Journal articles: 'RBF-SVM'

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Indraswari, Rarasmaya, and Agus Zainal Arifin. "RBF KERNEL OPTIMIZATION METHOD WITH PARTICLE SWARM OPTIMIZATION ON SVM USING THE ANALYSIS OF INPUT DATA’S MOVEMENT." Jurnal Ilmu Komputer dan Informasi 10, no. 1 (February 28, 2017): 36. http://dx.doi.org/10.21609/jiki.v10i1.410.

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SVM (Support Vector Machine) with RBF (Radial Basis Function) kernel is a frequently used classification method because usually it provides an accurate results. The focus about most SVM optimization research is the optimization of the the input data, whereas the parameter of the kernel function (RBF), the sigma, which is used in SVM also has the potential to improve the performance of SVM when optimized. In this research, we proposed a new method of RBF kernel optimization with Particle Swarm Optimization (PSO) on SVM using the analysis of input data’s movement. This method performed the optimization of the weight of the input data and RBF kernel’s parameter at once based on the analysis of the movement of the input data which was separated from the process of determining the margin on SVM. The steps of this method were the parameter initialization, optimal particle search, kernel’s parameter computation, and classification with SVM. In the optimal particle’s search, the cost of each particle was computed using RBF function. The value of kernel’s parameter was computed based on the particles’ movement in PSO. Experimental result on Breast Cancer Wisconsin (Original) dataset showed that this RBF kernel optimization method could improve the accuracy of SVM significantly. This method of RBF kernel optimization had a lower complexity compared to another SVM optimization methods that resulted in a faster running time.

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Schuhmann, Ricardo M., Andreas Rausch, and Thomas Schanze. "Parameter estimation of support vector machine with radial basis function kernel using grid search with leave-p-out cross validation for classification of motion patterns of subviral particles." Current Directions in Biomedical Engineering 7, no. 2 (October 1, 2021): 121–24. http://dx.doi.org/10.1515/cdbme-2021-2031.

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Abstract The classification of subviral particle motion in fluorescence microscopy video sequences is relevant to drug development. This work introduces a method for estimating parameters for support vector machines (SVMs) with radial basis function (RBF) kernels using grid search with leave-pout cross-validation for classification of subviral particle motion patterns. RBF-SVM was trained and tested with a large number of combinations of expert-evaluated training and test data sets for different RBF-SVM parameters using grid search. For each subtest, the mean and standard deviation of the accuracy of the RBF-SVM were calculated. The RBF-SVM parameters are selected according to the optimal accuracy. For the optimal parameters, the accuracy is 89% +- 13% for N = 100. Using the introduced computer intensive machine learning parameter adjustment method, an RBF-SVM has been successfully trained to classify the motion patterns of subviral particles into chaotic, moderate and linear movements.

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Harafani, Hani. "Forward Selection pada Support Vector Machine untuk Memprediksi Kanker Payudara." Jurnal Infortech 1, no. 2 (January 14, 2020): 131–39. http://dx.doi.org/10.31294/infortech.v1i2.7398.

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Kanker payudara merupakan masalah kesehatan yang serius, sehingga deteksi dini dari kanker payudara dapat berperan penting dalam perencanaan pengobatan. Pada penelitian ini Support Vector Machine dengan kernel (dot, polynomial, RBF) dan forward selection diterapkan. Perbandingan akurasi SVM tanpa forward selection dengan menggunakan forward selection menunjukkan selisih yang besar. Hasil penelitian menunjukkan SVM(RBF)+FS unggul dengan akurasi 85,38% dibandingkan dengan SVM(Polynomial & dot), selain itu SVM(RBF)+FS juga unggul dibandingkan algoritma machine learning lainnya dalam memprediksi dataset kanker payudara Coimbra.

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Zafari, Azar, Raul Zurita-Milla, and Emma Izquierdo-Verdiguier. "Evaluating the Performance of a Random Forest Kernel for Land Cover Classification." Remote Sensing 11, no. 5 (March 8, 2019): 575. http://dx.doi.org/10.3390/rs11050575.

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The production of land cover maps through satellite image classification is a frequent task in remote sensing. Random Forest (RF) and Support Vector Machine (SVM) are the two most well-known and recurrently used methods for this task. In this paper, we evaluate the pros and cons of using an RF-based kernel (RFK) in an SVM compared to using the conventional Radial Basis Function (RBF) kernel and standard RF classifier. A time series of seven multispectral WorldView-2 images acquired over Sukumba (Mali) and a single hyperspectral AVIRIS image acquired over Salinas Valley (CA, USA) are used to illustrate the analyses. For each study area, SVM-RFK, RF, and SVM-RBF were trained and tested under different conditions over ten subsets. The spectral features for Sukumba were extended by obtaining vegetation indices (VIs) and grey-level co-occurrence matrices (GLCMs), the Salinas dataset is used as benchmarking with its original number of features. In Sukumba, the overall accuracies (OAs) based on the spectral features only are of 81.34 % , 81.08 % and 82.08 % for SVM-RFK, RF, and SVM-RBF. Adding VI and GLCM features results in OAs of 82 % , 80.82 % and 77.96 % . In Salinas, OAs are of 94.42 % , 95.83 % and 94.16 % . These results show that SVM-RFK yields slightly higher OAs than RF in high dimensional and noisy experiments, and it provides competitive results in the rest of the experiments. They also show that SVM-RFK generates highly competitive results when compared to SVM-RBF while substantially reducing the time and computational cost associated with parametrizing the kernel. Moreover, SVM-RFK outperforms SVM-RBF in high dimensional and noisy problems. RF was also used to select the most important features for the extended dataset of Sukumba; the SVM-RFK derived from these features improved the OA of the previous SVM-RFK by 2%. Thus, the proposed SVM-RFK classifier is as at least as good as RF and SVM-RBF and can achieve considerable improvements when applied to high dimensional data and when combined with RF-based feature selection methods.

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Eckstein, Jan, Negin Moghadasi, Hermann Körperich, Elena Weise Valdés, Vanessa Sciacca, Lech Paluszkiewicz, Wolfgang Burchert, and Misagh Piran. "A Machine Learning Challenge: Detection of Cardiac Amyloidosis Based on Bi-Atrial and Right Ventricular Strain and Cardiac Function." Diagnostics 12, no. 11 (November 4, 2022): 2693. http://dx.doi.org/10.3390/diagnostics12112693.

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Background: This study challenges state-of-the-art cardiac amyloidosis (CA) diagnostics by feeding multi-chamber strain and cardiac function into supervised machine (SVM) learning algorithms. Methods: Forty-three CA (32 males; 79 years (IQR 71; 85)), 20 patients with hypertrophic cardiomyopathy (HCM, 10 males; 63.9 years (±7.4)) and 44 healthy controls (CTRL, 23 males; 56.3 years (IQR 52.5; 62.9)) received cardiovascular magnetic resonance imaging. Left atrial, right atrial and right ventricular strain parameters and cardiac function generated a 41-feature matrix for decision tree (DT), k-nearest neighbor (KNN), SVM linear and SVM radial basis function (RBF) kernel algorithm processing. A 10-feature principal component analysis (PCA) was conducted using SVM linear and RBF. Results: Forty-one features resulted in diagnostic accuracies of 87.9% (AUC = 0.960) for SVM linear, 90.9% (0.996; Precision = 94%; Sensitivity = 100%; F1-Score = 97%) using RBF kernel, 84.9% (0.970) for KNN, and 78.8% (0.787) for DT. The 10-feature PCA achieved 78.9% (0.962) via linear SVM and 81.8% (0.996) via RBF SVM. Explained variance presented bi-atrial longitudinal strain and left and right atrial ejection fraction as valuable CA predictors. Conclusion: SVM RBF kernel achieved competitive diagnostic accuracies under supervised conditions. Machine learning of multi-chamber cardiac strain and function may offer novel perspectives for non-contrast clinical decision-support systems in CA diagnostics.

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Kumar, Kapil. "Comprehensive Composition to Spot Intrusions by Optimized Gaussian Kernel SVM." International Journal of Knowledge-Based Organizations 12, no. 1 (January 2022): 1–27. http://dx.doi.org/10.4018/ijkbo.291689.

