Genetic Algorithm Support Vector Research Articles

Software Development and Design of Network Security System Under Big Data Analysis

To explore the prediction effect of network security situational awareness on network vulnerabilities and attacks under the background of big data, this study constructs a predictive index system based on the network security situational awareness model. Based on the improved cuckoo algorithm, the cuckoo search radial basis function neural network is used to predict the situation. The weight value in the model is determined by the hierarchical analysis method, vulnerability simulation is conducted by Nessus software and network attack simulation is conducted by Snort software, and then the situation is evaluated by a fuzzy comprehensive evaluation method. Finally, Jquery and Bootstrap software is used to develop the system. The results show that the cuckoo search radial basis function model proposed in this study could predict network security situations more accurately than the radial basis function model, cuckoo search back-propagation neural network model, genetic algorithm radial basis function model and Support vector machine model based on particle swarm optimization model.

On the prediction of chemical exergy of organic substances using least square support vector machine

ABSTRACTKnowledge of a technique which could significantly predict standard chemical exergy values seems to be beneficial in chemical processes. In this regard, a combination of genetic algorithm (GA) and least square support vector machine (LSSVM) was developed. A set of 134 data was utilized for model development from which 114 and 20 data were allotted to training and testing phases, respectively. In addition, the average absolute relative error and coefficient of determination values were 1.8351 and 0.99848, respectively. In this study, molecular weight of substance, atomic polarization, and number of atoms in any substances were selected as input data to introduce the subjected substances and consequently, the chemical exergy was selected for target of prediction. In conclusion, it is comprehensively established that the proposed model is considerably efficient due to its highly accurate prediction and GA-LSSVM introduced as an efficient model for the prediction of chemical exergy of organic substances.

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 and Least Squares Support Vector Machines in Laminar Cooling Process

Application of Genetic Algorithm and Least Squares Support Vector Machines in Laminar Cooling Process

Optimized Feature Subset Selection and Relevance Feedback for Image Retrieval Based on Multiresolution Enhanced Orthogonal Polynomials Model

In this paper, a simple image retrieval method incorporating relevance feedback based on the multiresolution enhanced orthogonal polynomials model is proposed. In the proposed method, the low level image features such as texture, shape and color are extracted from the reordered orthogonal polynomials model coefficients and linearly combined to form a multifeature set. Then the dimensionality of the multifeature set is reduced by utilizing multi objective Genetic Algorithm (GA) and multiclass binary Support Vector Machine (SVM). The obtained optimized multifeature set is used for image retrieval. In order to improve the retrieval accuracy and to bridge the semantic gap, a correlation based k-Nearest Neighbor (k-NN) method for relevance feedback is also proposed. In this method, an appropriate relevance score is computed for each image in the database based on relevant and non relevant set chosen by the user with correlation based k-NN method. The experiments are carried out with Corel and Caltech database images and the retrieval rates are computed. The proposed method with correlation based k-NN for relevance feedback gives an average retrieval rate of 94.67%.