In recent times, deep artificial neural networks have achieved many successes in pattern
recognition. Part of this success can be attributed to the reliance on big data to increase …

The accuracy of a predictive system is critical for predictive maintenance and to support the
right decisions at the right times. Statistical models, such as ARIMA and SARIMA, are unable …

Wastewater treatment plants (WWTPs) are energy-intensive facilities that play a critical role
in meeting stringent effluent quality regulations. Accurate prediction of energy consumption …

In this paper, a novel data augmentation method for time-series classification is proposed. In
the introduced method, a new time-series is obtained in warped space between …

Data Augmentation (DA) has emerged as an indispensable strategy in Time Series
Classification (TSC), primarily due to its capacity to amplify training samples, thereby …

Accurate and reliable measurement of the severity of dystonia is essential for the indication,
evaluation, monitoring and fine-tuning of treatments. Assessment of dystonia in children and …

Abstract Machine Learning methods require a huge amount of data to train. Real world
constraints and missing labels hinder the assimilation of large data sets and therefore limit …

Despite the rapid growth of machine learning (ML) and its far‐reaching applications in
various fields such as healthcare, finance, and urban heat management, there are still some …

In this paper, a hybrid data augmentation technique for short-term time series prediction is
proposed in order to overcome the underfitting problem in deep learning models based on …

In machine learning, data augmentation is called the process of generating synthetic
samples in order to augment sparse training datasets. Reducing the error-rate of classifiers …