In my latest Signal Spot, I had my Villanova students explore machine learning techniques to see if we could accurately ...

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 ...

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 ...

On Capitol Hill, calls are growing for lawmakers to regulate online betting platforms known as “prediction markets” amid allegations of insider trading that includes profiteering from U.S. military ...

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 ...

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 ...

This paper presents a machine learning–based nowcasting framework for estimating quarterly non-oil GDP growth in the Gulf Cooperation Council (GCC) countries. Leveraging machine learning models ...