Neuroimaging presents us with an in-depth understanding about brain structure and function, yet the data complexity poses significant analytical challenges. Current frameworks suffer from issues such ...
ABSTRACT: The visualization of high-dimensional data is a cornerstone of modern scientific inquiry, particularly in quantum physics, where complex non-linear interactions define system behavior. While ...
Data assimilation (DA) integrates observations with model forecasts to produce optimized atmospheric states, whose physical consistency is critical for stable weather forecasting and reliable climate ...
Abstract: Faced with high-dimensional expensive optimization problems (HEOPs), existing high-dimensional expensive optimization algorithms (HEOAs) struggle to locate promising areas quickly due to a ...
Today, the plastics industry stands at the threshold of a technological revolution, with artificial intelligence and machine learning poised to transform everything from material development to ...
Constructing high-quality features is critical to any quantitative data analysis. While feature engineering was historically addressed by carefully handcrafting data representations on the basis of ...
Rapidly estimating multiple trait indicators simultaneously, nondestructively, and with high precision is an important means of accurate diagnosis in modern phenomics. Increasing the accuracy of ...
Abstract: With the innovative application of machine learning neural networks, the problem of feature extraction and dimensionality reduction in big data processing has received extensive attention, ...
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