Academic literature on the topic 'Support Vector Classifier (SVC)'
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Journal articles on the topic "Support Vector Classifier (SVC)"
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Hashemi, H., D. M. J. Tax, R. P. W. Duin, A. Javaherian, and P. de Groot. "Gas chimney detection based on improving the performance of combined multilayer perceptron and support vector classifier." Nonlinear Processes in Geophysics 15, no. 6 (2008): 863–71. http://dx.doi.org/10.5194/npg-15-863-2008.
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Abstract. Seismic object detection is a relatively new field in which 3-D bodies are visualized and spatial relationships between objects of different origins are studied in order to extract geologic information. In this paper, we propose a method for finding an optimal classifier with the help of a statistical feature ranking technique and combining different classifiers. The method, which has general applicability, is demonstrated here on a gas chimney detection problem. First, we evaluate a set of input seismic attributes extracted at locations labeled by a human expert using regularized discriminant analysis (RDA). In order to find the RDA score for each seismic attribute, forward and backward search strategies are used. Subsequently, two non-linear classifiers: multilayer perceptron (MLP) and support vector classifier (SVC) are run on the ranked seismic attributes. Finally, to capitalize on the intrinsic differences between both classifiers, the MLP and SVC results are combined using logical rules of maximum, minimum and mean. The proposed method optimizes the ranked feature space size and yields the lowest classification error in the final combined result. We will show that the logical minimum reveals gas chimneys that exhibit both the softness of MLP and the resolution of SVC classifiers.
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Ramdas, Soorya, and Neenu N. T. Agnes. "Leveraging Machine Learning for Fraudulent Social Media Profile Detection." Cybernetics and Information Technologies 24, no. 1 (2024): 118–36. http://dx.doi.org/10.2478/cait-2024-0007.
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Abstract Fake social media profiles are responsible for various cyber-attacks, spreading fake news, identity theft, business and payment fraud, abuse, and more. This paper aims to explore the potential of Machine Learning in detecting fake social media profiles by employing various Machine Learning algorithms, including the Dummy Classifier, Support Vector Classifier (SVC), Support Vector Classifier (SVC) kernels, Random Forest classifier, Random Forest Regressor, Decision Tree Classifier, Decision Tree Regressor, MultiLayer Perceptron classifier (MLP), MultiLayer Perceptron (MLP) Regressor, Naïve Bayes classifier, and Logistic Regression. For a comprehensive evaluation of the performance and accuracy of different models in detecting fake social media profiles, it is essential to consider confusion matrices, sampling techniques, and various metric calculations. Additionally, incorporating extended computations such as root mean squared error, mean absolute error, mean squared error and cross-validation accuracy can further enhance the overall performance of the models.
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Anindya, Florentina Pramita, Dyah Erni Herwindiati, and Novario Jaya Perdana. "Pengenalan Suara Manusia Menggunakan Support Vector Classifier(SVC) Untuk Proses Otentikasi." Computatio : Journal of Computer Science and Information Systems 7, no. 1 (2023): 28–36. http://dx.doi.org/10.24912/computatio.v7i1.16230.
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Sistem pengenalan suara SEAUI merupakan salah satu aplikasi website untuk melakukan proses verifikasi suara sebagai salah satu teknik pengamanan tingkat lanjut dengan mengenali pemilik suara setelah melakukan proses login. Sistem ini dibuat untuk diterapkan pada aplikasi website. Sistem ini dibuat dengan menggunakan bahasa pemrograman Python dengan modul framework Flask dan basis data SQL Server Management Studio untuk penyimpanan data. Proses pengenalan suara pada sistem ini menggunakan metode Median Filter, metode ekstraksi fitur suara Mel Frequency Cepstral Coefficients dan metode klasifikasi Support Vector Classifier. Proses pengumpulan data dilakukan dengan teknik kuesioner dan didapatkan data suara dari 25 responden. Dalam tahap pengujian, sistem SEAUI sudah dapat melewati tes pengujian SQL Injection dengan 4 kali percobaan pengujian dan sistem dapat berjalan sesuai fungsionalitas. Data suara yang digunakan sebagai input adalah 1 data suara rekaman pada saat login dan kumpulan data suara yang terdapat dalam basis data. Akurasi tertinggi sistem SEAUI yang didapatkan adalah sebesar 67% untuk 15 kali percobaan pengujian dalam 1 akun pengguna.
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Hekmatian, Mohammad E., Vahid E. Ardestani, Mohammad A. Riahi, Ayyub M. K. Bagh, and Jalal Amini. "Estimating the Shapes of Gravity Sources through Optimized Support Vector Classifier (SVC)." Acta Geophysica 63, no. 4 (2015): 1000–1024. http://dx.doi.org/10.1515/acgeo-2015-0022.
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Saputri, Rezi Iwardani, Siti Khomsah, and Novian Adi Prasetyo. "Perbandingan Metode Naïve Bayes Classifier Dan Support Vector Machine Untuk Klasifikasi Cyber Harassment Pada Twitter." Algoritma: Jurnal Ilmu Komputer dan Informatika 8, no. 1 (2024): 10. http://dx.doi.org/10.30829/algoritma.v8i1.16601.
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<em>Cyber Harassment dapat disebut juga dengan pelecehan online dapat berupa mengancam atau melecehkan melalui email, pesan instan, media sosial atau memposting informasi secara online. Kasus ini kerap terjadi di media sosial seperti salah satunya adalah Twitter. Untuk itu dibutuhkan sebuah metode klasifikasi yang tepat agar mengatasi kasus Cyber Harassment dari data Twitter. Pada penelitian ini menggunakan bahasa pemrograman Python dan menggunakan dua metode yaitu Naïve Bayes Classifier dan Support Vector Machine untuk membandingkan metode yang memiliki akurasi yang baik dan mengetahui kinerja metode masing-masing. Pada metode Naïve Bayes Classifier menggunakan model Complement Naïve Bayes dan Support Vector Machine menggunakan model Support Vector Classifacation (SVC). Hasil kinerja masing – masing metode dengan pembagian data trainning dan data testing yaitu 80% : 20% menunjukkan metode Naïve Bayes Classifier dengan accuracy 86.30%, precision 84.51% dan recall 87.21%. dan Support Vector Machine dengan accuracy 89.56%, precision 83.62% dan recall 94.5%. Dengan demikian metode Support Vector Machine lebih baik dari metode Naïve Bayes Classifier dan dapat diimplementasikan untuk kasus Cyber Harassment di Twitter.</em>
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Cho, Byeong-Hyo, Yong-Hyun Kim, Ki-Beom Lee, Young-Ki Hong, and Kyoung-Chul Kim. "Potential of Snapshot-Type Hyperspectral Imagery Using Support Vector Classifier for the Classification of Tomatoes Maturity." Sensors 22, no. 12 (2022): 4378. http://dx.doi.org/10.3390/s22124378.
