The Experts below are selected from a list of 840 Experts worldwide ranked by ideXlab platform
S. Ben J. Yoo - One of the best experts on this subject based on the ideXlab platform.
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self taught anomaly detection with hybrid unsupervised supervised Machine Learning in optical networks
Journal of Lightwave Technology, 2019Co-Authors: Xiaoliang Chen, Baojia Li, Roberto Proietti, Zuqing Zhu, S. Ben J. YooAbstract:This paper proposes a self-taught anomaly detection framework for optical networks. The proposed framework makes use of a hybrid unsupervised and supervised Machine Learning Scheme. First, it employs an unsupervised data clustering module (DCM) to analyze the patterns of monitoring data. The DCM enables a self-Learning capability that eliminates the requirement of prior knowledge of abnormal network behaviors and therefore can potentially detect unforeseen anomalies. Second, we introduce a self-taught mechanism that transfers the patterns learned by the DCM to a supervised data regression and classification module (DRCM). The DRCM, whose complexity is mainly related to the scale of the applied supervised Learning model, can potentially facilitate more scalable and time-efficient online anomaly detection by avoiding excessively traversing the original dataset. We designed the DCM and DRCM based on the density-based clustering algorithm and the deep neural network structure, respectively. Evaluations with experimental data from two use cases (i.e., single-point detection and end-to-end detection) demonstrate that up to $99\%$ anomaly detection accuracy can be achieved with a false positive rate below $1\%$ .
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Self-Taught Anomaly Detection With Hybrid Unsupervised/Supervised Machine Learning in Optical Networks
Journal of Lightwave Technology, 2019Co-Authors: Xiaoliang Chen, Baojia Li, Roberto Proietti, Zuqing Zhu, S. Ben J. YooAbstract:This paper proposes a self-taught anomaly detection framework for optical networks. The proposed framework makes use of a hybrid unsupervised and supervised Machine Learning Scheme. First, it employs an unsupervised data clustering module (DCM) to analyze the patterns of monitoring data. The DCM enables a self-Learning capability that eliminates the requirement of prior knowledge of abnormal network behaviors and therefore can potentially detect unforeseen anomalies. Second, we introduce a self-taught mechanism that transfers the patterns learned by the DCM to a supervised data regression and classification module (DRCM). The DRCM, whose complexity is mainly related to the scale of the applied supervised Learning model, can potentially facilitate more scalable and time-efficient online anomaly detection by avoiding excessively traversing the original dataset. We designed the DCM and DRCM based on the density-based clustering algorithm and the deep neural network structure, respectively. Evaluations with experimental data from two use cases (i.e., single-point detection and end-to-end detection) demonstrate that up to 99% anomaly detection accuracy can be achieved with a false positive rate below 1%.
Xiaoliang Chen - One of the best experts on this subject based on the ideXlab platform.
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self taught anomaly detection with hybrid unsupervised supervised Machine Learning in optical networks
Journal of Lightwave Technology, 2019Co-Authors: Xiaoliang Chen, Baojia Li, Roberto Proietti, Zuqing Zhu, S. Ben J. YooAbstract:This paper proposes a self-taught anomaly detection framework for optical networks. The proposed framework makes use of a hybrid unsupervised and supervised Machine Learning Scheme. First, it employs an unsupervised data clustering module (DCM) to analyze the patterns of monitoring data. The DCM enables a self-Learning capability that eliminates the requirement of prior knowledge of abnormal network behaviors and therefore can potentially detect unforeseen anomalies. Second, we introduce a self-taught mechanism that transfers the patterns learned by the DCM to a supervised data regression and classification module (DRCM). The DRCM, whose complexity is mainly related to the scale of the applied supervised Learning model, can potentially facilitate more scalable and time-efficient online anomaly detection by avoiding excessively traversing the original dataset. We designed the DCM and DRCM based on the density-based clustering algorithm and the deep neural network structure, respectively. Evaluations with experimental data from two use cases (i.e., single-point detection and end-to-end detection) demonstrate that up to $99\%$ anomaly detection accuracy can be achieved with a false positive rate below $1\%$ .
