Feedback analysis of tunnel construction using a hybrid arithmetic based on Support Vector Machine and Particle Swarm Optimisation

The victory prediction of DotA 2 game is an interesting thing to know as it is one of the popular online games and is often played by players around the world. The winning opportunity of a team in this game is largely determined by the combination of heroes chosen so that a proper selection of heroes can give the team a victory. A classification is one of many ways used to make prediction. In this study, the classification will be divided into 2 classes, namely win and lose. One popular classification method is the Support Vector Machine (SVM). SVM method is suitable for classification based on 2 classes. However, in the SVM method, it is necessary to determine the optimum parameters to obtain good accuracy results, so that this research will use one of the optimization methods of Particle Swarm Optimization (PSO) for optimization of SVM parameters to increase the accuracy value. In this study, the SVM parameter to be optimized is C Parameter on linear kernel and it is proven that Optimization of C Parameter on SVM using PSO can increase SVM accuracy from 0.53866 to 0.600349718.

Support Vector Machine (SVM) has become a popular method in machine learning in recent years. SVM is widely used as a tool in many classifiction areas. The selection optimum values of the parameters (model selection) for SVM is an important step in SVM design. This paper presents the results of research and testing method Particle Swarm Optimization (PSO) in selecting the parameters for SVM. Here we focus on the selection parameter γ of the RBF function and soft margin parameter C of SVM for classification problems. In this paper we propose a Particle Swarm Optimization variation model which combine the cognition-only model with the social-only model and use randomised low discrepancy sequences for initialising particle swarms. We then evaluate the suggested strategies with a series of experiments on 6 benchmark datasets. Experimental results demonstrate the model we proposed better than grid search and full PSO-SVM model.

Network traffic prediction performs a main function in characterizing network community performance. An approach which could appropriately seize the salient characteristics of the network visitors could be very useful for network analysis and simulation. Network traffic prediction methods could be divided into two classes: one is the single models and the opposite is the hybrid fashions. The hybrid models integrate the merits of several single models and consequently can enhance the network traffic prediction accuracy. In this paper, a new hybrid network traffic prediction method (EPSVM) primarily based on Empirical Mode Decomposition (EMD), Particle Swarm Optimization (PSO), and Support Vector Machines (SVM) is presented. The EPSVM first utilizes EMD to eliminate the impact of noise signals. Then, SVM is applied to model training and fitting, and the parameters of SVM are optimized by PSO. The effectiveness of the presented method is examined by evaluating it with different methods, including basic SVM (BSVM), Empirical Mode Decomposition processed by SVM (ESVM), and SVM optimized by Particle Swarm Optimization (PSVM). Case studies have demonstrated that EPSVM performed better than the other three network traffic prediction models.

Forecasting agriculture water consumption is significant to optimize confiration of water resources. In the paper, we have combined particle swarm optimization (PSO) and support vector machines (SVM) for agriculture water consumption forecasting. Compared to GA, the advantages of PSO are that PSO is easy to implement and there are few parameters to adjust. Thus, PSO is very suitable to determine training parameters of support vector machine. The experimental results demonstrate that the proposed PSOSVM model has good forecasting results in agriculture water consumption Forecasting.

Software defect prediction could improve the reliability of software and reduce development costs. Traditional prediction models usually have a lower prediction accuracy. In order to solve this problem, a new model for software defect prediction using Particle Swarm Optimization (PSO) and Support Vector Machine (SVM) named P-SVM model is proposed in this paper, which takes advantage of non-linear computing capability of SVM and parameters optimization capability of PSO. Firstly, P-SVM model uses PSO algorithm to calculate the best parameters of SVM, and then it adopts the optimized SVM model to predict software defect. P-SVM model and other three different prediction models are used to predict the software defects in JM1 data set as an experiment, the results show that P-SVM model has a higher prediction accuracy than BP Neural Network model, SVM model, GA-SVM model.

The success of any Intrusion Detection Systems (IDSs) is a complicated problem due to its nonlinearity and the quantitative or qualitative network traffic data stream with irrelevant and redundant features. How to choose the effective and key features is very important topic for an intrusion detection problem. Support vector machine (SVM) has been employed to provide potential solutions for the IDSs problem. However, the practicability of SVM is affected due to the difficulty of selecting appropriate SVM parameters. Artificial Bee Colony algorithm (ABC) is an optimization method, which is not only has strong global search capability, but also is very easy to implement. Thus, the proposed ABC-SVM model is applied to determine free parameters of SVM and feature selection at building intrusion detection system. The standard ABC is used to determine free parameters of support vector machine and the binary ABC is to obtain the optimum feature selection for IDSs from KDD Cup 99 data set. The experimental results indicate that the ABC-SVM method can achieve higher accuracy rate than Particle swarm optimization (PSO) and GA-SVM algorithms in the same time.

