Data clustering remains an essential component of unsupervised learning, enabling the exploration and interpretation of complex datasets. The field has witnessed considerable advancements that address ...

Building fast and highly performant data science applications requires an intimate knowledge of how data can be organized in a computer and how to efficiently perform operations such as sorting, ...

A University of Hawaiʻi at Mānoa student-led team has developed a new algorithm to help scientists determine direction in complex two-dimensional (2D) data, with potential applications ranging from ...

We developed two algorithms to identify patients with stomach, lung, colorectal, breast, and cervical cancers: diagnosis only (algorithm 1), and combining diagnosis, treatments, and medicines ...

Artificial intelligence has become a popular tool for job recruiters, in part because programmers can code applicant-screening algorithms to avoid any explicit discrimination in their decision-making ...

shinyOPTIK, a User-Friendly R Shiny Application for Visualizing Cancer Risk Factors and Mortality Across the University of Kansas Cancer Center Catchment Area We trained and validated two-phase ML ...

Extracting and analyzing relevant medical information from large-scale databases such as biobanks poses considerable challenges. To exploit such "big data," attempts have focused on large sampling ...

Ph.D student Austin P. Wright wins a best paper runner-up award at an international conference for an algorithm used on the Mars Perseverance Rover than can be used in applications in earth science ...

Through data, algorithms communicate with their environments and get to “know about” and “learn from” what is happening around them. Algorithms without living data are no more than sheer mathematical ...

A new algorithm can identify the most predictive data points that a tipping point is near. Whether you're trying to predict a climate catastrophe or mental health crisis, mathematics tells us to look ...