Abstract: Learning over time for machine learning (ML) models is emerging as a new field, often called continual learning or lifelong Machine learning (LML). Today, deep learning and neural networks ...

Dhanapal Govindarajulu does not work for, consult, own shares in or receive funding from any company or organization that would benefit from this article, and has disclosed no relevant affiliations ...

Monitoring forest health typically relies on remote sensing tools such as light detection and ranging (LiDAR), radar, and multispectral photography. While radar and LiDAR penetrate canopies to reveal ...

OpenAI CEO Sam Altman’s new brain-computer interface startup, Merge Labs, is being spun out of the Los Angeles–based nonprofit Forest Neurotech, according to a source with direct knowledge of the ...

Researchers in New Brunswick, Canada, are utilizing artificial intelligence and satellite imaging to protect old forests, according to the CBC. Old forests are those that have developed over long ...

PythoC lets you use Python as a C code generator, but with more features and flexibility than Cython provides. Here’s a first look at the new C code generator for Python. Python and C share more than ...

According to Andrej Karpathy on Twitter, the Python random.seed() function produces identical random number generator (RNG) streams when seeded with positive and negative integers of the same ...

Industry Insight from Ethical Corporation Magazine, a part of Thomson Reuters. More than 90 countries supported a deforestation roadmap at COP30 Indigenous peoples won important concession on land ...

Arizona’s forests are more than beautiful landscapes. They are living systems that protect our water, shield us from wildfires and sustain our communities. That’s why we must join in urging Senators ...

This is read by an automated voice. Please report any issues or inconsistencies here. The U.S. Forest Service completed prescribed burns on more than 127,000 acres during the shutdown, largely keeping ...

AQI Predictor V2 use multiple Supervised Machine Learning with Hyper tuning. ML algorithms used Linear Regressor, Lasso Regressor, Decision Tree Regressor, Random Forest Regressor, XGboost Regressor.

Background: Heart failure (HF), with its distinct phenotypes, poses significant public health challenges. Early diagnosis of specific HF phenotypes is crucial for timely therapeutic intervention.