Parameters Of Support Vector Machine Research Articles

Load Forecasting Based on Weighted Grey Relational Degree and Improved ABC-SVM

The present study proposes a short-term load forecasting method based on weighted grey relational degree and improved support vector machines with the artificial bee colony algorithm (ABC-SVM). The entropy weight method was employed to obtain the weight of load-related physical information, and the historical and forecast load data selected based on the weighted grey relational degree were input into the support vector machine (SVM) to build a forecasting model. Meanwhile, the SVM parameters were optimized by the improved artificial bee colony algorithm before the model was used to perform load forecasting. The experimental results show that the proposed method could effectively improve the accuracy of the forecasting model and simplify the calculation, thus having research and practical value.

Challenges in evolutionary algorithm to find optimal parameters of SVM: A review

In rapidly changing classification and predation environment, optimization techniques in determining the hyper parameter of Support Vector Machine has become crucial for the accuracy of result. It’s an important tool for improving output quality of classification and prediction which includes modeling parameters relationship and resolution of optimal hyper parameter. However, determination of regularization (C) and gamma (γ), through mathematical models have undergone substantial development and expansion. In this paper, optimization techniques are categorized under several criteria. We also included the benchmarks for measuring the performance of classifier after parameter tuning, and found there is still a scope of improvement.

Unified integration of many-objective optimization algorithm based on temporary offspring for software defects prediction

Software defects prediction technology related to software products’ security and quality and provides guidance for software testing. To solve both the problem of datasets class imbalance in software defects prediction and support vector machine (SVM) parameter selection synchronously, high dimension software defects prediction model (HD-SDP) based on SVM is proposed. Including four objectives that the false positive rate of defects, probability of detection, F-metric, and Balance value. And a unified integration of many-objective optimization algorithm based on temporary offspring (UIMaOTO) is designed for this model to select the parameters of SVM and non-defective module synchronously. UIMaOTO adopts temporary offspring strategy to generate the formal offspring and then proposes the unified integration strategy to enhance the selection pressure of algorithm. UIMaOTO is compared to other state-of-the-art algorithms, and the experiment results are conducted on well-known DTLZ test suite. The results show that the proposed algorithm has better all-around performance and is competitive for many-objective optimization problems. At the same time, the UIMaOTO algorithm is used to address the HD-SDP model, and the performance is improved by 14.27% compared with other algorithms.

Investigating factors affecting severity of large truck-involved crashes: Comparison of the SVM and random parameter logit model

Introduction: Reducing the severity of crashes is a top priority for safety researchers due to its impact on saving human lives. Because of safety concerns posed by large trucks and the high rate of fatal large truck-involved crashes, an exploration into large truck-involved crashes could help determine factors that are influential in crash severity. The current study focuses on large truck-involved crashes to predict influencing factors on crash injury severity. Method: Two techniques have been utilized: Random Parameter Binary Logit (RPBL) and Support Vector Machine (SVM). Models have been developed to estimate: (1) multivehicle (MV) truck-involved crashes, in which large truck drivers are at fault, (2) MV track-involved crashes, in which large truck drivers are not at fault and (3) and single-vehicle (SV) large truck crashes. Results: Fatigue and deviation to the left were found as the most important contributing factors that lead to fatal crashes when the large truck-driver is at fault. Outcomes show that there are differences among significant factors between RPBL and SVM. For instance, unsafe lane-changing was significant in all three categories in RPBL, but only SV large truck crashes in SVM. Conclusions: The outcomes showed the importance of the complementary approaches to incorporate both parametric RPBL and non-parametric SVM to identify the main contributing factors affecting the severity of large truck-involved crashes. Also, the results highlighted the importance of categorization based on the at-fault party. Practical Applications: Unrealistic schedules and expectations of trucking companies can cause excessive stress for the large truck drivers, which could leads to further neglect of their fatigue. Enacting and enforcing comprehensive regulations regarding large truck drivers’ working schedules and direct and constant surveillance by authorities would significantly decrease large truck-involved crashes.

Quantitative analysis of multiple components based on support vector machine (SVM)

In this paper, support vector machine (SVM) is applied to the spectral detection of multiple components to establish a suitable prediction model in the water environment system. Firstly, the mixed fluorescence spectra of four common drugs with extremely similar fluorescence peaks are measured in water quality monitoring. Then, cross-validation is used to select the best parameters of SVM, which are given to obtain the relationship between the fluorescence spectra and corresponding concentration. The results show that the error between the real result and the predicted value is within the allowable error. With the features of effectiveness and robustness, the proposed method could be applied to the quantitative analysis of multiple components.

