Abstract: In recent years, deep learning methods have been playing an important role in extracting features from ground penetrating radar (GPR) images to detect underground targets rapidly. However, ...
WILLOW PARK, Texas, Aug. 18, 2025 /PRNewswire/ -- ProFrac Holding Corp. (NASDAQ: ACDC) ("ProFrac") and Seismos today announced a strategic partnership for the launch of Closed Loop Fracturing, now ...
Abstract: Effusion cytology analysis can be time consuming for cytopathologists, but the burden can be reduced through automatic malignancy detection. The main challenge in the automation process is ...
The proposed industrial anomaly detection model is computationally efficient, memory-friendly, and also suitable for low-light conditions, common in manufacturing environments, making it well-suited ...
1 Australian Centre for Robotics, University of Sydney, Sydney, NSW, Australia 2 Department of Marine Technology, Norwegian University of Science and Technology, Trondheim, Norway Visual surveys by ...
Summary: New research reveals that the brain may be learning even during unstructured, aimless exploration. By recording activity in tens of thousands of neurons, scientists found that the visual ...
Falls pose a significant public health concern, leading to numerous injuries and fatalities annually. Most existing fall detection methods often rely on supervised learning, necessitating extensive ...
One of my responsibilities as a magistrate judge is to conduct what are called felony-first appearances and arraignments in criminal cases. Both are initial hearings when individuals are read their ...
Nathan Eddy works as an independent filmmaker and journalist based in Berlin, specializing in architecture, business technology and healthcare IT. He is a graduate of Northwestern University’s Medill ...
Forbes contributors publish independent expert analyses and insights. Writes about the future of finance and technology, follow for more. As machine learning continues to reshape the financial ...
Supervised learning relies on historical, labeled data to train algorithms for specific outcomes. This approach is widely used in the financial sector, where reliable past data can guide future ...
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