AI-assisted design methods now allow for automated optimization, drastically shortening development cycles while boosting ...

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 ...

A new system that combines Gemini’s coding abilities with an evolutionary approach improves data center scheduling and chip design, and fine-tunes large language models.

Abstract This study addresses the growing demand for news text classification driven by the rapid expansion of internet information by proposing a classification algorithm based on a Bidirectional ...

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, ...

There is an increasing demand for data with the development of the world, and various fiber optic multiplexing techniques have become an important research direction to improve transmission capacity.

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.

Researchers have developed an algorithm to train an analog neural network just as accurately as a digital one, enabling the development of more efficient alternatives to power-hungry deep learning ...