Radial Basis Function Kernel Research Articles

Performance evaluation of housing price index prediction model using DNN and SVR

This study used the base interest rate, mortgage interest rate, deposit interest rate, consumer price index, exchange rate, and composite stock price index as variables to predict the housing price index. The variables were normalized using the min-max normalization method. Machine learning methods, Deep Neural Networks (DNN) and Support Vector Regression (SVR), were employed to construct a prediction model, and the fit of the model was evaluated using the Root Mean Square Error (RMSE). The analysis results of the DNN model indicated that when applying the softplus activation function, the RMSE was 0.1298, resulting in the smallest prediction error, and the correlation coefficient between the predicted and actual values was highest at 0.9342. The analysis results of the SVR model using the Gaussian RBF kernel showed that the RMSE was smallest at 0.2781 when the parameters were set to C=100 and Gamma=0.1. Therefore, the DNN model was found to be more fit for predicting the housing price index. Additionally, it was confirmed that the consumer price index had a non-linear effect with a positive relationship, and the base interest rate had a linear effect with a negative relationship.

Exploring attitudes and behavioral patterns in residential energy consumption: Data-driven by a machine learning approach

The present study focuses on two main objectives: firstly, to clarify the mechanisms by which attitudes impact behavioral changes related to household energy consumption, and secondly, to offer valuable insights to enhance the understanding of residential energy usage through a novel technique called Support Vector Regression (SVR). This method employs several feature space transformations to convert nNar relationships into linear ones. The results highlight the crucial role of psychological factors in determining energy consumption behaviors, demonstrating that cognitive factors significantly influence attitudes and behavioral patterns. The findings show that psychological variables have a major role in determining how people consume energy, with cognitive variables having a particularly large impact on attitudes and behavior patterns. Our findings demonstrate the superior performance of Support Vector Regression (SVR) with radial basis function kernels over traditional predictive models, with a prediction accuracy of 93.7 % for changes in behavior patterns (CHP) and 94.4 % for changes in attitudes (CHA). These results highlight the value of applying cutting-edge machine-learning approaches to create precise models for comprehending and directing energy-saving actions. The policy implications suggest that reducing cognitive barriers can significantly encourage energy-saving behaviors and contribute to a comprehensive approach for energy-efficiency initiatives

Anderson accelerated preconditioning iterative method for RBF interpolation

Traditional RBF interpolation involves solving a linear system, making it computationally expensive for large datasets. Iterative-based quasi-interpolation combines RBF interpolation with iterative methods to enhance accuracy and convergence. To enhance efficiency and accuracy, we in this paper propose a novel method for RBF quasi-interpolation that combines Anderson acceleration with the asynchronous DCPI, termed Anderson-DCPI. The method alternates between the preconditioning iterative method and Anderson extrapolation, aiming to improve convergence rates. We demonstrate the convergence of Anderson-DCPI for positive definite RBF kernel functions and validate its effectiveness through a series of numerical examples.

Intelligence computational analysis of letrozole solubility in supercritical solvent via machine learning models

Supercritical fluids (SCFs) can be used to prepare drugs nanoparticles with improved solubility. SCFs have shown superior advantages in pharmaceutical industry as an environmentally friendly alternative to toxic/harmful organic solvents. They possess gas-like transport characteristics and liquid-like solvation power for solutes. Evaluation of chemotherapeutic drugs’ solubility in supercritical carbon dioxide (SCCO2) has been recently an attractive subject for developing this method in pharmaceutical sector. To reach this purpose, the utilization of accurate models is of great necessity to estimate experimental-based solubility data. In this paper, the authors tried to employ machine learning (ML) approaches to estimate the solubility of Letrozole (LET) drug as chemotherapeutic agent and correlate its values in wide ranges of temperature and pressure. To do this, PAR (Passive Aggressive Regression), RF (Random Forest), and RBF-SVM are the models used (Support Vector Machine with RBF kernel). These models optimized in terms of their hyper-parameters using GA algorithm. The optimized PAR, RF, RBF-SVM models obtained coefficients of determination (R-squared) of 0.8277, 0.9534, and 0.9947. Also, the MSE error rate of the models are 0.1342, 0.0305, and 0.0045, in the same order. The final result of the evaluations shows the optimized RBF-SVM model as the most appropriate model in this research. The model exhibits a maximum prediction error of 0.1289.

