Gaussian Radial Basis Research Articles

Research on the Application of SVM in Ideology Analysis and the Enhancement of Ideological and Political Education Effectiveness

Abstract The consciousness and behavior of students in the Ideological and Political Education curriculum generate massive amounts of data information, how to allow educators to quickly obtain information from the massive amount of text data is very important to improve the energy efficiency of classroom education. SVM technology is employed in this paper for data mining and text classification. Firstly, using the web crawler method, the text of messages related to the Ideological and Political Science course posted by students on social networks is collected. The short text was preprocessed before performing sentiment analysis, which mainly included interaction information filtering, word segmentation, and lexical labeling. Then, the SVM model suitable for students’ ideology analysis is constructed, using the Gauss radial basis kernel function to accurately depict the distribution structure of the data, and the L1-SVM model with more stable computational performance is also proposed. The extension method of the classification algorithm in the real number domain is summarized at the end. This algorithm’s accuracy is 78%, and its F1 value is 80%, which is higher than the other three algorithms. DAG-SVM and recall are both optimized to a lesser extent. Overall, the classification efficiency of the algorithm in this paper has been improved. The positive effect of this paper’s algorithm on improving the effectiveness of Ideological and Political Education can be seen in the significant increase in the learning interest of the experimental class.

A New Approach for Assessing Heat Balance State along a Water Transfer Channel during Winter Periods

Ice problems in channels for water transfer in cold regions seriously affect the capacity and efficiency of water conveyance. Sometimes, ice problems such as ice jams in water transfer channels create risk during winter periods. Recently, water temperature and environmental factors at various cross-sections along the main channel of the middle route of the South-to-North Water Transfer Project in China have been measured. Based on these temperature data, the heat balance state of this water transfer channel has been investigated. A principal component analysis (PCA) method has been used to analyze the complex factors influencing the observed variations of the water temperature, by reducing eigenvector dimension and then extracting the principal component as the input feature. Based on the support vector machine (SVM) theory, a new approach for judging the heat loss or heat gain of flowing water in a channel during winter periods has been developed. The Gaussian radial basis is used as the kernel function in this new approach. Then, parameters have been optimized by means of various methods. Through the supervised machine learning process toward the observed water temperature data, it is found that the air–water temperature difference and thermal conditions are the key factors affecting the heat loss or heat absorption of water body. Results using the proposed method agree well with those of measurements. The changes of water temperature are well predicted using the proposed method together with the state of water heat balance.

Improving the Prediction Accuracy of Soil Nutrient Classification by Optimizing Extreme Learning Machine Parameters

Abstract: Agriculture is a non-technical sector where in technology can be incorporated for the betterment. In this paper, the soil test report values are used to classify several significant soil features like village wise soil fertility indices of Available Phosphorus (P), Available Potassium (K), Organic Carbon (OC) and Boron (B), as well as the parameter Soil Reaction (pH). The classification and prediction of the village wise soil parameters aids in reducing wasteful expenditure on fertilizer inputs, increase profitability, save the time of chemical soil analysis experts, improves soil health and environmental quality. These five classification problems are solved using the fast learning classification technique known as Extreme Learning Machine (ELM) with different activation functions like Gaussian radial basis, sine-squared, hyperbolic tangent, triangular basis, and hard limit.

Machine Learning Algorithm for Soil Analysis and Classification of Micronutrients in IoT‐Enabled Automated Farms

The available nutrient status of the mulberry gardens in the districts of Tamil Nadu is analyzed and evaluated to find the status. In this work, the soil is classified based on the test report to a number of features with fertility indices for boron (B), organic carbon (OC), potassium (K), phosphorus (P), and available boron (B), along with the parameter soil reaction (pH). A total of 10 steps are used for cross‐validation purposes wherein in every step, the data involves 10% for validation and the remaining for training data. A fast learning classification methodology known as extreme learning method (ELM) is trained using the data to identify the micronutrients present in the soil. Activation functions such as hard limit, triangular basis, hyperbolic tangent, sine‐squared, and Gaussian radial basis are used to optimize the methodology. Based on the analysis performed, the nutrients are classified and the optimal soil conditions are proposed for different regions that are analyzed. Based on the study conducted, it is found that the soils in Tamil Nadu have normal electrical conductivity and are red in colour. They are found to be rich in potassium (35% of the samples), nitrogen (80% of the samples), and sulphur (75% of the sample) and sufficient or poor in magnesium, boron, zinc, and copper.

