AZoSensors on MSN

Machine-learning technique shown to reduce sensor drift in electronic noses

Research reveals that knowledge distillation significantly compensates for sensor drift in electronic noses, improving ...
ZME Science

Scientists Finally Figured Out How Much Groundwater There Is Under the US and It Is Enough to Fill the Great Lakes 13 Times Over

Researchers used 1 million data points and a machine learning algorithm to estimate groundwater stores with higher resolution than ever before.
InfoWorld

16 open source projects transforming AI and machine learning

From fine-tuning open source models to building agentic frameworks on top of them, the open source world is ripe with ...

How Organizations Can Successfully Leverage Quantum-Inspired Techniques

Finally, the organizations that implement quantum-inspired techniques will have a headstart on quantum computing as the ...
AlphaGalileo

Fast AI for satellite learns to quantify uncertainty

Accurately tracking atmospheric greenhouse gases requires not only fast predictions but also reliable estimates of ...
Tech Xplore

Mistaken correlations: Why it's critical to move beyond overly aggregated machine-learning metrics

MIT researchers have identified significant examples of machine-learning model failure when those models are applied to data other than what they were trained on, raising questions about the need to ...
IEEE

Machine Learning-Based Algorithm for One-Dimensional Wave Spectrum Retrieval from SAR Imagery as Passing Oceanic Eddy

The inversion of the one-dimensional wave spectrum from dual-polarized synthetic aperture radar (SAR) data is performed using machine learning methods, namely Random Forest (RF), eXtreme Gradient ...
Frontiers

Integrated surgical treatment: a new model for treating secondary extremity lymphedema based on algorithms

Secondary extremity lymphedema (SEL) is a chronic and progressive disorder resulting from impaired lymphatic drainage, most commonly following oncologic interventions such as breast or gynecological ...
IEEE

Machine-Learning-Based Algorithm for Significant Wave Height Retrieval From Microwave Radiometer During Tropical Cyclones

This study develops a machine-learning-based approach to retrieve significant wave height (SWH) from soil moisture active passive (SMAP) radiometer data under tropical cyclone (TC) conditions, ...
Frontiers

Machine learning models for the prediction of levodopa response to tremor in Parkinson’s disease

Objectives: To develop and validate machine learning models to predict levodopa responsiveness of tremor in Parkinson’s disease (PD) patients. Methods: A total of 197 PD tremor patients underwent ...