Support Vector Machine (SVM) is a relatively novel classification technology, which has shown higher performance than traditional learning methods in many applications. Therefore, some security researchers have proposed an intrusion detection method based on SVM. However, the SVM algorithm is very sensitive to the choice of kernel function and parameter adjustment. Once the parameter selection is unscientific, it will lead to poor classification accuracy. To solve this problem, this paper presents a Grey Wolf Optimizer Algorithm based on Particle Swarm Optimization (PSOGWO) algorithm to improve the Intrusion Detection System (IDS) based on SVM. This method uses PSOGWO algorithm to optimize the parameters of SVM to improve the overall performance of intrusion detection based on SVM. The "optimal detection model" of SVM classifier is determined by the fusion of PSOGWO algorithm and SVM. The comparison experiments based on NSL-KDD dataset show that the intrusion detection method based on PSOGWO-SVM achieves the optimization of the parameters of SVM, and has improved significantly in terms of detection rate, convergence speed and model balance. This shows that the method has better performance for network intrusion detection.

This abstract presents the influence of meteorological and hydrological droughts on groundwater drought conditions through the calculation and analysis of four widely used drought indices: Standardized Precipitation Index (SPI), Reconnaissance Drought index (RDI), Streamflow Drought Index (SDI), and Groundwater Resource Index (GRI). These indices were computed using long-term observed data including precipitation, temperature, discharge, and groundwater levels to capture the temporal and spatial variability of drought events in the Chaliyar River Basin. By analyzing the spatial and temporal variability of groundwater levels and discharge rates and measuring the impact of future drought events under climate change conditions. The research utilizes historical data to understand the relationship between drought and groundwater availability, informing sustainable water management strategies. A Support Vector Machine (SVM) model was developed to simulate groundwater storage by using the Particle Swarm Optimization (PSO) to optimize SVM parameters. It begins with the collection of groundwater storage data, discharge data, and climate data, alongside outputs from global climate models (GCMs). The data is processed with a Gamma Test to determine the best combination of meteorological and hydrological drought indices as input variables. A Support Vector Machine (SVM) for groundwater simulation, which is optimized through Particle Swarm Optimization (PSO) to select the best SVM parameters. The developed SVM model undergoes validation using 5-fold cross- validation to ensure accuracy. Climate data downscaled for future scenarios, specifically RCP4.5 and RCP8.5, is then utilized to project drought events for the period 2022-2050. This enables an assessment of anticipated changes in groundwater storage and its behavior during future drought events under different climate conditions. This study investigates the interconnected effects of meteorological and hydrological droughts on groundwater storage in a changing climate. Using a combination of observational data, hydrological modelling, and climate projections, it assesses the impacts of drought on groundwater recharge and storage.

Based on soft morphological filters and support vector machine (SVM), a roller bearing fault diagnosis method is proposed. It is very difficult to filter the fault vibration signals from the strong noise background because the roller bearing fault diagnosis is a problem of multi-class classification. Soft morphology filter can not only identify the features of fringe and shape of the signals but also give improved performance under certain conditions. Support vector machine has good classification performance especially in the small-sample, nonlinear and high dimensional features and so on. The penalty factors and kernel parameters of SVM are optimized by using particle swarm optimization to avoid dependence on initial parameters and training samples. Firstly, vibration signals are filtered by the soft morphological filters. Secondly, the normalized energy of the different characteristic frequencies is utilized to identify the fault features of input parameters of SVM classifier. The SVM parameters are optimized by using the canonical particle swarm optimization. The experiment results indicate that the modeling method is correct.

Disease diagnosis is conducted with a machine learning method. We have proposed a novel machine learning method that hybridizes support vector machine (SVM), particle swarm optimization (PSO), and cuckoo search (CS). The new method consists of two stages: firstly, a CS based approach for parameter optimization of SVM is developed to find the better initial parameters of kernel function, and then PSO is applied to continue SVM training and find the best parameters of SVM. Experimental results indicate that the proposed CS-PSO-SVM model achieves better classification accuracy and F-measure than PSO-SVM and GA-SVM. Therefore, we can conclude that our proposed method is very efficient compared to the previously reported algorithms.

