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Self-assembling amphiphilic peptides have recently received special attention in medicine. Nonetheless, testing the myriad of combinations generated from at least 20 coded and several hundreds of noncoded amino acids to obtain candidate sequences for each application, if possible, is time-consuming and expensive. Therefore, rapid and accurate approaches are needed to select candidates from countless combinations. In the current study, we examined three conventional descriptor sets along with a novel descriptor set derived from the simulated aggregation propensity of di- and tripeptides to model the critical aggregation concentration (CAC) of amphiphilic peptides. In contrast to the conventional descriptors, the radial kernel model derived from the novel descriptor set accurately predicted the critical aggregation concentration of the test set with a residual standard error of 0.10. The importance of aromatic side chains, as well as neighboring amino acids in the self-assembly, was emphasized by analysis of the influential descriptors. The addition of very long peptides (70–100 residues) to the data set decreased the model accuracy and changed the influential descriptors. The developed model can be used to predict the CAC of self-assembling amphiphilic peptides and also to derive rules to apply in designing novel amphiphilic peptides with desired properties.

Carbon financial futures have both the characteristics of commodity futures and environmental protection and its price is affected by many factors. It is hard and complex for traditional analysis methods to get precise prediction results effectively. How to effectively predict the price trend of carbon financial futures has been focused on by both academia and traders. This study addresses the high prediction error of European allowance (EUA) futures price by constructing a novel approach by combining the support vector machine (SVM) and particle swarm optimization (PSO) algorithm. This article introduces a parameters optimization method, which provides the best parameters for SVM to improve the prediction performance of the EUA futures price. Furthermore, this research uses the realistic trading dataset containing 30,762 EUA futures closing prices to verify the effectiveness and efficiency of the PSO-SVM prediction model. The empirical results show that the prediction performance of the model, especially the radial kernel function, is significantly improved. And this approach can determine the parameters according to the characteristics of the dataset and input the parameters for training and prediction automatically. The PSO-SVM algorithm can effectively predict extreme price fluctuations and overcome the problem of high prediction error caused by parameter constraints.

Tourism in Bali is one of the major industries which play an important role in developing the global economy in Indonesia. Good forecasting of tourist arrival, especially from foreign countries, is needed to predict the number of tourists based on past information to minimize the prediction error rate. This study compares the performance of SVM and Backpropagation to find the model with the best prediction algorithm using data from foreign tourists in Bali Province. The results of this study recommend the best forecasting using the SVM model with the radial kernel function. The best accuracy of the SVM model obtained the lowest error values of MSE 0.0009, MAE 0.0186, and MAPE 0.0276, compared to Backpropagation which obtained MSE 0.0170, MAE 0.1066, and MAPE 0.1539.

Due to the concentration effect, there is a major challenge for the electronic nose system to identify different odor samples with multiple concentrations. The development of artificial intelligence provides new ways to solve such problems. This article attempts to use support vector machine (SVM) technology to distinguish four fragrance samples with three concentrations, including roman chamomile, jasmine, lavender, and orange. The responses of these samples were collected by an 11-sensor electronic nose. After baseline correction, data smoothing, and removal of non-responsive sensors, the signals of 8 sensors were used for subsequent model analysis. Due to the concentration effect, when the primary signal intensities were used as features, the electronic nose cannot distinguish between different aroma types (accuracy less than 50%). When the normalized maximum signal intensity Xmr was used, the accuracy of the model was greatly improved. Graphic analysis and PCA showed that the normalized feature effectively eliminates the concentration effect, and appropriately reducing some sensors can enhance the ability to distinguish odors. The SVM correctly classified all 14 aromas when feeding 8 sets of data to train the radial kernel C-classification SVM. This showed that the cross-interference of the sensors was reduced, and the resolving power of the electronic nose was enhanced after the feature reduction.

