With the latest advances in deep learning-based generative models, it has not taken long to
take advantage of their remarkable performance in the area of time series. Deep neural …

Time Series Classification and Extrinsic Regression are important and challenging machine
learning tasks. Deep learning has revolutionized natural language processing and computer …

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 …

Large datasets are necessary for deep learning as the performance of the algorithms used
increases as the size of the dataset increases. Poor data management practices and the low …

Neural networks have become a powerful tool in pattern recognition and part of their
success is due to generalization from using large datasets. However, unlike other domains …

We demonstrate the application of a recurrent neural network (RNN) to perform multistep
and multivariate time-series performance predictions for stirred and static mixers as …

Data Augmentation (DA) has become a critical approach in Time Series Classification
(TSC), primarily for its capacity to expand training datasets, enhance model robustness …

With the surge of electric vehicles, accurate estimation of their energy consumption becomes
increasingly critical. Data-driven models have been widely used for estimating the energy …

Time series data are essential in a wide range of machine learning (ML) applications.
However, temporal data are often scarce or highly sensitive, limiting data sharing and the …

Utilizing electronic noses (e-noses) with pattern recognition algorithms offers a promising
noninvasive method for the early detection of urinary bladder cancer (UBC). However …