Underwater image processing is an intelligence research field that has great potential to
help developers better explore the underwater environment. Underwater image processing …

Time series forecasting is a critical aspect of data analysis, with applications ranging from
finance and economics to weather prediction and industrial processes. This review paper …

In this paper, a robust recurrent neural network is presented in a Bayesian framework based
on echo state mechanisms. Since the new model is capable of handling outliers in the …

Time series measured in real world is often nonlinear, even chaotic. To effectively extract
desired information from measured time series, it is important to preprocess data to reduce …

Battery prognostics aims to predict the remaining life of a battery and to perform necessary
maintenance service if necessary using the past and current information. A reliable …

In structural vibration tests, one of the main factors which disturb the reliability and accuracy
of the results are the noise signals encountered. To overcome this deficiency, this paper …

Aiming at the problems of insufficient feature extraction, low prediction accuracy and
sensitivity to noise in the cooling load prediction, a hybrid model named WTD–CNN–LSTM …

Chaos is a ubiquitous phenomenon in nature, but the observed chaotic signals are often
contaminated by noises. In this work, we consider chaotic signal denoising from the …

Objective. Among the different approaches for denoising neural signals, wavelet-based
methods are widely used due to their ability to reduce in-band noise. All wavelet denoising …

Precise event detection within time‐series data is increasingly critical, particularly in noisy
environments. Reservoir computing, a robust computing method widely utilized with …