Cutting Process Model Design of Cutter Suction Dredger Based on Auto Regressive eXogenous and Radial Basis Function model

The dredging operation of the strander dredger is complex, and the mathematical model established according to its key equipment characteristics is not possible to describe such a system having time degeneration and non-linear. Therefore, based on the analysis of mud formation process of dredger, RBF-ARX model is used to model the cutting process, and mud concentration is taken as the output. This modeling method is a combination model based on the theory of Auto-Regressive eXogenous (ARX) model and Gauss radial basis function (Radial Basis Function) neural network (RBF). The comparison between the simulation results and the actual data shows that the model can accurately describe the dynamic characteristics of cutter suction dredger in the cutting process.

Fault Diagnosis Method of Mine Motor Based on Support Vector Machine

Background: In order to improve the efficiency of fault treatment of mining motor, the method of model construction is used to construct the type of kernel function based on the principle of vector machine classification and the optimization method of parameters. Methodology: One-to-many algorithm is used to establish two kinds of support vector machine (SVM) models for fault diagnosis of motor rotor of crusher. One of them is to obtain the optimal parameters C and g based on the input samples of the instantaneous power fault characteristic data of some motor rotors which have not been processed by rough sets. Patents on machine learning have also shows their practical usefulness in the selction of the feature for fault detection. Results: The results show that the instantaneous power fault feature extracted from the rotor of the crusher motor is obtained by the cross validation method of grid search k-weights (where k is 3) and the final data of the applied Gauss radial basis penalty parameter C and the nuclear parameter g are obtained. Conclusion: The model established by the optimal parameters is used to classify and diagnose the sample of instantaneous power fault characteristic measurement of motor rotor. Therefore, the classification accuracy of the sample data processed by rough set is higher.

Improved atmospheric weighted mean temperature modeling using sparse kernel learning

As a crucial parameter in the process of converting the zenith wet delay into precipitable water vapor at Global Navigation Satellite System (GNSS) stations, the weighted mean temperature (Tm) influences the accuracy of GNSS-based water vapor retrievals. We propose an improved atmospheric Tm modeling method by introducing sparse kernel learning to obtain high-accuracy and high-temporal-resolution Tm data. To establish the model, the temporal variation characteristics of Tm time series are first analyzed by spectral analysis using the Lomb–Scargle periodogram. Second, the sparse kernel learning method is introduced to model the residuals from spectral analysis, for which the Gauss radial basis function kernel, L1-norm regularization, and the highly efficient fast iterative shrinkage thresholding algorithm are employed. To verify the performance of the proposed method, we used ERA5 hourly data from China’s 9 International GNSS Service stations produced by the European Centre for Medium-Range Weather Forecasts. ERA5 Tm data with a temporal resolution of 12 h are used as the training data, and the ERA5 hourly Tm data (excluding the data employed for modeling) are used for testing. The results show that compared with the spectral analysis accuracy, the accuracy of the calculated Tm can be improved by approximately 48.3% when the residual sparse kernel learning method is used. Thus, this proposed method for GNSS-based water vapor retrievals can provide high-accuracy and high-temporal-resolution Tm data.