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It is necessary to convert to automation in a tomato hydroponic greenhouse because of the aging of farmers, the reduction in agricultural workers as a proportion of the population, COVID-19, and so on. In particular, agricultural robots are attractive as one of the ways for automation conversion in a hydroponic greenhouse. However, to develop agricultural robots, crop monitoring techniques will be necessary. In this study, therefore, we aimed to develop a maturity classification model for tomatoes using both support vector classifier (SVC) and snapshot-type hyperspectral imaging (VIS: 460–600 nm (16 bands) and Red-NIR: 600–860 nm (15 bands)). The spectral data, a total of 258 tomatoes harvested in January and February 2022, was obtained from the tomatoes’ surfaces. Spectral data that has a relationship with the maturity stages of tomatoes was selected by correlation analysis. In addition, the four different spectral data were prepared, such as VIS data (16 bands), Red-NIR data (15 bands), combination data of VIS and Red-NIR (31 bands), and selected spectral data (6 bands). These data were trained by SVC, respectively, and we evaluated the performance of trained classification models. As a result, the SVC based on VIS data achieved a classification accuracy of 79% and an F1-score of 88% to classify the tomato maturity into six stages (Green, Breaker, Turning, Pink, Light-red, and Red). In addition, the developed model was tested in a hydroponic greenhouse and was able to classify the maturity stages with a classification accuracy of 75% and an F1-score of 86%.
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Shahin, Makubhai, R. Pathak Ganesh, and R. Chandre Pankaj. "Comparative analysis of explainable artificial intelligence models for predicting lung cancer using diverse datasets." IAES International Journal of Artificial Intelligence (IJ-AI) 13, no. 2 (2024): 1980–91. https://doi.org/10.11591/ijai.v13.i2.pp1980-1991.
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Lung cancer prediction is crucial for early detection and treatment, and explainable artificial intelligence (XAI) models have gained attention for their interpretability. This study aims to compare various XAI models using diverse datasets for lung cancer prediction. Clinical, genomic, and imaging data from multiple sources were collected, preprocessed, and used to train models such as logistic regression (LR), support vector classifier (SVC)-linear, SVC-radial basis function (RBF), decision tree (DT), random forest (RF), adaboost classifier, and XGBoost classifier. Preliminary results indicate that RF achieved the highest accuracy of 98.9% across multiple datasets. Evaluation metrics such as accuracy, precision, recall, and F1 score were utilized, along with interpretability techniques like feature importance rankings and rule extraction methods. The study's findings will aid in identifying effective and interpretable AI models, facilitating early detection and treatment decisions for lung cancer.
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Setiawan, Assegaff, Rasywir Errissya, and Pratama Yovi. "Experimental of vectorizer and classifier for scrapped social media data." TELKOMNIKA 21, no. 04 (2023): 815–24. https://doi.org/10.12928/telkomnika.v21i4.24180.
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In this study, we used several classifiers and vectorizers to see their effect on processing social media data. In this study, the classifiers used were random forest, logistic regression, Bernoulli Naive Bayes (NB), and support vector clustering (SVC). Random forests are used to reduce spatial complexity, and also to minimize errors. Logistic regression is a method with a statistical model whose basic form uses a logistic function to represent the binary dependent variable. Then, the Naive Bayes function uses binary elements and SVC which has so far given good results rivals other guided learning. Our tests use social media data. Based on the tests that have been carried out on classifier variations and vectorizer variations, it was found that the best classifier is a linear regression algorithm based on predictive adaptive compared to the random forest method based on decision trees, probability-based Bernoulli NB and SVC which work by clustering. Meanwhile, from the test results on the count vectorizer, term frequency-inverse document frequency (TFIDF), and hashing, the best accuracy is achieved on the TFIDF vectorizer. In this case, it means that the TFIDF vectorizer has a better value in presenting word feature dimensions.
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G. Deena, K. Raja, M. Azhagiri, W. A. Breen, and S. Prema. "Application of support vector classifier for mango leaf disease classification." Scientific Temper 14, no. 04 (2023): 1163–69. http://dx.doi.org/10.58414/scientifictemper.2023.14.4.16.
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In India, Mango is the fruit of high economic and ecological importance as it exports in large quantities. 1000 varieties of mangoes are cultivated and mostly supported commercially. Among all the Indian fruits, mangoes are highly demand. In majority of the Indian region, mango crops are suffering from several diseases that reduce both the production and the quality and parallel reduces its value on the international market. Mangoes are highly affected by number of diseases, which hamper its appearance, taste and has huge impact on the economy the Indian commercial growth rate has not raised. Manually identifying those disease is a complex task and time consuming, since lack of knowledge, poverty, infrastructure and the facilities the identification of the disease in earlier stages are not done by the farmers. In recent years, the plant pathologists apply different techniques to identify the diseases but then again these techniques are time consuming and relatively expensive for mango growers and the solutions proposed are often not very accurate and sometimes biased. The disease has to diagnosed in order to provide solution to the farmers to increase the productivity with high quality. Currently, researchers have proposed several solutions to diagnosis of mango diseases automatically to gain high returns. The use of machine learning algorithms to identify diseases of plants from leaf photos is a very exciting field for advancement and research has carried in the proposed system using Support vector machine. Using non-linear SVC, achieved the accuracy of 88% for the dataset.
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Mutaz Rasmi, Abu Sara, Khaled Sabarna, and Jawad H. Alkhateeb. "The Analysis of Breast Cancer Classification Involves Utilizing Machine Learning (Ml) Techniques and Hyperparameter Adjustment - A Comparative Study." Ahliya Journal of Allied Medico-Technology Science 1, no. 2 (2024): 10–15. https://doi.org/10.59994/ajamts.2024.2.10.
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This study aims to analyze and classify breast cancer (BC) cases using machine learning (ML) techniques and hyperparameter tuning. The BC dataset from the University of California (UCI) was utilized, which comprises 569 cases classified as malignant (M) and benign (B), with 32 features. The algorithms employed in the study included Logistic Regression (LR), Support Vector Classifier (SVC), K-Nearest Neighbors (KNN), Decision Tree (DT), and Gaussian Naive Bayes (NB). The results indicated that the SVC algorithm performed the best, achieving an accuracy of 98% on the test set, along with a precision of 100%. Furthermore, all algorithms demonstrated high performance, reflecting the effectiveness of machine learning techniques in classifying breast cancer cases.
Dissertations / Theses on the topic "Support Vector Classifier (SVC)"
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Reyaz-Ahmed, Anjum B. "Protein Secondary Structure Prediction Using Support Vector Machines, Nueral Networks and Genetic Algorithms." Digital Archive @ GSU, 2007. http://digitalarchive.gsu.edu/cs_theses/43.
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Bioinformatics techniques to protein secondary structure prediction mostly depend on the information available in amino acid sequence. Support vector machines (SVM) have shown strong generalization ability in a number of application areas, including protein structure prediction. In this study, a new sliding window scheme is introduced with multiple windows to form the protein data for training and testing SVM. Orthogonal encoding scheme coupled with BLOSUM62 matrix is used to make the prediction. First the prediction of binary classifiers using multiple windows is compared with single window scheme, the results shows single window not to be good in all cases. Two new classifiers are introduced for effective tertiary classification. This new classifiers use neural networks and genetic algorithms to optimize the accuracy of the tertiary classifier. The accuracy level of the new architectures are determined and compared with other studies. The tertiary architecture is better than most available techniques.
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Shantilal. "SUPPORT VECTOR MACHINE FOR HIGH THROUGHPUT RODENT SLEEP BEHAVIOR CLASSIFICATION." UKnowledge, 2008. http://uknowledge.uky.edu/gradschool_theses/506.
