scholarly journals Improving the Data Augmentation Algorithm in the Two-Block Setup

2015 ◽  
Vol 24 (4) ◽  
pp. 1114-1133
Author(s):  
Subhadip Pal ◽  
Kshitij Khare ◽  
James P. Hobert
Keyword(s):  
Data Augmentation ◽  
Augmentation Algorithm

Component-mixing Strategy: A Decomposition-based Data Augmentation Algorithm for Motor Imagery Signals

2021 ◽  
Author(s):  
Binghua Li ◽  
Zhiwen Zhang ◽  
Feng Duan ◽  
Zhenglu Yang ◽  
Qibin Zhao ◽  
...  
Keyword(s):  
Motor Imagery ◽  
Data Augmentation ◽  
Augmentation Algorithm

scholarly journals Data Augmentation Algorithm Based on Generative Antagonism Networks (GAN) Model for Optical Transmission Networks (OTN)

2021 ◽  
Author(s):  
Liang Chen ◽  
Kunpeng Zheng ◽  
Yang Li ◽  
Xuelian Yang ◽  
Han Zhang ◽  
...  
Keyword(s):  
Optical Transmission ◽  
Fault Location ◽  
Data Augmentation ◽  
High Reliability ◽  
Data Traffic ◽  
Transmission Networks ◽  
Large Bandwidth ◽  
Augmentation Algorithm ◽  
Length Data ◽  
High Level

OTN (Optical Transmission Networks) is one of the mainstream infrastructures over the ground-transmission networks, with the characteristics of large bandwidth, low delay, and high reliability. To ensure a stable working of OTN, it is necessary to preform high-level accurate functions of data traffic analysis, alarm prediction, and fault location. However, there is a serious problem for the implementation of these functions, caused by the shortage of available data but a rather-large amount of dirty data existed in OTN. In the view of current pretreatment, the extracted amount of effective data is very less, not enough to support machine learning. To solve this problem, this paper proposes a data augmentation algorithm based on deep learning. Specifically, Data Augmentation for Optical Transmission Networks under Multi-condition constraint (MVOTNDA) is designed based on GAN Mode with the demonstration of variable-length data augmentation method. Experimental results show that MVOTNDA has better performances than the traditional data augmentation algorithms.

LDA-based data augmentation algorithm for acoustic scene classification

2020 ◽  
Vol 195 ◽  
pp. 105600
Author(s):  
Yan Leng ◽  
Weiwei Zhao ◽  
Chan Lin ◽  
Chengli Sun ◽  
Rongyan Wang ◽  
...  
Keyword(s):  
Data Augmentation ◽  
Scene Classification ◽  
Augmentation Algorithm

scholarly journals An Artificial Neural Network Approach and a Data Augmentation Algorithm to Systematize the Diagnosis of Deep-Vein Thrombosis by Using Wells’ Criteria

Electronics ◽  
2020 ◽  
Vol 9 (11) ◽  
pp. 1810
Author(s):  
María Berenice Fong-Mata  ◽  
Enrique Efrén García-Guerrero  ◽  
David Abdel Mejía-Medina ◽  
Oscar Roberto López-Bonilla  ◽  
Luis Jesús Villarreal-Gómez  ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Deep Vein Thrombosis ◽  
Data Augmentation ◽  
Training Dataset ◽  
Ann Model ◽  
Vein Thrombosis ◽  
Augmentation Algorithm ◽  
Artificial Neural ◽  
Deep Vein

The use of a back-propagation artificial neural network (ANN) to systematize the reliability of a Deep Vein Thrombosis (DVT) diagnostic by using Wells’ criteria is introduced herein. In this paper, a new ANN model is proposed to improve the Accuracy when dealing with a highly unbalanced dataset. To create the training dataset, a new data augmentation algorithm based on statistical data known as the prevalence of DVT of real cases reported in literature and from the public hospital is proposed. The above is used to generate one dataset of 10,000 synthetic cases. Each synthetic case has nine risk factors according to Wells’ criteria and also the use of two additional factors, such as gender and age, is proposed. According to interviews with medical specialists, a training scheme was established. In addition, a new algorithm is presented to improve the Accuracy and Sensitivity/Recall. According to the proposed algorithm, two thresholds of decision were found, the first one is 0.484, which is to improve Accuracy. The other one is 0.138 to improve Sensitivity/Recall. The Accuracy achieved is 90.99%, which is greater than that obtained with other related machine learning methods. The proposed ANN model was validated performing the k-fold cross validation technique using a dataset with 10,000 synthetic cases. The test was performed by using 59 real cases obtained from a regional hospital, achieving an Accuracy of 98.30%.

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