Objective Early diagnosis of many diseases is essential for their treatment. Furthermore, the existence of abundant and unknown variables makes more complicated decision making. For this reason, the diagnosis and classification of diseases using machine learning algorithms have attracted a lot of attention. Therefore, this study aimed to design a support vector machine (SVM) based decision-making support system to diagnosis various periodontal diseases. Data were collected from 300 patients referring to Periodontics department of Hamadan University of Medical Sciences, west of Iran. Among these patients, 160 were Gingivitis, 60 were localized periodontitis and 80 were generalized periodontitis. In the designed classification model, 11 variables such as age, sex, smoking, gingival index, plaque index and so on used as input and output variable show the individual’s status as a periodontal disease. Results Using different kernel functions in the design of the SVM classification model showed that the radial kernel function with an overall correct classification accuracy of 88.7% and the overall hypervolume under the manifold (HUM) value was to 0.912 has the best performance. The results of the present study show that the designed classification model has an acceptable performance in predicting periodontitis.

This study proposes a novel and efficient dual regression model for possibilistic regression analysis that incorporates the principles of support vector machine (SVM) theory. The dual regression model, which comprises an upper model and a lower model, approximates the observed fuzzy phenomena from the outside and inside directions, respectively, such that the inclusion relationship between those two models holds. The proposed dual regression model better explains the inherent vagueness that exists in a given dataset. It provides the outer and inner bounds for the estimated vagueness region, and allows an estimation of the degree of confidence in the predicted fuzzy output. Using the principles for a twin support vector machine (TSVM), the upper- and lower models are estimated by solving two smaller SVM-type quadratic programming problems (QPPs), instead of a single larger QPP. This strategy significantly increases the learning speed for the proposed algorithm. The structural risk minimization principle of SVM makes the proposed method to yield better generalization ability. The kernel function method offers a model-independent framework for the proposed dual regression model. This paper focuses on the class of radial kernels, which enables the proposed method to conquer the problem of increasing spreads. The radial kernel also gives the proposed method a unified framework that allows both crisp and fuzzy inputs. The experimental results verify the effectiveness and efficiency of the proposed method. In comparison with previous SVM-based dual regression model, the proposed approach significantly improves the sparsity, prediction speed, and training speed.

The purpose of this paper is to provide necessary and sufficient conditions on a continuous and matrix valued radial kernel on a Euclidean space in order that it be conditionally positive definite of a fixed order. Except for the one dimensional Euclidean space, the strict conditional positive definiteness of the kernel is fully characterized.

In this paper, we first derive two types of transformed Franklin polynomial: substituted and weighted radial Franklin polynomials. Two radial orthogonal moments are proposed based on these two types of polynomials, namely substituted Franklin-Fourier moments and weighted Franklin-Fourier moments &#x0028 SFFMs and WFFMs &#x0029, which are orthogonal in polar coordinates. The radial kernel functions of SFFMs and WFFMs are transformed Franklin functions and Franklin functions are composed of a class of complete orthogonal splines function system of degree one. Therefore, it provides the possibility of avoiding calculating high order polynomials, and thus the accurate values of SFFMs and WFFMs can be obtained directly with little computational cost. Theoretical and experimental results show that Franklin functions are not well suited for constructing higher-order moments of SFFMs and WFFMs, but compared with traditional orthogonal moments &#x0028 e.g., BFMs, OFMs and ZMs &#x0029 in polar coordinates, the proposed two types of Franklin-Fourier Moments have better performance respectively in lower-order moments.

Tuberculosis is a contagious disease that is generally transmitted through a sufferer’s cough and is deadly. Tuberculosis usually attacks the lungs but can also affect other parts of the body. Treatment of tuberculosis patients who do not recover with those who can recover is different, mishandling can cause death in patients. Therefore, we need a system that can predict whether the patient’s condition can recover normally, or the lungs cannot be recovered. Support vector machine is a learning system that uses a hypothetical linear function in a high dimensional space and is trained with an algorithm based on optimization theory by applying learning bias derived from statistical theory. In this study, the kernel function is used, namely the radial kernel and the polynomial. Based on the analysis and discussion that has been done, it can be concluded from this study that the performance of the radial and polynomial kernels is the same with an accuracy of 85% and a sensitivity value of 94%.