Research on Fault Diagnosis Method of Train Rolling Bearing Based on Variational Modal Decomposition and Bat Algorithm-Support Vector Machine

Aiming at the problem that the vibration signal of train rolling bearing presents nonlinear and non-stationary characteristics, which leads to the difficulty of fault feature extraction, a fault diagnosis method of train bogie rolling bearing based on variational mode decomposition (VMD) and bat algorithm optimization support vector machine (BA-SVM) is proposed. Firstly, the center frequency method is used to determine the K value of VMD algorithm. Then, the original signal is decomposed into a series of intrinsic mode components and the distribution entropy of each component is calculated as the feature vector, and the bat algorithm is used to optimize the model parameters of support vector machine. Finally, the BA-SVM model is used for fault pattern recognition of train rolling bearing. The experimental results show that this method can effectively extract the fault characteristics of train rolling bearings and realize fault diagnosis, and the recognition rate is better than that of the comparison method.

Characterization and Prediction of Presynaptic and Postsynaptic Neurotoxins Based on Reduced Amino Acids and Biological Properties

Background: Presynaptic and postsynaptic neurotoxins are two important neurotoxins. Due to the important role of presynaptic and postsynaptic neurotoxins in pharmacology and neuroscience, identification of them becomes very important in biology. Method: In this study, the statistical test and F-score were used to calculate the difference between amino acids and biological properties. The support vector machine was used to predict the presynaptic and postsynaptic neurotoxins by using the reduced amino acid alphabet types. Results: By using the reduced amino acid alphabet as the input parameters of support vector machine, the overall accuracy of our classifier had increased to 91.07%, which was the highest overall accuracy in this study. When compared with the other published methods, better predictive results were obtained by our classifier. Conclusion: In summary, we analyzed the differences between two neurotoxins in amino acids and biological properties, and constructed a classifier that could predict these two neurotoxins by using the reduced amino acid alphabet.

Predicting Rainfall-Induced Soil Erosion Based on a Hybridization of Adaptive Differential Evolution and Support Vector Machine Classification

Soil erosion induced by rainfall is a critical problem in many regions in the world, particularly in tropical areas where the annual rainfall amount often exceeds 2000 mm. Predicting soil erosion is a challenging task, subjecting to variation of soil characteristics, slope, vegetation cover, land management, and weather condition. Conventional models based on the mechanism of soil erosion processes generally provide good results but are time-consuming due to calibration and validation. The goal of this study is to develop a machine learning model based on support vector machine (SVM) for soil erosion prediction. The SVM serves as the main prediction machinery establishing a nonlinear function that maps considered influencing factors to accurate predictions. In addition, in order to improve the accuracy of the model, the history-based adaptive differential evolution with linear population size reduction and population-wide inertia term (L-SHADE-PWI) is employed to find an optimal set of parameters for SVM. Thus, the proposed method, named L-SHADE-PWI-SVM, is an integration of machine learning and metaheuristic optimization. For the purpose of training and testing the method, a dataset consisting of 236 samples of soil erosion in Northwest Vietnam is collected with 10 influencing factors. The training set includes 90% of the original dataset; the rest of the dataset is reserved for assessing the generalization capability of the model. The experimental results indicate that the newly developed L-SHADE-PWI-SVM method is a competitive soil erosion predictor with superior performance statistics. Most importantly, L-SHADE-PWI-SVM can achieve a high classification accuracy rate of 92%, which is much better than that of backpropagation artificial neural network (87%) and radial basis function artificial neural network (78%).

A new non-kernel quadratic surface approach for imbalanced data classification in online credit scoring

Imbalanced data sets are very common in online credit scoring. Their imbalanced structures may cause statistical bias and poor performance of those traditional models. Hence, how to properly deal with them and dig useful information from them are very crucial for the risk management. This paper proposes a new approach to address the imbalanced data classification in this area by directly generating two quadratic surfaces in the original space. In this way, it avoids the time-consuming task for searching a proper kernel function and its corresponding parameters in the traditional support vector machine (SVM) models, hence significantly improve the total efficiency of the approach. Moreover, the homocentric structure and maximization margin principle are applied to obtain a good performance of our model on the issue. Besides, fuzzy weight is also incorporated to further increase the classification accuracy and robustness. It is worth noting that the linear programming structure of our model not only guarantees the global optimality of its solution, but also leads to a much higher efficiency than those benchmark models. In addition, the fewer parameters in our model further saves more time in the tuning process. Hence it is quite suitable for handling those huge-sized problems in this big data era. Finally, we conduct a comprehensive experiment to compare the performances and efficiencies of different methods. The numerical results on various data sets strongly verify the superiority of our method to some benchmark methods in handling the imbalanced data classification in online credit scoring.