Rice Leaf Nitrogen Content Estimation Through A Methodological Framework Using Single-Sensor Multispectral Images

Using non-destructive evaluation tools based on imaging techniques, including single-sensor multispectral cameras, provides a cost-effective solution for optimizing rice nitrogen fertilization through site-specific nutrient management. However, their accuracy and precision have been identified as areas for improvement. This study aims to develop a methodology to improve the accuracy of estimations through field experiments. It utilizes multispectral images captured by MAPIR Survey3W Orange Cyan Near-Infrared and MAPIR Survey3W Red Edge cameras. The Normalized Difference Vegetation Index and Red Edge values derived from these images are correlated with Soil Plant Analysis Development values to assess rice nitrogen levels. A prediction model is then built using the Support Vector Regression algorithm. Findings from the experiments underscore the importance of addressing shadow effects, integrating the dataset on light intensity and image capture time, conducting radiometric calibration, filtering outlier data, employing image segmentation, and utilizing nonlinear Canova tests to enhance estimation accuracy. By configuring the Support Vector Regression model with RBF kernel, gamma set to 1.24, and epsilon set to 0.1, the R2 of the train data and validation data reaches 0.851, and 0.840 respectively. Meanwhile, the R2 of the test data achieves 0.793 with a mean absolute percentage error of 3.49 % and a root mean square error of 1.70. These findings underscore the potential of the proposed methodology to improve the estimation of rice nitrogen status based on single-sensor multispectral images, paving the way for more effective nutrient management strategies in rice cultivation.

Research on Sustainable Form Design of NEV Vehicle Based on Particle Swarm Algorithm Optimized Support Vector Regression

With the growing emphasis on eco-friendly and sustainable development concepts, new energy vehicles (NEVs) have emerged as a popular alternative to traditional fuel vehicles (FVs). Due to the absence of an internal combustion engine, electric vehicles (EVs) do not require a front air intake grille, allowing for a more minimalist and flexible design. Consequently, aligning EV styling with users’ visual cognition and emotional perception is a critical objective for automakers and designers. In this study, we establish the mapping relationship between users’ emotional cognition and NEV styling design based on experimental data. We introduce Particle Swarm Optimization Support Vector Regression (PSO-SVR) into the perceptual engineering (KE) research process to predict user emotions using Support Vector Regression (SVR). To optimize the three hyperparameters (penalty coefficient C, RBF kernel function parameter γ, and insensitivity loss coefficient ε) of the SVR model, we utilize the Particle Swarm Optimization (PSO) algorithm. The results indicate that the proposed PSO-SVR model outperforms traditional SVR and BPNN models in predicting NEV user emotions. This model effectively captures the nonlinear relationship between battery electric vehicle (BEV) morphological features and users’ emotional cognition, providing a novel method for enhancing NEV design. The results of this research are expected to drive design innovation and technological advancement in the new energy vehicle industry, contributing to the achievement of the ambitious goal of global eco-friendliness and sustainable development.

Machine Learning Methods for Evaluation of Technical Factors of Spraying in Permanent Plantations

Considering the demand for the optimization of the technical factors of spraying for a greater area coverage and minimal drift, field tests were carried out to determine the interaction between the area coverage, number of droplets per cm2, droplet diameter, and drift. The studies were conducted with two different types of sprayers (axial and radial fan) in an apple orchard and a vineyard. The technical factors of the spraying interactions were nozzle type (ISO code 015, code 02, and code 03), working speed (6 and 8 km h−1), and spraying norm (250–400 L h−1). The airflow of both sprayers was adjusted to the plantation leaf mass and the working pressure was set for each repetition separately. A method using water-sensitive paper and a digital image analysis was used to collect data on coverage factors. The data from the field research were processed using four machine learning models: quantile random forest (QRF), support vector regression with radial basis function kernel (SVR), Bayesian Regularization for Feed-Forward Neural Networks (BRNN), and Ensemble Machine Learning (ENS). Nozzle type had the highest predictive value for the properties of number of droplets per cm2 (axial = 69.1%; radial = 66.0%), droplet diameter (axial = 30.6%; radial = 38.2%), and area coverage (axial = 24.6%; radial = 34.8%). Spraying norm had the greatest predictive value for area coverage (axial = 43.3%; radial = 26.9%) and drift (axial = 72.4%; radial = 62.3%). Greater coverage of the treated area and a greater number of droplets were achieved with the radial sprayer, as well as less drift. The accuracy of the machine learning model for the prediction of the treated surface showed a satisfactory accuracy for most properties (R2 = 0.694–0.984), except for the estimation of the droplet diameter for an axial sprayer (R2 = 0.437–0.503).