In this paper, we estimate the dynamic parameters of a time-varying coefficient model through radial kernel functions in the context of a longitudinal study. Our proposal is based on a linear combination of weighted kernel functions involving a bandwidth, centered around a given set of time points. In addition, we study different alternatives of estimation and inference including a Frequentist approach using weighted least squares along with bootstrap methods, and a Bayesian approach through both Markov chain Monte Carlo and variational methods. We compare the estimation strategies mention above with each other, and our radial kernel functions proposal with an expansion based on regression spline, by means of an extensive simulation study considering multiples scenarios in terms of sample size, number of repeated measurements, and subject-specific correlation. Our experiments show that the capabilities of our proposal based on radial kernel functions are indeed comparable with or even better than those obtained from regression splines. We illustrate our methodology by analyzing data from two AIDS clinical studies.

Autism Spectrum Disorder (ASD) is on the rise and constantly growing. Earlier identify of ASD with the best outcome will allow someone to be safe and healthy by proper nursing. Humans are hard to estimate the present condition and stage of ASD by measuring primary symptoms. Therefore, it is being necessary to develop a method that will provide the best outcome and measurement of ASD. This paper aims to show several measurements that implemented in several classifiers. Among them, Support Vector Machine (SVM) provides the best result and under SVM, there are also some kernels to perform. Among them, the Gaussian Radial Kernel gives the best result. The proposed classifier achieves 95% accuracy using the publicly available standard ASD dataset.

In this work, the estimation of the characteristic impedance of a microstrip transmission line with the aid of support vector machines was conducted. In the estimation, first data was obtained by using empirical analytical formulas and this data was used for training and testing in support vector machine. Both classification and regression analysis were conducted using different kernel functions. Kernel functions used in this case were linear kernel and radial kernel. In the analysis, different regression techniques as ν-regression and e-regression were also used. It is concluded that using radial kernels produce more accurate results. Moreover, it can be concluded that support vector machines can be efficiently used in determining the characteristic impedance of microstrip lines.

We overview recent developments in the study of alignment hydrodynamics, driven by a general class of symmetric communication kernels. A main question of interest is to characterize the emergent behavior of such systems, which we quantify in terms of the spectral gap of a weighted Laplacian associated with the alignment operator. Our spectral analysis of energy fluctuation covers both long-range and short-range kernels and does not require thermal equilibrium (no closure for the pressure). In particular, in the prototypical case of metric-based short-range kernels, the spectral gap admits a lower-bound expressed in terms of the discrete Fourier coefficients of the radial kernel, which enables us to quantify an emerging flocking behavior for non-vacuous solutions. These large-time behavior results apply as long as the solutions remain smooth. It is known that global smooth solutions exist in one and two spatial dimensions, subject to sub-critical initial data. We settle the question for arbitrary dimension, obtaining non-trivial initial threshold conditions which guarantee existence of multiD global smooth solutions.

This research introduces a new and effective asymmetric dual regression model by combining the advantages of possibilistic regression model and paired nu support vector machine (pair-v SVM). Our algorithm is able to best examine the ambiguity in a given data set from internal and external sides. Our algorithm estimates the outer boundary and inner boundary of the uncertain area for the predicted output. Based on the strategy of pair-v SVM, our algorithm find the solutions of four smaller SVM types of quadratic programming problems (QPP) instead of one big QPP to seek the up and down limits of the necessity and possibility model. This scheme greatly speeds up the training speed for our algorithm. Our model adopts the radial kernel, which offers a unified structure for the proposed method, which can handle crisp and vague input at the same time. The experimental results prove the efficiency and effectiveness of our algorithm.

Student dropout is a significant problem affecting higher education institutions. This phenomenon is the result of multiple causes and has different forms. The aim of this work was to predict which students of the Departamento de Ingenieria Informatica, Universidad de Santiago de Chile, will migrate to a different programme. For this purpose, we considered logistic regression models and support vector machines (SVM) with linear and radial kernels. Results showed that radial kernel SVM can satisfactorily predict this phenomenon, with an accuracy of 88.0% for the 6-year programme and of 66.67% for the 4-year programme.

The objective of this paper is to compare the accuracy of different kernel functions of the SVM method for text classification. As a basis for the research film reviews are used. The authors try to detect the kernel functions and their parameters to achieve high accuracy in movie reviews classification. The studied kernel functions are: polynomial kernel of degree 2, a linear kernel and a radial base kernel. The achieved accuracy is higher than 83%. The experiments show that the sigmoid radial kernel is an inappropriate choice in text classification.

