Abstract: Developments in space-based hyperspectral sensors, advanced remote sensing, and machine learning can help crop yield measurement, modelling, prediction, and crop monitoring for loss ...

This project is targeting people who want to learn internals of ml algorithms or implement them from scratch. The code is much easier to follow than the optimized libraries and easier to play with.

From reproductive rights to climate change to Big Tech, The Independent is on the ground when the story is developing. Whether it's investigating the financials of Elon Musk's pro-Trump PAC or ...

Abstract: Prediction accuracy and model explainability are the two most important objectives when developing machine learning algorithms to solve real-world problems. Neural networks are known to ...

This study proposes a novel method for designing prosthetic heart valves (PHVs) by combining machine learning (ML) with optimization algorithms. This approach aims to overcome the limitations of ...

Many scientific problems entail labeling data items with one of a given, finite set of classes based on features of the data items. For example, oncologists classify tumors as different known cancer ...

The results show that Spain is favored to win with a probability of 14.5%. In times past, when we wanted to know which team would win the World Cup, we had to turn to seers with crystal balls, use ...

In complex environments such as dense forests, detecting and tracking moving targets presents significant challenges due to ...

In times past, when we wanted to know which team would win the World Cup, we had to turn to seers with crystal balls, use divination via tea leaves, or hope for Paul the Octopus to tell us what would ...

Support vector regression can predict numeric values effectively, and this article shows how to implement and train a kernel SVR model in C# using stochastic sub-gradient descent.