Results 181 to 190 of about 261,268 (307)

Terrain Classification for Planetary Rovers Using Wireless In‐Wheel Sensor Modules and Machine Learning

open access: yesJournal of Field Robotics, Volume 43, Issue 3, Page 1884-1904, May 2026.
ABSTRACT Safe and reliable mobility over different kinds of ground is important for planetary rovers on space missions. Since terrain changes might affect the mobility of the rover, energy consumption, and safety, detecting the type of ground in real‐time is vital.
Md Masrul Khan   +7 more
wiley   +1 more source

Interpretable CRAM‑Enhanced Lightweight Dual‑Branch CNN for Real‑Time Breast Cancer Histopathology in Internet‑of‑Medical‑Things Environments

open access: yesSmall, Volume 22, Issue 26, 8 May 2026.
This study presents an interpretable, lightweight hybrid deep learning model for real‐time analysis of breast cancer histopathology in IoMT‐enabled diagnostic systems. By integrating MobileNetV2 and EfficientNet‐B0 with a novel contextual recurrent attention module (CRAM), the framework achieves near‐perfect accuracy while providing transparent Grad ...
Roseline Oluwaseun Ogundokun   +4 more
wiley   +1 more source

Comparative Analysis of Gut Microbiota Among Captive Waterbird Species: Effects of Diet and Environmental Factors

open access: yesVeterinary Medicine and Science, Volume 12, Issue 3, May 2026.
This study reveals that diet drives gut microbiota differences in captive waterbirds (bar‐headed goose, ruddy shelduck, black‐necked crane), with protein‐rich diets shaping distinct microbial communities. Artificial lakes enhance microbial diversity compared to enclosures, offering insights for improving captive waterbird health.
He Liu   +4 more
wiley   +1 more source

An embedded deep learning framework for real-time violence detection and alert generation. [PDF]

open access: yesSci Rep
Salman M   +4 more
europepmc   +1 more source

Crowdsourcing the Frontier: Advancing Hybrid Physics‐ML Climate Simulation via a $50,000 Kaggle Competition

open access: yesJournal of Advances in Modeling Earth Systems, Volume 18, Issue 5, May 2026.
Abstract Subgrid machine‐learning (machine learning [ML]) parameterizations have the potential to introduce a new generation of climate models that incorporate the effects of higher‐resolution physics without incurring the prohibitive computational cost associated with more explicit physics‐based simulations.
Jerry Lin   +26 more
wiley   +1 more source

Home - About - Disclaimer - Privacy