The development of advanced technologies in variety of domains such as health care, sensor measurements, intrusion detection, motion capture, environment monitoring have directed to the emergence of large scale time stamped data that varies over time. These data are influenced by complexities such as missing values, multivariate attributes, time-stamped features. The objective of the paper is to construct temporal classification framework using stacked Gated Recurrent Unit (S-GRU) for predicting ozone level. Ozone level prediction plays a vital role for accomplishing healthy living environment. Temporal missing value imputation and temporal classification are two functions performed by the proposed system. In temporal missing value imputation, the temporal correlated k-nearest neighbors (TCO-KNN) approach is presented to address missing values. Using attribute dependency based KNN, the nearest significant set is identified for each missing value. The missing values are imputed using the mean values from the determined closest significant set. In temporal classification, the classification model is build using stacked gated recurrent unit (S-GRU). The performance of the proposed framework investigated using ozone multivariate temporal data sets shows improvement in classification accuracy compared to other state of art methods.
Coronavirus disease 2019 (Covid-19) is a contagious pandemic illness characterized by severe acute respiratory syndrome. The daily rise of Covid-19 instances and fatalities has resulted in worldwide lockdowns, quarantines and social distancing. Researchers have been working incredibly to develop precisely focused strategies to warfare the Covid-19 pandemic. This study aims to develop a cyclical learning rate optimized stacked generalization computational models (CLR-SGCM) for predicting Covid-19 pandemic outbreaks. Stacked generalization framework performs hierarchical two-phase prediction. In the first phase, deep learning models namely Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) and statistical models namely Auto Regressive Integrated Moving Average (ARIMA) are used as sub models to create pooled datasets (PDS). Cyclical learning rate (CLR) optimizer is used to enhance learning rate of ensemble deep learning models namely LSTM and GRU. In the second phase, meta learner is trained on dataset PDS using four different regression algorithms such as linear regression, polynomial regression, lasso regression and ridge regression to perform the final predictions. Time series data from India, Brazil, and the United States were utilized to forecast the Covid-19 pandemic outbreak. According to experimental finding, the presented stacking ensemble model outpaces the individual learners in terms of accuracy and error rate.
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