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

A sequential hybrid metaheuristic framework combining PSO, HHO, or BSO with the Binary Al-Biruni Earth Radius algorithm ...

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, ...

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 ...

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 ...

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 ...

Smartwatches are among the wearable devices that gather health data. Translating that data into useful information can be complicated and expensive. (iStock) The human body constantly generates a ...

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 ...