Sparse identification of nonlinear dynamical systems is an important project, directly addressing the physics community’s long-standing goal of data-driven discovery. Although many effective methods ...
Most of the models are completed in a single file and implemented in a simple way. The machine learning part of the code does not use any external libraries, except for the loading part of the ONNX ...
Abstract: Frequency domain spectroscopy (FDS) is widely applied in the insulation condition assessment of high-voltage generator stators. The evaluation process requires frequency–temperature ...
We present systematic computational analyses to investigate the influence of Matthew’s effect (i.e., “rich gets richer”) in the development, testing, and application of machine learning algorithms for ...
A new AI-driven system designed to optimize energy use and resource allocation in smart agriculture has demonstrated superior performance over conventional machine learning models, according to a 2025 ...
Creative Commons (CC): This is a Creative Commons license. Attribution (BY): Credit must be given to the creator. We present an artificial intelligence-guided approach to design durable and chemically ...
ML-GAP: machine learning-enhanced genomic analysis pipeline using autoencoders and data augmentation
The advent of RNA sequencing (RNA-Seq) has significantly advanced our understanding of the transcriptomic landscape, revealing intricate gene expression patterns across biological states and ...
Abstract: The recent development of artificial intelligence (AI) has opened new avenues in processing parts per million (ppm) for fault detection through dissolved gas analysis (DGA). According to the ...
aDepartment of Pathology & Immunology, Washington University in St. Louis School of Medicine, 660 South Euclid Avenue, Campus Box 8118, Saint Louis, MO, 63110, United States bThe F. Widjaja ...
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