In a recent paper, SFI Complexity Postdoctoral Fellow Yuanzhao Zhang and co-author William Gilpin show that a deceptively ...
The editorial, "Dynamics-driven medical big data mining: dynamic approaches to early disease forecasting and individualized care," published in Intelligent Medicine (February 2026, Volume 6, Issue 1), ...
The next major advance in medical AI may lie not in analyzing more data, but in understanding how health data change over time. A recent editorial in Intelligent Medicine argues that dynamics-driven ...
Flash floods are among the deadliest weather events in the world, killing more than 5,000 people each year. They’re also among the most difficult to predict. But Google thinks it has cracked that ...
Abstract: While Long Short-Term Memory (LSTM) networks are pivotal for time-series forecasting, their fixed recurrent structure struggles to capture the complex dynamics of financial markets, such as ...
Wildlife experts in Florida continue to prioritize the elimination of invasive Burmese pythons. A presentation on the impact of pythons will be given by scientist Michael Kirkland on Sanibel Island.
Corporate income tax (CIT) collections are among the most difficult revenues to forecast—even with adequate staffing, comprehensive data, and a stable tax design. In practice, forecasting units ...
├── src/ # Source code modules │ ├── lstm_model.py # LSTM implementation with PyTorch │ ├── forecasting_models.py # ARIMA, Prophet, and statistical models │ ├── anomaly_detection.py # Anomaly ...
ABSTRACT: 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 ...
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