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A COMPARATIVE APPROACH TO SVM KERNEL FUNCTIONS VIA ACCURATE EVALUATING ALGORITHMS
(2023-08-01)
Nurhidayat, Irfan
;
Pimpunchat, Busayamas
The Accurate Evaluation Algorithm in R Programming is used to assess the feasibility of classification using the SVM Radial Basis Function (RBF) kernel method. This algorithm encompasses some statistical tools. Among the various scenarios for improving dataset accuracy, employing the accurate evaluating algorithm before classifying with the SVM RBF kernel method is crucial. The current objective is to achieve an enhanced accuracy of up to 83% by leveraging this algorithm. The significance of this research lies in the development of an accurate evaluation algorithm for data analysis and its application using the SVM RBF kernel method. This contribution provides a solution for players in the digital insurance business within the data processing industry. The novelty lies in the accurate evaluating algorithm, which is thoroughly explained alongside simulation graphs for sum insured data. The accuracy comparisons based on the root-mean-square error (RMSE) and density are extensively investigated. The results conclude that employing this algorithm leads to more accurate data analysis (83% accuracy).
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A hierarchical approach based CBIR scheme using shape, texture, and color for accelerating retrieval process
(2023-07-01)
Kumar, Sumit
;
Pal, Arup Kumar
;
Varish, Naushad
;
Nurhidayat, Irfan
;
Eldin, Sayed M.
In the traditional content-based image retrieval (CBIR) framework, images are retrieved based on the combined primitive features. However, such a kind of fusion is always not effective as one feature may overshadow other image feature. To overcome this issue, in this particular paper, we have suggested a hierarchical framework where features like color, texture, and shape are considered in a single hierarchy only. The main concern of the hierarchical system is the proper selection of the order of retrieving visual features. So, semantic-based image retrieval has been carried out. Here, at the first level, the salience map-based region of interest has been identified, and then edge histogram descriptor-based shape features are incorporated. In the second hierarchy, we have proposed a novel directional texture feature extraction based on the Tamura features’ directionality. Further, color is considered another primitive feature of an image, but human visual perception is not sensitive to each color. The image can be visualized by the salient colors, and in this work, we have developed a color image quantization-based approach. Now, to validate the system, extensive experimental results and its comparison with its contemporaries through Corel-1 K, GHIM-10 K, Olivia-2688, and Produce-1400 databases have been carried out.
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More accurate simulation for insurance data based on a modified SVM polynomial method
(2023-06-01)
Nurhidayat, Irfan
;
Pimpunchat, Busayamas
;
Klomsungcharoen, Wiriyabhorn
This study aims to present the modified SVM polynomial method in order to evaluate insurance data. The research methodology discusses classical and modified SVM polynomial methods by R programming, and uses performance profiles to create the most preferable methods. It offers a new algorithm called an accurate evaluating algorithm as the way to construct the modified SVM polynomial method. The classical SVM polynomial method is also represented as the main idea in finding the modified polynomial SVM method. Model Performance Evaluation (MPE), Receiver Operating Characteristics (ROCs) Curve, Area Under Curve (AUC), partial AUC (pAUC), smoothing, confidence intervals, and thresholds are further named an accurate evaluating algorithm, employed to build the modified SVM polynomial method. The research paper also presents the best performance profiles based on the computing time and the number of iterations of both classical and modified SVM polynomial methods. Performance profiles show numerical comparisons based on both methods involving insurance data also displayed in this paper. It can be concluded that applying an accurate evaluating algorithm on the modified SVM polynomial method will improve the data accuracy up to 86% via computing time and iterations compared to the classical SVM polynomial method, which is only 79%. This accurate evaluating algorithm can be applied to various large-sized data by utilizing R programming with changing any suitable kernels for that data. This vital discovery will offer solutions for faster and more accurate data analysis that can benefit researchers, the private sector, or governments struggling with data.
