scholarly journals Predicting long‐term freedom from atrial fibrillation after catheter ablation by a machine learning algorithm: Validation of the CAAP‐AF score

2020 ◽  
Vol 36 (2) ◽  
pp. 297-303
Author(s):  
Koichi Furui ◽  
Itsuro Morishima ◽  
Yasuhiro Morita ◽  
Yasunori Kanzaki ◽  
Kensuke Takagi ◽  
...  
Keyword(s):  
Machine Learning ◽  
Atrial Fibrillation ◽  
Catheter Ablation ◽  
Learning Algorithm ◽  
Machine Learning Algorithm ◽  
Algorithm Validation

scholarly journals Analyzing the Impact of Climate Factors on GNSS-Derived Displacements by Combining the Extended Helmert Transformation and XGboost Machine Learning Algorithm

2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Hanlin Liu ◽  
Linqiang Yang ◽  
Linchao Li
Keyword(s):  
Machine Learning ◽  
Puerto Rico ◽  
Reference Frame ◽  
Learning Algorithm ◽  
Virgin Islands ◽  
Machine Learning Algorithm ◽  
Climate Factors ◽  
Helmert Transformation ◽  
The Impact

A variety of climate factors influence the precision of the long-term Global Navigation Satellite System (GNSS) monitoring data. To precisely analyze the effect of different climate factors on long-term GNSS monitoring records, this study combines the extended seven-parameter Helmert transformation and a machine learning algorithm named Extreme Gradient boosting (XGboost) to establish a hybrid model. We established a local-scale reference frame called stable Puerto Rico and Virgin Islands reference frame of 2019 (PRVI19) using ten continuously operating long-term GNSS sites located in the rigid portion of the Puerto Rico and Virgin Islands (PRVI) microplate. The stability of PRVI19 is approximately 0.4 mm/year and 0.5 mm/year in the horizontal and vertical directions, respectively. The stable reference frame PRVI19 can avoid the risk of bias due to long-term plate motions when studying localized ground deformation. Furthermore, we applied the XGBoost algorithm to the postprocessed long-term GNSS records and daily climate data to train the model. We quantitatively evaluated the importance of various daily climate factors on the GNSS time series. The results show that wind is the most influential factor with a unit-less index of 0.013. Notably, we used the model with climate and GNSS records to predict the GNSS-derived displacements. The results show that the predicted displacements have a slightly lower root mean square error compared to the fitted results using spline method (prediction: 0.22 versus fitted: 0.31). It indicates that the proposed model considering the climate records has the appropriate predict results for long-term GNSS monitoring.

scholarly journals Machine learning algorithm validation with a limited sample size

PLoS ONE ◽  
2019 ◽  
Vol 14 (11) ◽  
pp. e0224365 ◽  
Author(s):  
Andrius Vabalas ◽  
Emma Gowen ◽  
Ellen Poliakoff ◽  
Alexander J. Casson
Keyword(s):  
Machine Learning ◽  
Sample Size ◽  
Learning Algorithm ◽  
Machine Learning Algorithm ◽  
Limited Sample ◽  
Limited Sample Size ◽  
Algorithm Validation

scholarly journals Machine Learning Algorithm Validation

2020 ◽  
Vol 30 (4) ◽  
pp. 433-445
Author(s):  
Farhad Maleki ◽  
Nikesh Muthukrishnan ◽  
Katie Ovens ◽  
Caroline Reinhold ◽  
Reza Forghani
Keyword(s):  
Machine Learning ◽  
Learning Algorithm ◽  
Machine Learning Algorithm ◽  
Algorithm Validation

Enhancing Agent Intelligence with Behavior Duplication

2011 ◽  
Vol 403-408 ◽  
pp. 1266-1269 ◽  
Author(s):  
Wei Tang ◽  
Jun Lai
Keyword(s):  
Machine Learning ◽  
Learning Algorithm ◽  
Human Action ◽  
Machine Learning Algorithm ◽  
Agent Behavior ◽  
Input Action ◽  
Sample Data ◽  
Intelligent Behavior ◽  
Agent Intelligence

The traditional agent intelligence designing always lead to a fixed behavior manner. In this way, the NPC(Non-Player Character) in the game will act in a fixed and expectable way. It has greatly weakened the long-term attraction of single-played game. Extracting the human action patterns using a statistical-based machine learning algorithm can provide an easily-understanding way to implement the agent behavior intelligence. A daemon program records and sample the human player’s input action and related properties of character and virtual environment, and then apply certain statistical-based machine learning algorithm on the sample data. As a result, a human-similar intelligent behavior model was obtained. It can be used to help agent making an action decision. Repeating the learning process can give the agent a variety of intelligent behavior.

scholarly journals Application of a machine learning algorithm for detection of atrial fibrillation in secondary care

2020 ◽  
Vol 31 ◽  
pp. 100674
Author(s):  
Kevin G. Pollock ◽  
Sara Sekelj ◽  
Ellie Johnston ◽  
Belinda Sandler ◽  
Nathan R. Hill ◽  
...  
Keyword(s):  
Machine Learning ◽  
Atrial Fibrillation ◽  
Secondary Care ◽  
Learning Algorithm ◽  
Machine Learning Algorithm

scholarly journals An Integrated Machine Learning Algorithm for Separating the Long-Term Deflection Data of Prestressed Concrete Bridges

Sensors ◽  
2018 ◽  
Vol 18 (11) ◽  
pp. 4070 ◽  
Author(s):  
Xijun Ye ◽  
Xueshuai Chen ◽  
Yaxiong Lei ◽  
Jiangchao Fan ◽  
Liu Mei
Keyword(s):  
Machine Learning ◽  
Prestressed Concrete ◽  
Learning Algorithm ◽  
Component Analysis ◽  
Live Load ◽  
Machine Learning Algorithm ◽  
Prestress Loss ◽  
Butterworth Filter ◽  
Concrete Shrinkage

Deflection is one of the key indexes for the safety evaluation of bridge structures. In reality, due to the changing operational and environmental conditions, the deflection signals measured by structural health monitoring systems are greatly affected. These ambient changes in the system often cover subtle changes in the vibration signals caused by damage to the system. The deflection signals of prestressed concrete (PC) bridges are regarded as the superposition of different effects, including concrete shrinkage, creep, prestress loss, material deterioration, temperature effects, and live load effects. According to multiscale analysis theory of the long-term deflection signal, in this paper, an integrated machine learning algorithm that combines a Butterworth filter, ensemble empirical mode decomposition (EEMD), principle component analysis (PCA), and fast independent component analysis (FastICA) is proposed for separating the individual deflection components from a measured single channel deflection signal. The proposed algorithm consists of four stages: (1) the live load effect, which is a high-frequency signal, is separated from the raw signal by a Butterworth filter; (2) the EEMD algorithm is used to extract the intrinsic mode function (IMF) components; (3) these IMFs are utilized as input in the PCA model and some uncorrelated and dominant basis components are extracted; and (4) FastICA is applied to derive the independent deflection component. The simulated results show that each individual deflection component can be successfully separated when the noise level is under 10%. Verified by a practical application, the algorithm is feasible for extracting the structural deflection (including concrete shrinkage, creep, and prestress loss) only caused by structural damage or material deterioration.

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