the trade-off between high detection rate and less false alarm rate make the design of an efficient and robust Intrusion Detection System, an open research challenge. In this way, we present Hyper Clique—Improved Binary Gravitational Search Algorithm based Support Vector Machine (HC-IBGSA SVM), an efficient and adaptive intrusion detection technique to improve the performance of SVM in terms of detection rate and false alarm rate. HC-IBGSA SVM employs hyper clique property of hypergraph, novel mutation operator, and Newton–Raphson inspired position update function to fasten the search for an optimal solution and to prevent premature convergence. Further, HC-IBGSA uses a weighted objective function to maintain the trade-off between maximizing detection rate and minimizing the false alarm rate and the optimal number of features. The experimental evaluations were carried out using two benchmark intrusion datasets, namely NSL-KDD CUP dataset and UNSW-NB15 dataset under two scenarios (1) SVM trained with all features, and (2) SVM trained with the optimal feature subset and model parameters obtained from HC-IBGSA in terms of various quality metrics, stability analysis and statistical test.

A Novel MOGA-SVM Multinomial Classification for Organ Inflammation Detection

Wrist pulse signal (WPS) contains crucial information of humans’ health condition. It can serve as an alternative method for diagnosing of organ inflammation instead of traditional clinical measurement. In this paper, a novel multi-objective genetic algorithm based support vector machine (MOGA-SVM) has been proposed for the multinomial classification of the inflammations of appendix, pancreas, and duodenum. A customized similarity kernel (KCS) has been optimally designed. The performance of multinomial classification using KCS is compared with five types of kernels, linear, radial basis function (RBF), polynomial and sigmoid kernel, as well as mixtures of polynomial and RBF, to verify the effectiveness of KCS. The sensitivity, specificity and accuracy (Acc) of the proposed method are 92%, 91.2%, and 91.6% respectively. The results have demonstrated that KCS improves the accuracy of classification from 8.9% to 59.6%. When compared to related work, the proposed method increases the performance by more than 10%. It is believed that WPS can serve as alternative measures to diagnose organ inflammations.

A feature extraction method based on composite multi-scale permutation entropy and Laplacian score for shearer cutting state recognition

In the field of coal mining, accurate cutting state recognition of shearer is the basis and premise for achieving automatic coal cutting. In this paper, a novel shearer cutting state recognition method is presented based on composite multi-scale permutation (CMPE), Laplacian score (LS) and fly optimization algorithm-based support vector machine classification (FOA-SVM). CMPE is proposed to overcome the shortcomings of MPE and can extract the hidden state characteristics from the vibration signals of shearer rocker arm. Some simulations are provided to select the appropriate parameter settings and prove the superiority of CMPE to MPE. In addition, LS algorithm is employed to sort the extracted features over different scales according to their importance and the sensitive feature combinations can be generated scientifically. The FOA-SVM classifier is constructed to achieve intelligent recognition of shearer cutting state. Finally, some experiments are presented and the comparison results indicated that the proposed method can realize the recognition of shearer cutting state with higher accuracy than the existing methods.

Short-term PV power forecasting using hybrid GASVM technique

The static, clean and movement free characteristics of solar energy along with its contribution towards global warming mitigation, enhanced stability and increased efficiency advocates solar power systems as one of the most feasible energy generation resources. Considering the influence of stochastic weather conditions over the output power of photovoltaic (PV) systems, the necessity of a sophisticated forecasting model is increased rapidly. A genetic algorithm-based support vector machine (GASVM) model for short-term power forecasting of residential scale PV system is proposed in this manuscript. The GASVM model classifies the historical weather data using an SVM classifier initially and later it is optimized by the genetic algorithm using an ensemble technique. In this research, a local weather station was installed along with the PV system at Deakin University for accurately monitoring the immediate surrounding environment avoiding the inaccuracy caused by the remote collection of weather parameters (Bureau of Meteorology). The forecasting accuracy of the proposed GASVM model is evaluated based on the root mean square error (RMSE) and mean absolute percentage error (MAPE). Experimental results demonstrated that the proposed GASVM model outperforms the conventional SVM model by the difference of about 669.624 W in the RMSE value and 98.7648% of the MAPE error.

Hybrid Grey Wolf Optimization Algorithm–Based Support Vector Machine for Groutability Prediction of Fractured Rock Mass

AbstractGroutability determination is a very important task in grouting quality control. There is little research on the groutability of cement-based grout in a fractured rock mass, and the predict...