Evaluation of geological hazard susceptibility in Xixia Xichuan area based on support vector machine

Abstract Geological hazards seriously affect the personal and property safety of regional residents, in the Xichuan area, a real-time monitoring and early warning system could have provided timely alerts before the landslide, potentially averting the tragic loss of life and destruction.So it is very important to carry out geological hazard susceptibility assessment and hazard prevention. The Xixia area of Xichuan is selected as the study area, and the support vector machine model is used to evaluate the susceptibility of geological hazards in the study area. Through Kendall factor correlation analysis, the susceptibility evaluation indexes of the study area are selected, the evaluation index system of the study area is established, and the thematic layers and attribute data of the evaluation indexes are extracted through GIS technology. The MATLAB platform was used to establish linear kernel function (LN-SVM), polynomial kernel function (PL-SVM), radial basis kernel function (RBF-SVM), sigmoid kernel function (Sigmoid-SVM) based on the four support vector machine models of the study area were used to evaluate and classify the susceptibility of geological hazards. ROC curves were plotted to check the accuracy of the evaluation results of the four kernel functions, and the most suitable kernel function model for the study area was selected. The results show that the RBF-SVM model has high evaluation accuracy and is most suitable for the evaluation of geological hazard susceptibility in the study area. Finally, GIS technology was applied to partition the evaluation results into susceptibility zones. The evaluation results show that 86.14% of the geological hazards in Xixia Xichuan area are of medium and high susceptibility, and the evaluation results have certain practical significance for the prevention and control of geological hazards in Xixia Xichuan area.

Tourism and uncertainty: a machine learning approach

ABSTRACT In this paper, we attempt to create a unique forecasting model to forecast out-of-sample the tourism demand in 24 European Union countries. The initial dataset included 34 relevant variables of annual frequency that span the period from 2010 to 2020 for 40 countries. A data prefiltering process resulted in a final set of 17 relevant variables for 24 countries. Additionally, in the effort to investigate the impact of uncertainty on international tourism, apart from the traditional factors that affect tourism, we also include variables that measure various forms of uncertainty: we use the World Pandemic Uncertainty (WPU) Index, the Global CBOE Volatility Index, the Political Globalisation Index, the Economic Globalisation Index, and the Political Stability Index. In the empirical part of our research, we employ and compare in terms of their forecasting accuracy a set of six state-of-the-art machine learning algorithms, the Support Vector Regression with both a linear and an RBF kernel, the Random Forests, the Decision Trees, the KNN, and gradient-boosting trees. The results show that the Gradient-Boosting Trees algorithm outperforms the other five models providing the most accurate forecasts with a MAPE of 0.10% and 1.36% in the training and the out-of-sample tests, respectively.

Enhancing pilot vigilance assessment: The role of flight data and continuous performance test in detecting random attention loss in short IFR flights