Financial Risk Early Warning Based on Wireless Network Communication and the Optimal Fuzzy SVM Artificial Intelligence Model

Since the beginning of the new century, risk events such as the world economic crisis have occurred, which have greatly impacted the real economy of our country. A wireless network is a network implemented using wireless communication technology. It includes both global voice and data networks that allow users to establish long‐distance wireless connections, as well as infrared technology and radio frequency technology optimized for short‐distance wireless connections. These events have a great impact on many small‐ and medium‐sized listed companies, resulting to many small‐ and medium‐sized listed companies going bankrupt. Indeed, one of the important reasons for the frequent bankruptcy of small‐ and medium‐sized listed companies is the lack of awareness of risk prevention and effective financial risk early warning mechanism. The support vector machine is a machine learning method based on the VC dimension theory of statistical learning and the principle of structural risk minimization. This method shows many unique advantages when dealing with classification problems and has been widely used in many fields. The purpose of this article is to realize the financial risk analysis of listed companies through wireless network communication and the optimal fuzzy SVM artificial intelligence model, which help small‐ and medium‐sized listed companies find abnormalities in their business management activities in advance and deal with market risks in a timely manner. Taking 81 small‐ and medium‐sized listed companies as the research object, this paper chooses the small‐ and medium‐sized listed companies in every quarter of 2018 as the research sample. By using the financial and nonfinancial data of small‐ and medium‐sized listed companies and introducing the support vector machine (SVM) with the fuzzy method, the model of the fuzzy support vector machine (FSVM) is constructed. And the performance of the FSVM under four different kernel functions is compared and studied. At the same time, the performance of the FSVM is compared with other artificial intelligence models. The empirical results show that different kernel functions have different effects on the prediction performance of the FCM‐SVM model. Under the Gauss radial basis function, the prediction accuracy of the FCM‐SVM is over 86%. It can be seen that in predicting the financial crisis of small‐ and medium‐sized listed companies, the FCM‐SVM model with Gauss radial basis function has the best predictive performance. The FSVM model based on Gauss radial basis function not only has the advantages of linearity, being polynomial, and nonlinearity of neurons but also is significantly superior to the traditional artificial intelligence model.

M.R. brain tumor classification employing ICA and kernel-based support vector machine

A revolutionary and robust magnetic resonance (MR) brain tumor detection and segmentation approach has been presented in this work. We have put forward a robust technique to extract the tumor and classify the same as benign or malignant. The extraction of features from detected lesions is achieved through usage of wavelet transform. There is an immediate need to eliminate the redundant features from extracted subset because they degrade the performance of classification. Now, these reduced features are fed to kernel-based SVM (K-SVM). The kernel that is being utilized in this framework is Gaussian radial basis as it is quite efficient. Once the MR images are classified as benign or malignant, the next obvious step is to segment out the infected portion. We have relied upon region growing technique for the segmentation of the infected area. K-fold cross-validation approach has been adopted to optimize the performance K-SVM. We have compared the outcomes of our approach to that of various in-class conventional approaches. Experimental outcomes clearly suggest that our approach has performed efficiently and robustly for almost entire set of data and has performed way better when compared to existing in-class methodologies both qualitatively as well as quantitatively.

Gaussian hybrid fuzzy clustering and radial basis neural network for automatic brain tumor classification in MRI images

Magnetic Resonance Imaging (MRI) is an emerging research area, employed extensively in radiology for the diagnosis of various neurological diseases. Here, as the classification of MRI gains significant importance for the tumor diagnosis, various techniques are developed in the literature for the automatic classification of MRI images. In this paper, a clustering scheme, namely gaussian hybrid fuzzy clustering (GHFC) is developed by hybridizing the fuzzy c-means (FCM) clustering and sparse FCM along with the Gaussian function for the segmentation purpose. After segmenting the image, the suitable features are extracted from the image and given to the Exponential cuckoo based Radial Basis Neural Network (Exponential cuckoo based RBNN) classifier. The features serve as the training information for the Exponential cuckoo based RBNN classifier, and it finally detects the training class. Simulation of the proposed work is done using BRATS and SIMBRATS databases, and the results are compared with several state-of-art techniques. Simulation results depict that the proposed GHFC along with the RBNN classifier achieved improved accuracy and mean squared error results with the values of 0.8952, and 0.0074, respectively, for the BRATS dataset and 0.8719 and 0.0036, for the SIMBRATS dataset.

