This review covers recent advances in artificial intelligence (AI) for aquatic species identification and conservation, ...

This paper proposes an adaptive direct-discretized recurrent neural network (ADD-RNN) algorithm with fuzzy factor to address the problem of future distinct-layer inequality and equation system (FDLIES ...

Specifically, to process heterogeneous sequential data, we propose a tensor-based RNN model. To guarantee privacy, we develop a tensor-based back-propagation through time algorithm with perturbation ...

During the process of determining the arrival directions and beam conformation at the antennas, different types of algorithms can be used, namely deterministic algorithms and heuristics. Genetic ...

Recurrent neural networks (RNNs) are a powerful class of neural networks that are commonly used to model sequential processing applications, such as natural language processing, speech recognition, ...

RNN variants are among the most prominent in use today. There is great variety in how they are implemented. The most common is the long short-term memory (LSTM) network.

Various traditional and deep machine learning (DML) algorithms, including convolutional neural network (CNN), recurrent neural network (RNN), support vector machine (SVM), decision tree (DT), random ...

Amid all the hype and hysteria about ChatGPT, Bard, and other generative large language models (LLMs), it’s worth taking a step back to look at the gamut of AI algorithms and their uses. After ...