When people think about geological faults, they usually think about earthquakes. Yet faults do not move only during ...
Forensic science plays a vital role in identifying, characterizing, and quantifying physical and biological traces recovered from crime scenes — a task that ...
In recent years, as generative AI has shifted from model training toward large-scale inference, an increasing number of AI ...
Multiomics data integration with machine learning has become the standard approach for combining genomic, transcriptomic, proteomic, and metabolomic measurements collected from the same biological ...
Summary: Researchers deployed an advanced, data-driven framework combining integrative network pharmacology, transcriptomics, machine learning, and molecular docking simulations. Their computational ...
As energy companies push AI deeper into industrial operations, success increasingly depends on governance, trusted data, and ...
Abstract: Autoencoder is a widely used deep learning method, which first extracts features from all data through unsupervised reconstruction, and then fine-tunes the network with labeled data. However ...
ABSTRACT: Video-based anomaly detection in urban surveillance faces a fundamental challenge: scale-projective ambiguity. This occurs when objects of different physical sizes appear identical in camera ...
Dr. James McCaffrey of Microsoft Research tackles the process of examining a set of source data to find data items that are different in some way from the majority of the source items. Data anomaly ...
Abstract: Deep learning with ability to feature learning and nonlinear function approximation has shown its effectiveness for machine fault prediction. While, how to transfer a deep network trained by ...
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