Open AccessOn the domain aided performance boosting technique for deep predictive networks: A COVID-19 scenario
Author(s) -
Soumya Jyoti Raychaudhuri,
C. Narendra Babu
Publication year - 2022
Publication title -
intelligent decision technologies
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.206
H-Index - 13
eISSN - 1875-8843
pISSN - 1872-4981
DOI - 10.3233/idt-200167
Subject(s) - computer science , mean squared error , mean absolute percentage error , boosting (machine learning) , time series , recurrent neural network , convolutional neural network , artificial intelligence , deep learning , time domain , series (stratigraphy) , artificial neural network , algorithm , pattern recognition (psychology) , machine learning , statistics , mathematics , paleontology , computer vision , biology
Deep learning models are one of the widely used techniques for forecasting time series data in various applications. It has already been established that the Recurrent Neural Networks (RNN) such as the Long Short-Term Memory (LSTM), Gated Recurrent Units (GRU), etc., perform well in analyzing sequence data for accurate time-series predictions. But, these specialized recurrent architectures suffer from certain drawbacks due to their computational complexity and also their dependency on short term historical data. Hence, there is a scope for further improvement. This paper analyzes the effects of various optimizers and hyper-parameter tuning, on the precision and time efficiency of different deep neural architectures. The analysis has been conducted on COVID-19 pandemic data. Since Convolutional Neural Networks (CNN) are known for their super-human ability in identifying patterns from images, the time-series data has been transformed into a slope-information domain for analyzing the slope patterns over time. The domain patterns have been projected on a 2D plane for further analysis using a restricted recursive CNN (RRCNN) algorithm. The experimental results reveal that the proposed methodology reduces the error over benchmarked sequence models by almost 20% and further reduces the training time by nearly 50%. The prediction models considered in this study have been evaluated using Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), and Mean Absolute Percentage Error (MAPE%).