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This thesis examines the application of a Support Vector Machine (SVM) classifier to automatically detect sleep and quiet wake (rest) behavior in mice from pressure signals on their cage floor. Previous work employed Neural Networks (NN) and Linear Discriminant Analysis (LDA) to successfully detect sleep and wake behaviors in mice. Although the LDA was successful in distinguishing between the sleep and wake behaviors, it has several limitations, which include the need to select a threshold and difficulty separating additional behaviors with subtle differences, such as sleep and rest. The SVM has advantages in that it offers greater degrees of freedom than the LDA for working with complex data sets. In addition, the SVM has direct methods to limit overfitting for the training sets (unlike the NN method). This thesis develops an SVM classifier to characterize the linearly non separable sleep and rest behaviors using a variety of features extracted from the power spectrum, autocorrelation function, and generalized spectrum (autocorrelation of complex spectrum). A genetic algorithm (GA) optimizes the SVM parameters and determines a combination of 5 best features. Experimental results from over 9 hours of data scored by human observation indicate 75% classification accuracy for SVM compared to 68% accuracy for LDA.
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Beltrami, Monica. "Método Grid-Quadtree para seleção de parâmetros do algoritmo support vector classification (SVC)." reponame:Repositório Institucional da UFPR, 2016. http://hdl.handle.net/1884/44061.
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Orientador : Prof. Dr. Arinei Carlos Lindbeck da Silva<br>Tese (doutorado) - Universidade Federal do Paraná, Setor de Tecnologia, Programa de Pós-Graduação em Métodos Numéricos em Engenharia. Defesa: Curitiba, 01/06/2016<br>Inclui referências : f. 143-149<br>Área de concentração : Programação matemática<br>Resumo: O algoritmo Support Vector Classification (SVC) é uma técnica de reconhecimento de padrões, cuja eficiência depende da seleção de seus parâmetros: constante de regularização C, função kernel e seus respectivos parâmetros. A escolha equivocada dessas variáveis impacta diretamente na performance do algoritmo, acarretando em fenômenos indesejáveis como o overfitting e o underfitting. O problema que estuda a procura de parâmetros ótimos para o SVC, em relação às suas medidas de desempenho, é denominado seleção de modelos do SVC. Em virtude do amplo domínio de convergência do kernel gaussiano, a maioria dos métodos destinados a solucionar esse problema concentra-se na seleção da constante C e do parâmetro ? do kernel gaussiano. Dentre esses métodos, a busca por grid é um dos de maior destaque devido à sua simplicidade e bons resultados. Contudo, por avaliar todas as combinações de parâmetros (C, ?) dentre o seu espaço de busca, a mesma necessita de muito tempo de processamento, tornando-se impraticável para avaliação de grandes conjuntos de dados. Desta forma, o objetivo deste trabalho é propor um método de seleção de parâmetros do SVC, usando o kernel gaussiano, que combine a técnica quadtree à busca por grid, para reduzir o número de operações efetuadas pelo grid e diminuir o seu custo computacional. A ideia fundamental é empregar a quadtree para desenhar a boa região de parâmetros, evitando avaliações desnecessárias de parâmetros situados nas áreas de underfitting e overfitting. Para isso, desenvolveu-se o método grid-quadtree (GQ), utilizando-se a linguagem de programação VB.net em conjunto com os softwares da biblioteca LIBSVM. Na execução do GQ, realizou-se o balanceamento da quadtree e criou-se um procedimento denominado refinamento, que permitiu delinear a curva de erro de generalização de parâmetros. Para validar o método proposto, empregaram-se vinte bases de dados referência na área de classificação, as quais foram separadas em dois grupos. Os resultados obtidos pelo GQ foram comparados com os da tradicional busca por grid (BG) levando-se em conta o número de operações executadas por ambos os métodos, a taxa de validação cruzada (VC) e o número de vetores suporte (VS) associados aos parâmetros encontrados e a acurácia do SVC na predição dos conjuntos de teste. A partir das análises realizadas, constatou-se que o GQ foi capaz de encontrar parâmetros de excelente qualidade, com altas taxas VC e baixas quantidades de VS, reduzindo em média, pelo menos, 78,8124% das operações da BG para o grupo 1 de dados e de 71,7172% a 88,7052% para o grupo 2. Essa diminuição na quantidade de cálculos efetuados pelo quadtree resultou em uma economia de horas de processamento. Além disso, em 11 das 20 bases estudadas a acurácia do SVC-GQ foi superior à do SVC-BG e para quatro delas igual. Isso mostra que o GQ é capaz de encontrar parâmetros melhores ou tão bons quanto os da BG executando muito menos operações. Palavras-chave: Seleção de modelos do SVC. Kernel gaussiano. Quadtree. Redução de operações.<br>Abstract: The Support Vector Classification (SVC) algorithm is a pattern recognition technique, whose efficiency depends on its parameters selection: the penalty constant C, the kernel function and its own parameters. A wrong choice of these variables values directly impacts on the algorithm performance, leading to undesirable phenomena such as the overfitting and the underfitting. The task of searching for optimal parameters with respect to performance measures is called SVC model selection problem. Due to the Gaussian kernel wide convergence domain, many model selection approaches focus in determine the constant C and the Gaussian kernel ? parameter. Among these, the grid search is one of the highlights due to its easiest way and high performance. However, since it evaluates all parameters combinations (C, ?) on the search space, it requires high computational time and becomes impractical for large data sets evaluation. Thus, the aim of this thesis is to propose a SVC model selection method, using the Gaussian kernel, which integrates the quadtree technique with the grid search to reduce the number of operations performed by the grid and its computational cost. The main idea of this study is to use the quadtree to determine the good parameters region, neglecting the evaluation of unnecessary parameters located in the underfitting and the overfitting areas. In this regard, it was developed the grid-quadtree (GQ) method, which was implemented on VB.net development environment and that also uses the software of the LIBSVM library. In the GQ execution, it was considered the balanced quadtree and it was created a refinement procedure, that allowed to delineate the parameters generalization error curve. In order to validate the proposed method, twenty benchmark classification data set were used, which were separated into two groups. The results obtained via GQ were compared with the traditional grid search (GS) ones, considering the number of operations performed by both methods, the cross-validation rate (CV) and the number of support vectors (SV) associated to the selected parameters, and the SVC accuracy in the test set. Based on this analyzes, it was concluded that GQ was able to find excellent parameters, with high CV rates and few SV, achieving an average reduction of at least 78,8124% on GS operations for group 1 data and from 71,7172% to 88,7052% for group 2. The decrease in the amount of calculations performed by the quadtree lead to savings on the computational time. Furthermore, the SVC-GQ accuracy was superior than SVC-GS in 11 of the 20 studied bases and equal in four of them. These results demonstrate that GQ is able to find better or as good as parameters than BG, but executing much less operations. Key words: SVC Model Selection. Gaussian kernel. Quadtree. Reduction Operations
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WANDEKOKEN, E. D. "Support Vector Machine Ensemble Based on Feature and Hyperparameter Variation." Universidade Federal do Espírito Santo, 2011. http://repositorio.ufes.br/handle/10/4234.
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tese_4163_.pdf: 479699 bytes, checksum: 04f01a137084c0859b4494de6db8b3ac (MD5)
Previous issue date: 2011-02-23<br>Classificadores do tipo máquina de vetores de suporte (SVM) são atualmente considerados
uma das técnicas mais poderosas para se resolver problemas de classificação com duas classes.
