Optimized Support Vector Machine Assisted BOTDA for Temperature Extraction With Accuracy Enhancement

Zhu, Hongna; Lei, Yu; Zhang, Yufeng; Cheng, Le; Zhu, Zhenyu; Song, Jiayin; Zhang, Jinli; Luo, Bin; Yang, Kai

doi:10.1109/jphot.2019.2957410

Cited by 9 publications

(6 citation statements)

References 29 publications

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“…The SVM model with sequential minima optimization (SMO) is used for training and validation on binary classification [40,41] . Aside from SVM-SMO, a naïve Bayes classifier with Bayesian optimization is used to test CMFD performance [42,43] .…”

Section: Methodsmentioning

confidence: 99%

Copy-Move Forgery Verification in Images Using Local Feature Extractors and Optimized Classifiers

Babu

Rao

2023

Big Data Min. Anal.

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Passive image forgery detection methods that identify forgeries without prior knowledge have become a key research focus. In copy-move forgery, the assailant intends to hide a portion of an image by pasting other portions of the same image. The detection of such manipulations in images has great demand in legal evidence, forensic investigation, and many other fields. The paper aims to present copy-move forgery detection algorithms with the help of advanced feature descriptors, such as local ternary pattern, local phase quantization, local Gabor binary pattern histogram sequence, Weber local descriptor, and local monotonic pattern, and classifiers such as optimized support vector machine and optimized NBC. The proposed algorithms can classify an image efficiently as either copy-move forged or authenticated, even if the test image is subjected to attacks such as JPEG compression, scaling, rotation, and brightness variation. CoMoFoD, CASIA, and MICC datasets and a combination of CoMoFoD and CASIA datasets images are used to quantify the performance of the proposed algorithms. The proposed algorithms are more efficient than state-of-the-art algorithms even though the suspected image is post-processed.

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Section: Methodsmentioning

confidence: 99%

Copy-Move Forgery Verification in Images Using Local Feature Extractors and Optimized Classifiers

Babu

Rao

2023

Big Data Min. Anal.

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“…The hyperplane is the best separator between two predefined classes [15] [16]. The basic principle of SVM is a linear classifier, and then it was developed so that it can work on non-linear problems, namely by incorporating the concept of kernel tricks in high-dimensional workspaces [17]. The SVM kernels used in this research are Linear, Radial Basis Function (RBF), and Polynomial kernels.…”

Section: Data Classification Based On Support Vector Machinementioning

confidence: 99%

Comparison of Support Vector Machine and Naïve Bayes on Twitter Data Sentiment Analysis

Styawati,

Isnain,

Hendrastuty

et al. 2021

JPIT

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Twitter is a social media that is widely used by the public. Twitter social media can be used to express opinions or opinions about an object. This shows that there is a huge opportunity for data sources, so they can be used for sentiment analysis. There are many algorithms for performing sentiment analysis, including Support Vector Machine (SVM) and Naive Bayes (NB). Because of the many opinions regarding the performance of the two methods, the researcher is interested in classifying the data using the SVM and NB methods. The data used in this study is data on public opinion regarding the Covid-19 vaccination policy. The first classification process is carried out by the SVM method using various kernels. After getting the highest accuracy result, then the accuracy result is compared with the accuracy value from the NB method classification results.

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“…Recently, it has been found that such a trade-off problem can be solved by introducing machine learning (ML). Among various types of ML, support vector machine (SVM), a supervised machine learning model applicable to classification and regression, has been successfully used in Brillouin fiber optic sensors [17][18][19].…”

Section: Introductionmentioning

confidence: 99%

Brillouin Frequency Shift Estimation for Brillouin Optical Time Domain Analysis Using Brillouin Gain and Loss Spectra With SVC

Nonogaki,

Hoshino,

Saito

et al. 2024

IEEE Sensors J.

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We propose and investigate a method to estimate Brillouin frequency shift (BFS) for Brillouin optical time domain analysis (BOTDA) using Brillouin gain and loss spectra (BGS and BLS) along with support vector machine classifier (SVC) that is one of typical machine learning models. BGS and BLS are simultaneously swept by dual frequency probe light, where a curve with double peaks is obtained as a function of sweep frequency. We call the obtained curve the virtual gain spectrum (VGS), from which BFS is estimated. We conducted simulation to investigate the effect of the signal-to-noise ratio (SNR) and the number of sweep points in terms of BFS estimation. Besides, the accuracy of proposed BFS estimation method was evaluated by the experiments. Both simulation and experimental results showed that the proposed method using VGS exhibits less error in BFS estimation compared to the conventional method using BGS. In the experiment, when the frequency of probe light was swept with a step of 1 MHz, the average standard deviation of estimated BFS was 0.713 MHz in VGS SVC, while it was 1.434 MHz in BGS SVC. Even when the frequency sweep step was 10 MHz, the average standard deviation was 0.930 MHz in VGS SVC, whereas it was 3.301 MHz in BGS SVC.

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Optimized Support Vector Machine Assisted BOTDA for Temperature Extraction With Accuracy Enhancement

Cited by 9 publications

References 29 publications

Copy-Move Forgery Verification in Images Using Local Feature Extractors and Optimized Classifiers

Copy-Move Forgery Verification in Images Using Local Feature Extractors and Optimized Classifiers

Comparison of Support Vector Machine and Naïve Bayes on Twitter Data Sentiment Analysis

Brillouin Frequency Shift Estimation for Brillouin Optical Time Domain Analysis Using Brillouin Gain and Loss Spectra With SVC

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