XL, dynamic interest modeling, and distributed stream computing to analyze large-scale e-commerce user behavior. By improving long-sequence prediction, real-time processing, and behavioral clustering, ...

A privacy-preserving marketing framework applies homomorphic encryption to perform machine learning on encrypted ...

Introduction National essential medicines lists (NEMLs) guide medicine selection and procurement and are key tools for ...

Abstract: Data clustering is a fundamental machine learning task that seeks to categorize a dataset into homogeneous groups. However, real data usually contain noise, which poses significant ...

Abstract: In 1997, we proposed the fuzzy-possibilistic c-means (FPCM) model and algorithm that generated both membership and typicality values when clustering unlabeled data. FPCM constrains the ...

Probabilistic models, such as hidden Markov models or Bayesian networks, are commonly used to model biological data. Much of their popularity can be attributed to the existence of efficient and robust ...

Older patients represent the fastest growing patient group in clinical care. They are a heterogeneous group, for whom evidence for making treatment decisions is often scarce. Important domains for ...

Objectives Elective non-emergent surgical wait times have increased across countries such as Canada, straining operating room ...

Find out more about undergraduate study at the School of Electronic Engineering and Computer Science.

⚽ World Cup 2026 🏳️‍🌈 Pride Week 🍼 Meet the zoo's baby binturongs 🎵 Mariachi Museum loses its home 💥 Marvel returning to Comic-Con A long overdue update of SeaWorld's master plan received a warm ...

Meta’s AI chief says new Muse Spark update will sharpen coding, agentic AI Alexandr Wang said the upcoming Muse Spark update will significantly improve coding and agentic capabilities, while analysts ...