Situational awareness (SA) and fatigue management are crucial aspects of aviation safety, particularly during demanding flight phases. This study introduces an innovative approach employing flight data, machine learning, and Continuous Performance Test (CPT) metrics to predict pilot performance and SA during instrument approaches under Instrument Meteorological Conditions (IMC). Data were collected from over 10 pilots across more than 40 flights in a high-fidelity Cessna 172 analog flight simulator.Significant correlations were observed between dynamic cognitive performance parameters and the exceedance shape factor, a novel measure of pilot sustained attention introduced in this research. Key variables identified through correlation analysis included variability, interstimulus change, and reaction time standard deviation.Importantly, commission scores and reaction time standard deviation emerged as key predictors in the machine learning model, specifically the Optimizable Gaussian Process Regression (GPR) model with a radial basis function kernel. The model achieved a validation R-squared of 0.90 and a test R-squared of 0.70. These systems could incorporate additional data sources, such as eye-tracking and scan pattern analysis, for a better assessment of pilot SA and fatigue levels. While post-flight measurements are inherently reactive, they are effective for monitoring the degradation of pilot CPT scores after each leg of high-frequency, short-duration flights.Notable limitations include the need to understand individual cognitive differences among pilots, such as age, experience, and cognitive style. The predictive model also requires validation in actual flight conditions to determine its ecological validity. Future research should aim to address these limitations, generalize the findings, and integrate CPT data with other sensor inputs to provide a more comprehensive understanding of pilot performance.

Developing an electronic nose for formalin detection in meatballs using Support Vector Machine (SVM) method and Raspberry Pi 4

This study presents the development of an Electronic Nose system using Arduino Mega and Raspberry Pi 4, capable of accurately detecting odors and gases. Previously, sensor analysis revealed that variations in meatball and formalin mixtures yield diverse sensor responses, with some sensors exhibiting high sensitivity to formalin. Additionally, Linear Discriminant Analysis (LDA) demonstrated clear separation among different classes, facilitating comprehensive data analysis. The study also evaluated the performance of the SVM model, showing precise SVM parameter optimization with high accuracy for classification, achieving up to 100% accuracy at C = 0.1, kernel RBF, and gamma 0.1. These findings highlight the potential of the developed system to effectively detect formalin in meatballs, providing valuable insights for ensuring food safety and quality assurance. Overall, the optimal selection of the C parameter plays a key role in enhancing SVM model performance and contributes to advancing detection technology in food industry applications.

Implementasi Model Support Vector Machine Dalam Analisa Sentimen Masyarakat Mengenai Kebijakan Penerapan Aplikasi Mypertamina

The policy for using the MyPertamina application issued does not rule out the possibility of differences of opinion due to changes in the policy. There are many positive, neutral, and negative responses to the MyPertamina application implementation policy. To see the public's reaction to the MyPertamina application implementation policy, it can be seen through various media, including social media. Twitter is a social network that is widely used by people in Indonesia. The number of Twitter users in Indonesia reached 18.45 million in 2022, making Indonesia the fifth largest Twitter user country in the world. Researchers conducted a sentiment analysis of the search results for tweets containing the keyword "MyPertamina" using the support vector machine algorithm. 382 tweet data were obtained and classified using the support vector machine algorithm. Support vector machine is a supervised learning algorithm for data classification. SVM is very fast and effective in solving text data problems. Text data is suitable for classification with the SVM algorithm because the basic nature of text tends to be high-dimensional. Of the 382 data analyzed, the support vector machine classification using the RBF kernel with parameter C=2 gave the highest accuracy value of 80.51%, precision value of 81%, recall value of 81%, and F1 score value of 80%.

Bayesian learning of feature spaces for multitask regression

This paper introduces a novel approach to learn multi-task regression models with constrained architecture complexity. The proposed model, named RFF-BLR, consists of a randomised feedforward neural network with two fundamental characteristics: a single hidden layer whose units implement the random Fourier features that approximate an RBF kernel, and a Bayesian formulation that optimises the weights connecting the hidden and output layers. The RFF-based hidden layer inherits the robustness of kernel methods. The Bayesian formulation enables promoting multioutput sparsity: all tasks interplay during the optimisation to select a compact subset of the hidden layer units that serve as common non-linear mapping for every tasks. The experimental results show that the RFF-BLR framework can lead to significant performance improvements compared to the state-of-the-art methods in multitask nonlinear regression, especially in small-sized training dataset scenarios.

