Predicting stock market is one of the most difficult tasks in the field of computation. There are
many factors involved in the prediction–physical factors vs. physiological, rational and …

Coded recurrent neural networks with three levels of sparsity are introduced. The first level is
related to the size of messages that are much smaller than the number of available neurons …

In this paper, an asynchronous filter is proposed for Markov jump neural networks (NNs) with
time delay and quantized measurements where a logarithmic quantizer is employed. The …

The volatile and non-linear nature of stock market data, particularly in the post-pandemic
era, poses significant challenges for accurate financial forecasting. To address these …

Porosity estimation plays an important role in geophysical exploration and reservoir
development, which is a highly complicated and extremely challenging problem. Usually …

Recurrent neural networks (RNNs) continue to show outstanding performance in sequence
learning tasks such as language modeling, but it remains difficult to train RNNs for long …

In this research work, Efficient Stock forecasting model using Log Bilinear and Long Short
term memory (LBL-LSTM) is designed, considering external fluctuating factors to analyze …

Abstract Protein Secondary Structure Prediction (PSSP) is regarded as a challenging task in
bioinformatics, and numerous approaches to achieve a more accurate prediction have been …

Neuro-evolution and neural architecture search algorithms have gained significant interest
due to the challenges of designing optimal artificial neural networks (ANNs). While these …

This paper mainly studies the parameters estimation and synchronization of coupling
recurrent dynamical neural networks (CRDNNs). Here, the weights and coupling parameters …