Machine learning enhances proteomics by optimizing peptide identification, structure prediction, and biomarker discovery.

The automatic detection of surface-level irregularities—defects or anomalies—in 3D data is of significant interest for various real-world purposes, such as industrial quality inspection, ...

6G visions include immersive extended reality, holographic communications, tactile internet applications, and large-scale digital twins. Supporting these services will demand fully autonomous network ...

Advances in ultrasound—the same imaging technology that uses sound waves to allow doctors to monitor babies in utero—are being applied by engineers at the University of California San Diego to make ...

Kennesaw State University (KSU) is stepping into the future of workforce-ready education with the launch of a new Bachelor’s degree ...

Machine learning is an essential component of artificial intelligence. Whether it’s powering recommendation engines, fraud detection systems, self-driving cars, generative AI, or any of the countless ...

Unsupervised learning is a branch of machine learning that focuses on analyzing unlabeled data to uncover hidden patterns, structures, and relationships. Unlike supervised learning, which requires pre ...

Machine learning requires humans to manually label features while deep learning automatically learns features directly from raw data. ML uses traditional algorithms like decision tress, SVM, etc., ...

Abstract: We introduce a fully unsupervised framework designed to reconstruct X-ray CT images from truncated projections without requiring prior truncation correction. By incorporating a Radon ...

In this talk, I will present a series of new results in supervised learning from contaminated datasets, based on a general outlier removal algorithm inspired by recent work on learning with ...