Researchers at Yazd University developed a binary version of the Puma Optimization Algorithm that achieved 91.53 percent ...

Systems controlled by next-generation computing algorithms could give rise to better and more efficient machine learning products, a new study suggests. Systems controlled by next-generation computing ...

Researchers combined a genetic algorithm with machine learning price forecasting to cut 643 tonnes of steel from a 15 MW ...

Feature selection (FS) is a critical step in hyperspectral image (HSI) classification, essential for reducing data dimensionality while preserving classification accuracy. However, FS for HSIs remains ...

If you've ever wondered whether an AI feature on your phone is doing anything useful, Ben Khalesi has probably asked the same question. He has covered AI and Android for Android Police since 2023, ...

Supervised learning algorithms learn from labeled data, where the desired output is known. These algorithms aim to build a model that can predict the output for new, unseen input data. Let’s take a ...

Deep learning finds numerous applications in machine vision solutions, particularly in enhancing image analysis and recognition tasks. Algorithmic models can be trained to recognize patterns, shapes ...

This paper comprehensively surveys existing works of chip design with ML algorithms from an algorithm perspective. To accomplish this goal, the authors propose a novel and systematical taxonomy for ...

Testing two machine learning algorithms — extra trees and gradient boosting — a team including Australian researchers set out ...

A Diagnostic Cost Group (DCG) machine learning algorithm succeeded in generating risk adjustment models and predicted healthcare spending better than the current HHS hierarchical condition category ...

The current MA risk adjustment model has shortcomings, both in predictive accuracy and payment equity across the Medicare program, which could be mitigated using lessons from machine learning. MA ...