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The intrusion interjects network devices and holds a switch of the network with the command which regulates the programmer and programmer govern the nasty code inoculated in the device for attaining intelligence about the devices. In this paper, the researchers organized the IDS framework by using machine learning algorithms like Linear SVM, RBF SVM, Sigmoid SVM, and Polynomial SVM to detect intrusions and estimate the performance of numerous algorithms for attaining the optimized algorithm. The researchers utilized the KDDCUP99 for equating the accuracy, precision, and recall of the algorithms, and for classifications, the researchers utilized the binary encoder tools. The performance analysis calculates that RBF SVM is the finest classifier amongst the other SVMs, and the prediction report predicts that Linear SVM results with 99.2% accuracy, Sigmoid SVM results with 99.7% accuracy, Polynomial SVM results with 99.5% accuracy, and RBF SVMs results with 99.99% accuracy.

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Jahed Armaghani, Danial, Panagiotis G. Asteris, Behnam Askarian, Mahdi Hasanipanah, Reza Tarinejad, and Van Van Huynh. "Examining Hybrid and Single SVM Models with Different Kernels to Predict Rock Brittleness." Sustainability 12, no. 6 (March 12, 2020): 2229. http://dx.doi.org/10.3390/su12062229.

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The aim of this study was twofold: (1) to assess the performance accuracy of support vector machine (SVM) models with different kernels to predict rock brittleness and (2) compare the inputs’ importance in different SVM models. To this end, the authors developed eight SVM models with different kernel types, i.e., the radial basis function (RBF), the linear (LIN), the sigmoid (SIG), and the polynomial (POL). Four of these models were developed using only the SVM method, while the four other models were hybridized with a feature selection (FS) technique. The performance of each model was assessed using five performance indices and a simple ranking system. The results of this study show that the SVM models developed using the RBF kernel achieved the highest ranking values among single and hybrid models. Concerning the importance of variables for predicting the brittleness index (BI), the Schmidt hammer rebound number (Rn) was identified as the most important variable by the three single-based models, developed by POL, SIG, and LIN kernels. However, the single SVM model developed by RBF identified density as the most important input variable. Concerning the hybrid SVM models, three models that were developed using the RBF, POL, and SIG kernels identified the point load strength index as the most important input, while the model developed using the LIN identified the Rn as the most important input. All four single-based SVM models identified the p-wave velocity (Vp) as the least important input. Concerning the least important factors for predicting the BI of the rock in hybrid-based models, Vp was identified as the least important factor by FS-SVM-POL, FS-SVM-SIG, and FS-SVM-LIN, while the FS-SVM-RBF identified Rn as the least important input.

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Mohammed, Ahmed Saud, Atheer Saleem Almawla, and Salah Sabbar Thameel. "Prediction of Monthly Evaporation Model Using Artificial Intelligent Techniques in the Western Desert of Iraq-Al-Ghadaf Valley." Mathematical Modelling of Engineering Problems 9, no. 5 (December 13, 2022): 1261–70. http://dx.doi.org/10.18280/mmep.090513.

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The use of traditional methods to predict evaporation may face many obstacles due to the influence of many factors on the pattern of evaporation's shape. Therefore, the use of existing methods of artificial intelligence is a reliable prediction model in many applications in engineering. Monthly measurements were employed in the present work to predict for duration eighteen years, from beginning of January 2000 until December 2017. The best model was chosen using ANNs (MLP, RBF) and AI (SVM) techniques. The best evaporation model prediction was made using ANNs (MLP, RBF) and AI (SVM) technologies, with temperature, wind speed, relative humidity, and sunshine hours used as independent variables. Several statistical metrics have been used to evaluate the effectiveness of the proposed model to other popular artificial intelligence models. The obtained result denotes the superiority of the MLP models over the RBF and SVM models. It is concluded that the MLP model is better than RBF and SVM for evaporation prediction for both groups. A comparison of the model performance between MLP, RBF, and SVM models indicated that the MLP-ANN method presents the best estimates of monthly evaporation rate with minimum RMSE 0.033, minimum MAE 0.026, and maximum determination coefficient 0.967.

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Amelia, Octavia Dwi, Agus M. Soleh, and Septian Rahardiantoro. "Pemodelan Support Vector Machine Data Tidak Seimbang Keberhasilan Studi Mahasiswa Magister IPB." Xplore: Journal of Statistics 2, no. 1 (June 30, 2018): 33–40. http://dx.doi.org/10.29244/xplore.v2i1.76.

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Bogor Agricultural University Postgraduate School (SPs-IPB) can maintain its reputation by applying a more selective admissions system. This research predicts the success of student using Support Vector Machine (SVM) modeling by considering the characteristics and educational background of the students. But there is an imbalance of data class. SVM modeling on unbalanced data produces poor performance with a sensitivity value of 0.00%. Unbalanced data handling using Sythetic Minority Oversampling Technique (SMOTE) succeeded in improving SVM classification performance in classifying unsuccessful students. Based on accuracy, sensitivity, and specificity with the default cut off, the exact type of SVM to model student success is SVM RBF. When using the optimum cut-off value from each type of SVM, the sensitivity value can be improved again. SVM RBF still gives the best result when using cut off 0.6. The final model that will be used to predict the success of the SPs-IPB student is obtained from SVM RBF modeling with cut off 0.6 using the entire data that has been through the SMOTE stage.

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Syahrial, Syahrial, Rosmin Ilham, Zulaika F. Asikin, and St Surya Indah Nurdin. "Stunting Classification in Children's Measurement Data Using Machine Learning Models." Journal La Multiapp 3, no. 2 (March 31, 2022): 52–60. http://dx.doi.org/10.37899/journallamultiapp.v3i2.614.

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The study conducted a stunting classification of measurement data for children under 5 years old. The dataset has attributes such as: gender, age, weight (BB), height (TB), weight / height (BBTB), weight / age (BBU), and height / age (TBU). The research uses the CRISP-DM methodology in processing the data. The data were tested on several classification models, namely: logistic regression (LR), linear discriminant analysis (LDA), quadratic discriminant analysis (QDA), k-nearest neighbor (KNN), classification and regression trees (CART), nave bayes (NB), support vector machine - linear kernel (SVM-Linear), support vector machine - rbf kernel (SVM-RBF), random forest classifier (RPC), adaboost (ADA), and neural network (MLPC). These models were tested on the dataset to find out the best model in accuracy. The test results show that SVM-RBF produces an accuracy of 78%. SVM-RBF has consistently been at the highest accuracy in several tests. Testing through k-fold cross validation with k=10.

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Sun, Qiong, Zhiyong Tan, and Xiaolu Zhou. "Workload prediction of cloud computing based on SVM and BP neural networks." Journal of Intelligent & Fuzzy Systems 39, no. 3 (October 7, 2020): 2861–67. http://dx.doi.org/10.3233/jifs-191266.

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In this study, support vector machine (SVM) and back-propagation (BP) neural networks were combined to predict the workload of cloud computing physical machine, so as to improve the work efficiency of physical machine and service quality of cloud computing. Then, the SVM and BP neural network was simulated and analyzed in MATLAB software and compared with SVM, BP and radial basis function (RBF) prediction models. The results showed that the average error of the SVM and BP based model was 0.670%, and the average error of SVM, BP and RBF was 0.781%, 0.759% and 0.708%, respectively; in the multi-step prediction, the prediction accuracy of SVM, BP, RBF and SVM + BP in the first step was 89.3%, 94.6%, 96.3% and 98.5%, respectively, the second step was 87.4%, 93.1%, 95.2% and 97.8%, respectively, the third step was 83.5%, 90.3%, 93.1% and 95.7%, the fourth step was 79.1%, 87.4%, 90.5% and 93.2%, respectively, the fifth step was 75.3%, 81.3%, 85.9% and 91.1% respectively, and the sixth step was 71.1%, 76.6%, 82.1% and 89.4%, respectively.

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Miao, Man Xiang, and Yi Jin Gang. "Algorithms Research in the Application of Lorenz Time Series Prediction." Advanced Materials Research 268-270 (July 2011): 1017–20. http://dx.doi.org/10.4028/www.scientific.net/amr.268-270.1017.