Para aumentar o desempenho alcançado por classificadores SVM individuais, uma abordagem
bem estabelecida é usar uma combinação de SVMs, a qual corresponde a um conjunto de classificadores SVMs que são, simultaneamente, individualmente precisos e coletivamente divergentes em suas decisões. Este trabalho propõe uma abordagem para se criar combinações de
SVMs, baseada em um processo de três estágios. Inicialmente, são usadas execuções complementares de uma busca baseada em algoritmos genéticos (GEFS), com o objetivo de investigar
globalmente o espaço de características para definir um conjunto de subconjuntos de características. Em seguida, para cada um desses subconjuntos de características definidos, uma SVM
que usa parâmetros otimizados é construída. Por fim, é empregada uma busca local com o
objetivo de selecionar um subconjunto otimizado dessas SVMs, e assim formar a combinação
de SVMs que é finalmente produzida. Os experimentos foram realizados num contexto de detecção de defeitos em máquinas industriais. Foram usados 2000 exemplos de sinais de vibração
de moto bombas instaladas em plataformas de petróleo. Os experimentos realizados mostram
que o método proposto para se criar combinação de SVMs apresentou um desempenho superior
em comparação a outras abordagens de classificação bem estabelecidas.
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Lai, Guojun, and Bing Li. "Handwritten Document Binarization Using Deep Convolutional Features with Support Vector Machine Classifier." Thesis, Blekinge Tekniska Högskola, Institutionen för datavetenskap, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:bth-20090.
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Background. Since historical handwritten documents have played important roles in promoting the development of human civilization, many of them have been preserved through digital versions for more scientific researches. However, various degradations always exist in these documents, which could interfere in normal reading. But, binarized versions can keep meaningful contents without degradations from original document images. Document image binarization always works as a pre-processing step before complex document analysis and recognition. It aims to extract texts from a document image. A desirable binarization performance can promote subsequent processing steps positively. For getting better performance for document image binarization, efficient binarization methods are needed. In recent years, machine learning centered on deep learning has gathered substantial attention in document image binarization, for example, Convolutional Neural Networks (CNNs) are widely applied in document image binarization because of the powerful ability of feature extraction and classification. Meanwhile, Support Vector Machine (SVM) is also used in image binarization. Its objective is to build an optimal hyperplane that could maximize the margin between negative samples and positive samples, which can separate the foreground pixels and the background pixels of the image distinctly. Objectives. This thesis aims to explore how the CNN based process of deep convolutional feature extraction and an SVM classifier can be integrated well to binarize handwritten document images, and how the results are, compared with some state-of-the-art document binarization methods. Methods. To investigate the effect of the proposed method on document image binarization, it is implemented and trained. In the architecture, CNN is used to extract features from input images, afterwards these features are fed into SVM for classification. The model is trained and tested with six different datasets. Then, there is a performance comparison between the proposed model and other binarization methods, including some state-of-the-art methods on other three different datasets. Results. The performance results indicate that the proposed model not only can work well but also perform better than some other novel handwritten document binarization method. Especially, evaluation of the results on DIBCO 2013 dataset indicates that our method fully outperforms other chosen binarization methods on all the four evaluation metrics. Besides, it also has the ability to deal with some degradations, which demonstrates its generalization and learning ability are excellent. When a new kind of degradation appears, the proposed method can address it properly even though it never appears in the training datasets. Conclusions. This thesis concludes that the CNN based component and SVM can be combined together for handwritten document binarization. Additionally, in certain datasets, it outperforms some other state-of-the-art binarization methods. Meanwhile, its generalization and learning ability is outstanding when dealing with some degradations.
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Venkatachari, Sidhaarth. "Application of Neural Networks to Inverter-Based Resources." Thesis, Virginia Tech, 2021. http://hdl.handle.net/10919/103376.
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With the deployment of sensors in hardware equipment and advanced metering infrastructure, system operators have access to unprecedented amounts of data. Simultaneously, grid-connected power electronics technology has had a large impact on the way electrical energy is generated, transmitted, and delivered to consumers. Artificial intelligence and machine learning can help address the new power grid challenges with enhanced computational abilities and access to large amounts of data. This thesis discusses the fundamentals of neural networks and their applications in power systems such as load forecasting, power system stability analysis, and fault diagnosis. It extends application of neural networks to inverter-based resources by studying the implementation and performance of a neural network controller emulator for voltage-sourced converters. It delves into how neural networks could enhance cybersecurity of a component through multiple hardware and software implementations of the same component. This ensures that vulnerabilities inherent in one form of implementation do not affect the system as a whole. The thesis also proposes a comprehensive support vector classifier (SVC)--based submodule open-circuit fault detection and localization method for modular multilevel converters. This method eliminates the need for extra hardware. Its efficacy is discussed through simulation studies in PSCAD/EMTDC software. To ensure efficient usage of neural networks in power system simulation softwares, this thesis entails the step by step implementation of a neural network custom component in PSCAD/EMTDC. The custom component simplifies the process of recreating a neural network in PSCAD/EMTDC by eliminating the manual assembly of predefined library components such as summers, multipliers, comparators, and other miscellaneous blocks.<br>Master of Science<br>Data analytics and machine learning play an important role in the power grids of today, which are continuously evolving with the integration of renewable energy resources. It is expected that by 2030 most of the electric power generated will be processed by some form of power electronics, e.g., inverters, from the point of its generation. Machine learning has been applied to various fields of power systems such as load forecasting, stability analysis, and fault diagnosis. This work extends machine learning applications to inverter-based resources by using artificial neural networks to perform controller emulation for an inverter, provide cybersecurity through heterogeneity, and perform submodule fault detection in modular multilevel converters. The thesis also discusses the step by step implementation of a neural network custom component in PSCAD/EMTDC software. This custom component simplifies the process of creating a neural network in PSCAD/EMTDC by eliminating the manual assembly of predefined library components.
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Abo, Al Ahad George, and Abbas Salami. "Machine Learning for Market Prediction : Soft Margin Classifiers for Predicting the Sign of Return on Financial Assets." Thesis, Linköpings universitet, Produktionsekonomi, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-151459.
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Forecasting procedures have found applications in a wide variety of areas within finance and have further shown to be one of the most challenging areas of finance. Having an immense variety of economic data, stakeholders aim to understand the current and future state of the market. Since it is hard for a human to make sense out of large amounts of data, different modeling techniques have been applied to extract useful information from financial databases, where machine learning techniques are among the most recent modeling techniques. Binary classifiers such as Support Vector Machines (SVMs) have to some extent been used for this purpose where extensions of the algorithm have been developed with increased prediction performance as the main goal. The objective of this study has been to develop a process for improving the performance when predicting the sign of return of financial time series with soft margin classifiers. An analysis regarding the algorithms is presented in this study followed by a description of the methodology that has been utilized. The developed process containing some of the presented soft margin classifiers, and other aspects of kernel methods such as Multiple Kernel Learning have shown pleasant results over the long term, in which the capability of capturing different market conditions have been shown to improve with the incorporation of different models and kernels, instead of only a single one. However, the results are mostly congruent with earlier studies in this field. Furthermore, two research questions have been answered where the complexity regarding the kernel functions that are used by the SVM have been studied and the robustness of the process as a whole. Complexity refers to achieving more complex feature maps through combining kernels by either adding, multiplying or functionally transforming them. It is not concluded that an increased complexity leads to a consistent improvement, however, the combined kernel function is superior during some of the periods of the time series used in this thesis for the individual models. The robustness has been investigated for different signal-to-noise ratio where it has been observed that windows with previously poor performance are more exposed to noise impact.