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Self-Taught Anomaly Detection With Hybrid Unsupervised/Supervised Machine Learning in Optical Networks
Journal of Lightwave Technology, 2019Co-Authors: Xiaoliang Chen, Baojia Li, Roberto Proietti, Zuqing Zhu, S. Ben J. YooAbstract:This paper proposes a self-taught anomaly detection framework for optical networks. The proposed framework makes use of a hybrid unsupervised and supervised Machine Learning Scheme. First, it employs an unsupervised data clustering module (DCM) to analyze the patterns of monitoring data. The DCM enables a self-Learning capability that eliminates the requirement of prior knowledge of abnormal network behaviors and therefore can potentially detect unforeseen anomalies. Second, we introduce a self-taught mechanism that transfers the patterns learned by the DCM to a supervised data regression and classification module (DRCM). The DRCM, whose complexity is mainly related to the scale of the applied supervised Learning model, can potentially facilitate more scalable and time-efficient online anomaly detection by avoiding excessively traversing the original dataset. We designed the DCM and DRCM based on the density-based clustering algorithm and the deep neural network structure, respectively. Evaluations with experimental data from two use cases (i.e., single-point detection and end-to-end detection) demonstrate that up to 99% anomaly detection accuracy can be achieved with a false positive rate below 1%.
Chen Hoffmann - One of the best experts on this subject based on the ideXlab platform.
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Application of MR morphologic, diffusion tensor, and perfusion imaging in the classification of brain tumors using Machine Learning Scheme
Neuroradiology, 2019Co-Authors: Shai Shrot, Moshe Salhov, Nir Dvorski, Eli Konen, Amir Averbuch, Chen HoffmannAbstract:Purpose While MRI is the modality of choice for the assessment of patients with brain tumors, differentiation between various tumors based on their imaging characteristics might be challenging due to overlapping imaging features. The purpose of this study was to apply a Machine Learning Scheme using basic and advanced MR sequences for distinguishing different types of brain tumors. Methods The study cohort included 141 patients (41 glioblastoma, 38 metastasis, 50 meningioma, and 12 primary central nervous system lymphoma). A computer-assisted classification Scheme, combining morphologic MRI, perfusion MRI, and DTI metrics, was developed and used for tumor classification. The proposed multistep Scheme consists of pre-processing, ROI definition, features extraction, feature selection, and classification. Feature subset selection was performed using support vector Machines (SVMs). Classification performance was assessed by leave-one-out cross-validation. Given an ROI, the entire classification process was done automatically via computer and without any human intervention. Results A binary hierarchical classification tree was chosen. In the first step, selected features were chosen for distinguishing glioblastoma from the remaining three classes, followed by separation of meningioma from metastasis and PCNSL, and then to discriminate PCNSL from metastasis. The binary SVM classification accuracy, sensitivity and specificity for glioblastoma, metastasis, meningiomas, and primary central nervous system lymphoma were 95.7, 81.6, and 91.2%; 92.7, 95.1, and 93.6%; 97, 90.8, and 58.3%; and 91.5, 90, and 96.9%, respectively. Conclusion A Machine Learning Scheme using data from anatomical and advanced MRI sequences resulted in high-performance automatic tumor classification algorithm. Such a Scheme can be integrated into clinical decision support systems to optimize tumor classification.
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application of mr morphologic diffusion tensor and perfusion imaging in the classification of brain tumors using Machine Learning Scheme
Neuroradiology, 2019Co-Authors: Shai Shrot, Moshe Salhov, Nir Dvorski, Eli Konen, Amir Averbuch, Chen HoffmannAbstract:Purpose While MRI is the modality of choice for the assessment of patients with brain tumors, differentiation between various tumors based on their imaging characteristics might be challenging due to overlapping imaging features. The purpose of this study was to apply a Machine Learning Scheme using basic and advanced MR sequences for distinguishing different types of brain tumors.
Baojia Li - One of the best experts on this subject based on the ideXlab platform.
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self taught anomaly detection with hybrid unsupervised supervised Machine Learning in optical networks
Journal of Lightwave Technology, 2019Co-Authors: Xiaoliang Chen, Baojia Li, Roberto Proietti, Zuqing Zhu, S. Ben J. YooAbstract:This paper proposes a self-taught anomaly detection framework for optical networks. The proposed framework makes use of a hybrid unsupervised and supervised Machine Learning Scheme. First, it employs an unsupervised data clustering module (DCM) to analyze the patterns of monitoring data. The DCM enables a self-Learning capability that eliminates the requirement of prior knowledge of abnormal network behaviors and therefore can potentially detect unforeseen anomalies. Second, we introduce a self-taught mechanism that transfers the patterns learned by the DCM to a supervised data regression and classification module (DRCM). The DRCM, whose complexity is mainly related to the scale of the applied supervised Learning model, can potentially facilitate more scalable and time-efficient online anomaly detection by avoiding excessively traversing the original dataset. We designed the DCM and DRCM based on the density-based clustering algorithm and the deep neural network structure, respectively. Evaluations with experimental data from two use cases (i.e., single-point detection and end-to-end detection) demonstrate that up to $99\%$ anomaly detection accuracy can be achieved with a false positive rate below $1\%$ .
