Improving Power Grid Monitoring Data Quality: An Efficient Machine Learning Framework for Missing Data Prediction

2015 ◽  
Author(s):  
Weiwei Shi ◽  
Yongxin Zhu ◽  
Jinkui Zhang ◽  
Xiang Tao ◽  
Gehao Sheng ◽  
...  
Keyword(s):  
Machine Learning ◽  
Missing Data ◽  
Data Quality ◽  
Power Grid ◽  
Monitoring Data ◽  
Learning Framework ◽  
Data Prediction ◽  
Grid Monitoring ◽  
Efficient Machine ◽  
Missing Data Prediction

scholarly journals CDL4CDRP: A Collaborative Deep Learning Approach for Clinical Decision and Risk Prediction

Processes ◽  
2019 ◽  
Vol 7 (5) ◽  
pp. 265 ◽  
Author(s):  
Mingrui Sun ◽  
Tengfei Min ◽  
Tianyi Zang ◽  
Yadong Wang
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Missing Data ◽  
Clinical Data ◽  
Clinical Decision ◽  
Disease Diagnosis ◽  
Personalized Recommendation ◽  
Data Prediction ◽  
Latent Features ◽  
Missing Data Prediction

(1) Background: Recommendation algorithms have played a vital role in the prediction of personalized recommendation for clinical decision support systems (CDSSs). Machine learning methods are powerful tools for disease diagnosis. Unfortunately, they must deal with missing data, as this will result in data error and limit the potential patterns and features associated with obtaining a clinical decision; (2) Methods: Recent years, collaborative filtering (CF) have proven to be a valuable means of coping with missing data prediction. In order to address the challenge of missing data prediction and latent feature extraction, neighbor-based and latent features-based CF methods are presented for clinical disease diagnosis. The novel discriminative restricted Boltzmann machine (DRBM) model is proposed to extract the latent features, where the deep learning technique is adopted to analyze the clinical data; (3) Results: Proposed methods were compared to machine learning models, using two different publicly available clinical datasets, which has various types of inputs and different quantity of missing. We also evaluated the performance of our algorithm, using clinical datasets that were missing at random (MAR), which were missing at various degrees; and (4) Conclusions: The experimental results demonstrate that DRBM can effectively capture the latent features of real clinical data and exhibits excellent performance for predicting missing values and result classification.

scholarly journals Missing Data Prediction using Correlation Genetic Algorithm and SVM Approach

2020 ◽  
Vol 11 (2) ◽  
Author(s):  
Aysh Alhroob ◽  
Wael Alzyadat ◽  
Ikhlas Almukahel ◽  
Hassan Altarawneh
Keyword(s):  
Genetic Algorithm ◽  
Missing Data ◽  
Data Prediction ◽  
Missing Data Prediction

scholarly journals Development of missing data prediction model for carbon monoxide

2019 ◽  
Vol 15 (1) ◽  
pp. 13-17
Author(s):  
Nurul Latiffah Abd Rani ◽  
Azman Azid ◽  
Muhamad Shirwan Abdullah Sani ◽  
Mohd Saiful Samsudin ◽  
Ku Mohd Kalkausar Ku Yusof ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Carbon Monoxide ◽  
Missing Data ◽  
Prediction Model ◽  
Optimum Number ◽  
Data Prediction ◽  
Artificial Neural ◽  
Input Variables ◽  
Missing Data Prediction

Carbon monoxide (CO) is one of the most important pollutants since it is selected for API calculation. Therefore, it is paramount to ensure that there is no missing data of CO during the analysis. There are numbers of occurrences that may contribute to the missing data problems such as inability of the instrument to record certain parameters. In view of this fact, a CO prediction model needs to be developed to address this problem. A dataset of meteorological and air pollutants value was obtained from the Air Quality Division, Department of Environment Malaysia (DOE). A total of 113112 datasets were used to develop the model using sensitivity analysis (SA) through artificial neural network (ANN). SA showed particulate matter (PM10) and ozone (O3) were the most significant input variables for missing data prediction model of CO. Three hidden nodes were the optimum number to develop the ANN model with the value of R2 equal to 0.5311. Both models (artificial neural network-carbon monoxide-all parameters (ANN-CO-AP) and artificial neural network-carbon monoxide-leave out (ANN-CO-LO)) showed high value of R2 (0.7639 and 0.5311) and low value of RMSE (0.2482 and 0.3506), respectively. These values indicated that the models might only employ the most significant input variables to represent the CO rather than using all input variables.

scholarly journals An Efficient Missing Data Prediction Method Based on Kronecker Compressive Sensing in Multivariable Time Series

IEEE Access ◽  
2018 ◽  
Vol 6 ◽  
pp. 57239-57248 ◽  
Author(s):  
Yan Guo ◽  
Xiaoxiang Song ◽  
Ning Li ◽  
Dagang Fang
Keyword(s):  
Time Series ◽  
Missing Data ◽  
Compressive Sensing ◽  
Prediction Method ◽  
Data Prediction ◽  
Missing Data Prediction
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