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Amlathe, Prakhar. "Standard Machine Learning Techniques in Audio Beehive Monitoring: Classification of Audio Samples with Logistic Regression, K-Nearest Neighbor, Random Forest and Support Vector Machine." DigitalCommons@USU, 2018. https://digitalcommons.usu.edu/etd/7050.
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Honeybees are one of the most important pollinating species in agriculture. Every three out of four crops have honeybee as their sole pollinator. Since 2006 there has been a drastic decrease in the bee population which is attributed to Colony Collapse Disorder(CCD). The bee colonies fail/ die without giving any traditional health symptoms which otherwise could help in alerting the Beekeepers in advance about their situation.
Electronic Beehive Monitoring System has various sensors embedded in it to extract video, audio and temperature data that could provide critical information on colony behavior and health without invasive beehive inspections. Previously, significant patterns and information have been extracted by processing the video/image data, but no work has been done using audio data. This research inaugurates and takes the first step towards the use of audio data in the Electronic Beehive Monitoring System (BeePi) by enabling a path towards the automatic classification of audio samples in different classes and categories within it. The experimental results give an initial support to the claim that monitoring of bee buzzing signals from the hive is feasible, it can be a good indicator to estimate hive health and can help to differentiate normal behavior against any deviation for honeybees.
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Naram, Hari Prasad. "Classification of Dense Masses in Mammograms." OpenSIUC, 2018. https://opensiuc.lib.siu.edu/dissertations/1528.
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This dissertation material provided in this work details the techniques that are developed to aid in the Classification of tumors, non-tumors, and dense masses in a Mammogram, certain characteristics such as texture in a mammographic image are used to identify the regions of interest as a part of classification. Pattern recognizing techniques such as nearest mean classifier and Support vector machine classifier are also used to classify the features. The initial stages include the processing of mammographic image to extract the relevant features that would be necessary for classification and during the final stage the features are classified using the pattern recognizing techniques mentioned above. The goal of this research work is to provide the Medical Experts and Researchers an effective method which would aid them in identifying the tumors, non-tumors, and dense masses in a mammogram. At first the breast region extraction is carried using the entire mammogram. The extraction is carried out by creating the masks and using those masks to extract the region of interest pertaining to the tumor. A chain code is employed to extract the various regions, the extracted regions could potentially be classified as tumors, non-tumors, and dense regions. Adaptive histogram equalization technique is employed to enhance the contrast of an image. After applying the adaptive histogram equalization for several times which will provide a saturated image which would contain only bright spots of the mammographic image which appear like dense regions of the mammogram. These dense masses could be potential tumors which would need treatment. Relevant Characteristics such as texture in the mammographic image are used for feature extraction by using the nearest mean and support vector machine classifier. A total of thirteen Haralick features are used to classify the three classes. Support vector machine classifier is used to classify two class problems and radial basis function (RBF) kernel is used to find the best possible (c and gamma) values. Results obtained in this research suggest the best classification accuracy was achieved by using the support vector machines for both Tumor vs Non-Tumor and Tumor vs Dense masses. The maximum accuracies achieved for the tumor and non-tumor is above 90 % and for the dense masses is 70.8% using 11 features for support vector machines. Support vector machines performed better than the nearest mean majority classifier in the classification of the classes. Various case studies were performed using two distinct datasets in which each dataset consisting of 24 patients’ data in two individual views. Each patient data will consist of both the cranio caudal view and medio lateral oblique views. From these views the region of interest which could possibly be a tumor, non-tumor, or a dense regions(mass).
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Xia, Junshi. "Multiple classifier systems for the classification of hyperspectral data." Thesis, Grenoble, 2014. http://www.theses.fr/2014GRENT047/document.
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Dans cette thèse, nous proposons plusieurs nouvelles techniques pour la classification d'images hyperspectrales basées sur l'apprentissage d'ensemble. Le cadre proposé introduit des innovations importantes par rapport aux approches précédentes dans le même domaine, dont beaucoup sont basées principalement sur un algorithme individuel. Tout d'abord, nous proposons d'utiliser la Forêt de Rotation (Rotation Forest) avec différentes techiniques d'extraction de caractéristiques linéaire et nous comparons nos méthodes avec les approches d'ensemble traditionnelles, tels que Bagging, Boosting, Sous-espace Aléatoire et Forêts Aléatoires. Ensuite, l'intégration des machines à vecteurs de support (SVM) avec le cadre de sous-espace de rotation pour la classification de contexte est étudiée. SVM et sous-espace de rotation sont deux outils puissants pour la classification des données de grande dimension. C'est pourquoi, la combinaison de ces deux méthodes peut améliorer les performances de classification. Puis, nous étendons le travail de la Forêt de Rotation en intégrant la technique d'extraction de caractéristiques locales et l'information contextuelle spatiale avec un champ de Markov aléatoire (MRF) pour concevoir des méthodes spatio-spectrale robustes. Enfin, nous présentons un nouveau cadre général, ensemble de sous-espace aléatoire, pour former une série de classifieurs efficaces, y compris les arbres de décision et la machine d'apprentissage extrême (ELM), avec des profils multi-attributs étendus (EMaPS) pour la classification des données hyperspectrales. Six méthodes d'ensemble de sous-espace aléatoire, y compris les sous-espaces aléatoires avec les arbres de décision, Forêts Aléatoires (RF), la Forêt de Rotation (RoF), la Forêt de Rotation Aléatoires (Rorf), RS avec ELM (RSELM) et sous-espace de rotation avec ELM (RoELM), sont construits par multiples apprenants de base. L'efficacité des techniques proposées est illustrée par la comparaison avec des méthodes de l'état de l'art en utilisant des données hyperspectrales réelles dans de contextes différents<br>In this thesis, we propose several new techniques for the classification of hyperspectral remote sensing images based on multiple classifier system (MCS). Our proposed framework introduces significant innovations with regards to previous approaches in the same field, many of which are mainly based on an individual algorithm. First, we propose to use Rotation Forests with several linear feature extraction and compared them with the traditional ensemble approaches, such as Bagging, Boosting, Random subspace and Random Forest. Second, the integration of the support vector machines (SVM) with Rotation subspace framework for context classification is investigated. SVM and Rotation subspace are two powerful tools for high-dimensional data classification. Therefore, combining them can further improve the classification performance. Third, we extend the work of Rotation Forests by incorporating local feature extraction technique and spatial contextual information with Markov random Field (MRF) to design robust spatial-spectral methods. Finally, we presented a new general framework, Random subspace ensemble, to train series of effective classifiers, including decision trees and extreme learning machine (ELM), with extended multi-attribute profiles (EMAPs) for classifying hyperspectral data. Six RS ensemble methods, including Random subspace with DT (RSDT), Random Forest (RF), Rotation Forest (RoF), Rotation Random Forest (RoRF), RS with ELM (RSELM) and Rotation subspace with ELM (RoELM), are constructed by the multiple base learners. The effectiveness of the proposed techniques is illustrated by comparing with state-of-the-art methods by using real hyperspectral data sets with different contexts
Book chapters on the topic "Support Vector Classifier (SVC)"
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Montesinos López, Osval Antonio, Abelardo Montesinos López, and Jose Crossa. "Support Vector Machines and Support Vector Regression." In Multivariate Statistical Machine Learning Methods for Genomic Prediction. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-89010-0_9.
