Devdiscourse

High-accuracy AI models improve IoT threat detection

The results show that the Decision Tree model emerged as the top-performing algorithm, achieving an accuracy rate of 99.36 percent. Random Forest followed closely with 99.27 percent accuracy, while ...
Frontiers

Computational Frameworks for Decision-Making: From Bayesian Inference to Reinforcement Learning Models

The ability to make adaptive decisions in uncertain environments is a fundamental characteristic of biological intelligence. Historically, computational ...
IEEE

Hierarchical UAV Autonomous Navigation Algorithm Based on Event-Triggered Deep Reinforcement Learning

Abstract: In unknown environments, achieving autonomous navigation for uncrewed aerial vehicles (UAVs) is a complex task. Ensuring that UAVs reach their destinations safely and efficiently remains a ...

Machine learning tool pinpoints optimal locations for tree planting, offering a powerful tool for climate mitigation

Afforestation—establishing forests on previously non-forested land, or where forests have not existed for a long time—is one ...
Frontiers

Beyond the Algorithm: Scalable, Explainable, and Adaptive AI for High-Stakes Clinical Decision Support

Artificial Intelligence (AI) has shown strong potential in supporting clinical decision-making through Clinical Decision Support Systems (CDSSs). However, ...