Music recommendations are one of the important things, such as music streaming platforms. Classification of music genres is one of the important initial stages in the process of music recommendation based on genre. Many music classifications are proposed by extracting audio features that require a not light computing process. This research aims to analyze and test the performance of music genre classification based on metadata using three different classifiers, namely Support Vector Machine (SVM) with radial kernel base function (RBF), K Nearest Neighbors (K-NN), and Naive Bayes (NB). The Spotify music dataset was chosen because it has complete metadata on each of its music. Based on the results of tests conducted by the SVM classifier has the best classification performance with 80% accuracy, then followed by KNN with 77.18% and NB with 76.08%. The accuracy results are relatively the same as music classification based on audio feature extraction, so the classification with the extraction of metadata features can continue to be developed if the metadata in the dataset is well managed.

The paper presents the application of support vector regression technique for the modelling of antennas. Two different antennas were modelled with different properties. The first modelling was for a helical antenna with varying parameters to determine the gain of antenna. In the second modelling, an equilateral triangular patch antenna with varying properties for the design was considered for the determination of the directivity of the antenna. The support vector regression modelled both of the antennas very well. In helical antenna, the radial kernel with ν-regression produced only 0.143 dBi average error. In equilateral triangular patch antenna, the radial kernel with e-regression produced only 0.126 dBi average error.

In this paper, we prove some properties of algebraic cone metric spaces and introduce the notion of algebraic distance in an algebraic cone metric space. As an application, we obtain some famous fixed point results in the framework of this algebraic distance.

The aim of this study is to assess the performance of linear discriminate analysis, support vector machines (SVMs) with linear and radial basis, classification and regression trees and random forest (RF) in the classification of radionuclide data obtained from three different types of rocks. Radionuclide data were obtained for metamorphic, sedimentary and igneous rocks using gamma spectroscopic method. A P-type high-purity germanium detector was used for the radiometric study. For analysis purpose, we have determined activity concentrations of 232Th, 226Ra and 40K radionuclides, published elsewhere (Rafique et al. in Russ Geol Geophys 55:1073–1082, 2014), in different rock samples and built the classification model after pre-processing the data using three times tenfold cross-validation. Using this model, we have classified the new samples into known categories of sedimentary, igneous and metamorphic. The statistics depicts that RF and SVM with radial kernel outperform as compared to other classification methods in terms of error rate, area under the curve and with respect to other performance measures.

Sufficient conditions on the kernel and the grandizer that ensure the boundedness of integral operators with homogeneous kernels in grand Lebesgue spaces on ℝ n as well as an upper bound for their norms are obtained. For some classes of grandizers, necessary conditions and lower bounds for the norm of these operators are also obtained. In the case of a radial kernel, stronger estimates are established in terms of one-dimensional grand norms of spherical means of the function. A sufficient condition for the boundedness of the operator with homogeneous kernel in classical Lebesgue spaces with arbitrary radial weight is obtained. As an application, boundedness in grand spaces of the one-dimensional operator of fractional Riemann–Liouville integration and of a multidimensional Hilbert-type operator is studied.

In this paper we show an interior discontinuity and its piecewise regularity for a stationary compressible Stokes problem with inflow jump datum. The discontinuity is preserved along the interior curve directed by the ambient vector field. The difficulty lies in controlling the gradient of the pressure across the interior curve starting at the jump point that the inflow datum has. We deal with this issue by constructing a vector function K corresponding to the interior jump and splitting the vector K from the velocity vector. Finally the remainder for the velocity vector and pressure function is shown to be in the space H 2 , q × H 1 , q for q ≥ 2 .

