Jessica Lin and Zhenqi (Pete) Shi from Genentech describe a novel machine learning approach to predicting retention times for ...
Kernel methods represent a cornerstone in modern machine learning, enabling algorithms to efficiently derive non-linear patterns by implicitly mapping data into high‐dimensional feature spaces. At the ...
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, ...
In food drying applications, machine learning has demonstrated strong capability in predicting drying rates, moisture ...
As data privacy collides with AI’s rapid expansion, the Berkeley-trained technologist explains how a new generation of models is learning without crossing ethical lines.
20+ Machine Learning Methods in Groundbreaking Periodic Table From MIT, Google, Microsoft Your email has been sent A new “periodic table for machine learning” is reshaping how researchers explore AI, ...
A scoping review shows machine learning models may help predict response to biologic and targeted synthetic DMARDs in ...
A scientist in Sweden has developed a new hybrid local features-based method using thermographs to identify faulty solar panels. A researcher from Sweden’s Jönköping University has proposed a machine ...
Cancer is the second leading cause of death worldwide. Since age is one of the risk factors for the development of cancer, ...
9don MSN
A urine-based biological aging clock: Machine learning and microRNA offer accurate prediction
Craif Inc. in Nagoya, Japan, working with Nagoya University's Institute of Innovation for Future Society, has developed a ...
NITK develops SVALSA, a machine learning-based landslide warning system for the Western Ghats, enhancing disaster ...
Some results have been hidden because they may be inaccessible to you
Show inaccessible results
Feedback