A modified zebra optimization algorithm shows dramatic improvements in wireless sensor network performance. The IZOACP protocol extends network lifetime by up to 129% while reducing energy consumption and latency.
A comprehensive study evaluating active learning strategies combined with automated machine learning reveals substantial performance variations in materials science applications. Researchers found that strategies incorporating model gradient information outperformed traditional approaches, while model-free methods consistently underperformed across multiple datasets.
Researchers have conducted a comprehensive evaluation of active learning strategies combined with automated machine learning for small-sample regression tasks in materials science, according to recent reports published in Scientific Reports. The study systematically compared 18 distinct AL strategies across 14 single-output regression tasks derived from 9 materials datasets, providing new insights into optimal approaches for data-efficient machine learning in scientific applications.
Researchers have developed an advanced AI model that significantly improves early wildfire detection through drone-based monitoring. The enhanced system addresses key challenges in identifying small-scale fire sources while reducing false alarms in complex natural environments.
Researchers have developed an improved artificial intelligence model that reportedly addresses critical limitations in early forest fire detection, according to recent scientific reports. The enhanced system builds upon the YOLOv8 architecture and demonstrates significant improvements in identifying small-scale fire sources and smoke while reducing false detection rates in complex natural environments.
The Evolution of Learning: From Biological Inspiration to Machine Discovery For decades, artificial intelligence researchers have looked to biological systems…
Unlocking China’s Lake Carbon Mysteries Through Four Decades of Satellite Data In a groundbreaking scientific achievement, researchers have compiled the…
A groundbreaking study reveals how data bias has inflated performance metrics in drug discovery AI models. The new GEMS system and PDBbind CleanSplit dataset demonstrate superior generalization by eliminating structural redundancies that previously hampered accurate binding affinity predictions.
Researchers have uncovered significant data bias issues that have been inflating the performance metrics of artificial intelligence models used in drug discovery, according to a recent study published in Nature Machine Intelligence. Sources indicate that structural similarities between training and testing datasets have created a “data leakage” problem, allowing models to achieve artificially high performance through memorization rather than genuine understanding of protein-ligand interactions.