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Prediction of Lorenz Chaotic Time Series is a vital problem in nonlinear dynamics .Support vector machine (SVM) is a kind of novel machine learning methods based on statistical learning theory, which have been provided an efficient algorithm thought in prediction of Chaotic Time Series. This paper combined SVM with neural network which based on the similarity of structure between SVM and RBF Networks, using SVM to obtain the centers of RBF Networks, then to predict the Lorenz Chaotic Time Series. Simulation results show that the effect is better than other methods.

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Kumar, Gyanendra. "IMPROVING DIGITAL SIGNATURE VERIFICATION ACCURACY THROUGH SUPPORT VECTOR MACHINE LEARNING: A COMPARATIVE STUDY." International Journal of Advance Scientific Research 03, no. 05 (May 1, 2023): 75–79. http://dx.doi.org/10.37547/ijasr-03-05-11.

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Digital signatures are widely used in electronic documents, and their verification is crucial to ensure document authenticity and security. However, digital signature verification can be challenging, especially when dealing with large amounts of data. In this paper, we present a comparative study of three Support Vector Machine (SVM) based methods for improving digital signature verification accuracy. We used a dataset of 10,000 digital signatures and compared the performance of linear SVM, polynomial SVM, and radial basis function (RBF) SVM. Our results showed that all three SVM-based methods improved the accuracy of digital signature verification compared to traditional methods. The RBF SVM method was found to be the most effective method for improving accuracy, with an accuracy of 98%.

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Wang, Cheng. "Optimization of SVM Method with RBF Kernel." Applied Mechanics and Materials 496-500 (January 2014): 2306–10. http://dx.doi.org/10.4028/www.scientific.net/amm.496-500.2306.

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Usually there is no a uniform model to the choice of SVMs kernel function and its parameters for SVM. This paper presents a bilinear grid search method for the purpose of getting the parameter of SVM with RBF kernel, with the approach of combining grid search with bilinear search. Experiment results show that the proposed bilinear grid search has combined both the advantage of moderate training quantity by the bilinear search and of high predict accuracy by the grid search.

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Razaque, Abdul, Mohamed Ben Haj Frej, Muder Almi’ani, Munif Alotaibi, and Bandar Alotaibi. "Improved Support Vector Machine Enabled Radial Basis Function and Linear Variants for Remote Sensing Image Classification." Sensors 21, no. 13 (June 28, 2021): 4431. http://dx.doi.org/10.3390/s21134431.

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Remote sensing technologies have been widely used in the contexts of land cover and land use. The image classification algorithms used in remote sensing are of paramount importance since the reliability of the result from remote sensing depends heavily on the classification accuracy. Parametric classifiers based on traditional statistics have successfully been used in remote sensing classification, but the accuracy is greatly impacted and rather constrained by the statistical distribution of the sensing data. To eliminate those constraints, new variants of support vector machine (SVM) are introduced. In this paper, we propose and implement land use classification based on improved SVM-enabled radial basis function (RBF) and SVM-Linear for image sensing. The proposed variants are applied for the cross-validation to determine how the optimization of parameters can affect the accuracy. The accuracy assessment includes both training and test sets, addressing the problems of overfitting and underfitting. Furthermore, it is not trivial to determine the generalization problem merely based on a training dataset. Thus, the improved SVM-RBF and SVM-Linear also demonstrate the outstanding generalization performance. The proposed SVM-RBF and SVM-Linear variants have been compared with the traditional algorithms (Maximum Likelihood Classifier (MLC) and Minimum Distance Classifier (MDC)), which are highly compatible with remote sensing images. Furthermore, the MLC and MDC are mathematically modeled and characterized with new features. Also, we compared the proposed improved SVM-RBF and SVM-Linear with the current state-of-the-art algorithms. Based on the results, it is confirmed that proposed variants have higher overall accuracy, reliability, and fault-tolerance than traditional as well as latest state-of-the-art algorithms.

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Bhushan, Shashi, Mohammed Alshehri, Neha Agarwal, Ismail Keshta, Jitendra Rajpurohit, and Ahed Abugabah. "A Novel Approach to Face Pattern Analysis." Electronics 11, no. 3 (February 1, 2022): 444. http://dx.doi.org/10.3390/electronics11030444.

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Recognizing facial expressions is a major challenge and will be required in the latest fields of research such as the industrial Internet of Things. Currently, the available methods are useful for detecting singular facial images, but they are very hard to extract. The main aim of face detection is to capture an image in real-time and search for the image in the available dataset. So, by using this biometric feature, one can recognize and verify the person’s image by their facial features. Many researchers have used Principal Component Analysis (PCA), Support Vector Machine (SVM), a combination of PCA and SVM, PCA with an Artificial Neural Network, and even the traditional PCA-SVM to improve face recognition. PCA-SVM is better than PCA-ANN as PCA-ANN has the limitation of a small dataset. As far as classification and generalization are concerned, SVM requires fewer parameters and generates less generalization errors than an ANN. In this paper, we propose a new framework, called FRS-DCT-SVM, that uses GA-RBF for face detection and optimization and the discrete cosine transform (DCT) to extract features. FRS-DCT-SVM using GA-RBF gives better results in terms of clustering time. The average accuracy received by FRS-DCT-SVM using GA-RBF is 98.346, which is better than that of PCA-SVM and SVM-DCT (86.668 and 96.098, respectively). In addition, a comparison is made based on the training, testing, and classification times.

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Abakar, Khalid AA, and Chongwen Yu. "The Spinning Quality Control Management Based on Decision Making by Data Mining Techniques." International Journal of Emerging Research in Management and Technology 7, no. 1 (June 11, 2018): 72. http://dx.doi.org/10.23956/ijermt.v7i1.25.

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This work demonstrated the possibility of using the data mining techniques such as artificial neural networks (ANN) and support vector machine (SVM) based model to predict the quality of the spinning yarn parameters. Three different kernel functions were used as SVM kernel functions which are Polynomial and Radial Basis Function (RBF) and Pearson VII Function-based Universal Kernel (PUK) and ANN model were used as data mining techniques to predict yarn properties. In this paper, it was found that the SVM model based on Person VII kernel function (PUK) have the same performance in prediction of spinning yarn quality in comparison with SVM based RBF kernel. The comparison with the ANN model showed that the two SVM models give a better prediction performance than an ANN model.

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Hasanah, Siti Hadijah. "CLASSIFICATION SUPPORT VECTOR MACHINE IN BREAST CANCER PATIENTS." BAREKENG: Jurnal Ilmu Matematika dan Terapan 16, no. 1 (March 21, 2022): 129–36. http://dx.doi.org/10.30598/barekengvol16iss1pp129-136.

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Support vector machine is one of the supervised learning methods in machine learning that is used in classification. The purpose of this study is to measure the accuracy of classification by using 3 hyperplane functions in SVM, namely linear, sigmoid, polynomial, and radial basis function (RBF). Based on the simulation results of training data and testing data on female breast cancer patients, SVM with hyperplane RBF has better accuracy than hyperplane polynomial, linear and sigmoid. The RBF results for the training and testing data were 89.1% and 73.2%, respectively. Based on the results of the classification of training data for female breast cancer patients, 88.07% had no recurrence and 93.33% had recurrence events. Meanwhile, based on the results of the classification of testing data, female patients did not recurrence events and recurrence events was 72.55% and 80.00%, respectively. So from this article, it can be concluded that SVM with hyperplane RBF is one of the best methods in the application of the method of classifying female breast cancer patients.

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Imanuela Mustamu, Laura, and Yuliant Sibaroni. "FUEL INCREASE SENTIMENT ANALYSIS USING SUPPORT VECTOR MACHINE WITH PARTICLE SWARM OPTIMIZATION AND GENETIC ALGORITHM AS FEATURE SELECTION." Jurnal Teknik Informatika (Jutif) 4, no. 3 (June 26, 2023): 521–28. http://dx.doi.org/10.52436/1.jutif.2023.4.3.881.

