The vision of a fully connected world is rapidly becoming a reality through the Internet of Things (IoT)—a growing network of ...

Rainfall prediction has advanced rapidly with the adoption of machine learning, but most models remain optimized for overall ...

Researchers have employed Bayesian neural network approaches to evaluate the distributions of independent and cumulative ...

AI is the broad goal of creating intelligent systems, no matter what technique is used. In comparison, Machine Learning is a specific technique to train intelligent systems by teaching models to learn ...

In some ways, Java was the key language for machine learning and AI before Python stole its crown. Important pieces of the data science ecosystem, like Apache Spark, started out in the Java universe.

AI Steam updates AI disclosure form to specify that it's focused on AI-generated content that is 'consumed by players,' not efficiency tools used behind the scenes AI Stellar Blade's director says AI ...

In late September 2017, Palestinian American activist Linda Sarsour, once the darling of the Women’s March and the self-declared face of the "resistance" against Donald Trump, was facing mounting ...

Machine learning, a key enabler of artificial intelligence, is increasingly used for applications like self-driving cars, medical devices, and advanced robots that work near humans — all contexts ...

This project implements state-of-the-art deep learning models for financial time series forecasting with a focus on uncertainty quantification. The system provides not just point predictions, but ...

An AI-driven digital-predistortion (DPD) framework can help overcome the challenges of signal distortion and energy inefficiency in power amplifiers for next-generation wireless communication.

Tumor Site–Specific Radiation-Induced Lymphocyte Depletion Models After Fractionated Radiotherapy: Considerations of Model Structure From an Aggregate Data Meta-Analysis Lymphocytes play critical ...

Accurate disaster prediction combined with reliable uncertainty quantification is crucial for timely and effective decision-making in emergency management. However, traditional deep learning methods ...