scholarly journals Machine-Learning Method for Quality of Transmission Prediction of Unestablished Lightpaths

2018 ◽  
Vol 10 (2) ◽  
pp. A286 ◽  
Author(s):  
Cristina Rottondi ◽  
Luca Barletta ◽  
Alessandro Giusti ◽  
Massimo Tornatore
Keyword(s):  
Machine Learning ◽  
Machine Learning Method ◽  
Learning Method ◽  
Quality Of Transmission

scholarly journals Single Image De-Raining using Supervised CNN Model

2021 ◽  
Vol 9 (10) ◽  
pp. 244-250
Author(s):  
Dr. Geeta Hanji
Keyword(s):  
Machine Learning ◽  
High Frequency ◽  
Accurate Result ◽  
Supervised Machine Learning ◽  
Machine Learning Method ◽  
Learning Method ◽  
Single Image ◽  
Elapsed Time ◽  
Difficult Issue

Abstract: An image captured in rain reduces the visibility quality of image which affects the analytical task like detecting objects and classifying pictures. Hence, image de-raining became important in last few years. Since pictures taken in rain include rain streaks of all sizes, single image de-raining is becoming much difficult issue to solve, which may flow in different direction and the density of each rain streak is different. Rain streaks have a varied effect on various areas of picture, and hence it becomes important for removing rain streak from rainy pictures as rainy images tend to lose its high frequency information; previously many methods were proposed for this purpose but they failed to provide accurate results. Hence we have studied and implemented a supervised machine learning method using convolutional neural network (CNN) algorithm to get more accurate result of rain streak removal from an image captured during rain and in less elapsed time by preserving high rated information of image during removal of rain streak. Keywords: CNN, elapsed time, single image de-raining, supervised machine learning, rain streaks.

scholarly journals An unsupervised machine learning method for assessing quality of tandem mass spectra

2012 ◽  
Vol 10 (Suppl 1) ◽  
pp. S12 ◽  
Author(s):  
Wenjun Lin ◽  
Jianxin Wang ◽  
Wen-Jun Zhang ◽  
Fang-Xiang Wu
Keyword(s):  
Machine Learning ◽  
Mass Spectra ◽  
Tandem Mass ◽  
Machine Learning Method ◽  
Learning Method ◽  
Unsupervised Machine Learning ◽  
Tandem Mass Spectra

scholarly journals Learning Using Concave and Convex Kernels: Applications in Predicting Quality of Sleep and Level of Fatigue in Fibromyalgia

Entropy ◽  
2019 ◽  
Vol 21 (5) ◽  
pp. 442 ◽  
Author(s):  
Elyas Sabeti ◽  
Jonathan Gryak ◽  
Harm Derksen ◽  
Craig Biwer ◽  
Sardar Ansari ◽  
...  
Keyword(s):  
Machine Learning ◽  
Sleep Quality ◽  
Medical Condition ◽  
Electrodermal Activity ◽  
Supervised Machine Learning ◽  
Poor Sleep ◽  
Machine Learning Method ◽  
Learning Approaches ◽  
Learning Method

Fibromyalgia is a medical condition characterized by widespread muscle pain and tenderness and is often accompanied by fatigue and alteration in sleep, mood, and memory. Poor sleep quality and fatigue, as prominent characteristics of fibromyalgia, have a direct impact on patient behavior and quality of life. As such, the detection of extreme cases of sleep quality and fatigue level is a prerequisite for any intervention that can improve sleep quality and reduce fatigue level for people with fibromyalgia and enhance their daytime functionality. In this study, we propose a new supervised machine learning method called Learning Using Concave and Convex Kernels (LUCCK). This method employs similarity functions whose convexity or concavity can be configured so as to determine a model for each feature separately, and then uses this information to reweight the importance of each feature proportionally during classification. The data used for this study was collected from patients with fibromyalgia and consisted of blood volume pulse (BVP), 3-axis accelerometer, temperature, and electrodermal activity (EDA), recorded by an Empatica E4 wristband over the courses of several days, as well as a self-reported survey. Experiments on this dataset demonstrate that the proposed machine learning method outperforms conventional machine learning approaches in detecting extreme cases of poor sleep and fatigue in people with fibromyalgia.

Speech Organ Contour Extraction Using Real-Time MRI and Machine Learning Method

2019 ◽  
Author(s):  
Hironori Takemoto ◽  
Tsubasa Goto ◽  
Yuya Hagihara ◽  
Sayaka Hamanaka ◽  
Tatsuya Kitamura ◽  
...  
Keyword(s):  
Machine Learning ◽  
Real Time ◽  
Machine Learning Method ◽  
Learning Method ◽  
Contour Extraction
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