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BBM, or fuel oil, is one of the essential needs of the Indonesian people. The government's policy regarding the increase in fuel prices raises many opinions from the public. Twitter is one of the social media that Indonesian people often use to express opinions on a topic. In this study, sentiment analysis was carried out on public opinion regarding the fuel price increase policy from Twitter social media. This research is expected to help determine public opinion regarding the fuel price increase policy with positive, neutral and negative sentiments. The sentiment analysis method used is the Support Vector Machine (SVM) classification algorithm. The results of the accuracy of SVM were compared with accuracy by adding a feature selection process. The Particle Swarm Optimization (PSO) and Genetic Algorithm (GA) algorithms are used for the feature selection method. After several experiments using the three methods, the SVM method with the Radial Basis Function (RBF) kernel produced the best accuracy of 71.2%. The combination of the SVM method with the RBF and PSO kernels obtained an accuracy of 68.84%, and the combination of the RBF and GA kernel SVM methods obtained an accuracy of 69.52%.

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Ananda, Fadhilah Dwi, and Yoga Pristyanto. "Analisis Sentimen Pengguna Twitter Terhadap Layanan Internet Provider Menggunakan Algoritma Support Vector Machine." MATRIK : Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer 20, no. 2 (May 30, 2021): 407–16. http://dx.doi.org/10.30812/matrik.v20i2.1130.

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Media sosial saat ini merupakan media komunikasi yang sering digunakan oleh kalangan masyarakat Indonesia dalam menyampaikan sebuah opini. Salah satu media yang sering digunakan masyarat adalah twitter. Twitter merupakan media sosial yang memberikan banyak informasi melalui tweet, dari informasi yang ditulis tersebut terdapat data yang dapat diolah. Penelitian ini menggunakan teknik text mining dengan menerapkan algoritma Support Vector Machine dipergunakan untuk klasifikasi sentimen pengguna twitter terhadap layanan internet Biznet. Kernel yang digunakan adalah kernel Linear dan kernel RBF. Pengujian dilakukan dengan 3 skenario, pada skenario 1 menggunakan 800 data, skenario 2 menggunakan 900 data dan skenario 3 menggunakan 1000 data, untuk pembagiannya yaitu 90% data training dan 10% data testing dari masing-masing skenario. Berdasarkan hasil pengujian yang dilakukan menggunakan kernel linear dan kernel RBF dapat diambil kesimpulan sebagai berikut. Algoritma SVM menggunakan dengan kernel linear maupun kernel RBF memiliki hasil kinerja evaluasi baik dari sisi akurasi, presisi dan recall yang relatif sama. Sehingga dapat dikatakan bahwa algoritma SVM baik dengan kernel RBF maupun Linear sama sama dapat digunakan dengan baik dalam menentukan sentimen pengguna internet Biznet. Selain itu dengan 3 skenario pengujian dengan jumlah data yang berbeda algoritma SVM baik dengan kernel RBF maupun Linear sama sama konsisten kinerjanya.

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Wu, Jun, and Jin Hui Zou. "Combination Prediction Based on RBF-SVM Model for Short-Term Trafficflow." Applied Mechanics and Materials 475-476 (December 2013): 729–32. http://dx.doi.org/10.4028/www.scientific.net/amm.475-476.729.

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For short-term trafficflow prediction,this paper applies a combination prediction base on RBF-SVM model.At first,it is to use RBF and SVM to get separately two prediction values,then each error can be calculated by each prediction value .Using the two errors to adjust the two weights.At last adding the prediction values multiplied by weights can get a more approximate value to the real value. Simulated values demonstrate that it is an accurate and efficient method.

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Orangi-Fard, Negar, Alireza Akhbardeh, and Hersh Sagreiya. "Predictive Model for ICU Readmission Based on Discharge Summaries Using Machine Learning and Natural Language Processing." Informatics 9, no. 1 (January 26, 2022): 10. http://dx.doi.org/10.3390/informatics9010010.

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Predicting ICU readmission risk will help physicians make decisions regarding discharge. We used discharge summaries to predict ICU 30-day readmission risk using text mining and machine learning (ML) with data from the Medical Information Mart for Intensive Care III (MIMIC-III). We used Natural Language Processing (NLP) and the Bag-of-Words approach on discharge summaries to build a Document-Term-Matrix with 3000 features. We compared the performance of support vector machines with the radial basis function kernel (SVM-RBF), adaptive boosting (AdaBoost), quadratic discriminant analysis (QDA), least absolute shrinkage and selection operator (LASSO), and Ridge Regression. A total of 4000 patients were used for model training and 6000 were used for validation. Using the bag-of-words determined by NLP, the area under the receiver operating characteristic (AUROC) curve was 0.71, 0.68, 0.65, 0.69, and 0.65 correspondingly for SVM-RBF, AdaBoost, QDA, LASSO, and Ridge Regression. We then used the SVM-RBF model for feature selection by incrementally adding features to the model from 1 to 3000 bag-of-words. Through this exhaustive search approach, only 825 features (words) were dominant. Using those selected features, we trained and validated all ML models. The AUROC curve was 0.74, 0.69, 0.67, 0.70, and 0.71 respectively for SVM-RBF, AdaBoost, QDA, LASSO, and Ridge Regression. Overall, this technique could predict ICU readmission relatively well.

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Sharma, Reena, and Gurjot Kaur. "E-Mail Spam Detection Using SVM and RBF." International Journal of Modern Education and Computer Science 8, no. 4 (April 8, 2016): 57–63. http://dx.doi.org/10.5815/ijmecs.2016.04.07.

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Alemayehu, Dagnachew Melesew, Abrham Debasu Mengistu, and Seffi Gebeyehu Mengistu. "Computer vision for Ethiopian agricultural crop pest identification." Indonesian Journal of Electrical Engineering and Computer Science 3, no. 1 (July 1, 2016): 209. http://dx.doi.org/10.11591/ijeecs.v3.i1.pp209-214.

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Crop pest is an organism that creates damage on to the agriculture by feeding crops. The research focuses on four major types of crop pest which occurs on teff, wheat, sorghum, barley and maize these are Black tef beetles, Ageda korkur, Degeza and Yesinde Kish Kish. The aim of this paper is identification of the four types of agricultural crop pest using a computer vision technique. The image of crop pest were taken from Amhara regions of Ethiopia i.e. Adiet, Dejen, Gonder, Debremarkos (places where images were taken). In this paper, artificial neural network (ANN), a hybrid of self organizing map (SOM) with Radial basis function (RBF) and a hybrid of support vector machine (SVM) with Radial basis function (RBF) are used, and also we used Otsu and Kmeans segmentation techniques. Finally we selected Kmeans techniques for segmenting crop pest. In general, the overall result showed that using kmeans segmentation in RBF and SVM perform better than using otsu method in the other algorithm and the recognition performance of the combination of RBF (Radial basis function) and SVM (support vector machine) is 93.33%.

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Zhang, Hong, Zhi Guo Lei, Jian Guo, and Zhao Yu Pian. "Short Term Load Forecasting Based on Improved RBF Neural Network." Advanced Materials Research 860-863 (December 2013): 2610–13. http://dx.doi.org/10.4028/www.scientific.net/amr.860-863.2610.

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An improved radial basis function neural network is proposed that preprocessing is the key to improving the precision of short-term load forecasting. This paper presents a new model which is based on classical RBF neural network, combine the GA-optimized SVM radial basis function and RBF neural network. According to the date of the type, temperature, weather conditions and other factors ,The Application of combined GA-optimized SVM radial basis function is used to extract useful data to improve the load forecasting accuracy of RBF neural network. Spring load data of California were applied for simulation. The simulation indicates that the new method is feasible and the forecasting precision is greatly improved.

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Zhao, Qinghe, Zifang Zhang, Yuchen Huang, and Junlong Fang. "TPE-RBF-SVM Model for Soybean Categories Recognition in Selected Hyperspectral Bands Based on Extreme Gradient Boosting Feature Importance Values." Agriculture 12, no. 9 (September 13, 2022): 1452. http://dx.doi.org/10.3390/agriculture12091452.