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AbstractIn this chapter, the support vector machines (svm) methods are studied. We first point out the origin and popularity of these methods and then we define the hyperplane concept which is the key for building these methods. We derive methods related to svm: the maximum margin classifier and the support vector classifier. We describe the derivation of the svm along with some kernel functions that are fundamental for building the different kernels methods that are allowed in svm. We explain how the svm for binary response variables can be expanded for categorical response variables and give examples of svm for binary and categorical response variables with plant breeding data for genomic selection. Finally, general issues for adopting the svm methodology for continuous response variables are provided, and some examples of svm for continuous response variables for genomic prediction are described.
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Anwar, Suzan, Arthur Rahming, Mikea Fernander, Otito Udedibor, and Shereen Ali. "Breast Cancer Diagnosing System: Using a Rough Set-Ensemble Classifier Approach." In Lecture Notes in Computer Science. Springer Nature Switzerland, 2025. https://doi.org/10.1007/978-3-031-88220-3_2.
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Abstract Breast cancer occurs when normal breast cells turn cancerous, grow abnormally and form tumors. The most common cancer impacting women worldwide is breast cancer. Diagnosing breast cancer early and accurately is crucial for giving the correct treatment and ensuring patients receive the best care possible. Due to human error, misdiagnosis is a possibility in the medical field. Over-diagnosis can cause patients to go through unnecessary treatments. Under-diagnosis can allow malignant tumors to become more aggressive and life-threatening. The aim of our research is to create a dependable model to correctly diagnose breast cancer. We propose to use a rough set ensemble classifier approach to assist doctors in making more accurate diagnosis. The rough set reduct algorithm will be used for feature reductions and the model will be built with logistic regression algorithm, Support Vector Machine (SVM) algorithms and random forest algorithm. The proposed model produced an accuracy of 93% for logistic regression algorithm, 97% for SVM, and 92% for Random Forest when classifying the image data and overall produced a 96% accuracy.
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da Silva, Camilla, Jed Nisenson, and Jeff Boisvert. "Comparing and Detecting Stationarity and Dataset Shift." In Springer Proceedings in Earth and Environmental Sciences. Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-19845-8_3.
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AbstractMachine learning algorithms have been increasingly applied to spatial numerical modeling. However, it is important to understand when such methods will underperform. Machine learning algorithms are impacted by dataset shift; when modeling domains of interest present non-stationarities there is no guarantee that the trained models are effective in unsampled areas. This work aims to compare the stationarity requirement of geostatistical methods to the concept of dataset shift. Also, workflow is developed to detect dataset shift in spatial data prior to modeling, this involves applying a discriminative classifier and a two sample Kolmogorv-Smirnov test to model areas. And, when required a lazy learning modification of support vector regression is proposed to account for dataset shift. The benefits of the lazy learning algorithm are demonstrated on the well-known non-stationary Walker Lake dataset and improves root mean squared error up to 25% relative to standard SVR approach, in areas where dataset shift is present.
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López-González, G., Nancy Arana-Daniel, and Eduardo Bayro-Corrochano. "Quaternion Support Vector Classifier." In Advanced Information Systems Engineering. Springer Berlin Heidelberg, 2014. http://dx.doi.org/10.1007/978-3-319-12568-8_88.
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Shilton, Alistair, and Marimuthu Palaniswami. "A Unified Approach to Support Vector Machines." In Pattern Recognition Technologies and Applications. IGI Global, 2008. http://dx.doi.org/10.4018/978-1-59904-807-9.ch014.
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This chapter presents a unified introduction to support vector machine (SVM) methods for binary classification, one-class classification, and regression. The SVM method for binary classification (binary SVC) is introduced first, and then extended to encompass one-class classification (clustering). Next, using the regularized risk approach as a motivation, the SVM method for regression (SVR) is described. These methods are then combined to obtain a single unified SVM formulation that encompasses binary classification, one-class classification, and regression (as well as some extensions of these), and the dual formulation of this unified model is derived. A mechanical analogy for binary and one-class SVCs is given to give an intuitive explanation of the operation of these two formulations. Finally, the unified SVM is extended to implement general cost functions, and an application of SVM classifiers to the problem of spam e-mail detection is considered.
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Seref, Onur, O. Erhun Kundakcioglu, and Michael Bewernitz. "Support Vector Machines in Neuroscience." In Encyclopedia of Healthcare Information Systems. IGI Global, 2008. http://dx.doi.org/10.4018/978-1-59904-889-5.ch161.
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The underlying optimization problem for the maximal margin classifier is only feasible if the two classes of pattern vectors are linearly separable. However, most of the real life classification problems are not linearly separable. Nevertheless, the maximal margin classifier encompasses the fundamental methods used in standard SVM classifiers. The solution to the optimization problem in the maximal margin classifier minimizes the bound on the generalization error (Vapnik, 1998). The basic premise of this method lies in the minimization of a convex optimization problem with linear inequality constraints, which can be solved efficiently by many alternative methods (Bennett & Campbell, 2000).
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Ramirez L., Durdle N.G., and Raso V.J. "A Machine Learning Approach to Assess Changes in Scoliosis." In Studies in Health Technology and Informatics. IOS Press, 2008. https://doi.org/10.3233/978-1-58603-888-5-254.
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This paper presents a machine learning approach that can be used to evaluate the validity of the results obtained with an automated system to measure changes in scoliotic curves. The automated system was used to measure the inclinations of 141 vertebral endplates in spine radiographs of patients with scoliosis. The resulting dataset was divided into training and test set. The training set was used to configure three classifiers: a support vector classifier (SVC), a decision tree classifier (DT) and a logistic regression classifier (LR). Their performance was evaluated on the test set. The SVC had an accuracy of 86% discriminating Good Results (those in which the error was less than 3&deg;) from Bad Results. This accuracy was better than that of the LR (76%) and DT (68%). The differentiation between Good and Bad Results using the proposed machine learning approach was achieved successfully.
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Jaladanki, Ravindrababu, Syed Imran Patel, Imran Khan, Karim Ishtiaque Ahmed Mohammed, Thenmozhi M., and Arun Kumar Tripathi. "Machine Learning Models in Detecting Cyber Crimes and Cyber Terrorism in India." In Advances in Digital Crime, Forensics, and Cyber Terrorism. IGI Global, 2022. http://dx.doi.org/10.4018/978-1-6684-3942-5.ch004.
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Cyber-physical systems (CPSs), which are more susceptible to a range of cyber-attacks, play an increasingly crucial role in power system security today. Digital communication has become a global phenomenon in the last decade. Sadly, cyber terrorism is on the rise, and abusers are able to hide behind the anonymity of the internet. A hybrid model for detecting instances of cyber terrorism in Twitter datasets was proposed in this study after a survey of prominent classification algorithms. Logistic regression, linear support vector classifier, and naive bayes are the methods utilised for evaluation. Four metrics were used to evaluate the performance of the classifiers in experiments: precision, F1, accuracy, and recall. The findings show how well each of the algorithms worked, along with the metrics that went along with them. Linear support vector classifier (SVC) was the least effective, while hybrid model (EM) was the most successful.