Classification and Novelty Detection of Tampered ICs Using ResCav

This paper investigates the capabilities of the resonant cavity system (ResCav) for detecting tampered integrated circuits (ICs) within supply chains. Prior research showcased ResCav’s ability to discern minor circuit variations, this study focuses on enhancing supervised classification results and introduces a one-class support vector machine (SVM) approach with a modified radial basis function kernel for novelty detection. Through finer hyperparameter tuning, the system achieves improved classification accuracy, demonstrating its potential to identify nuanced alterations with even higher precision and recall rates. Additionally, the application of a one-class SVM enables the detection of tampered ICs without reliance on labeled datasets, expanding utility in scenarios where access is limited to golden ICs. These advancements in ResCav’s capabilities signify progress in failure prevention methodologies, offering an efficient and non-destructive solution crucial for safeguarding against counterfeit and non-conforming components infiltrating critical supply chains.

Non‐linear phylogenetic regression using regularised kernels

Abstract Phylogenetic regression is a type of generalised least squares (GLS) method that incorporates a modelled covariance matrix based on the evolutionary relationships between species (i.e. phylogenetic relationships). While this method has found widespread use in hypothesis testing via phylogenetic comparative methods, such as phylogenetic ANOVA, its ability to account for non‐linear relationships has received little attention. To address this, here we implement a phylogenetic Kernel Ridge Regression (phyloKRR) method that utilises GLS in a high‐dimensional feature space, employing linear combinations of phylogenetically weighted data to account for non‐linearity. We analysed two biological datasets using the Radial Basis Function and linear kernel function. The first dataset contained morphometric data, while the second dataset comprised discrete trait data and diversification rates as response variable. Hyperparameter tuning of the model was achieved through cross‐validation rounds in the training set. In the tested biological datasets, phyloKRR reduced the error rate (as measured by RMSE) by around 20% compared to linear‐based regression when data did not exhibit linear relationships. In simulated datasets, the error rate decreased almost exponentially with the level of non‐linearity. These results show that introducing kernels into phylogenetic regression analysis presents a novel and promising tool for complementing phylogenetic comparative methods. We have integrated this method into Python package named phyloKRR, which is freely available at: https://github.com/ulises‐rosas/phylokrr.

CHINESE YUAN EXCHANGE RATE AGAINST THE INDONESIAN RUPIAH PREDICTION USING SUPPORT VECTOR REGRESSION

This study aims to forecast the exchange rate between the Chinese Yuan (CNY) and the Indonesian Rupiah (IDR) using Support Vector Regression (SVR), a machine-learning technique that can handle nonlinear and complex data. The authors utilize the monthly selling exchange rate of CNY against IDR from January 2012 to October 2023 sourced from the “investing” platform. The optimal SVR model is obtained by splitting the data into 113 training samples and 28 testing samples and using the Radial Basis Function (RBF) kernel. The model achieves high accuracy, with a Mean Absolute Percentage Error (MAPE) of 1.738%, a Root Mean Squared Error (RMSE) of 50.661 for the training data and a MAPE of 2.516%, and an RMSE of 64.735 for the testing data. The results of this paper can provide valuable insights for policymakers, investors, and traders who are interested in the CNY/IDR exchange rate dynamics and the economic implications of the Belt and Road Initiative (BRI). The study aligns with the Sustainable Development Goals (SDGs), specifically SDG 8, aiming to promote sustained, inclusive, and sustainable economic growth.

Flood Prediction Using Support Vector Regression (Case Study of Floodgates in Jakarta)

Flood can be interpreted as an event that occurs suddenly and quickly enough where the water discharge in the drainage channel cannot be accommodated, so that the blocked area causes the water discharge in the drainage channel in several surrounding areas to overflow and is one of the natural disasters that occurs at an unexpected time and cannot be prevented, because of this, a prediction must be made to detect floods for the next day. Flood prediction is a crucial aspect of disaster management and mitigation, particularly in flood-prone areas such as Jakarta, Indonesia. This study aims to leverage Support Vector Regression (SVR) to predict flood events by analyzing various environmental and hydrological factors that influence flooding. The primary data sources include historical wheater data, river water levels, floodgate positions in Jakarta. The data preprocessing involved cleaning, handling missing values, and normalizing the datasets to ensure compatibility with the SVR model. Feature selection was conducted to identify the most relevant predictors of flooding, such as wheater data, and river water levels. The dataset was then split into training and testing sets, maintaining an 80-20 ratio to ensure robust model validation. An SVR model with a radial basis function (RBF) kernel was trained on the standardized training data. The model's performance was evaluated using Root Mean Squared Error (RMSE) as the primary metric. The RMSE produced in this study was 0.112 with an R Square accuracy of 0.977. The results indicated that the SVR model could effectively predict flood events with a reasonable degree of accuracy, demonstrating its potential as a valuable tool in flood forecasting.

