Although large language models (LLMs) have the potential to transform biomedical research, their ability to reason accurately across complex, data-rich domains remains unproven. To address this ...
A deep learning framework combines convolutional and bidirectional recurrent networks to improve protein function prediction from genomic sequences. By automating feature extraction and capturing long ...
Microsoft has added official Python support to Aspire 13, expanding the platform beyond .NET and JavaScript for building and running distributed apps. Documented today in a Microsoft DevBlogs post, ...
ABSTRACT: Under the context of global climate change, the frequent occurrence of strong winds in Guyuan has significantly hindered the development of local facility agriculture. Using hourly ...
Objective: To compare the application of the ARIMA model, the Long Short-Term Memory (LSTM) model and the ARIMA-LSTM model in forecasting foodborne disease incidence. Methods: Monthly case data of ...
This project implements an LSTM that learns to isolate individual frequency components from a mixed signal through one-hot conditioning.
Imagine a world where machines don’t just follow instructions but actively make decisions, adapt to new information, and collaborate to solve complex problems. This isn’t science fiction, it’s the ...
With the in-depth digital transformation of the global shipping industry, the accurate prediction of smart port operation efficiency has become a key factor in enhancing the competitiveness of ...
When using Tesseract OCR to extract text from an image containing asterisks (*), the output does not preserve the asterisk character. Instead, it is replaced with seemingly random characters or ...
Abstract: Earthquake forecasting using traditional methods remains a complex task due to the inherent nonlinearity and stochastic nature of seismic activity. Therefore, this study examines the ...
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