Stock Price Prediction, Deep Learning, LSTM, GRU, Attention Mechanism, Financial Time Series Share and Cite: Kirui, D. (2026) ...

Qiskit and Q# are major quantum programming languages from IBM and Microsoft, respectively, used for creating and testing ...

In a recent study published in Nature Communications, researchers created a memristor that uses a built-in oxygen gradient to ...

Naoris debuts its quantum-resistant mainnet, which uses algorithms approved by the U.S. National Institute of Standards and ...

A recent study explored rapid evaporative ionization mass spectrometry (REIMS) as a high-throughput, real-time alternative. By analyzing metabolomic fingerprints from pig neck fat, REIMS was combined ...

In a recent webinar organized by the National Academy of Engineering in connection with its forthcoming Fall ...

In this tutorial, we implement a reinforcement learning agent using RLax, a research-oriented library developed by Google DeepMind for building reinforcement learning algorithms with JAX. We combine ...

The CMS Collaboration has shown, for the first time, that machine learning can be used to fully reconstruct particle collisions at the LHC. This new approach can reconstruct collisions more quickly ...

Supervised learning algorithms like Random Forests, XGBoost, and LSTMs dominate crypto trading by predicting price directions or values from labeled historical data, enabling precise signals such as ...

Abstract: To address the issues of slow convergence speed and poor path planning performance in dynamic obstacle environments. This paper proposes an improved Q-Learning path planning algorithm for ...

The original version of this story appeared in Quanta Magazine. Imagine a town with two widget merchants. Customers prefer cheaper widgets, so the merchants must compete to set the lowest price.

A machine learning algorithm used gene expression profiles of patients with gout to predict flares. The PyTorch neural network performed best, with an area under the curve of 65%. The PyTorch model ...