Multi-sensor prediction of Eucalyptus stand volume: A support vector approach

Stem volume is a key attribute of Eucalyptus forest plantations upon which decision-making is based at diverse levels of planning. Quantifying volume through remote sensing can support a proper management of forests. Because of limitations on spaceborne optical and synthetic aperture radar sensors, this study integrated both types of datasets assembled using support vector regression (SVR) to retrieve the stand volume of Eucalyptus plantations. We assessed different combinations of sensors and a minimum number of plots to develop an SVR model. Finally, the best SVR performance was compared with other analytical methods already tested and in the literature: multilinear regression, artificial neural networks (ANN), and random forest (RF). Here, we introduce a test for comparative analysis of the performance of different methods. We found that SVR accurately predicted stem volume of Brazilian fast-growing Eucalyptus forest plantations. Gaussian radial basis was the most suitable kernel function. Integrating the optical and L-band backscatter data increased the predictive accuracy compared to a single sensor model. Combining NIR-band data from ALOS AVNIR-2 and backscatter of L-band horizontal emitted and vertical received (HV) electric fields from ALOS PALSAR produced the most accurate SVR model (with an R2 of 0.926 and root mean square error of 11.007 m3/ha). The number of field plots sufficient for model development with non-redundant explanatory variables was 77. Under this condition, SVR performed similarly to ANN and outperformed the multiple linear regression and random forest methods.

Improving the prediction accuracy of soil nutrient classification by optimizing extreme learning machine parameters

The soil, Soul of Infinite Life, is the entity responsible for sustaining life on earth. In spite of significant advances in the service sector, agriculture remains the major provider of employment and source of revenue in India. Soil testing is a valuable tool for evaluating the available nutrient status of soil and helps to determine the proper amount of nutrients to be added to a given soil based on its fertility and crop needs. In the current study, the soil test report values are used to classify several significant soil features like village wise soil fertility indices of Available Phosphorus (P), Available Potassium (K), Organic Carbon (OC) and Boron (B), as well as the parameter Soil Reaction (pH). The classification and prediction of the village wise soil parameters aids in reducing wasteful expenditure on fertilizer inputs, increase profitability, save the time of chemical soil analysis experts, improves soil health and environmental quality. These five classification problems are solved using the fast learning classification technique known as Extreme Learning Machine (ELM) with different activation functions like gaussian radial basis, sine-squared, hyperbolic tangent, triangular basis, and hard limit. After the performance analysis of ELMs with diverse activation functions for these soil parameter classifications, the gaussian radial basis function attains the maximum performance for four out of five problems, which goes above 80% in most of the accuracy rate calculations in every problem, followed by hyperbolic tangent, hard limit, triangular basis, and sine-squared. However, the performance of the final classification problem, i.e. the pH classification, gives moderate values with the gaussian radial basis and best performance (near 90%), with the hyperbolic tangent.

Driving energy management of front-and-rear-motor-drive electric vehicle based on hybrid radial basis function

This paper presents mathematical methods to develop a high-efficiency and real-time driving energy management for a front-and-rear-motor-drive electric vehicle (FRMDEV), which is equipped with an induction motor (IM) and a permanent magnet synchronous motor (PMSM). First of all, in order to develop motor-loss models for energy optimization, database of with three factors, which are speed, torque and temperature, was created to characterize motor operation based on HALTON sequence method. The response surface model of motor loss, as the function of the motor-operation database, was developed with the use of Gauss radial basis function (RBF). The accuracy of the motor-loss model was verified according to statistical analysis. Then, in order to create a two-factor energy management strategy, the modification models of the torque required by driver (Td) and the torque distribution coefficient (β) were constructed based on the state of charge (SOC) of battery and the motor temperature, respectively. According to the motor-loss models, the fitness function for optimization was designed, where the influence of the non-work on system consumption was analyzed and calculated. The optimal β was confirmed with the use of the off-line particle swarm optimization (PSO). Moreover, to achieve both high accuracy and real-time performance under random vehicle operation, the predictive model of the optimal β was developed based on the hybrid RBF. The modeling and predictive accuracies of the predictive model were analyzed and verified. Finally, a hardware-in-loop (HIL) test platform was developed and the predictive model was tested. Test results show that, the developed predictive model of β based on hybrid RBF can achieve both real-time and economic performances, which is applicable to engineering application. More importantly, in comparison with the original torque distribution based on rule algorithm, the torque distribution based on hybrid RBF is able to reduce driving energy consumption by 9.51% under urban cycle.