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Soybeans with insignificant differences in appearance have large differences in their internal physical and chemical components; therefore, follow-up storage, transportation and processing require targeted differential treatment. A fast and effective machine learning method based on hyperspectral data of soybeans for pattern recognition of categories is designed as a non-destructive testing method in this paper. A hyperspectral-image dataset with 2299 soybean seeds in four categories is collected. Ten features are selected using an extreme gradient boosting algorithm from 203 hyperspectral bands in a range of 400 to 1000 nm; a Gaussian radial basis kernel function support vector machine with optimization by the tree-structured Parzen estimator algorithm is built as the TPE-RBF-SVM model for pattern recognition of soybean categories. The metrics of TPE-RBF-SVM are significantly improved compared with other machine learning algorithms. The accuracy is 0.9165 in the independent test dataset, which is 9.786% higher for the vanilla RBF-SVM model and 10.02% higher than the extreme gradient boosting model.

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KOÇER, SABRI. "CLASSIFYING MYOPATHY AND NEUROPATHY NEUROMUSCULAR DISEASES USING ARTIFICIAL NEURAL NETWORKS." International Journal of Pattern Recognition and Artificial Intelligence 24, no. 05 (August 2010): 791–807. http://dx.doi.org/10.1142/s0218001410008184.

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The aim of this study is to classify myopathy and neuropathy neuromuscular diseases using artificial neural networks. Coefficients were obtained from these EMG signals by applying Fast Fourier Transform (FFT), Autoregressive (AR), and Cepstral spectral analysis methods. Each of these coefficients was used as input data for Multilayer Perceptron (MLP), Radial Basis Function (RBF), and Support Vector Machine (SVM). After these inputs were individually trained in MLP, RBF and SVM classification systems, their classification and test performances were examined. The results revealed that the highest prediction was in SVM classification system, whereas the best analysis method was found to be FFT. The results show that the combination of FFT with SVM topology has provided the area under the ROC curve of 0.953, which is considered within the acceptable range.

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LIU, HAN-BING, and YU-BO JIAO. "APPLICATION OF GENETIC ALGORITHM-SUPPORT VECTOR MACHINE (GA-SVM) FOR DAMAGE IDENTIFICATION OF BRIDGE." International Journal of Computational Intelligence and Applications 10, no. 04 (December 2011): 383–97. http://dx.doi.org/10.1142/s1469026811003215.

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A support vector machine (SVM) optimized by genetic algorithm (GA)-based damage identification method is proposed in this paper. The best kernel parameters are obtained by GA from selection, crossover and mutation, and utilized as the model parameters of SVM. The combined vector of mode shape ratio and frequency rate is used as the input variable. A numerical example for a simply supported bridge with five girders is provided to verify the feasibility of the method. Numerical simulation shows that the maximal relative errors of GA-SVM for the damage identification of single, two and three suspicious damaged elements is 1.84%. Meanwhile, comparative analyzes between GA-SVM and radical basis function (RBF), back propagation networks optimized by GA (GA-BP) were conducted, the maximal relative errors of RBF and GA-BP are 6.91% and 5.52%, respectively. It indicates that GA-SVM can assess the damage conditions with better accuracy.

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Nti, Isaac Kofi, Owusu Nyarko-Boateng, Felix Adebayo Adekoya, and Benjamin Asubam Weyori. "An empirical assessment of different kernel functions on the performance of support vector machines." Bulletin of Electrical Engineering and Informatics 10, no. 6 (December 1, 2021): 3403–11. http://dx.doi.org/10.11591/eei.v10i6.3046.

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Artificial intelligence (AI) and machine learning (ML) have influenced every part of our day-to-day activities in this era of technological advancement, making a living more comfortable on the earth. Among the several AI and ML algorithms, the support vector machine (SVM) has become one of the most generally used algorithms for data mining, prediction and other (AI and ML) activities in several domains. The SVM’s performance is significantly centred on the kernel function (KF); nonetheless, there is no universal accepted ground for selecting an optimal KF for a specific domain. In this paper, we investigate empirically different KFs on the SVM performance in various fields. We illustrated the performance of the SVM based on different KF through extensive experimental results. Our empirical results show that no single KF is always suitable for achieving high accuracy and generalisation in all domains. However, the gaussian radial basis function (RBF) kernel is often the default choice. Also, if the KF parameters of the RBF and exponential RBF are optimised, they outperform the linear and sigmoid KF based SVM method in terms of accuracy. Besides, the linear KF is more suitable for the linearly separable dataset.

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Zhang, Mei Jun, Hao Chen, Jie Huang, and Kai Chai. "Combined Improved EEMD with SVM in the Application of Intellgent Fault Diagnosis." Advanced Materials Research 706-708 (June 2013): 1774–77. http://dx.doi.org/10.4028/www.scientific.net/amr.706-708.1774.

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Intelligent diagnosis is the development direction of mechahnical condition monitoring and fault diagnosis.Conbined improved EEMD with SVM in fault intelligent diagnosis is researched in this paper.To bearing normal and fault as an example,impove EEMD decomposed 9D normalized energy for characteristic vector applied to the binary classification and identification.Compared to the SVM classification accuracy using different kernel functions that is linear,polynomial,RBF and Sigmoid kernel function.In the same parameters,SVM classification accuracy based on linear and polynomial kernel function is a hundred percent.Bearing normal and fault two kinds of state is completely correct apart. And the normal and fault state of the binary classification and identification using RBF and Sigmoid kernel function appeared wtong points.

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Octaviani, Aulia, Nuryani Nuryani, Umi Salamah, and Trio Pambudi Utomo. "Identification of high blood pressure using support vector machine and time-domain heart rate variability from photoplethysmography." Journal of Physics: Conference Series 2498, no. 1 (May 1, 2023): 012003. http://dx.doi.org/10.1088/1742-6596/2498/1/012003.

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Abstract Hypertension is one of the serious threats to human health by accelerating the cardiovascular disease. The way to prevent hypertension complications is to detect and prevent high blood pressure. This study aimed to identify hypertension using photoplethysmography (PPG) records. The method used time-domain Heart Rate Variability (HRV) from PPG. It used a Support Vector Machine (SVM) with Radial Basis Function (RBF). Variations of SVM-C and RBF gamma were conducted to find the good performance of identification. Using clinical data, the identification system performed with a training accuracy of 99.33 % and a testing accuracy of 71.75%. Best performing results occur when using SVM-C 100 with a gamma of 400,000.

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Al-Faiz, Mohammed Z., and Ammar A. Al-hamadani. "IMPLEMENTATION OF EEG SIGNAL PROCESSING AND DECODING FOR TWO-CLASS MOTOR IMAGERY DATA." Biomedical Engineering: Applications, Basis and Communications 31, no. 04 (June 27, 2019): 1950028. http://dx.doi.org/10.4015/s1016237219500285.

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This work decodes two-class motor imagery (MI) based on four main processing steps: (i) Raw electroencephalographic (EEG) signal is decomposed to single trials and spatial filters are estimated for each trial by common spatial filtering (CSP) method; (ii) features are extracted by taking the log transformation (normal distribution) of the spatially filtered EEG signal; (iii) optimal channel selection algorithm is proposed to reduce the number of EEG channels, such approach is regarded as key technological advantage in the implementation of brain–computer interface (BCI) to reduce the system processing time; (iv) finally, support vector machine (SVM) is employed to discriminate two classes of left and right hand MI. Two variations of SVM were proposed: polynomial function kernel and radial-based function RBF kernel. The results revealed that CSP succeeded in removing the strong correlation bound between the EEG samples by maximizing the variance of class 2 samples while minimizing the variance of class 1 samples. The channel selection algorithm achieved its goal to reduce the data dimension by selecting two channels out of three having the lowest variance entropies of 0.239 and 0.261 for channel 1 and channel 2, respectively. The features vector was divided into 80% train and 20% test with five-fold cross validation. The classification performance of SVM-polynomial kernel was 87.86% while it is 95.72% for SVM-RBF kernel as average accuracy of five-folds for both. Thus SVM-RBF is superior to SVM-Poly in the proposed framework.

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Hosseini Monjezi, Pejman, Morteza Taki, Saman Abdanan Mehdizadeh, Abbas Rohani, and Md Shamim Ahamed. "Prediction of Greenhouse Indoor Air Temperature Using Artificial Intelligence (AI) Combined with Sensitivity Analysis." Horticulturae 9, no. 8 (July 26, 2023): 853. http://dx.doi.org/10.3390/horticulturae9080853.

