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Yufan Qian, Limei Tian, Baichen Zhai, Shufan Zhang and Rui Wu
Missing observations in time series will distort the data characteristics, change the dataset expectations, high-order distances, and other statistics, and increase the difficulty of data analysis. Therefore, data imputation needs to be performed first. ...
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Fahima Noor, Sanaulla Haq, Mohammed Rakib, Tarik Ahmed, Zeeshan Jamal, Zakaria Shams Siam, Rubyat Tasnuva Hasan, Mohammed Sarfaraz Gani Adnan, Ashraf Dewan and Rashedur M. Rahman
Bangladesh is in the floodplains of the Ganges, Brahmaputra, and Meghna River delta, crisscrossed by an intricate web of rivers. Although the country is highly prone to flooding, the use of state-of-the-art deep learning models in predicting river water ...
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Jamal Mamkhezri,Alok K. Bohara,Alejandro Islas Camargo
Pág. 249 - 267
We utilize a time-series semi-parametric Poisson regression approach, incorporating natural cubic splines for temperature, to study the short-term associations between PM10 and daily mortality due to cardiovascular, respiratory, and cardiorespiratory eve...
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Youngdoo Son and Wonjoon Kim
Estimating stature is essential in the process of personal identification. Because it is difficult to find human remains intact at crime scenes and disaster sites, for instance, methods are needed for estimating stature based on different body parts. For...
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Iguniwari Thomas Ekeu-wei, George Alan Blackburn and Philip Pedruco
In developing regions missing data are prevalent in historical hydrological datasets, owing to financial, institutional, operational and technical challenges. If not tackled, these data shortfalls result in uncertainty in flood frequency estimates and co...
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