Predicting borrower default risk using support vector machine AI models

Precise prediction on the likelihood of borrower default is pivotal for credit institution and decision makers to mitigate the loss of capital and rationalize decision process. This article reviewed the Effects of Support Vector Machine (SVM) models with radial basis function (RBF) kernel in predicting the mortality rate of borrowers. By integrating with a dataset of approximately 100,000 borrowers profile harvested through historical loan performance, we set up the SVM model, and employed a feature-distribution method utilizing grid search and cross-validation technique to fine-tune the predictive model of SVM. Results indicated that the model accomplished an excellent performance with accuracy of 92%, precision of 89%, the recall and F1-score of 85% and 87%, respectively, alongside an Area Under the Curve -Receiver Operating Characteristic (AUC-ROC value of 0.95). It was evinced that the model performed substantially better than traditional logistic regression and decision trees in discriminating defaulter from non-defaulter. The outcome informs that an in-depth process should be implemented on data preprocessing, featureselection, and parameter tuning to achieve a robust predictive model for credit risk assessment. The article concludes the potentials of AI based on the resort to artificial technology in revolutionising the risk assessment scheme within the financial industry.

RTSMFFDE-HKRR: A fault diagnosis method for train bearing in noise environment

The bearings have been exposed to a noisy environment for an extended period, making it challenging to identify fault characteristics accurately and resulting in low accuracy. In this study, we proposed a technique for diagnosing train-bearing faults in noisy environments using refined time-shift multiscale fractional order fuzzy dispersion entropy and hybrid kernel ridge regression (RTSMFFDE-HKRR) to enhance the precision of fault detection. Firstly, RTSMFFDE is proposed based on multiscale fuzzy dispersion entropy by combining the theories of refinement, time-shifting, and fractional order. Revising the refinement process helps to stabilize the entropy value when dealing with a large scale factor. Implementing time-shifting techniques can help preserve the original signal features more effectively. The theory of fractional order strengthens the noise-resistant performance of the algorithm, and the proposed RTSMFFDE has a superior feature extraction capability. Subsequently, a hybrid kernel function is formulated through the combination of the radial basis kernel function (RBF) and the linear kernel function, aiming to improve the nonlinear mapping ability and anti-noise ability of ridge regression classification algorithm. Finally, two sets of experimental cases were used to verify the proposed RTSMFFDE-HKRR. The results show that the fault feature information extracted by RTSMFFDE in noisy environment is more comprehensive, and the classification effect of HKRR is more significant. In the fault diagnosis of two bearing simulation test beds, the accuracy rate of RTSMFFDE-HKRR is up to 100%. In the noisy environment, the accuracy rate is 98.84% and 97.32%, which is much higher than other diagnostic models. RTSMFFDE-HKRR is suitable for train bearing fault diagnosis in noisy environment.

Revealing the Impact of the Combination of Parameters on SVM Performance in COVID-19 Classification

Non-linear SVM functions to modify the kernel in the SVM. Each kernel function in linear and non-linear SVMs has several parameters that are used in the classification process. SVM is a method that has advantages in classification, but there are still obstacles in selecting optimal parameters. This research investigates the effect of parameter variations on SVM classification performance on the COVID-19 dataset, using linear, RBF, Sigmoid and polynomial kernels. The analysis shows that the polynomial kernel is superior with the highest performance compared to other kernels. The highest accuracy of 77.57% was achieved with a combination of C values ??of 0.75 and Gamma of 0.75, and an F1-Score value of 76.67% indicating an optimal balance between precision and recall. The performance stability produced by the polynomial kernel provides advantages in classifying the COVID-19 dataset, with more controlled fluctuations compared to other kernels. The interaction between the C and Gamma parameters shows that a Gamma value of 0.75 consistently provides good results, while adjusting the C parameter shows more controlled performance variations. This confirms that appropriate Gamma parameter settings are key in improving the accuracy and consistency of SVM model predictions in this case.