SEMG-Based Dynamic Muscle Fatigue Classification Using SVM With Improved Whale Optimization Algorithm

During robot-assisted rehabilitation, failure to detect muscle fatigue in time may cause severe damage to human muscles. Surface electromyography (sEMG) signals are widely used in muscle fatigue analysis, but the dynamic fatigue classification is rarely reported and the accuracy is not satisfactory. In this article, an accurate classification model incorporating support vector machine (SVM) is established to accommodate the muscle fatigue prediction in dynamic conditions by proposing an improved whale optimization algorithm (WOA). Multidomain sEMG features are extracted and then fused to effectively classify the muscle fatigue statuses. WOA’s global optimization capability is able to find out the optimal parameters for SVM, but it will be greatly affected by the initial population. The differential evolution (DE) algorithm is adopted here to generate a more appropriate initial population. Experiments were carried out to distinguish the normal and fatigue status by using sEMG signals only. Results demonstrate the effectiveness and feasibility of the proposed method in dynamic muscle fatigue prediction with an average accuracy of 85.50% in ankle dorsiflexion (DF) and 84.75% in ankle plantarflexion (PF).

Application Analysis of Artificial Intelligence in Library Network Security

In the information age, the rapid development of computer network technology and artificial intelligence technology has created conditions for the development of traditional libraries to digital libraries. While computer network technology and artificial intelligence technology continue to promote the development of digital libraries, library network security issues also arise. The purpose of this paper is to explore the application analysis of artificial intelligence technology in library network security. This article puts forward the problem of library network system security, describes the concept and characteristics of library network system security, and aims at the basic factors affecting the security of digital library network system, constructs artificial intelligence-based books from the perspective of management and technology Preventive mechanism for library network system security. In terms of situation prediction, this article uses artificial intelligence technology and analytic hierarchy process to evaluate the network security situation. This method can quickly extract parameters and can quickly evaluate the current network, thereby improving the real-time performance of the situation assessment. The optimized support vector machine method overcomes the subjectivity of support vector machine parameter selection, thereby improving the prediction accuracy of the network security situation. The simulation experiment verifies that the optimized support vector machine prediction model has a higher value for the network security situation. Forecast accuracy. Experimental research shows that the PSO-SVR prediction model used in this article can predict the future situation value relatively closely, help library management network administrators analyze the future network situation, and prompt library management network administrators to adjust defense strategies in time based on threat assessment to maintain library management network Safe and healthy operation.

Machine-Learning-Based Fast Angle-of-Arrival Recognition for Vehicular Communications

Obtaining angle-of-arrival (AOA) information is of great significance to improve the performance of communication systems. Real-time AOA recognition can reduce the complexity of beamforming design for massive multiple-input multiple-output (MIMO) systems, and can be used to construct an outer precoder to optimize the system and rate. However, for vehicular communications, AOA will change with environment and positions of vehicles, and it is difficult to obtain accurate AOA in real-time. Therefore, a fast AOA recognition method is needed to adapt to the rapid changes of channels. The traditional spectral- or parametric-based AOA estimation methods are difficult to obtain real-time AOA information because of the relatively high computational complexity. In order to solve this problem, this paper proposes a machine-learning-based fast AOA recognition approach. The proposed method includes off-line training and on-line estimation processes. In the off-line training process, an estimation model is obtained by using the support vector machine (SVM) based on a large number of actual measurement data in vehicular scenarios. Then, in the on-line estimation process, the obtained model is used to realize fast AOA recognition according to the channel snapshots collected by antenna array. Furthermore, the performance is verified under the different conditions of SVM parameters, training features, antenna numbers, and training data sizes. The experimental results show that the proposed method has satisfactory accuracy in real-time AOA recognition, and the optimal configuration and implementation scheme are also discussed.

Comparative study on credit card fraud detection based on different support vector machines

Credit card fraud is the new financial fraud crime accompanied by the gradual development of the economy which causes billions of dollars of losses every year. Credit card fraud case not only seriously violated the cardholder benefits and financial institutions, but also undermined the credit management order. However, fraudsters keep exploring new crime strategies constantly which exacerbates the crime rate of fraud. Thus, a predictive model for credit card fraud detection is essential to minimize its losses. By distinguishing between fraud and non-fraud, machine learning is one of the most efficient solutions for detecting fraud. Support vector machines have proven to be a novel algorithm with excellent performance. Nevertheless, the performance of SVM depends largely on the correct choice of model parameters (C and g), which could cause that the false positive was very high if the kernel function type and parameter cannot be selected properly. In this paper, based on the real transaction data of the credit card business, firstly, it will find the optimal kernel function suitable for the data set. Secondly, this paper will propose the method of optimizing the support vector machine parameters by the cuckoo search algorithm, genetic algorithm and particle swarm optimization algorithm. Last but not least, the Linear kernel function was found to be the best kernel function with an accuracy rate of 91.56%. Furthermore, the Radial basis function is used to optimize the kernel function, which can improve the accuracy from 42.86% to the highest accuracy rate of 98.05%. Compared with CS-SVM and GA-SVM, PSO-SVM has the best overall performance.

