Supervised learning algorithms like Random Forests, XGBoost, and LSTMs dominate crypto trading by predicting price directions ...

Over the last several decades, urban planners and municipalities have sought to identify and better manage the socioeconomic dynamics associated with rapid development in established neighborhoods.

Firms integrating AI and machine learning into their investment processes are building compounding advantages that late adopters may never overcome.

Rules-based automation (RBA) and learning are two training mechanisms in robotics. While there are many others, these are two ...

AI algorithms analyse complex medical images with speed and precision, supporting early disease detection.Radiology and ...

AI-powered penetration testing is an advanced approach to security testing that uses artificial intelligence, machine learning, and autonomous agents to simulate real-world cyberattacks, identify ...

Researchers at the Department of Energy's Oak Ridge National Laboratory have developed a deep learning algorithm that ...

No discussion of AI is complete without addressing fear—particularly fear of job loss. History offers perspective. Each ...

Musculoskeletal (MSK) conditions drive a large share of global pain, disability, and lost productivity. Rehabilitation can be effective, but outcomes vary ...

Machine learning holds great promise for classifying and identifying fossils, and has recently been marshaled to identify trackmakers of dinosaur ...

The “one big breakthrough” pattern suggests that total citation counts can mislead. A researcher with one highly-cited paper and several uncited ones may have a more impactful trajectory than one with ...

A signal-processing–based framework converts DNA sequences into numerical signals to identify protein-coding regions. By integrating spectral ...