A study led by UC Riverside researchers offers a practical fix to one of artificial intelligence's toughest challenges by ...

Machine learning (ML) and deep learning (DL) as two well-known methods of artificial intelligence (AI) have emerged as powerful tools in extracting insights and patterns from vast amounts of data. In ...

For a project in Bangladesh, Prof. Mushfiq Mobarak and his team used machine-learning models applied to mobile phone records ...

Due to their error-prone hardware, quantum computers have not yet found practical use. One promising solution is quantum error correction: special methods are used to find and correct errors in the ...

California’s AB 2013, also known as the Generative Artificial Intelligence: Training Data Transparency Act (TDTA), took effect on January 1, 2026.

According to the paper, artificial intelligence addresses a core weakness in Parkinson’s care: reliance on subjective ...

Abstract: Artificial intelligence (AI) is transforming social science research by enabling scalable data analysis, predictive modeling, and causal inference, thereby reshaping the methodological ...

Abstract: One of the prominent challenges encountered in real-world data is an imbalance, characterized by unequal distribution of observations across different target classes, which complicates ...

Remotely sensed geospatial data are critical for applications including precision agriculture, urban planning, disaster monitoring and response, and climate change research, among others. Deep ...

No audio available for this content. High-precision GNSS applications, such as real-time displacement monitoring and vehicle navigation, rely heavily on resolving carrier-phase ambiguities. However, ...