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Mohsen, Farida, Md Rafiul Biswas, Hazrat Ali, Tanvir Alam, Mowafa Househ, and Zubair Shah. "Customized and Automated Machine Learning-Based Models for Diabetes Type 2 Classification." In Studies in Health Technology and Informatics. IOS Press, 2022. http://dx.doi.org/10.3233/shti220779.
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This study aims to develop models to accurately classify patients with type 2 diabetes using the Practice Fusion dataset. We use Random Forest (RF), Support Vector Classifier (SVC), AdaBoost classifier, an ensemble model, and automated machine learning (AutoML) model. We compare the performance of all models in a five-fold cross-validation scheme using four evaluation measures. Experimental results demonstrate that the AutoML model outperformed individual and ensemble models in all evaluation measures.
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Fu, Xiuju, Lipo Wang, GihGuang Hung, and Liping Goh. "Linguistic Rule Extraction from Support Vector Machine Classifiers." In Research and Trends in Data Mining Technologies and Applications. IGI Global, 2007. http://dx.doi.org/10.4018/978-1-59904-271-8.ch010.
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Classification decisions from linguistic rules are more desirable compared to complex mathematical formulas from support vector machine (SVM) classifiers due to the explicit explanation capability of linguistic rules. Linguistic rule extraction has been attracting much attention in explaining knowledge hidden in data. In this chapter, we show that the decisions from an SVM classifier can be decoded into linguistic rules based on the information provided by support vectors and decision function. Given a support vector of a certain class, cross points between each line, which is extended from the support vector along each axis, and an SVM decision hyper-curve are searched first. A hyper-rectangular rule is derived from these cross points. The hyper-rectangle is tuned by a tuning phase in order to exclude those out-class data points. Finally, redundant rules are merged to produce a compact rule set. Simultaneously, important attributes could be highlighted in the extracted rules. Rule extraction results from our proposed method could follow SVM classifier decisions very well. We compare the rule extraction results from SVM with RBF kernel function and linear kernel function. Experiment results show that rules extracted from SVM with RBF nonlinear kernel function are with better accuracy than rules extracted from SVM with linear kernel function. Comparisons between our method and other rule extraction methods are also carried out on several benchmark data sets. Higher rule accuracy is obtained in our method with fewer number of premises in each rule.
Conference papers on the topic "Support Vector Classifier (SVC)"
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Miraliakbar, Alireza, and Zheyu Jiang. "Fast, Accurate, and Robust Fault Detection and Diagnosis of Industrial Processes." In Foundations of Computer-Aided Process Design. PSE Press, 2024. http://dx.doi.org/10.69997/sct.184473.
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Modern industrial processes are continuously monitored by a large number of sensors. Despite having access to large volumes of historical and online sensor data, industrial practitioners still face challenges in the era of Industry 4.0 in effectively utilizing them to perform online process monitoring and fast fault detection and diagnosis. To target these challenges, in this work, we present a novel framework named �FARM� for Fast, Accurate, and Robust online process Monitoring. FARM is a holistic monitoring framework that integrates (a) advanced multivariate statistical process control (SPC) for fast anomaly detection of nonparametric, heterogeneous data streams, and (b) modified support vector machine (SVM) for accurate and robust fault classification. Unlike existing general-purpose process monitoring frameworks, FARM�s unique hierarchical architecture decomposes process monitoring into two fault detection and diagnosis, each of which is conducted by targeted algorithms. Here, we test and validate the performance of our FARM monitoring framework on Tennessee Eastman Process (TEP) benchmark dataset. We show that SPC achieves faster fault detection speed at a lower false alarm rate compared to state-of-the-art benchmark fault detection methods. In terms of fault classification diagnosis, we show that our modified SVM algorithm successfully classifies 17 out of 20 of the fault scenarios present in the TEP dataset. Compared with the results of standard SVM trained directly on the original dataset, our modified SVM improves the fault classification accuracy significantly.
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Meesala, Gopichand, Barkha Soni, Manish Pandey, and Nilay Khare. "Epileptic Seizure Detection Using Quantum Support Vector Classifier." In 2024 International Conference on Computational Intelligence for Green and Sustainable Technologies (ICCIGST). IEEE, 2024. http://dx.doi.org/10.1109/iccigst60741.2024.10717615.
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Tan, Jie Ying, and Andy Sai Kit Chow. "Sentiment Analysis on Game Reviews: A Comparative Study of Machine Learning Approaches." In International Conference on Digital Transformation and Applications (ICDXA 2021). Tunku Abdul Rahman University College, 2021. http://dx.doi.org/10.56453/icdxa.2021.1023.
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Sentiment analysis is one of the major topics of natural language processing which is used to determine whether data is positive, negative or neutral. It is often performed on textual data to help businesses monitor brand and product sentiment in customer feedback to understand their customers’ needs. This paper explores various machine learning algorithms including Logistic Regression (LR), Multinomial Naïve Bayes (MNB), Support Vector Classifier (SVC), Multi-layer Perceptron Classifier (MLP) and Extreme Gradient Boosting Classifier (XGB) to build sentiment analysis models tailored for the gaming domain to classify reviews into positive, negative and neutral. The models were trained on game reviews obtained from Metacritic and Steam. Various data preprocessing and model optimization techniques have been employed and the performance of the models were evaluated and compared. SVC has been determined as the best-performing model among all the models. Keywords: Sentiment Analysis, Natural Language Processing, Machine Learning, Support Vector Machine, Game Reviews
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Xu, Jie, Xianglong Liu, Zhouyuan Huo, Cheng Deng, Feiping Nie, and Heng Huang. "Multi-Class Support Vector Machine via Maximizing Multi-Class Margins." In Twenty-Sixth International Joint Conference on Artificial Intelligence. International Joint Conferences on Artificial Intelligence Organization, 2017. http://dx.doi.org/10.24963/ijcai.2017/440.
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Support Vector Machine (SVM) is originally proposed as a binary classification model, and it has already achieved great success in different applications. In reality, it is more often to solve a problem which has more than two classes. So, it is natural to extend SVM to a multi-class classifier. There have been many works proposed to construct a multi-class classifier based on binary SVM, such as one versus all strategy, one versus one strategy and Weston's multi-class SVM. One versus all strategy and one versus one strategy split the multi-class problem to multiple binary classification subproblems, and we need to train multiple binary classifiers. Weston's multi-class SVM is formed by ensuring risk constraints and imposing a specific regularization, like Frobenius norm. It is not derived by maximizing the margin between hyperplane and training data which is the motivation in SVM. In this paper, we propose a multi-class SVM model from the perspective of maximizing margin between training points and hyperplane, and analyze the relation between our model and other related methods. In the experiment, it shows that our model can get better or compared results when comparing with other related methods.
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Fan, XiaoJing, LaiBin Zhang, Wei Liang, and ZhaoHui Wang. "Leak Detection Method Based on Support Vector Machine." In 2008 7th International Pipeline Conference. ASMEDC, 2008. http://dx.doi.org/10.1115/ipc2008-64118.