Joint Modulation Classification and OSNR Estimation Enabled by Support Vector Machine

By adopting the cumulative distribution function of the received signal's amplitude as feature, a support vector machine-based algorithm is proposed to jointly classify the modulation format and estimate the optical signal-to-noise ratio (OSNR) in coherent optical communication systems. Three commonly-used quadrature-amplitude modulation (QAM) formats are considered. Numerical simulations have been carried out in the OSNR ranges from 5 to 30 dB, and results show that the proposed algorithm achieves a very good modulation classification (MC) performance, as well as high OSNR estimation accuracy with a maximum estimation error of 0.8 dB. Optical back-to-back experiments are also conducted in OSNR ranges of interest. A 99% average correct MC rate is observed, and mean OSNR estimation errors of 0.38, 0.68, and 0.62 dB are noticed for 4-QAM, 16-QAM, and 64-QAM, respectively. Furthermore, compared with the neural networks-based joint estimation algorithm, the proposed algorithm attains better performance with comparable complexity.

Optimizing Biodiesel Production from Waste Cooking Oil Using Genetic Algorithm-Based Support Vector Machines

The ever increasing fuel demands and the limitations of oil reserves have motivated research of renewable and sustainable energy resources to replace, even partially, fossil fuels, which are having a serious environmental impact on global warming and climate change, excessive greenhouse emissions and deforestation. For this reason, an alternative, renewable and biodegradable combustible like biodiesel is necessary. For this purpose, waste cooking oil is a potential replacement for vegetable oils in the production of biodiesel. Direct transesterification of vegetable oils was undertaken to synthesize the biodiesel. Several variables controlled the process. The alkaline catalyst that is used, typically sodium hydroxide (NaOH) or potassium hydroxide (KOH), increases the solubility and speeds up the reaction. Therefore, the methodology that this study suggests for improving the biodiesel production is based on computing techniques for prediction and optimization of these process dimensions. The method builds and selects a group of regression models that predict several properties of biodiesel samples (viscosity turbidity, density, high heating value and yield) based on various attributes of the transesterification process (dosage of catalyst, molar ratio, mixing speed, mixing time, temperature, humidity and impurities). In order to develop it, a Box-Behnken type of Design of Experiment (DoE) was designed that considered the variables that were previously mentioned. Then, using this DoE, biodiesel production features were decided by conducting lab experiments to complete a dataset with real production properties. Subsequently, using this dataset, a group of regression models—linear regression and support vector machines (using linear kernel, polynomial kernel and radial basic function kernel)—were constructed to predict the studied properties of biodiesel and to obtain a better understanding of the process. Finally, several biodiesel optimization scenarios were reached through the application of genetic algorithms to the regression models obtained with greater precision. In this way, it was possible to identify the best combinations of variables, both independent and dependent. These scenarios were based mainly on a desire to improve the biodiesel yield by obtaining a higher heating value, while decreasing the viscosity, density and turbidity. These conditions were achieved when the dosage of catalyst was approximately 1 wt %.

Application of Genetic Algorithm Based Support Vector Machine Model in Second Virial Coefficient Prediction of Pure Compounds

In this work, a Genetic Algorithm boosted Least Square Support Vector Machine model by a set of linear equations instead of a quadratic program, which is improved version of Support Vector Machine model, was used for estimation of 98 pure compounds second virial coefficient. Compounds were classified to the different groups. Finest parameters were obtained by Genetic Algorithm method for training data. The accuracy of the Genetic Algorithm boosted Least Square Support Vector Machine was compared with four empirical equations that are well-known and are claimed can predict all compounds second virial coefficients (Pitzer, Tesonopolos, Gasanov RK and Long Meng). Results showed that in all classes of compounds, the Genetic Algorithm boosted Least Square Support Vector Machine method was more accurate than these empirical correlations. The Average Relative Deviation percentage of overall data set was 2.53 for the Genetic Algorithm boosted Least Square Support Vector Machine model while the best Average Relative Deviation percentage for empirical models (Tesonopolos) was 15.38. When the molecules become more complex, the difference in accuracy becomes sharper for empirical models where the proposed Genetic Algorithm boosted Least Square Support Vector Machine model have predicted good results for classes of compounds that empirical correlations usually fail to give good estimates.

Short-term load forecasting for microgrids based on DA-SVM

PurposeThe study aims to accomplish the short-term load forecasting for microgrids. Short-term load forecasting is a vital component of economic dispatch in microgrids, and the forecasting error directly affects the economic efficiency of operation. To some extent, short-term load forecasting is more difficult in microgrids than in macrogrids.Design/methodology/approachThis paper presents the method of Dragonfly Algorithm-based support vector machine (DA-SVM) to forecast the short-term load in microgrids. This method adopts the combination of penalty factor C and kernel parameters of SVM which needs to be optimized as the position of dragonfly to find the solution. It takes the forecast accuracy calculated by SVM as the current fitness value of dragonfly and the optimal position of dragonfly obtained through iteration is considered as the optimal combination of parameters C and s of SVM.FindingsDA-SVM algorithm was used to do short-term load forecast in the microgrid of an offshore oilfield group in the Bohai Sea, China and the forecasting results were compared with those of PSO-SVM, GA-SVM and BP neural network models. The experimental results indicate that the DA-SVM algorithm has better global searching ability. In the case of study, the root mean square errors of DA-SVA are about 1.5 per cent and its computation time is saved about 50 per cent.Originality/valueThe DA-SVM model presented in this paper provides an efficient and effective method of short-term load forecasting for a microgrid electric power system.

