Reports demonstrates that whole-brain Single Photon Emission Computed Tomography (SPECT) imaging, combined with advanced ...

The human brain is known to naturally change with age, shrinking in size and volume after people reach their 30s or 40s. In ...

Aims To develop prediction models for identifying cases with poor visual outcomes after surgery for primary rhegmatogenous ...

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.

The emerging convergence of AI-first design principles and environmental consciousness is reshaping how we think about ...

Supervised machine learning improves predictions of compressive strength in industrial waste-modified concrete, supporting ...

While machine learning (ML) has garnered increasing attention in health care applications, effective early prediction tools remain limited in current clinical practice. Recent investigations have ...

Abstract: This work introduces an innovative application of established machine learning methods to calculate transformer no-load losses. An accurate estimation of losses is crucial to cost-effective ...

Machine learning is increasingly used in content-based recommender algorithms to guide journal submissions for academic articles. Objective: We sought to evaluate the performance of open-source ...

Department of Neurology, Weill Institute for Neuroscience, University of California San Francisco, San Francisco, California 94158, United States ...

Abstract: In this article, we propose using new machine learning (ML)-based optimization methods as an alternative to traditional optimization methods, for complex antenna designs. This is an ...