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Assumptive and uncertain factors, few leak samples, complex non-linear pipeline systems are the problems often involved in the process of pipeline leak detection. Furthermore, the pressure wave changes of leakage are similar to these of valve regulation and pump closure. Thus it is difficult to establish a reliable model and to distinguish the leak signal pattern from others in pipeline leak detection. The veracity of leak detection system is limited. This paper presents a novel technique based on the statistical learning theory, support vector machine (SVM) for pipeline leak detection. Support Vector Machine (SVM) is learning system that uses a hypothesis space of linear functions in a high dimensional feature space, trained with a learning algorithm from optimization theory that implements a learning bias derived from statistical learning theory. SVM is based on the principle of structure risk minimization as opposed to the principle of empirical risk minimization in conventional techniques. Thus, SVM has good performance for classification over small sample set. In this paper, an overview of the limitations of traditional statistics and the advantage of statistical learning theory will be introduced. In this paper, an SVM classifier is used to classify the signal pattern with few samples. Firstly, the algorithm of the SVM classifier and steps of using the model to identify leakage signals are studied. Secondly, the classification results of the experiment show that SVM classifier has high recognition accuracy. In addition, SVM is compared with neural network method. Then the paper concludes that in terms of classification ability and generalization performance, SVM has clearly advantages than neural network method over small sample set, so SVM is more applicable to pipeline leak detection.
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Bender, Dieter, Ali Jalali, Daniel J. Licht, and C. Nataraj. "Prediction of Periventricular Leukomalacia Occurrence in Neonates Using a Novel Support Vector Machine Classifier Optimization Method." In ASME 2015 Dynamic Systems and Control Conference. American Society of Mechanical Engineers, 2015. http://dx.doi.org/10.1115/dscc2015-9984.
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Prior work has documented that Support Vector Machine (SVM) classifiers can be powerful tools in predicting clinical outcomes of complex diseases such as Periventricular Leukomalacia (PVL). Our previous study showed that SVM performance can be improved significantly by optimizing the supervised training set used during the learning stage of the overall SVM algorithm. This study fully develops the initial idea using the reliable Leave-One-Out Cross-validation (LOOCV) technique. The work presented in this paper confirms previous results and improves the performance of the SVM even further. In addition, using the LOOCV technique, the computational time is decreased and the structure of the algorithm simplified, making this framework more feasible. Furthermore, we evaluate the performance of the resulting optimized SVM classifier on an unseen set of data. This demonstrates that the developed SVM algorithm outperforms normal SVM type classifiers without any loss of generalization.
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Kalimullah, Nur M. M., Shivam Ojha, Amit Shelke, and Anowarul Habib. "Acoustic Emission Source Localization in Plates Through Support Vector Machine with Genetic Optimization." In 2023 50th Annual Review of Progress in Quantitative Nondestructive Evaluation. American Society of Mechanical Engineers, 2023. http://dx.doi.org/10.1115/qnde2023-108711.
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Abstract Support vector machine (SVM) is used as the classifier as well as a regressor in the machine learning techniques. However, the application of SVM for acoustic emission (AE) source localization is limited. For the composite materials, the use of guided ultrasonic waves (GUV) helps in identifying the location of defects as they can be transmitted and simultaneously received by the sparse array of piezoelectric sensors. Various methods based on minimizing the error function and Bayesian filters have already been implemented to identify the location of the acoustic emission (AE) source. The current study focuses on identifying the location of impact in the plate using guided waves through a layered support vector machine framework. Under the framework, the carbon-fiber-reinforced polymer (CFRP) plate is divided into several small regions. The SVM classifier is trained for each of these regions, and thus a set of layers containing different SVM classifiers is formed. The training dataset is generated through the centroidal Voronoi tessellations. The test point generated through Markov Chain is passed through the different layers of SVM to identify the region of AE source location. Moreover, the proposed approach is validated using an experimental program that makes use of a CFRP composite panel instrumented with a sparse array of piezoelectric transducers.
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Bender, Dieter, Ali Jalali, and C. Nataraj. "Prediction of Periventricular Leukomalacia Occurrence in Neonates Using a Novel Unsupervised Learning Method." In ASME 2014 Dynamic Systems and Control Conference. American Society of Mechanical Engineers, 2014. http://dx.doi.org/10.1115/dscc2014-6304.
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Prior work has documented that Support Vector Machine (SVM) classifiers can be powerful tools in predicting clinical outcomes of complex diseases such as Periventricular Leukomalacia (PVL). A preceding study indicated that SVM performance can be improved significantly by optimizing the supervised training set used during the learning stage of the overall SVM algorithm. This preliminary work, as well as the complex nature of the PVL data suggested integration of the active learning algorithm into the overall SVM framework. The present study supports this initial hypothesis and shows that active learning SVM type classifier performs considerably well and outperforms normal SVM type classifiers when dealing with clinical data of high dimensionality.
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Coelho, Clyde, and Aditi Chattopadhyay. "Feature Reduction for Computationally Efficient Damage State Classification Using Binary Tree Support Vector Machines." In ASME 2008 Conference on Smart Materials, Adaptive Structures and Intelligent Systems. ASMEDC, 2008. http://dx.doi.org/10.1115/smasis2008-640.
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This paper proposes a computationally efficient methodology for classifying damage in structural hotspots. Data collected from a sensor instrumented lug joint subjected to fatigue loading was preprocessed using a linear discriminant analysis (LDA) to extract features that are relevant for classification and reduce the dimensionality of the data. The data is then reduced in the feature space by analyzing the structure of the mapped clusters and removing the data points that do not affect the construction of interclass separating hyperplanes. The reduced data set is used to train a support vector machines (SVM) based classifier and the results of the classification problem are compared to those when the entire data set is used for training. To further improve the efficiency of the classification scheme, the SVM classifiers are arranged in a binary tree format to reduce the number of comparisons that are necessary. The experimental results show that the data reduction does not reduce the ability of the classifier to distinguish between classes while providing a nearly fourfold decrease in the amount of training data processed.
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Bobby, Thomas Christy, and Swaminathan Ramakrishnan. "Evaluation of Human Femur Bone Radiographic Images Using AdaBoost and Support Vector Machines." In ASME 2011 International Mechanical Engineering Congress and Exposition. ASMEDC, 2011. http://dx.doi.org/10.1115/imece2011-65107.
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In this work, classification of normal and abnormal human femur bone images are carried out using Support Vector Machines (SVM) and AdaBoost classifiers. The trabecular (soft bone) regions of human femur bone images (N = 44) recorded under standard conditions are used for the study. The acquired images are subjected to auto threshold binarization algorithm to recognize the presence of mineralization and trabecular structures in the digitized images. The mechanical strength regions such as primary compressive and tensile are delineated by semi-automated image processing methods from the digitized femur bone images. The first and higher order statistical parameters are calculated from the intensity values of the delineated regions of interest and their gray level co-occurrence matrices respectively. The significant parameters are found using principal component analysis. The first two most significant parameters are used as input to the classifiers. Statistical classification tools such as SVM and AdaBoost are employed for the classification. Results show that the AdaBoost classifier performs better in terms of sensitivity and specificity for the chosen parameters for primary compressive and tensile regions compared to SVM.