scholarly journals Machine Learning Techniques for Optical Performance Monitoring From Directly Detected PDM-QAM Signals

2017 ◽  
Vol 35 (4) ◽  
pp. 868-875 ◽  
Author(s):  
Jakob Thrane ◽  
Jesper Wass ◽  
Molly Piels ◽  
Julio C. M. Diniz ◽  
Rasmus Jones ◽  
...  
Keyword(s):  
Machine Learning ◽  
Performance Monitoring ◽  
Machine Learning Techniques ◽  
Optical Performance ◽  
Optical Performance Monitoring ◽  
Learning Techniques

scholarly journals Machine Learning-Aided Optical Performance Monitoring Techniques: A Review

2022 ◽  
Vol 2 ◽  
Author(s):  
Dativa K. Tizikara ◽  
Jonathan Serugunda ◽  
Andrew Katumba
Keyword(s):  
Machine Learning ◽  
Communication Systems ◽  
Performance Monitoring ◽  
High Capacity ◽  
Machine Learning Algorithms ◽  
Heterogeneous Traffic ◽  
Optical Fiber Communications ◽  
Optical Performance ◽  
Optical Performance Monitoring ◽  
Monitoring Techniques

Future communication systems are faced with increased demand for high capacity, dynamic bandwidth, reliability and heterogeneous traffic. To meet these requirements, networks have become more complex and thus require new design methods and monitoring techniques, as they evolve towards becoming autonomous. Machine learning has come to the forefront in recent years as a promising technology to aid in this evolution. Optical fiber communications can already provide the high capacity required for most applications, however, there is a need for increased scalability and adaptability to changing user demands and link conditions. Accurate performance monitoring is an integral part of this transformation. In this paper, we review optical performance monitoring techniques where machine learning algorithms have been applied. Moreover, since many performance monitoring approaches in the optical domain depend on knowledge of the signal type, we also review work for modulation format recognition and bitrate identification. We additionally briefly introduce a neuromorphic approach as an emerging technique that has only recently been applied to this domain.

Using machine learning techniques to reduce data annotation time

2006 ◽  
Author(s):  
Christopher Schreiner ◽  
Kari Torkkola ◽  
Mike Gardner ◽  
Keshu Zhang
Keyword(s):  
Machine Learning ◽  
Machine Learning Techniques ◽  
Data Annotation ◽  
Learning Techniques

Using Machine Learning Algorithms on Prediction of Stock Price

2020 ◽  
Vol 12 (2) ◽  
pp. 84-99
Author(s):  
Li-Pang Chen
Keyword(s):  
Machine Learning ◽  
Stock Price ◽  
Short Term Memory ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Short Term ◽  
Learning Techniques ◽  
Historical Database ◽  
Long Short Term Memory

In this paper, we investigate analysis and prediction of the time-dependent data. We focus our attention on four different stocks are selected from Yahoo Finance historical database. To build up models and predict the future stock price, we consider three different machine learning techniques including Long Short-Term Memory (LSTM), Convolutional Neural Networks (CNN) and Support Vector Regression (SVR). By treating close price, open price, daily low, daily high, adjusted close price, and volume of trades as predictors in machine learning methods, it can be shown that the prediction accuracy is improved.

389-P: Ability for Detecting or Predicting Hypoglycemia with the Aid of Machine Learning Techniques: A Meta-analysis

Diabetes ◽  
2020 ◽  
Vol 69 (Supplement 1) ◽  
pp. 389-P
Author(s):  
SATORU KODAMA ◽  
MAYUKO H. YAMADA ◽  
YUTA YAGUCHI ◽  
MASARU KITAZAWA ◽  
MASANORI KANEKO ◽  
...  
Keyword(s):  
Machine Learning ◽  
Meta Analysis ◽  
Machine Learning Techniques ◽  
Learning Techniques
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