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Greenhouses are essential for agricultural production in unfavorable climates. Accurate temperature predictions are critical for controlling Heating, Ventilation, Air-Conditioning, and Dehumidification (HVACD) and lighting systems to optimize plant growth and reduce financial losses. In this study, several machine models were employed to predict indoor air temperature in an even-span Mediterranean greenhouse. Radial Basis Function (RBF), Support Vector Machine (SVM), and Gaussian Process Regression (GPR) were applied using external parameters such as outside air, relative humidity, wind speed, and solar radiation. The results showed that an RBF model with the LM learning algorithm outperformed the SVM and GPR models. The RBF model had high accuracy and reliability with an RMSE of 0.82 °C, MAPE of 1.21%, TSSE of 474.07 °C, and EF of 1.00. Accurate temperature prediction can help farmers manage their crops and resources efficiently and reduce energy inefficiencies and lower yields. The integration of the RBF model into greenhouse control systems can lead to significant energy savings and cost reductions.

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Tomasouw, Berny Pebo, and Francis Yunito Rumlawang. "Penerapan Metode SVM Untuk Deteksi Dini Penyakit Stroke (Studi Kasus : RSUD Dr. H. Ishak Umarella Maluku Tengah dan RS Sumber Hidup-GPM)." Tensor: Pure and Applied Mathematics Journal 4, no. 1 (June 19, 2023): 37–44. http://dx.doi.org/10.30598/tensorvol4iss1pp37-44.

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Stroke is a significant health problem in today's modern society. Early detection of stroke usually takes a long time. To prevent the risk of a significant disabling stroke, it is good to pay attention and recognize the symptoms of a stroke early on. In this study, the Support Vector Machine (SVM) method was used to detect stroke based on risk factors for stroke consisting of blood pressure, age, LDL, and blood sugar. Based on the results obtained, the nonlinear SVM method has a better level of accuracy than the linear SVM. This is because of the two data-sharing schemes, the linear SVM only has an accuracy rate of 81.25%, while the nonlinear SVM has an accuracy rate of 84.38%. Especially for the nonlinear SVM, the RBF kernel has a better level of accuracy than the polynomial kernel. This can be seen from the results of testing the two data sharing schemes, the RBF kernel has the best results, namely the highest accuracy rate of 84.38% and 84% respectively

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Purwaningsih, Esty. "IMPROVING THE PERFORMANCE OF SUPPORT VECTOR MACHINE WITH FORWARD SELECTION FOR PREDICTION OF CHRONIC KIDNEY DISEASE." JITK (Jurnal Ilmu Pengetahuan dan Teknologi Komputer) 8, no. 1 (August 31, 2022): 18–24. http://dx.doi.org/10.33480/jitk.v8i1.3327.

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Chronic kidney disease is a disorder that affects the kidneys and arises due to various factors. Chronic kidney disease, usually develops slowly and is chronic. For prevention and control, proper treatment is needed, so that detection of this disease can play a very important role. This study aims to determine the level of accuracy in predicting chronic kidney disease through SVM based on forward selection and to determine the performance of Feature Selection which is applied to the SVM method in solving problems in chronic kidney disease. This research was conducted an experiment on the SVM method using various kinds of kernels and it was seen that SVM with the dot kernel was 98.50% with AUC 1,000 which was superior to the polynominal kernel and RBF. However, when the experiment was carried out again by applying FS to SVM, it was found that SVM+FS with the RBF kernel outperformed the other kernels by 99.75% with AUC 1,000. So it can be concluded that the Forward Selection of SVM has succeeded in improving its performance, especially in this case, namely the prediction of chronic kidney disease

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Romero, R., E. L. Iglesias, and L. Borrajo. "A Linear-RBF Multikernel SVM to Classify Big Text Corpora." BioMed Research International 2015 (2015): 1–14. http://dx.doi.org/10.1155/2015/878291.

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Support vector machine (SVM) is a powerful technique for classification. However, SVM is not suitable for classification of large datasets or text corpora, because the training complexity of SVMs is highly dependent on the input size. Recent developments in the literature on the SVM and other kernel methods emphasize the need to consider multiple kernels or parameterizations of kernels because they provide greater flexibility. This paper shows a multikernel SVM to manage highly dimensional data, providing an automatic parameterization with low computational cost and improving results against SVMs parameterized under a brute-force search. The model consists in spreading the dataset into cohesive term slices (clusters) to construct a defined structure (multikernel). The new approach is tested on different text corpora. Experimental results show that the new classifier has good accuracy compared with the classic SVM, while the training is significantly faster than several other SVM classifiers.

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Gong, Lihong, Zhuxin Li, and Zhen Zhang. "Diagnosis Model of Pipeline Cracks According to Metal Magnetic Memory Signals Based on Adaptive Genetic Algorithm and Support Vector Machine." Open Mechanical Engineering Journal 9, no. 1 (November 2, 2015): 1076–80. http://dx.doi.org/10.2174/1874155x01509011076.

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Metal magnetic memory (MMM) signals can reflect stress concentration and cracks on the surface of ferromagnetic components, but the traditional criteria used to distinguish the locations of these stress concentrations and cracks are not sufficiently accurate. In this study, 22 indices were extracted from the original MMM signals, and the diagnosis results of 4 kernel functions of support vector machine (SVM) were compared. Of these 4, the radial basis function (RBF) kernel performed the best in the simulations, with a diagnostic accuracy of 94.03%. Using the principles of adaptive genetic algorithms (AGA), a combined AGA-SVM diagnosis model was created, resulting in an improvement in accuracy to 95.52%, using the same training and test sets as those used in the simulation of SVM with an RBF kernel. The results show that AGA-SVM can accurately distinguish stress concentrations and cracks from normal points, enabling them to be located more accurately.

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Zhang, Mei Jun, Jie Huang, Kai Chai, and Hao Chen. "Bearing Binary Classification Intelligent Diagnosis by Combined Improved EEMD with SVM." Applied Mechanics and Materials 341-342 (July 2013): 1066–70. http://dx.doi.org/10.4028/www.scientific.net/amm.341-342.1066.

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In order to perform the bearing intelligent fault diagnosis,combined improved EEMD with SVM respectively applied to the binary classification identification of bearing normal and ball fault, normal and inner circle fault,normal and outer ring fault in this paper.Improve EEMD decomposed 9d normalized energy for characteristic vector,the SVM binary classification and recognition of bearings normal and ball fault, normal and inner circle fault, normal and outer ring fault is researched.Compared to the SVM classification accuracy using different kernel functions that is linear kernel function, polynomial kernel function, RBF kernel function and Sigmoid kernel function.In the same parameters,SVM classification accuracy based on linear kernel function and polynomial kernel function is a hundred percent.Bearing normal and ball fault,normal and inner circle fault,normal and outer ring fault is completely correct apart.And there are the classification errors based on RBF kernel function and Sigmoid kernel functions.

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Sahak, Rohilah, Nooritawati Md Tahir, Ahmad Ihsan Mohd Yassin, and Fadhlan Hafizhelmi Kamaruzaman. "Human gait recognition using orthogonal least square as feature selection." Indonesian Journal of Electrical Engineering and Computer Science 17, no. 3 (March 1, 2020): 1355. http://dx.doi.org/10.11591/ijeecs.v17.i3.pp1355-1361.

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This study investigates the potential gait features that are related to human recognition using orthogonal least square (OLS). Firstly, video of 30 subjects walking in oblique view was recorded using Kinect. Next, all 20 skeleton joints in 3D space were extracted and further selected using OLS. Additionally, SVM with linear, polynomial and radial basis function (RBF) kernel was used to classify the selected features. As consequences, OLS was proven to be able to identify the significant features using all three kernels of SVM since all recognition accuracy attained is higher as compared to the original gait features. Results attained showed that the highest recognition accuracy was 90.67% using 48 skeleton joint points for SVM with linear as kernel, followed by 46 skeleton joint points for SVM with RBF kernel namely 88.33% and accuracy of 86.33% for 38 skeleton joint points using polynomial kernel.

