The application of the current new generation communication technology is gradually diversified, and global Internet users are increasing, leading some large enterprises to increasingly rely on faster more efficient big data processing technology. In order solve shortcomings algorithms, such as slow computing speed, accuracy be improved, poor online real-time learning ability, this research com...

Artificial intelligence (AI) and machine learning (ML) have influenced every part of our day-to-day activities in this era technological advancement, making a living more comfortable on the earth. Among several AI ML algorithms, support vector (SVM) has become one most generally used algorithms for data mining, prediction other (AI ML) domains. The SVM’s performance is significantly centred ker...

The eigenstructure of the second-order statistics of a multivariate random population can be inferred from the matrix of pairwise combinations of inner products of the samples. Therefore, it can be also efficiently obtained in the implicit, high-dimensional feature spaces defined by kernel functions. We elaborate on this property to obtain general expressions for immediate derivation of nonline...

This paper presents a simple estimate to determine the width of Gaussian kernel with the adaptive scaling technique. The Gaussian kernel is widely employed in Radial Basis Function (RBF) network, Support Vector Machine (SVM), Least Squares Support Vector Machine (LS-SVM), Kriging, and so on. It is widely known that the width of the Gaussian kernel in these machine learning techniques plays an i...

One commonly finds in applications of smooth radial basis functions (RBFs) that scaling the kernels so they are ‘flat’ leads to smaller discretization errors. However, the direct numerical approach for computing with flat RBFs (RBF-Direct) is severely ill-conditioned. We present an algorithm for bypassing this ill-conditioning that is based on a new method for rational approximation (RA) of vec...

Abstract The growth of urbanization in Klang District was considered to be fast and has increased the concern policy makers town planners. This paper assess changes urban development using Support Vector Machine (SVM) classification by different kernel for purpose studying built up area within year 2017 2021. At initial stage image processing, Land Use Cover (LULC) been classified based on use ...

Prediction of barley yield is an attempt to accurately forecast the outcome of a specific situation, using as input information extracted from a set of data features that potentially describe the situation. In this study, an attempt has been made to analyze and compare multiple linear regression (MLR), and artificial neural network (ANN) including multi-layer p erceptron (MLP) and r adial basis...

We present an approach for learning an anisotropic RBF kernel in a game theoretical setting where the value of the game is the degree of separation between positive and negative training examples. The method extends a previously proposed method (KOMD) to perform feature re-weighting and distance metric learning in a kernel-based classification setting. Experiments on several benchmark datasets ...

Automatic diagnosis of COVID-19 has an active role in reducing the spread disease by minimizing interaction with people. Machine learning models using various signals and images form basis automatic diagnosis. This study presents machine based for detecting infection ‘Virufy’ dataset containing cough sound labeled as Non-COVID-19. Since number COVID positive coughs set is less than those negati...

While kernel support vector machines are powerful classification algorithms, their computational overhead can be significant, especially for large and high-dimensional data sets. A recent biomedical dataset, for instance, could take as long as 3 weeks to compute its RBF kernel matrix on a modern, single-processor workstation. In this paper, we develop methods for high-performance parallel compu...