Online-Purchasing Behavior Forecasting with a Firefly Algorithm-based SVM Model Considering Shopping Cart Use

Due to the complexity of the e-commerce system, a hybrid model for online-purchasing behavior forecasting is developed to predict whether or not a customer makes a purchase during the next visit to the online store based on the previous behaviors, i.e., online-purchasing behavior. The proposed model makes contributions to literature from two perspectives: (1) a classification model is proposed based on the “hybrid modeling” concept, in which an effective artificial intelligence (AI) technique of support vector machine (SVM) is employed for classification forecasting and further extended by introducing the promising AI optimization tool of firefly algorithm (FA), to solve the crucial but tough task of parameters selection, i.e., the FA-based SVM model; (2) an appropriate predictor set is carefully designed especially considering online shopping cart use which was otherwise neglected in existing models, apart from other common online behaviors, e.g., clickstream behavior, previous purchase behavior and customer heterogeneity. To verify the superiority of the proposed model, an online furniture store is focused on as study sample, and the empirical results statistically support that the proposed FA-based SVM model considering online shopping cart use significantly beat all benchmarking models (with other popular classification methods and/or different predictor sets) in terms of prediction accuracy.

Application of firefly algorithm-based support vector machines for prediction of field capacity and permanent wilting point

Soil field capacity (FC) and permanent wilting point (PWP) are significant parameters in numerous biophysical models and agricultural activities. Although these parameters can be measured directly, their measurements are quite expensive. The purpose of this study was to develop a hybrid Support Vector Machine (SVM) combined with Firefly Algorithm (FFA) techniques (SVM-FFA) to predict the FC and PWP using some easily available soil properties. The data consist of 215 soil samples collected from different horizons of soil profiles located in the East Azerbaijan provinces, North-west of Iran. Several important parameters, including the sand,silt, clay, bulk density, and organic matter content were used as inputs, while the soil FC and PWP were the output parameters. The predictions from the SVM-FFA model were compared with SVM and artificial neural network (ANN) models. The model results were compared with regard to root mean square error (RMSE), correlation coefficient (CC) and relative root mean square error (RRMSE). A comparison of models indicated that the SVM-FFA model predicted better than SVM and ANN models with RMSE=2.402%, CC=0.972, RRMSE=7.677% for FC and RMSE=1.720%, CC=0.969, RRMSE=5.512% for PWP in the training data set while RMSE=2.873%, CC=0.962, RRMSE=8.745% for FC and RMSE=1.935%, CC=0.965, RRMSE=10.619% for PWP were obtained in the testing data set.

Assessing the performance of multiple spectral–spatial features of a hyperspectral image for classification of urban land cover classes using support vector machines and artificial neural network

Accurate and spatially detailed mapping of complex urban environments is essential for land managers. Classifying high spectral and spatial resolution hyperspectral images is a challenging task because of its data abundance and computational complexity. Approaches with a combination of spectral and spatial information in a single classification framework have attracted special attention because of their potential to improve the classification accuracy. We extracted multiple features from spectral and spatial domains of hyperspectral images and evaluated them with two supervised classification algorithms; support vector machines (SVM) and an artificial neural network. The spatial features considered are produced by a gray level co-occurrence matrix and extended multiattribute profiles. All of these features were stacked, and the most informative features were selected using a genetic algorithm-based SVM. After selecting the most informative features, the classification model was integrated with a segmentation map derived using a hidden Markov random field. We tested the proposed method on a real application of a hyperspectral image acquired from AisaFENIX and on widely used hyperspectral images. From the results, it can be concluded that the proposed framework significantly improves the results with different spectral and spatial resolutions over different instrumentation.

Neuroadaptive Variable Speed Control of Wind Turbine With Wind Speed Estimation

It is difficult to measure the wind speed accurately in short term. This reveals challenges for wind turbine control, especially for maximum power point tracking with adaptive control strategies. In this paper, a genetic algorithm based support vector machine model is adopted to estimate the wind speed, using physically measurable signals, such as the electrical power, pitch angle, and rotor speed, while the desired rotor speed can be obtained accordingly. Further, by combining the radial basis function neural networks with adaptive algorithms, a novel virtual parameter based neuroadaptive controller is developed to accommodate the system uncertain and external disturbances. The effectiveness and performances of the proposed method are validated and demonstrated with FAST (Fatigue, Aerodynamics, Structures, and Turbulence) and Simulink.