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Na, Wen Bo, Qing Feng Jiang, and Zhi Wei Su. "Research of Fault Diagnosis Method Based on Improved Extreme Learning Machine." Applied Mechanics and Materials 727-728 (January 2015): 872–75. http://dx.doi.org/10.4028/www.scientific.net/amm.727-728.872.

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In order to improve the accuracy of diagnosis pumping, and accelerate the speed of diagnosis, a fault diagnosis model based on improved extreme learning machine (RWELM) was proposed. Firstly, it extracted the energy characteristic eigenvector of dynamometer cards of an oilfield in northern Shanxi by using wavelet packet decomposition method. Then through simulation of fault diagnosis, and compare with the extreme learning machine (ELM), RBF neural networks and support vector machine (SVM). The experimental results show that the accuracy and the speed of fault diagnosis based on the RWELM are better than the ELM, RBF neural network and SVM.

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Ibrahim, Y., E. Okafor, and B. Yahaya. "Optimization of RBF-SVM hyperparameters using genetic algorithm for face recognit." Nigerian Journal of Technology 39, no. 4 (March 24, 2021): 1190–97. http://dx.doi.org/10.4314/njt.v39i4.27.

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Manual grid-search tuning of machine learning hyperparameters is very time-consuming. Hence, to curb this problem, we propose the use of a genetic algorithm (GA) for the selection of optimal radial-basis-function based support vector machine (RBF-SVM) hyperparameters; regularization parameter C and cost-factor γ. The resulting optimal parameters were used during the training of face recognition models. To train the models, we independently extracted features from the ORL face image dataset using local binary patterns (handcrafted) and deep learning architectures (pretrained variants of VGGNet). The resulting features were passed as input to either linear-SVM or optimized RBF-SVM. The results show that the models from optimized RBFSVM combined with deep learning or hand-crafted features yielded performances that surpass models obtained from Linear-SVM combined with the aforementioned features in most of the data splits. The study demonstrated that it is profitable to optimize the hyperparameters of an SVM to obtain the best classification performance. Keywords: Face Recognition, Feature Extraction, Local Binary Patterns, Transfer Learning, Genetic Algorithm and Support Vector Machines.

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Bilski, Piotr, and Bartosz Polok. "Analysis of the RBF ANN-based classifier for the diagnostics of electronic circuit." ACTA IMEKO 7, no. 1 (April 1, 2018): 42. http://dx.doi.org/10.21014/acta_imeko.v7i1.516.

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The paper presents the application of the Radial Basis Function (RBF) Artificial Neural Network (ANN) to the diagnostics of the analog circuit. Such networks are in most cases useful in the approximation tasks as the alternative to multilayered perceptrons (MLP) or Support Vector Machines (SVM). In this work the analysis of various RBF ANN-based classifier configurations for the fault detection and identification module are is conducted. The considered parameters included the optimal number of neurons in the hidden layer, coding schemes for the output layer neurons and operation duration during the training and testing the classifier. The efficiency of the diagnostic system is verified using the fifth order lowpass filter. The circuit was also analyzed in terms of the testability, depending on the set of accessible nodes, confronted against the output node only. Experiments cover also accuracy comparison between the RBF, MLP and SVM classifiers. Results show advantages and drawbacks of the RBF ANN-based diagnostic module, compared to other available solutions.

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Mujib, Khusnil, Achmad Hidayatno, and Teguh Prakoso. "PENGENALAN WAJAH MENGGUNAKAN LOCAL BINARY PATTERN (LBP) DAN SUPPORT VECTOR MACHINE (SVM)." TRANSIENT 7, no. 1 (March 13, 2018): 123. http://dx.doi.org/10.14710/transient.7.1.123-130.

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Terdapat banyak metode yang digunakan untuk mengenali identitas seseorang, misalkan nomor unik, kartu identitas dan sandi rahasia. Kekurangan metode-metode tersebut antara lain, kartu dapat hilang, nomor unik dan sandi rahasia dapat terlupakan. Salah satu solusi untuk masalah ini adalah sistem identifikasi seseorang berdasarkan metode biometrik jenis fisiologis. Penelitian ini merancang sebuah sistem untuk mengidentifikasi wajah. Citra wajah diambil menggunakan kamera web kemudian diekstraksi cirinya dengan metode local binary pattern (LBP). Ciri wajah yang diperoleh diklasifikasi menggunakan support vector machine (SVM). Model terbaik SVM dibangun berdasarkan validasi silang grid search. Kernel linier terbaik dibentuk dengan dan parameter . Kernel radial basis function (RBF) terbaik dicapai dengan dan parameter dan . Berdasarkan pengujian terhadap keseluruhan citra wajah, akurasi kedua kernel adalah 96,0%. Pada pengujian lima ekspresi wajah dengan SVM kernel linier, akurasi 100,0% diperoleh untuk ekspresi sedih, netral dan mata tertutup. Sedangkan SVM kernel RBF menghasilkan akurasi 100,0% untuk ekspresi terkejut, netral dan mata tertutup. Hasil pengujian tersebut menunjukkan sistem pengenalan wajah yang dirancang telah berfungsi baik.

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Tinaliah, Tinaliah, and Triana Elizabeth. "Analisis Sentimen Ulasan Aplikasi PrimaKu Menggunakan Metode Support Vector Machine." JATISI (Jurnal Teknik Informatika dan Sistem Informasi) 9, no. 4 (December 13, 2022): 3436–42. http://dx.doi.org/10.35957/jatisi.v9i4.3586.

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Fase tumbuh kembang anak sangat berpengaruh terhadap fase selanjutnya. Dimana orangtua harus lebih cermat dalam memantau tumbuh kembang anaknya. Perkembangan anak dapat dipantau dengan aplikasi tumbuh kembang anak, salah satunya adalah aplikasi PrimaKu yang dapat diakses di google play store ataupun appstore. Saat ini aplikasi PrimaKu telah didownload sebanyak 500ribu kali dengan rating 4.8. Pada google play store dapat dilihat pada kolom komentar mengenai ulasan yang telah diisi oleh pengguna aplikasi PrimaKu ini. SVM merupakan metode yang memberikan hasil yang lebih baik dalam hal klasifikasi, dengan kelebihan SVM dapat mencari hyperplane terbaik, memisahkan suatu kelas dengan kelas lain. Pada penelitian ini akan dilakukan analisis sentimen terhadap ulasan aplikasi PrimaKu menggunakan metode SVM dengan variasi kernel, yaitu linear kernel, polynomial kernel, dan RBF Kernel. Berdasarkan hasil pengujian dapat disimpulkan bahwa SVM dapat melakukan analisis sentimen ulasan pengguna aplikasi PrimaKu dengan baik menggunakan linear kernel dengan nilai akurasi, yaitu 97.5% dibandingkan dengan menggunakan polynomial kernel atau RBF kernel.

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Yang, Liman, Lianming Su, Yixuan Wang, Haifeng Jiang, Xueyao Yang, Yunhua Li, Dongkai Shen, and Na Wang. "Metal Roof Fault Diagnosis Method Based on RBF-SVM." Complexity 2020 (December 3, 2020): 1–12. http://dx.doi.org/10.1155/2020/9645817.

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Metal roof enclosure system is an important part of steel structure construction. In recent years, it has been widely used in large-scale public or industrial buildings such as stadiums, airport terminals, and convention centers. Affected by bad weather, various types of accidents on metal roofs frequently occurred, causing huge property losses and adverse effects. Because of wide span, long service life and hidden fault of metal roof, the manual inspection of metal roof has low efficiency, poor real-time performance, and it is difficult to find hidden faults. On the basis of summarizing the working principle of metal roof and cause of accidents, this paper classifies the fault types of metal roofs in detail and establishes a metal roof monitoring and fault diagnosis system using distributed multisource heterogeneous sensors and Zigbee wireless sensor networks. Monitoring data from strain gauge, laser ranging sensor, and ultrasonic ranging sensor is utilized comprehensively. By extracting time domain feature, the data trend characteristics and correlation characteristics are analyzed and fused to eliminate erroneous data and find superficial faults such as sensor drift and network interruption. Aiming to the hidden faults including plastic deformation and bolt looseness, an SVM fault diagnosis algorithm based on RBF kernel function is designed and applied to diagnose metal roof faults. The experimental results show that the RBF-SVM algorithm can achieve high classification accuracy.