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Self-Taught Anomaly Detection With Hybrid Unsupervised/Supervised Machine Learning in Optical Networks
Journal of Lightwave Technology, 2019Co-Authors: Xiaoliang Chen, Baojia Li, Roberto Proietti, Zuqing Zhu, S. Ben J. YooAbstract:This paper proposes a self-taught anomaly detection framework for optical networks. The proposed framework makes use of a hybrid unsupervised and supervised Machine Learning Scheme. First, it employs an unsupervised data clustering module (DCM) to analyze the patterns of monitoring data. The DCM enables a self-Learning capability that eliminates the requirement of prior knowledge of abnormal network behaviors and therefore can potentially detect unforeseen anomalies. Second, we introduce a self-taught mechanism that transfers the patterns learned by the DCM to a supervised data regression and classification module (DRCM). The DRCM, whose complexity is mainly related to the scale of the applied supervised Learning model, can potentially facilitate more scalable and time-efficient online anomaly detection by avoiding excessively traversing the original dataset. We designed the DCM and DRCM based on the density-based clustering algorithm and the deep neural network structure, respectively. Evaluations with experimental data from two use cases (i.e., single-point detection and end-to-end detection) demonstrate that up to 99% anomaly detection accuracy can be achieved with a false positive rate below 1%.
Roberto Proietti - One of the best experts on this subject based on the ideXlab platform.
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self taught anomaly detection with hybrid unsupervised supervised Machine Learning in optical networks
Journal of Lightwave Technology, 2019Co-Authors: Xiaoliang Chen, Baojia Li, Roberto Proietti, Zuqing Zhu, S. Ben J. YooAbstract:This paper proposes a self-taught anomaly detection framework for optical networks. The proposed framework makes use of a hybrid unsupervised and supervised Machine Learning Scheme. First, it employs an unsupervised data clustering module (DCM) to analyze the patterns of monitoring data. The DCM enables a self-Learning capability that eliminates the requirement of prior knowledge of abnormal network behaviors and therefore can potentially detect unforeseen anomalies. Second, we introduce a self-taught mechanism that transfers the patterns learned by the DCM to a supervised data regression and classification module (DRCM). The DRCM, whose complexity is mainly related to the scale of the applied supervised Learning model, can potentially facilitate more scalable and time-efficient online anomaly detection by avoiding excessively traversing the original dataset. We designed the DCM and DRCM based on the density-based clustering algorithm and the deep neural network structure, respectively. Evaluations with experimental data from two use cases (i.e., single-point detection and end-to-end detection) demonstrate that up to $99\%$ anomaly detection accuracy can be achieved with a false positive rate below $1\%$ .
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Self-Taught Anomaly Detection With Hybrid Unsupervised/Supervised Machine Learning in Optical Networks
Journal of Lightwave Technology, 2019Co-Authors: Xiaoliang Chen, Baojia Li, Roberto Proietti, Zuqing Zhu, S. Ben J. YooAbstract:This paper proposes a self-taught anomaly detection framework for optical networks. The proposed framework makes use of a hybrid unsupervised and supervised Machine Learning Scheme. First, it employs an unsupervised data clustering module (DCM) to analyze the patterns of monitoring data. The DCM enables a self-Learning capability that eliminates the requirement of prior knowledge of abnormal network behaviors and therefore can potentially detect unforeseen anomalies. Second, we introduce a self-taught mechanism that transfers the patterns learned by the DCM to a supervised data regression and classification module (DRCM). The DRCM, whose complexity is mainly related to the scale of the applied supervised Learning model, can potentially facilitate more scalable and time-efficient online anomaly detection by avoiding excessively traversing the original dataset. We designed the DCM and DRCM based on the density-based clustering algorithm and the deep neural network structure, respectively. Evaluations with experimental data from two use cases (i.e., single-point detection and end-to-end detection) demonstrate that up to 99% anomaly detection accuracy can be achieved with a false positive rate below 1%.
