Supervised learning algorithms like Random Forests, XGBoost, and LSTMs dominate crypto trading by predicting price directions ...

A new study suggests humans can sense hidden objects without touching them, by detecting faint movements in sand. This unexpected form of “remote touch” challenges traditional ideas about how the ...

Machine learning and other modeling approaches could aid in forecasting the arrival of floating Sargassum rafts that clog ...

Abstract: Credit card fraud detection is a critical task in financial systems, requiring effective algorithms to accurately classify transactions as fraudulent or non-fraudulent. This paper proposes a ...

Users can note which content they would like to view more frequently. Instagram is handing users some control in deciding what content they see. The social media giant is allowing users to have a say ...

├── src/ # Source code modules │ ├── lstm_model.py # LSTM implementation with PyTorch │ ├── forecasting_models.py # ARIMA, Prophet, and statistical models │ ├── anomaly_detection.py # Anomaly ...

ABSTRACT: Forecasting fuel prices is a critical endeavor in energy economics, with significant implications for policy formulation, market regulation, and consumer decision-making. This study ...

Introduction: To address the dilemma that the small sample size of hospital energy consumption data makes it difficult to predict short-term electricity consumption, a combination of the Firefly ...

ABSTRACT: Since transformer-based language models were introduced in 2017, they have been shown to be extraordinarily effective across a variety of NLP tasks including but not limited to language ...

This study aimed to develop a machine learning‐based model to predict the risk of major adverse cardiac events (MACE) in patients presenting to the emergency department (ED) with chest pain, for whom ...