Establishment of cellular automata image model and its application in image dimension measurement

Aiming at how to improve the efficiency of image edge detection, an image edge detection method based on least squares support vector machine (LSSVM) and cellular automata is proposed. Firstly, a new kernel function is constructed based on the Gauss radial basis kernel and polynomial kernel, which enables the LSSVM to fit the gray values of the image pixels accurately. Then, the gradient operator of the image is deduced, and the gradient value of the image is obtained by convolution with the gray value of the image. Then, the cellular automata evolves the gradient value according to the designed local rules to locate and detect the image edge. Simulation results show that the proposed edge detection algorithm is effective, and the new algorithm has higher detection performance than Sobel and Canny algorithms.

Characterization and Identification of Coal and Carbonaceous Shale Using Visible and Near-Infrared Reflectance Spectroscopy

Because of the high organic carbon concentration in carbonaceous shale, a large proportion of carbonaceous shales are often misclassified into coals using visible and near-infrared (VIS-NIR) reflectance spectroscopy in the field of coal-gangue identification of hyperspectral remote sensing of coal mine. In order to study spectral characterization of coal and carbonaceous shale, three bituminite samples and three carbonaceous shales were collected from a coal mine of China, and their spectral reflectance curves were obtained by a field spectrometer in the wavelength range of 350–2500 nm. Only one carbonaceous shale could be easily identified from the three bituminite samples according to obvious absorption valleys near 1400 nm, 1900 nm, and 2200 nm of its reflectance curve while the other two carbonaceous shales have similar reflectance curves to the three bituminite samples. The effect of carbon concentration on reflectance curve was simulated by the mixed powder of ultralow ash bituminite and clay in 0.5 mm grain size under various mixing ratios. It was found that absorption valleys near 1400 nm, 1900 nm, and 2200 nm of the mixed powder become not obvious when the bituminite content is more than 30%. In order to establish an effective identification method of coal and carbonaceous shale, 250 other samples collected from the same coal mine were divided into 150 training samples and 100 prediction samples. Principal component analysis (PCA) and Gauss radial basis kernel principal component analysis (GRB-KPCA) were employed to extract principal components (PCs) of continuum removed (CR) spectra of the training samples in eight selected wavelength regions which are related to the main mineral and organic compositions. Two support vector machine- (SVM-) based models PCA-SVM and GRB-KPCA-SVM were established. The results showed that the GRB-KPCA-SVM model had better identification accuracies of 94% and 92% for powder and nature block prediction samples, respectively.

Analysis of tuberculosis disease through Raman spectroscopy and machine learning.

We present the effectiveness of Raman spectroscopy (RS) in combination with machine learning for screening and analysis of blood sera collected from tuberculosis patients. Blood samples of 60 patients have confirmed active pulmonary tuberculosis and 14 samples of healthy age matched control were used in the current study. Spectra from entire sera samples were acquired using 785 nm laser Raman system. Support Vector Machine (SVM) together with Principal Component Analysis (PCA) has been used for highlighting variations spectral intensities between healthy and pathological samples. SVM model using Gaussian radial basis is able to discriminate between healthy and diseased patients based on the differences in the concentration of essential biomolecules such as lactate, β-carotene, and amide-I. Diagnostic accuracy of 92%, with precision, specificity and sensitivity of 95%, 98% and 81%, respectively, were achieved considering PC3 and PC4. Automatic analysis of the variations in the concentration of these molecules together with chemometrics can effectively be utilized for an early screening of tuberculosis through minimum invasion.