Feature‐level fusion of laser‐induced breakdown spectroscopy and Raman spectroscopy for improving support vector machine in clinical bacteria identification

AbstractIn clinical field, the diagnosis of many diseases and their development stages depend on the detection of the corresponding bacteria. Raman spectroscopy and laser‐induced breakdown spectroscopy (LIBS) are two novel spectral diagnostic technologies for clinical bacteria identification. Both of them have been used in clinical detection combined with optimized support vector machine (SVM). In this paper, two feature‐level fusion methods (before feature selection fusion [BFSF] and after feature selection fusion [AFSF]) were proposed to improve the performance of SVM classifier and reduce the analyzing time (including the parameter tuning, model training, and testing time) simultaneously by combining data of LIBS and Raman spectroscopy. Using the most important 10 feature lines as the inputs of the optimized classifier, the analyzing time could be reduced to 1 to 2 min and the correct classification rate (CCR) achieved 95.67%. Without optimizing SVM parameters, the two proposed methods could achieve rapid and accurate classification of pathogenic bacteria further. The AFSF method showed better results with less fusion features while the BFSF method achieved higher CCR at 100% with more features. Both two methods costed around 0.2 s for analysis. These indicate that the proposed feature‐level fusion methods can improve the performance of SVM for bacteria detection. Maintaining the highest diagnostic accuracy, they can achieve the minimum analyzing time.

Fault Diagnosis of High-Speed Brushless Permanent-Magnet DC Motor Based on Support Vector Machine Optimized by Modified Grey Wolf Optimization Algorithm

With the development of reliability theory, people realized that “absolutely reliable” machines could not be made. With its incomparable advantages, the high-speed permanent-magnet brushless DC motor is usually used in the symmetrical structure of high-speed operation working systems, which at present are widely used in aerospace and other fields. The structure of the manufacturing process involves a strict processing, but in the process of work failure could still occur. No matter what field the high-speed permanent magnet brushless DC motor is applied to, it is very important to identify states and run fault diagnosis, which is of great significance to maintain the reliability of the motor and its working system. In this study, the fault diagnosis method of a high-speed permanent-magnet brushless DC motor is studied, and a combination model of modified gray wolf optimization algorithm (MGWO) and support vector machine (SVM) have been proposed for the motor fault diagnosis research. Based on the traditional gray wolf optimization (GWO) algorithm, the optimization performance of the algorithm is improved by initializing the population through a tent map and introducing a sine wave dynamic adaptive factor. Then the modified algorithm is used to optimize the internal parameters of SVM to improve the diagnostic accuracy of the model. Through the signal acquisition test, the current signals under different fault states and faultless states were collected, and the current signal data set required for the experiment is obtained. The experimental result showed that, compared with GWO or sailfish optimization (SFO) optimized SVM models, Extreme learning machine and Back Propagation neural network classical classification models, the fault diagnosis accuracy of the proposed model is the highest, proving the excellent classification performance and good robustness of the MGWO-SVM model.

Building an efficient intrusion detection system using grasshopper optimization algorithm for anomaly detection

Intrusion detection is one of the most crucial activities for security infrastructures in network environments, and it is widely used to detect, identify and track malicious threats. A common approach in intrusion detection systems (IDSs) specifically in anomaly detection is evolutionary algorithm that works as intrusion detector. Still, it has been challenging to design a precise and reliable IDS to determine security threats due to the large capacity of network data which contains redundant and irrelevant features. It does not only decrease the process of classification but also prevents a classifier from making precise decisions. To increase the accuracy and reduce the false alarm rate, in this study integration of ensemble feature selection (EFS) and grasshopper optimization algorithm (GOA), called EFSGOA is developed. Firstly, EFS method is applied to rank the features for selecting the top subset of relevant features. Afterward, GOA is utilized to identify significant features from the obtained reduced features set produced by EFS technique that can contribute to determine the type of attack. Furthermore, GOA utilizes support vector machine (SVM) as a fitness function to obtain the noteworthy features and to optimize penalty factor, kernel parameter, and tube size parameters of SVM for maximizing the classification performance. The experimental results demonstrate that EFSGOA method has performed better and obtained high detection rate of 99.69%, accuracy of 99.98% and low false alarm rate of 0.07 in NSL-KDD and high detection rate of 99.26%, accuracy of 99.89% and low false alarm rate of 0.097 in KDD Cup 99 data. Moreover, the proposed method has succeeded in achieving higher performance compared to other state-of-art techniques in terms of accuracy, detection rate, false alarm rate, and CPU time.

