Abstract: This study discusses the development and evaluation of a cellular network traffic forecasting system utilizing statistical, machine learning, and deep learning methodologies. The system has ...

Azillah Binti Othman, IAEA Department of Nuclear Sciences and Applications Ayhan Evrensel, IAEA Department of Nuclear Sciences and Applications The IAEA is inviting research organizations to join a ...

Add Futurism (opens in a new tab) More information Adding us as a Preferred Source in Google by using this link indicates that you would like to see more of our content in Google News results. YouTube ...

AI cyberattacks are rapidly transforming the cybersecurity landscape, enabling attackers to automate and scale operations with unprecedented speed. Through machine learning hacking, adversaries can ...

Code/ ├── app.py # Main Flask web application ├── model.py # STMLP neural network architecture ├── training.py # Model training script ├── users.db # User authentication database │ ├── Data Files: │ ...

A machine-learning model developed by Weill Cornell Medicine investigators may provide clinicians with an early warning of a complication that can occur late in pregnancy. Preeclampsia is a sudden ...

A multidisciplinary team led by MUSC Hollings Cancer Center researcher Sophie Paczesny, M.D., Ph.D., developed an AI-based tool that can identify patients at higher risk for serious post-transplant ...

Copyright: © 2025 The Author(s). Published by Elsevier Ltd. Machine learning for health data science, fuelled by proliferation of data and reduced computational ...

Prediction markets like Kalshi and Polymarket have surged in popularity. While some trading firms are actively using the platforms, hedge funds are mostly interested in their data. Dysrupt Labs in ...

Depression is one of the most widespread mental health disorders worldwide, affecting approximately 4% of the global population. It is characterized by a persistent low mood, disruptions in typical ...