Wildlife imaging shows that AI models aren't as smart as we think

Using AI to identify wildlife reveals a potential "transferability crisis," researchers say. Marketing for AI imaging systems ...
IEEE

Neural-XGBoost: A Hybrid Approach for Disaster Prediction and Management Using Machine Learning

Abstract: Effective disaster prediction is essential for disaster management and mitigation. This study addresses a multi-classification problem and proposes the Neural-XGBoost disaster prediction ...

Exploring electron microscopy and AI as key players for identifying pollen grains

Have you ever seen a hibiscus flower? Although its petals have a range of colors, what makes the trumpet-shaped flower more beautiful is the central stalk, which houses the anthers that produce pollen ...
IEEE

Benchmarking Machine Learning Algorithms for Bearing Fault Classification Using Vibration Data: A Deployment-Oriented Study

Abstract: This study presents a comprehensive benchmarking of 33 machine learning (ML) algorithms for bearing fault classification using vibration data, with a focus on real-world deployment in ...