scholarly journals Optical spectrum feature analysis and recognition for optical network security with machine learning

2019 ◽  
Vol 27 (17) ◽  
pp. 24808 ◽  
Author(s):  
Yanlong Li ◽  
Nan Hua ◽  
Jiading Li ◽  
Zhizhen Zhong ◽  
Shangyuan Li ◽  
...  
Keyword(s):  
Machine Learning ◽  
Network Security ◽  
Optical Spectrum ◽  
Optical Network ◽  
Feature Analysis ◽  
Spectrum Feature

Light Source and Trail Recognition via Optical Spectrum Feature Analysis for Optical Network Security

2018 ◽  
Vol 22 (5) ◽  
pp. 982-985 ◽  
Author(s):  
Yanlong Li ◽  
Nan Hua ◽  
Yufang Yu ◽  
Qingsong Luo ◽  
Xiaoping Zheng
Keyword(s):  
Network Security ◽  
Light Source ◽  
Optical Spectrum ◽  
Optical Network ◽  
Feature Analysis ◽  
Spectrum Feature

scholarly journals Machine Learning for Optical Network Security Monitoring: A Practical Perspective

2020 ◽  
pp. 1-1 ◽  
Author(s):  
Marija Furdek ◽  
Carlos Natalino ◽  
Fabian Lipp ◽  
David Hock ◽  
Andrea Di Giglio ◽  
...  
Keyword(s):  
Machine Learning ◽  
Network Security ◽  
Optical Network ◽  
Security Monitoring

Binary Spectrum Feature for Improved Classifier Performance

2020 ◽  
Author(s):  
Nalika Ulapane ◽  
Karthick Thiyagarajan ◽  
sarath kodagoda
Keyword(s):  
Machine Learning ◽  
Classification Performance ◽  
Feature Reduction ◽  
Sensor Data ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Svm Classifier ◽  
Monitoring Task ◽  
Classifier Performance ◽  
Spectrum Feature

<div>Classification has become a vital task in modern machine learning and Artificial Intelligence applications, including smart sensing. Numerous machine learning techniques are available to perform classification. Similarly, numerous practices, such as feature selection (i.e., selection of a subset of descriptor variables that optimally describe the output), are available to improve classifier performance. In this paper, we consider the case of a given supervised learning classification task that has to be performed making use of continuous-valued features. It is assumed that an optimal subset of features has already been selected. Therefore, no further feature reduction, or feature addition, is to be carried out. Then, we attempt to improve the classification performance by passing the given feature set through a transformation that produces a new feature set which we have named the “Binary Spectrum”. Via a case study example done on some Pulsed Eddy Current sensor data captured from an infrastructure monitoring task, we demonstrate how the classification accuracy of a Support Vector Machine (SVM) classifier increases through the use of this Binary Spectrum feature, indicating the feature transformation’s potential for broader usage.</div><div><br></div>

scholarly journals Machine Learning Classification and Regression Approaches for Optical Network Traffic Prediction

Electronics ◽  
2021 ◽  
Vol 10 (13) ◽  
pp. 1578
Author(s):  
Daniel Szostak ◽  
Adam Włodarczyk ◽  
Krzysztof Walkowiak
Keyword(s):  
Machine Learning ◽  
Optical Networks ◽  
Network Traffic ◽  
Optical Network ◽  
Optimization Methods ◽  
Supervised Machine Learning ◽  
Traffic Prediction ◽  
Machine Learning Classification ◽  
Classification And Regression ◽  
Network Technologies

Rapid growth of network traffic causes the need for the development of new network technologies. Artificial intelligence provides suitable tools to improve currently used network optimization methods. In this paper, we propose a procedure for network traffic prediction. Based on optical networks’ (and other network technologies) characteristics, we focus on the prediction of fixed bitrate levels called traffic levels. We develop and evaluate two approaches based on different supervised machine learning (ML) methods—classification and regression. We examine four different ML models with various selected features. The tested datasets are based on real traffic patterns provided by the Seattle Internet Exchange Point (SIX). Obtained results are analyzed using a new quality metric, which allows researchers to find the best forecasting algorithm in terms of network resources usage and operational costs. Our research shows that regression provides better results than classification in case of all analyzed datasets. Additionally, the final choice of the most appropriate ML algorithm and model should depend on the network operator expectations.

A Machine Learning Approach to First Pass Reperfusion in Mechanical Thrombectomy: Prediction and Feature Analysis

2021 ◽  
Vol 30 (7) ◽  
pp. 105796
Author(s):  
Lohit Velagapudi ◽  
Nikolaos Mouchtouris ◽  
Richard F. Schmidt ◽  
David Vuong ◽  
Omaditya Khanna ◽  
...  
Keyword(s):  
Machine Learning ◽  
Mechanical Thrombectomy ◽  
Feature Analysis ◽  
Learning Approach ◽  
Machine Learning Approach ◽  
First Pass
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