Tracking Control Based on Improved Fuzzy Gauss Radial Basis Function Neural Network for Omni-Directional Intelligent Wheelchair

In conventional fuzzy neural network based on sigmoid function, the number of nodes at the corresponding intermediate layer grows larger with the increase of fuzzy rule numbers. In order to obtain real time performance of the control system, an improved fuzzy Gaussian radial basis function neural network controller for omni-directional intelligent wheelchair was proposed. The structure and the design method of the improved controller were described in detail. The optimized fuzzy neural network reduced the numbers of intermediate lay, thus the computation real time ability could be guaranteed. Furthermore, the stability of the controller was proved through Lyapunov stability theory. Finally, the proposed optimized controller was applied to an omni-directional intelligent wheelchair, and the simulation results of tracking control verified the real time, effectiveness and feasibility of the proposed method.

Multifeature energy optimization framework and parameter adjustment-based nonrigid point set registration

Nonrigid point set registration is a key technology in the field of remote sensing image registration, which is widely used in military and civil fields such as natural disaster damage assessment, agricultural and urban land-use planning, environmental quality monitoring, and ground target identification. We present a multifeature energy optimization framework and parameter adjustment-based nonrigid point set registration that has three contributions: (1) an energy optimization framework is designed to freely combine multiple features for estimating correspondences between two point sets, (2) the thin-plate spline and Gauss radial basis function transformation models can be optionally implemented for solving two-dimensional, three-dimensional, or higher-dimensional registration problems, and (3) the free parameters can be adjusted by the proposed three parameter adjustment approaches to yield higher registration accuracy in varied registration patterns. We test the performances of our method in contour point sets, sequence images, remote sensing images, medical images, and real images and compare with 10 state-of-the-art methods, where our method shows favorable performances in most scenarios.

An efficient magnetic resonance brain image classifier using tetrolet transform and kernel support vector machine based on OTSU binarization

Classification of tumor from cancer causing to non-cancer causing, it plays a main role in diagnosing the disease effectively without any flaws. In this proposed paper, a novel methodology is presented in order to classify a given Magnetic resonant brain image as normal or abnormal using OTSU binarization segmentation with help of tetrolate transform. By replacing wavelet transform with tetrolate transform the classification is made efficient in case of images with geometric shapes. A good number of features are extracted by using OTSU binarization from edge-based segmentation, the more number of features makes the classification for effective and accurate. The image is finely segmented pixel by pixel for good accuracy and about 12 features like We calculate four different type of accuracy like RBF (Radial Basis Function), linear, polygonal and quadratic based on image features. We performed our proposed methods with four different kernels LIN (Linear), HPOL (Homogeneous Polynomial), and IPOL and GRB (Gaussian Radial Basis function) kernel to achieve the highest classification accuracy. The work is added with advancement by using Graphical user interface (GUI), which makes the user comfortable in accessing the method where most of the users are from clinical background and are not aware of any software and their usag.

SVM-based Implicit Stochastic Scheduling Mode for Cascade Hydropower Stations

In view of the uncertainty of inflow of cascade reservoirs, an implicit stochastic joint scheduling function model for cascade hydropower stations based on support vector machine (SVM) was established. Taking the cascade hydropower station on the lower reaches of the Yalong River as an example, according to the long series of optimized scheduling simulation operation data, the Gauss radial basis (BRF) kernel function is utilized by LIBSVM for the scheduling function fitting of the cascade reservoirs in the lower reaches of the Yalong River. Besides, combined with particle swarm optimization algorithm, the support vector machine (SVM) model parametersc(penalty coefficient) andg(relaxation coefficient) were optimized. Eventually, the optimized scheduling function model was used for the cascade hydropower station simulation operation. The results show that compared with the existing scheduling technology, the nonlinear SVM scheduling function is better than the linear regression model, and the effect of the nonlinear SVM scheduling function is equivalent to that of the threshold regression model. Therefore, the SVM-based Implicit Stochastic Scheduling method can provide references for the actual operation of the cascade hydropower station.