Machine learning to predict how fast biodegradable plastics break down in nature

Testing how quickly a biodegradable plastic actually breaks down in the environment can take months, sometimes years, of lab ...
Frontiers

Benchmarking, Evaluation, and Reproducibility in Earth Observation Machine Learning

Earth observation relies on diverse imaging systems whose varying spatial, spectral, radiometric, and temporal ...
AZoBuild

Machine Learning Improves Strength Predictions in Concrete

Supervised machine learning improves predictions of compressive strength in industrial waste-modified concrete, supporting ...
IEEE

An Effective Classification of DDoS Attacks in a Distributed Network by Adopting Hierarchical Machine Learning and Hyperparameters Optimization Techniques

Abstract: Data privacy is essential in the financial sector to protect client’s sensitive information, prevent financial fraud, ensure regulatory compliance, and safeguard intellectual property. It ...
Oak Ridge National Lab

Machine-learned closure of URANS for stably stratified turbulence: connecting physical timescales & data hyperparameters of d...

Stably stratified turbulence (SST), a model that is representative of the turbulence found in the oceans and atmosphere, is strongly affected by fine balances between forces and becomes more ...
IEEE

Effective Diagnosis Approach for Broken Rotor Bar Fault Using Bayesian-Based Optimization of Machine Learning Hyperparameters

Abstract: Rotor fault diagnosis plays a critical role in ensuring the safety and reliability of rotating machinery. Recently, there has been increasing interest in leveraging advanced signal ...