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Muflikhah, Lailil, and Dimas Joko Haryanto. "High Performance of Polynomial Kernel at SVM Algorithm for Sentiment Analysis." Journal of Information Technology and Computer Science 3, no. 2 (November 7, 2018): 194. http://dx.doi.org/10.25126/jitecs.20183260.

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Sentiment analysis is a text mining based on the opinion collection towards the review of online product. Support Vector Machine (SVM) is an algorithm of classification that applicable to review the analysis of product. The hyperplane kernel function of SVM has importance role to classify the certain category. Therefore, this research is address to investigate the performance between Polynomial and Radial Basis Function (RBF) kernel functions for sentiment analysis of review product. They are examined to 200 comments using 10-fold validation and various parameter values (learning rate, lambda, c value, epsilon and iteration). As general, the performance for polynomial kernel of 88.75% is slightly higher than RBF kernel of 83.25%.

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Azis, Tukhfatur Rizmah. "Klasifikasi Tingkat Kebingungan Siswa Terhadap Video Pembelajaran Massive Open Online Source (MOOC) Menggunakan Metode Support Vector Machine (SVM)." MATHunesa: Jurnal Ilmiah Matematika 9, no. 2 (August 31, 2021): 359–65. http://dx.doi.org/10.26740/mathunesa.v9n2.p359-365.

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Pembelajaran online telah dilakukan oleh beberapa dosen maupun siswa dalam menyampaikan dan menerima materi. Hal ini biasa dilakukan dengan memberikan video di platform kelas online atau mengajak bergabung dalam ruang obrolan online. MOOC atau Massive Open Online Course adalah salah satu platform kursus online yang telah digunakan oleh kalangan dosen, peneliti, dan siswa dengan menyuguhkan video pembelajaran kepada siswa dari dosen. Namun setelah adanya pembelajaran online terdapat perbedaan perilaku siswa ketika menerima pembelajaran online dan offline. Tidak seperti pendidikan di kelas, di mana guru dapat menilai apakah siswa dapat memahami materi melalui pertanyaan verbal atau bahasa tubuh mereka. Maka dalam hal ini penelitian akan difokuskan pada salah satu permasalahan yakni mendeteksi tingkat kebingungan pada siswa saat menonton video pembelajaran dengan menggunakan metode Support Vector Machine (SVM) dan data sinyal electroencephalography (EEG). Analisis dilakukan dengan melakukan perbandingan nilai ketepatan klasifikasi dari tiga fungsi kernel SVM yakni linear, Radial Basic Function (RBF), serta Polinomial Regresi. Berdasarkan pengolahan data yang telah diperoleh pada metode SVM Linear pada pre-defined label mendapatkan hasil akurasi mencapai 63,16% pada user-defined label mendapatkan hasil akurasi mencapai 63,16%. Sedangkan metode Polinomial Regresi pada pre-defined label mendapatkan hasil akurasi mencapai 68,42%, pada user-defined label mendapatkan hasil akurasi mencapai 57,89%. Serta metode RBF pada pre-defined label mendapatkan hasil akurasi mencapai 63,16% pada user-defined label mendapatkan hasil akurasi mencapai 57,89%. Hal ini menunjukkan bahwa metode SVM dapat digunakan untuk mengklasifikasikan data sinyal EEG. Kata Kunci: SVM, MOOC, Sinyal Electroencephalography, Linear, RBF, Polinomial Regresi.

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Isman Kurniawan, Reza Rendian Septiawan, and Bambang Hadi Prakoso. "DPP IV Inhibitors Activities Prediction as An Anti-Diabetic Agent using Particle Swarm Optimization-Support Vector Machine Method." Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) 6, no. 6 (December 29, 2022): 974–80. http://dx.doi.org/10.29207/resti.v6i6.4470.

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Diabetes mellitus is a chronic illness that can affect anyone, while the medicine that can entirely cure diabetes has not been discovered yet. Dipeptidyl Peptidase IV (DPP IV) inhibitor is one of the agents with potency as an anti-diabetic treatment. In this work, we utilized the machine learning method to predict the activity of DPP IV as an anti-diabetic agent. We combined Particle Swarm Optimization (PSO) method for features selection and the Support Vector Machine (SVM) for the prediction model. Three SVM kernels, i.e., radial basis function (RBF), polynomial, and linear, were utilized, and their performance was compared. A Hyperparameter tuning procedure was conducted to improve the performance of models. According to the results, we found that the best model obtained from SVM with RBF kernel with the value R2 of train and test set are 0.79 and 0.85, respectively.

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Zuriel, Heart Parasian PR, and Achmad Fahrurozi. "IMPLEMENTASI ALGORITMA KLASIFIKASI SUPPORT VECTOR MACHINE UNTUK ANALISA SENTIMEN PENGGUNA TWITTER TERHADAP KEBIJAKAN PSBB." Jurnal Ilmiah Informatika Komputer 26, no. 2 (2021): 149–62. http://dx.doi.org/10.35760/ik.2021.v26i2.4289.

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Pada saat ini penggunaan Twitter semakin luas. Pengguna twitter dapat dengan bebas untuk berpendapat dan membagikan sudut pandang mereka mengenai isu tren dunia. Hal ini membuat konten twitter menjadi beragam dan menarik untuk dianalisa, termasuk dengan tren kebijakan pemerintah yang ramai diperbincangkan di Indonesia. Munculnya pandemi Covid-19 ini membuat pemerintah mengeluarkan kebijakan yang bertujuan untuk menekan laju pertambahan orang yang terinfeksi virus. Kebijakan ini diberi nama Pembatasan Sosial Berskala Besar atau yang dikenal PSBB. Kebijakan ini pun hangat diperbincangkan di berbagai sosial media, tak terkecuali Twitter. Analisa sentimen dilakukan dengan menggunakan Support Vector Machine (SVM) sebagai algoritma klasifikasi pada data tweet yang berjumlah 22.335 data. Pelabelan data dalam penelitian ini dilakukan menggunakan metode Lexicon Based. Pada penelitian ini terdapat 4 model SVM yang dibangun menggunakan 4 fungsi kernel yaitu kernel Linear, RBF, Polynomial dan Sigmoid. Hasil klasifikasi dari masing-masing model diukur performanya menggunakan k-fold cross validation. Berdasarkan perhitungan, diperoleh bahwa performa model klasifikasi SVM dengan kernel RBF merupakan yang terbaik dibanding kernel lainnya dalam kasus penelitian analisa sentimen ini. Nilai accuracy, precision, recall, dan f1-score-nya yang diperoleh model klasifikasi dengan kernel RBF secara berturut-turut adalah 95.94%, 94.41 %, 97.8%, dan 96.08%. Model klasifikasi dengan kernel RBF ini memberikan mengklasifikasikan 11.764 (52.7%) data tweet ke dalam kelas positif dan 10.571 (47.3%) data tweet ke dalam kelas negative. Hasil tersebut memberikan kesimpulan bahwa pengguna twitter cenderung bersentimen positif terhadap kebijakan PSBB.

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Wang, Ping An, Xu Sheng Gan, and Deng Kai Yao. "Anomaly Intrusion Detection Based on Support Vector Machine with Mexico Hat Wavelet Kernel Function." Applied Mechanics and Materials 687-691 (November 2014): 3897–900. http://dx.doi.org/10.4028/www.scientific.net/amm.687-691.3897.

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The selection of kernel function in Support Vector Machine (SVM) has a great influence on the model performance. In the paper, Mexico hat wavelet kernel is introduced to employ the kernel function of SVM, and theoretically it has be prove that, Mexico hat wavelet kernel satisfies the Merce condition, that is the necessary condition as the kernel function of SVM. Simulation on the anomaly detection shows that the capability of SVM based on Mexico hat wavelet kernel is better than that of SVM based on RBF kernel with a satisfactory result for anomaly intrusion detection.

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Journal articles on the topic 'RBF-SVM'

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