CMBA-SVM: a clinical approach for Parkinson disease diagnosis

Different intelligence models are used by researchers for an easy and successful diagnosis of neurodegenerative diseases like Parkinson’s disease (PD) but none of the adopted methods is efficient. Early-stage identification of disease and diagnosis based upon vocal measurements is important to enhance the productive lives of the patient. An innovative intelligence model with a combination of a chaos-mapped bat algorithm (CMBA) and a support vector machine (SVM) called CMBA-SVM is introduced. The coordination of the CMBA method effectively resolved the SVM parameter tuning issues. CMBA is used for the identification of featured and consider input for SVM to develop an intelligence model. The effectiveness and performance of the proposed model are compared to ECFA-SVM and CFA-SVM. The performance of the proposed model was tested with considering various parameters in terms of accuracy, sensitivity, specificity, and AUC by taking two sets of PD data set (from UCI repository and Istanbul collected data set). The performance results and perspective of this study were compared with the different intelligence methods described literature study that used the same data, and the CMBA-SVM showed better efficiency than the other. The CMBA-SVM has a very good prospect, which may provide huge expediency to the clinicians to improve quality during PD diagnosis.

Forecasting annual natural gas consumption via the application of a novel hybrid model.

Accurate prediction of natural gas consumption (NGC) can offer effective information for energy planning and policy-making. In this study, a novel hybrid forecasting model based on support vector machine (SVM) and improved artificial fish swarm algorithm (IAFSA) is proposed to predict annual NGC. An adaptive learning strategy based on sigmoid function is introduced to improve the performance of traditional artificial fish swarm algorithm (AFSA), which provides a dynamic adjustment for parameter moving step step and visual scope visual. IAFSA is used to obtain the optimal parameters of SVM. In addition, the annual NGC data of China is selected as an example to evaluate the prediction performance of the proposed model. Experimental results reveal that the proposed model in this study outperforms the benchmark models such as artificial neural network (ANN) and partial least squares regression (PLS). The mean absolute percentage error (MAPE), root mean squared error (RMSE), and mean absolute error (MAE) values are as low as 0.512, 1.4958, and 1.0940. Finally, the proposed model is employed to predict NGC in China from 2020 to 2025.

A hybrid feature descriptor with Jaya optimised least squares SVM for facial expression recognition

Facial expression recognition has been a long-standing problem in the field of computer vision. This paper proposes a new simple scheme for effective recognition of facial expressions based on a hybrid feature descriptor and an improved classifier. Inspired by the success of stationary wavelet transform in many computer vision tasks, stationary wavelet transform is first employed on the pre-processed face image. The pyramid of histograms of orientation gradient features is then computed from the low-frequency stationary wavelet transform coefficients to capture more prominent details from facial images. The key idea of this hybrid feature descriptor is to exploit both spatial and frequency domain features which at the same time are robust against illumination and noise. The relevant features are subsequently determined using linear discriminant analysis. A new least squares support vector machine parameter tuning strategy is proposed using a contemporary optimisation technique called Jaya optimisation for classification of facial expressions. Experimental evaluations are performed on Japanese female facial expression and the Extended Cohn–Kanade (CK+) datasets, and the results based on 5-fold stratified cross-validation test confirm the superiority of the proposed method over state-of-the-art approaches.

The key frame extraction algorithm based on the indigenous disturbance variation difference video

Abstract In view of the traditional support vector machine (SVM) learning algorithm for widespread learning parameters is not easy determined in the process of video key frame extraction, the problem of low accuracy, an independent perturbation variable difference SVM algorithm used for video key frame extraction. First of all, the study of the biological mechanism of differential evolution algorithm, put forward an improved way of autonomic disturbance variation. Secondly, combined with improved forms of independent disturbance differential evolution algorithm for SVM parameters optimization, designed the key frame extraction algorithm of the video based on the improved differential of SVM algorithm. Through the standard test functions and video test database experiments show that the improvement of autonomic disturbance variation difference video key frame extraction algorithm can more effectively optimize parameters of support vector machine, so as to contribute to the improvement of the recall of video retrieval rate of (quasi) two algorithms performance evaluation standard.