An Accurate ECG-Based Transportation Safety Drowsiness Detection Scheme

Many traffic injuries and deaths are caused by the drowsiness of drivers during driving. Existing drowsiness detection schemes are not accurate due to various reasons. To resolve this problem, an accurate driver drowsiness classifier (DDC) has been developed using an electrocardiogram genetic algorithm-based support vector machine (ECG GA-SVM). In existing studies, a cross correlation kernel and a convolution kernel have both been applied for performing the classification. The DDC is designed by a Mercer kernel ${{\rm{K}}_{{\mathrm{DDC}}}}$ formed by commuting the cross correlation kernel ${{\rm{K}}_{{\mathrm{xcorr}},\mathrm{ij}}}$ and the convolution kernel ${{\rm{K}}_{{\mathrm{conv}},\mathrm{ij}}}$ . ${{\rm{K}}_{{\mathrm{xcorr}},\mathrm{ij}}}$ , and captures the symmetric information among ECG signals from different classes, while ${{\rm{K}}_{{\mathrm{conv}},\mathrm{ij}}}$ captures the antisymmetric information among ECG signals from the same class. The final ${{\rm{K}}_{{\mathrm{DDC}}}}$ (a precomputed kernel) is obtained by a genetic mutation using a multi-objective genetic algorithm. This renders an optimal ${{\rm{K}}_{{\mathrm{DDC}}}}$ that confidently serves as the full descriptor of the drowsiness. The performance of ${{\rm{K}}_{{\mathrm{DDC}}}}$ is compared with the most prevailing kernels. The obtained DDC yields an overall accuracy of 97.01%, sensitivity of 97.16%, and specificity of 96.86%. The analysis reveals that the accuracy of ${{\rm{K}}_{{\mathrm{DDC}}}}$ is better than those of both ${{\rm{K}}_{{\mathrm{xcorr}},\mathrm{ij}}}$ and ${{\rm{K}}_{{\mathrm{conv}},\mathrm{ij}}}$ by more than 11%, and typical kernels including linear, quadratic, third order polynomial, and Gaussian radial basis function by 17–63%, respectively. Comparing with related works using the image-based method and the biometric signal-based method, K DDC improves the accuracy by 48.4–87.2%. Testing results showed that KDDC has a less than 1% deviation from simulated results. Also, the average delay of DDC was bounded by 0.55 ms. This renders the real time implementation. Thus, the developed ECG GA-SVM provides an accurate and instantaneous warning to the drivers before they fall into sleep. As a result this ensures the public transport safety.

Optimizing mixture properties of biodiesel production using genetic algorithm-based evolutionary support vector machine

ABSTRACTNowadays, biodiesel is used as one of the alternative renewable energy due to the increasing energy demand. However, optimum production of biodiesel still requires a huge number of expensive and time-consuming laboratory tests. To address the problem, this research develops a novel Genetic Algorithm-based Evolutionary Support Vector Machine (GA-ESIM). The GA-ESIM is an Artificial Intelligence (AI)-based tool that combines K-means Chaotic Genetic Algorithm (KCGA) and Evolutionary Support Vector Machine Inference Model (ESIM). The ESIM is utilized as a supervised learning technique to establish a highly accurate prediction model between the input--output of biodiesel mixture properties; and the KCGA is used to perform the simulation to obtain the optimum mixture properties based on the prediction model. A real biodiesel experimental data is provided to validate the GA-ESIM performance. Our simulation results demonstrate that the GA-ESIM establishes a prediction model with better accuracy than other AI-based tool and thus obtains the mixture properties with the biodiesel yield of 99.9%, higher than the best experimental data record, 97.4%.

Application of genetic algorithm-based support vector machines for prediction of soil liquefaction

This paper presents a hybrid genetic algorithm (GA) and support vector machine (SVM) techniques to predict the potential of soil liquefaction. GA is employed in selecting the optimal values of the kernel function and the penalty parameter in SVM model to improve the forecasting accuracy. The database used in this study includes 109 CPT-based field observations from five major earthquakes between 1964 and 1983. Several important parameters, including the cone resistance, total vertical stress, effective vertical stress, mean grain size, normalized peak horizontal acceleration at ground surface, cyclic stress ratio, and earthquake magnitude, were used as the input parameters, while the potential of soil liquefaction was the output parameter. The predictions from the GA-SVM model were compared with those from three methods: grid search (GS) method, artificial neural network (ANN) model, and C4.5 decision tree approach. The overall classification success rates for the entire dataset predicted by GA-SVM, ANN, C4.5 decision tree, and GS-SVM models are 97.25, 97.2, 96.3, and 92.66 %, respectively. The study concluded that the proposed GA-SVM model improves the classification accuracy and is a feasible method in predicting soil liquefaction.