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An Interplay of Wigner–Ville Distribution and 2D Hyper-Complex Quadratic-Phase Fourier Transform
(2023-02-01)
Bhat, Mohammad Younus
;
Dar, Aamir Hamid
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Nurhidayat, Irfan
;
Pinelas, Sandra
Two-dimensional hyper-complex (Quaternion) quadratic-phase Fourier transforms (Q-QPFT) have gained much popularity in recent years because of their applications in many areas, including color image and signal processing. At the same time, the applications of Wigner–Ville distribution (WVD) in signal analysis and image processing cannot be ruled out. In this paper, we study the two-dimensional hyper-complex (Quaternion) Wigner–Ville distribution associated with the quadratic-phase Fourier transform (WVD-QQPFT) by employing the advantages of quaternion quadratic-phase Fourier transforms (Q-QPFT) and Wigner–Ville distribution (WVD). First, we propose the definition of the WVD-QQPFT and its relationship with the classical Wigner–Ville distribution in the quaternion setting. Next, we investigate the general properties of the newly defined WVD-QQPFT, including complex conjugate, symmetry-conjugation, nonlinearity, boundedness, reconstruction formula, Moyal’s formula, and Plancherel formula. Finally, we propose the convolution and correlation theorems associated with WVD-QQPFT.
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Uncertainty Principles for the Two-Sided Quaternion Windowed Quadratic-Phase Fourier Transform
(2022-12-01)
Bhat, Mohammad Younus
;
Dar, Aamir Hamid
;
Nurhidayat, Irfan
;
Pinelas, Sandra
A recent addition to the class of integral transforms is the quaternion quadratic-phase Fourier transform (Q-QPFT), which generalizes various signal and image processing tools. However, this transform is insufficient for addressing the quadratic-phase spectrum of non-stationary signals in the quaternion domain. To address this problem, we, in this paper, study the (two sided) quaternion windowed quadratic-phase Fourier transform (QWQPFT) and investigate the uncertainty principles associated with the QWQPFT. We first propose the definition of QWQPFT and establish its relation with quaternion Fourier transform (QFT); then, we investigate several properties of QWQPFT which includes inversion and the Plancherel theorem. Moreover, we study different kinds of uncertainty principles for QWQPFT such as Hardy’s uncertainty principle, Beurling’s uncertainty principle, Donoho–Stark’s uncertainty principle, the logarithmic uncertainty principle, the local uncertainty principle, and Pitt’s inequality.
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Comparisons of SVM Kernels for Insurance Data Clustering
(2022-08-01)
Nurhidayat, Irfan
;
Pimpunchat, Busayamas
;
Noeiaghdam, Samad
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Fernández-Gámiz, Unai
This paper will study insurance data clustering using Support Vector Machine (SVM) approaches. It investigates the optimum condition employing the three most popular kernels of SVM, i.e., linear, polynomial, and radial basis kernel. To explore sum insured datasets, kernel comparisons for Root Mean Square Error (RMSE) and density analysis have been provided. It employs these kernels to classify based on sum insured datasets. The objective of this research is to demonstrate to industrial researchers that data grouping may be accomplished in an organized, error-free, and efficient manner utilizing R programming and the SVM approach. In this study, we check the insurance data for the sum insured with statistical methods in the form of Model Performance Evaluation (MPE), Receiver Operating Characteristics (ROC), Area Under Curve (AUC), partial AUC (pAUC), smoothing, confidence intervals, and thresholds. Then, sum insured data are followed up to classify using SVM kernels. This paper finds new ideas for evaluating insurance data using the SVM approach with multiple kernels. This novel research emphasizes the statistical analysis methods for insurance data and uses the SVM method for more accurate data classification. Finally, it informs that this research is a pure finding, and there has never been any research on this subject. This research was conducted using the sum insured data as a sample from the Office of the Insurance Commission (OIC) in Thailand as an independent insurance institution providing actual data.
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