Localization is an essential task in Wireless Sensor Networks (WSN) for various use cases such as target tracking and object monitoring. Anchor nodes play a critical role in this task since they can find their location via GPS signals or manual setup mechanisms and help other nodes in the network determine their locations. Therefore, the optimal placement of anchor nodes in a WSN is of particular interest for reducing the energy consumption while yielding better accuracy at finding locations of the nodes. In this paper, we propose a novel approach for finding the optimal number of anchor nodes and an optimal placement strategy of them in a large-scale WSN, based on the output of Grey Wolf Optimization (GWO) and Particle Swarm Optimization (PSO) methods. As an initial step in this approach, the virtual localization process is executed over a virtual coordinate system in order to optimize the efficiency of the localization process. GWO and PSO methods are compared with a coverage-based analytical method and machine learning approaches such as Support Vector Machine (SVM) regression and Multiple Regression. The simulations we run with different numbers of nodes in a WSN and different communication ranges of nodes demonstrate that the proposed approaches are superior for minimizing the localization errors while reducing the number of anchor nodes.

Displacement prediction of tunnel surrounding rock plays a significant role for safety estimation during tunnel construction. This paper presents an approach to use support vector machines (SVM) to predict tunnel surrounding rock displacement. A stepwise search is also introduced to optimize the parameters in SVM. The data of Fangtianchong tunnel is use to evaluate the proposed model. The comparison between artificial neural network (ANN) and SVM shows that SVM has a high-accuracy prediction than ANN. Results also show SVM seems to be a powerful tool for tunnel surrounding rock displacement prediction.

The main objective of this paper is to accurately estimate the fault location in a series compensated transmission line using integration of discrete wavelet transform, particle swarm optimization (PSO) and support vector machine based algorithm. Estimation of accurate fault location will lead to quicker restoration of the supply. Instantaneous values of faulty current, voltage and power signals are available at the relay location. Using 10-level Discrete Wavelet Transform (DWT), the available signals are decomposed and thereafter the statistical features are obtained from the decomposed signals. Forward feature selection algorithm is used to select the best feature set. Selected features are then applied to the support vector machine (SVM) for estimating the fault distance. PSO algorithm is used to select the best SVM parameter by global searching technique. Proposed fault locator has been trained and tested for different fault scenarios (fault resistance and phase difference). The test results demonstrate that the adopted technique is a reliable method for estimating fault locations accurately.

Automatic recognition of skin symptom plays an importance role in the skin diagnosis and treatment. Feature selection is to increase the classification performance of skin symptom. In this paper, the effects of feature selection on the classification of 4-class skin symptoms (chloasma, blackhead, freckled and comedone) are analyzed. Support vector machine (SVM) is employed to construct classifier, the parameters setting is very crucial to the learning results and generalization ability of SVM, and many irrelevant and redundant features degrade the performance of classification, furthermore, the choice of SVM parameters is influenced by the feature subset taken into account and vice versa. So a joint optimization method for selecting features and SVM parameters is proposed. Genetic algorithms, which have a lot of improvements about fitness, crossover and mutation in comparison with simple genetic algorithms, is to achieve a balance between the classification accuracy and the size of the feature subsets selected and have a fast convergence rate. Furthermore, ‘one-against-one’ approach is used to solve multi-class classification problems and a comparison of the model performances is made among the results of the joint optimization and the single optimization of features or SVM parameters in the experiments. The simulation result shows that our approach can find subsets of features for 4-class skin symptoms classification with higher average cross validation accuracy and a higher rate of convergence compared with single optimization of features or SVM parameters.

This Dissertation dealt with the development of computational methods for the diagnosis and estimation of fetal condition. The proposed methods analyzed and extracted information from the Fetal Heart Rate (FHR) signal, since this is one of the few available tools for the estimation of fetal oxygenation and the assessment of fetal condition during labor. For the evaluation of the proposed methods the correlation of the FHR signal with short term indices were employed and to be more specific, its correlation with the pH values of fetal blood, which is an indirect sign of the development of fetal hypoxia during labor. In the context of this Dissertation, Independent Component Analysis (ICA) for feature extraction from the FHR signal was used for the first time. Moreover we used Hidden Markov Models in an attempt to “capture” the evolution in time of the fetal condition. Furthermore, new features based on the Discrete Wavelet Transform were proposed and used. Using a new hybrid method based on grammatical evolution new features were constructed based on already extracted features by conventional methods. Moreover, for the first (and only) time, Support Vector Machine (SVM) classifiers were employed in the field of FHR processing and the Particle Swarm Optimization (PSO) method was proposed for tuning their parameters. Finally, a new family of neural networks, the Wavelet Neural Networks (WNN) was proposed and used, trained using the PSO method. By conducting a number of experiments we managed to show that the FHR signal conveys valuable information, which by the use of advanced data processing and classification techniques can be associated with fetal pH, something which was not regarded feasible during the 90’s.