Machine Learning (ML) algorithms have been used for assessing soil quality parameters along with non-destructive methodologies. Among spectroscopic analytical methodologies, energy dispersive X-ray fluorescence (EDXRF) is one of the more quick, environmentally friendly and less expensive when compared to conventional methods. However, some challenges in EDXRF spectral data analysis still demand more efficient methods capable of providing accurate outcomes. Using Multi-target Regression (MTR) methods, multiple parameters can be predicted, and also taking advantage of inter-correlated parameters the overall predictive performance can be improved. In this study, we proposed the Multi-target Stacked Generalisation (MTSG), a novel MTR method relying on learning from different regressors arranged in stacking structure for a boosted outcome. We compared MTSG and 5 MTR methods for predicting 10 parameters of soil fertility. Random Forest and Support Vector Machine (with linear and radial kernels) were used as learning algorithms embedded into each MTR method. Results showed the superiority of MTR methods over the Single-target Regression (the traditional ML method), reducing the predictive error for 5 parameters. Particularly, MTSG obtained the lowest error for phosphorus, total organic carbon and cation exchange capacity. When observing the relative performance of Support Vector Machine with a radial kernel, the prediction of base saturation percentage was improved in 19%. Finally, the proposed method was able to reduce the average error from 0.67 (single-target) to 0.64 analysing all targets, representing a global improvement of 4.48%., 20 pages, 5 figures

In this work, we modelled the exchange rates using Support Vector Regression (SVR). The currency selected in this work was Turkish Lira (TRY) and compared the rates between United States Dollars (USD), Euro (EUR), and British Pounds (GBP). The modelling was done for the period of January 2018 till October 2019 data, with the aim to investigate three different issues. The difference in using ν-regression and e–regression was investigated. The effect of using different kernel functions was examined. Also, the effect of using multiple different exchange rates together (multi-rate) compared with modeling only one rate (single-rate) was explored. The results showed very successful modelling of the currencies producing very accurate results using the ν-regression modelling with radial kernel and using multi-rate modelling.

In this work our aim was to use support vector regression technique to predict unemployment rate and gross domestic product (GDP) of the Organization for the Economic Cooperation and Development (OECD) countries. The original data for unemployment rate and GDP taken from OECD was tested with the models generated by support vector regression. The analysis of using different kernels as well as analysis of using extended data from four countries in predicting the unemployment rate and GDP were considered separately. For unemployment rate, the relative error in average was 2.055% using the radial kernel and using extended data from four countries. Similarly, for GDP the relative error in average was 0.503%. It was observed that in fact support vector regression is a very powerful modelling tool in using the existing historical data for predicting the values of interest.

Building occupancy maps of the environment is a fundamental problem for robot autonomy. A common assumption in early work was that the occupancy states of different map elements are independent. Recently, Gaussian Process (GP) techniques were proposed to capture correlation, which is important not only for improved accuracy but also for uncertainty quantification and autonomous exploration based on the predicted occupancy of nearby unexplored areas. Despite these desirable properties, current GP mapping techniques are limited to small maps and slow inference speeds. This paper proposes an information space formulation of the GP mapping problem. If a decomposable radial kernel is evaluated over a latent grid of pseudo-input points, the resulting kernel matrix has a Kronecker-product-of-Toeplitz-matrices structure that allows very efficient representation of the occupancy distribution. We utilize this structure to design an information filter occupancy mapping algorithm with linear time and memory complexity that still permits continuous space observations and predictions.

Skin condition closely reflects human health, and skin roughness is one of the factors to analyze skin condition. This paper presents a classification algorithm of skin roughness based on improved support vector machine. The improved gray co-occurrence matrix, Tamura texture feature, fractal dimension, Gabor feature and other 24 properties affecting skin roughness were extracted by using human skin samples captured by visible light as data sources. Genetic algorithm was used to optimize the feature space, and the most important feature combinations were selected according to the fitness values. In order to improve the performance of classifier, the improved particle swarm optimization algorithm is used to optimize the penalty factor C and radial kernel function coefficient Gamma of support vector machine. The experiment shows that the model established in this paper has an obvious effect on the classification of skin roughness.

In this paper, we analyse three interior point continuous trajectories for convex programming with general linear constraints. The three continuous trajectories are derived from the primal–dual path-following method, the primal–dual affine scaling method and the central path, respectively. Theoretical properties of the three interior point continuous trajectories are fully studied. The optimality and convergence of all three interior point continuous trajectories are obtained for any interior feasible point under some mild conditions. In particular, with proper choice of some parameters, the convergence for all three interior point continuous trajectories does not require the strict complementarity or the analyticity of the objective function. These results are new in the literature.

