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Anomaly detection in system log files using machine learning algorithms / Zahedeh Zamanian
Published 2019“…This study applies unsupervised Isolation Forest and One Class SVM as ML algorithms to detect anomalies. Isolation Forest area under curve (AUC) successfully achieved 96.6% with applying PCA and without PCA, lowest value of AUC was 76%. …”
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Thesis -
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KopiCulture: Unveiling Customer Loyalty in Malaysia's Coffee Market through Clustering Algorithms for Local Cafe Insights
Published 2024“…This study aims to identify customer loyalty patterns in the Malaysian coffee market, focusing on the Malaysia Starbucks customer survey dataset. Using clustering algorithms such as KMeans, KMeans with Principal Component Analysis (PCA), single linkage, complete linkage, DBScan, and DBScan in conjunction with PCA, we identify distinct customer segments based on loyalty patterns. …”
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Combining Recursive Least Square and Principal Component Analysis for Assisted History Matching
Published 2014“…Therefore, in this project, Principal Component Analysis (PCA) is used to reduce the number of parameters. …”
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Feature Subset Selection in Intrusion Detection Using Soft Computing Techniques
Published 2011“…Instead of using traditional approach of selecting features with the highest eigenvalues such as PCA, this research applied a Genetic Algorithm (GA) to search the principal feature space that offers a subset of features with optimal sensitivity and the highest discriminatory power. …”
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Feature Subset Selection in Intrusion Detection Using Soft Computing Techniques
Published 2011“…Instead of using traditional approach of selecting features with the highest eigenvalues such as PCA, this research applied a Genetic Algorithm (GA) to search the principal feature space that offers a subset of features with optimal sensitivity and the highest discriminatory power. …”
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A Systematic Review of Anomaly Detection within High Dimensional and Multivariate Data
Published 2023“…It has been found that PCA, DOBIN, Stray algorithm, and DAE-KNN have a high learning rate compared to Random projection, ROBEM, and OCP methods. …”
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A Systematic Review of Anomaly Detection within High Dimensional and Multivariate Data
“…It has been found that PCA, DOBIN, Stray algorithm, and DAE-KNN have a high learning rate compared to Random projection, ROBEM, and OCP methods. …”
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A Systematic Review of Anomaly Detection within High Dimensional and Multivariate Data
Published 2023“…It has been found that PCA, DOBIN, Stray algorithm, and DAE-KNN have a high learning rate compared to Random projection, ROBEM, and OCP methods. …”
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A Systematic Review of Anomaly Detection within High Dimensional and Multivariate Data
Published 2023“…It has been found that PCA, DOBIN, Stray algorithm, and DAE-KNN have a high learning rate compared to Random projection, ROBEM, and OCP methods. …”
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A Systematic Review of Anomaly Detection within High Dimensional and Multivariate Data
Published 2023“…It has been found that PCA, DOBIN, Stray algorithm, and DAE-KNN have a high learning rate compared to Random projection, ROBEM, and OCP methods. …”
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A Systematic Review of Anomaly Detection within High Dimensional and Multivariate Data
Published 2023“…It has been found that PCA, DOBIN, Stray algorithm, and DAE-KNN have a high learning rate compared to Random projection, ROBEM, and OCP methods. …”
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A Systematic Review of Anomaly Detection within High Dimensional and Multivariate Data
Published 2023“…It has been found that PCA, DOBIN, Stray algorithm, and DAE-KNN have a high learning rate compared to Random projection, ROBEM, and OCP methods. …”
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Digital Quran With Storage Optimization Through Duplication Handling And Compressed Sparse Matrix Method
Published 2024thesis::doctoral thesis -
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A new model for iris data set classification based on linear support vector machine parameter's optimization
Published 2020“…In this study, we proposed a newly mode for classifying iris data set using SVM classifier and genetic algorithm to optimize c and gamma parameters of linear SVM, in addition principle components analysis (PCA) algorithm was use for features reduction.…”
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Characterization and pathogenicity of Rhizoctonia spp isolated from various crop species in different agroecosystems in Malaysia
Published 2017“…Phylogenetic analysis using different algorithms separated Rhizoctonia spp. to the distinct clades. …”
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