scholarly journals Predicting the New Cases of Coronavirus [COVID-19] in India by Using Time Series Analysis as Machine Learning Model in Python

2021 ◽  
Author(s):  
Vikas Kulshreshtha ◽  
N. K. Garg
Keyword(s):  
Machine Learning ◽  
Time Series ◽  
Time Series Analysis ◽  
Learning Model ◽  
Series Analysis ◽  
Machine Learning Model

Forecasting PV Panel Output Using Prophet Time Series Machine Learning Model

2020 ◽  
Author(s):  
Md. Mehedi Hasan Shawon ◽  
Sumaiya Akter ◽  
Md. Kamrul Islam ◽  
Sabbir Ahmed ◽  
Md. Mosaddequr Rahman
Keyword(s):  
Machine Learning ◽  
Time Series ◽  
Learning Model ◽  
Pv Panel ◽  
Machine Learning Model

scholarly journals Time Series-Analysis Based Engineering of High-Dimensional Wide-Area Stability Indices for Machine Learning

IEEE Access ◽  
2021 ◽  
pp. 1-1
Author(s):  
Raoult Teukam Dabou ◽  
Innocent Kamwa ◽  
C. Y. Chung ◽  
C. F. Mugombozi
Keyword(s):  
Machine Learning ◽  
Time Series ◽  
Time Series Analysis ◽  
Wide Area ◽  
High Dimensional ◽  
Series Analysis ◽  
Stability Indices

scholarly journals Machine Learning Methods for Improved Understanding of a Pumping Test in Heterogeneous Aquifers

Water ◽  
2020 ◽  
Vol 12 (5) ◽  
pp. 1342 ◽  
Author(s):  
Yong Fan ◽  
Litang Hu ◽  
Hongliang Wang ◽  
Xin Liu
Keyword(s):  
Machine Learning ◽  
Time Series ◽  
Time Series Analysis ◽  
Analytical Solutions ◽  
Pumping Test ◽  
Learning Methods ◽  
Pumping Tests ◽  
Machine Learning Methods ◽  
Series Analysis ◽  
Pumping Wells

Pumping tests are very important means for investigating aquifer properties; however, interpreting the data using common analytical solutions become invalid in complex aquifer systems. The paper aims to explore the potential of machine learning methods in retrieving the pumping tests information in a field site in the Democratic Republic of Congo. A newly planned mining site with a pumping test of three pumping wells and 28 observation wells over one month was chosen to analyze the significance of machine learning methods in the pumping test analysis. Widely used machine learning methods, including correlation, cluster, time-series analysis, artificial neural network (ANN), support vector machine (SVR), random forest (RF) method, and linear regression, are all used in this study. Correlation and cluster analyses among wells provide visual pictures of possible hydraulic connections. The pathway with the best permeability ranges from the depth of 250 m to 350 m. Time-series analysis perfectly captured changes of drawdowns within the three pumping wells. The RF method is found to have the higher accuracy and the lower sensitivity to model parameters than ANN and SVR methods. The coupling of the linear regressive model and analytical solutions is applied to estimate hydraulic conductivities. The results found that ML methods can significantly and effectively improve our understanding of pumping tests by revealing inherent information hidden in those tests.

scholarly journals Time-Series Growth Prediction Model Based on U-Net and Machine Learning in Arabidopsis

2021 ◽  
Vol 12 ◽  
Author(s):  
Sungyul Chang ◽  
Unseok Lee ◽  
Min Jeong Hong ◽  
Yeong Deuk Jo ◽  
Jin-Baek Kim
Keyword(s):  
Machine Learning ◽  
Time Series ◽  
Time Series Analysis ◽  
Time Series Data ◽  
Developmental Stages ◽  
Series Data ◽  
Future Application ◽  
Yield Prediction ◽  
Flowering Stage ◽  
Series Analysis

Yield prediction for crops is essential information for food security. A high-throughput phenotyping platform (HTPP) generates the data of the complete life cycle of a plant. However, the data are rarely used for yield prediction because of the lack of quality image analysis methods, yield data associated with HTPP, and the time-series analysis method for yield prediction. To overcome limitations, this study employed multiple deep learning (DL) networks to extract high-quality HTTP data, establish an association between HTTP data and the yield performance of crops, and select essential time intervals using machine learning (ML). The images of Arabidopsis were taken 12 times under environmentally controlled HTPP over 23 days after sowing (DAS). First, the features from images were extracted using DL network U-Net with SE-ResXt101 encoder and divided into early (15–21 DAS) and late (∼21–23 DAS) pre-flowering developmental stages using the physiological characteristics of the Arabidopsis plant. Second, the late pre-flowering stage at 23 DAS can be predicted using the ML algorithm XGBoost, based only on a portion of the early pre-flowering stage (17–21 DAS). This was confirmed using an additional biological experiment (P < 0.01). Finally, the projected area (PA) was estimated into fresh weight (FW), and the correlation coefficient between FW and predicted FW was calculated as 0.85. This was the first study that analyzed time-series data to predict the FW of related but different developmental stages and predict the PA. The results of this study were informative and enabled the understanding of the FW of Arabidopsis or yield of leafy plants and total biomass consumed in vertical farming. Moreover, this study highlighted the reduction of time-series data for examining interesting traits and future application of time-series analysis in various HTPPs.

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