By means of the Minkowski-type nonlinear scalarization functional, in this paper, we establish some nonlinear separation theorems via relative algebraic interior and vector closure in a general real linear space, via relative topological interior and topological closure in a real topological linear space and via quasi relative interior and topological closure in a real separated locally convex topological linear space, respectively. These new separation theorems can be applied to study those vector optimization problems with the ordering cones having possibly empty topological interior and even relative topological interior or relative algebraic interior. As their applications, we give some nonlinear scalarization characterizations of the corresponding weak efficient solutions of vector optimization problems. Moreover, we also present some concrete examples to illustrate the main results in some infinite dimensional spaces.

Article focuses on the prediction of bankruptcy of the 850 medium-sized retail business companies in EU from which 48 companies gone bankrupt in 2014 with respect to lag of the used features. From various types of classification models we chose Support vector machines method with linear, polynomial and radial kernels to acquire best results. Pre-processing is enhanced with filter based feature selection like Gain ratio, Chi-square and Relief algorithm to acquire attributes with the best information value. On this basis we deal with random samples of financial data to measure prediction accuracy with the confusion matrices and area under curve values for different kernel types and selected features. From the results it is obvious that with the rising distance to the bankruptcy there drops precision of bankruptcy prediction. The last year (2013) with avaible financial data offers best total prediction accuracy, thus we also infer both the Error I and II types for better recognizance. The 3rd order polynomial kernel offers better accuracy for bankruptcy prediction than linear and radial versions. But in terms of the total accuracy we recommend to use radial kernel without feature selection.

Log-based reservoir characterization is one of the widely used techniques to estimate the reservoir properties and make decisions about future plans for hydrocarbon production. Use of the machine learning tools is becoming a more accessible approach for predictive model development in oil and gas engineering. The objective of this research is to identify and rank the most contributing log variables for estimation of water saturation using the machine learning tools. The multilayer perception artificial neural network (MLP-ANN) and kernel function-based least-squares support vector machine (LS-SVM) techniques are employed to develop predictive models for water saturation. The model can capture the non-linear behaviors and high-dimensional complex relationships among field log data variables. Based on the prediction performance of the models, the Levenberg-Marquardt algorithm-based MLP-ANN and the radial kernel function-based LS-SVM model optimized with coupled simulated annealing optimization technique were found to perform better compared to other models. The MLP-ANN and radial kernel function-based LS-SVM approaches lead to the same feature ranking of predictor variables. It was found that the significance order (higher to lower) of influential log variables are the true resistivity, bulk density, neutron porosity, photoelectric factor, gamma-ray, and sonic travel time based on their relative contribution to the water saturation. The strategy introduced in this study assists to forecast water saturation with a relatively few number of log variables, and thus, reduces the number of necessary logs to run during exploration stage, considerably lowering the exploration costs.

In this paper, we develop and analyze a new divergence-free kernel approximation method for the time-dependent incompressible Stokes equations on surfaces. The novelty of our proposed method comes from the surface Helmholtz decomposition, which can convert the surface Stokes equations into a coupled equations in which the velocity is deadly divergence-free so that no inf–sup conditions have to be satisfied. Spatial discretization of velocity is implemented by the radial kernel divergence-free approximation spaces, which only need scatter nodes on surfaces. Using the radial basis function collocation method in space, we derive the rigorous stability and convergence result. Numerical examples are presented, demonstrating the efficiency of some model problems on more general surfaces.

In this paper, the author has proposed the combination of deep convolutional neural networks as feature learning and long short-term memory as time series forecasting algorithm to forecast appliances energy consumption in residential houses using 28 data features collected from IoT sensors database. It is shown in experiment that the forecasting performance can be improved with comparison methods such as GBM (Gradient Boosting Machine), Support vector Machine with Radial Kernel, and RF (Random Forest).

How to obtain the sharp constant of the Hardy–Littlewood inequality remains unsolved. In this paper, the new analytical technique is to convert the exact constant factor to the norm of the corresponding operator, the multiple Hardy–Littlewood integral operator norm inequalities are proved. As its generalizations, some new integral operator norm inequalities with the radial kernel on n-dimensional vector spaces are established. The discrete versions of the main results are also given.