Plants are constantly exposed to a wide array of biotic and abiotic stresses in their natural environments, posing ...

An overview of attention detection using EEG signals, which includes six steps: an experimental paradigm design, in which the task and the stimuli are defined and presented to the subjects; EEG data ...

In data analysis, time series forecasting relies on various machine learning algorithms, each with its own strengths. However, we will talk about two of the most used ones. Long Short-Term Memory ...

AI, Machine Learning & Robotics research at Drexel University's College of Computing & Informatics (CCI) explores algorithms, mathematics, and applications of artificial intelligence (AI) through ...

Machine learning is a multibillion-dollar business with seemingly endless potential, but it poses some risks. Here's how to avoid the most common machine learning mistakes. Machine learning technology ...

In the rapidly evolving landscape of data science and machine learning, ensuring accessibility of data is critical for obtaining meaningful insights. Continuous data plays a pivotal role in various ...

This system utilizes machine learning algorithms to optimize the operation of particle accelerators, reducing manual intervention and enhancing precision in real-time control. By integrating virtual ...

Machine learning, a type of artificial intelligence, has many applications in science, from finding gravitational lenses in the distant universe to predicting virus evolution. Hubble Space Telescope ...

Lithium-ion batteries have become the quiet workhorses of the energy transition, but the way they are designed and tested has ...

Electron density prediction for a four-million-atom aluminum system using machine learning, deemed to be infeasible using traditional DFT method. × Researchers from Michigan Tech and the University of ...