TreeGrad: Transferring Tree Ensembles to Neural Networks
, 2019 Gradient Boosting Decision Tree (GBDT) are popular machine learning algorithms with implementations such as LightGBM and in popular machine learning toolkits like Scikit-Learn.
C Siu +7 more
core +1 more source
Comparison of decision tree with common machine learning models for prediction of biguanide and sulfonylurea poisoning in the United States: an analysis of the National Poison Data System [PDF]
, 2023 Omid Mehrpour +6 more
openalex +1 more source
Toward the 3rd Generation of Smart Farming: Materials, Devices, and Systems for E‐Plant Technologies
Advanced Functional Materials, EarlyView.This review explores the latest developments in e‐plant technologies, which are revolutionizing smart farming by enabling real‐time monitoring of plant and environmental conditions. It covers the design, applications, and systems of e‐plant devices, detailing how they integrate data analytics to optimize agricultural practices, enhance crop yields, and
Daegun Kim +5 more
wiley +1 more source
Design and Applications of Multi‐Frequency Programmable Metamaterials for Adaptive Stealth
Advanced Functional Materials, EarlyView.This article provides a comprehensive overview of metamaterials, including their fundamental principles, properties, synthesis techniques, and applications in stealth, as well as their challenges and future prospects. It covers topics that are more advanced than those typically discussed in existing review articles, while still being closely connected ...
Jonathan Tersur Orasugh +4 more
wiley +1 more source
CORR Synthesis: Can Decision Tree Learning Advance Orthopaedic Surgery Research? [PDF]
Clin Orthop Relat Res, 2023 Wilson A.
europepmc +1 more source
Verifiable Reinforcement Learning via Policy Extraction
, 2019 While deep reinforcement learning has successfully solved many challenging control tasks, its real-world applicability has been limited by the inability to ensure the safety of learned policies. We propose an approach to verifiable reinforcement learning
Bastani, Osbert +2 more
core
Unleashing the Power of Machine Learning in Nanomedicine Formulation Development
Advanced Functional Materials, EarlyView.A random forest machine learning model is able to make predictions on nanoparticle attributes of different nanomedicines (i.e. lipid nanoparticles, liposomes, or PLGA nanoparticles) based on microfluidic formulation parameters. Machine learning models are based on a database of nanoparticle formulations, and models are able to generate unique solutions
Thomas L. Moore +7 more
wiley +1 more source
Decision tree learning in Neo4j on homogeneous and unconnected graph nodes from biological and clinical datasets. [PDF]
BMC Med Inform Decis Mak, 2023 Mondal R +7 more
europepmc +1 more source
Peptide Sequencing With Single Acid Resolution Using a Sub‐Nanometer Diameter Pore
Advanced Functional Materials, EarlyView.To sequence a single molecule of Aβ1−42–sodium dodecyl sulfate (SDS), the aggregate is forced through a sub‐nanopore 0.4 nm in diameter spanning a 4.0 nm thick membrane. The figure is a visual molecular dynamics (VMD) snapshot depicting the translocation of Aβ1−42–SDS through the pore; only the peptide, the SDS, the Na+ (yellow/green) and Cl− (cyan ...
Apurba Paul +8 more
wiley +1 more source
Exclusive Biosynthesis of Pullulan Using Taguchi's Approach and Decision Tree Learning Algorithm by a Novel Endophytic Aureobasidium pullulans Strain. [PDF]
Polymers (Basel), 2023 Saber WIA, Al-Askar AA, Ghoneem KM.
europepmc +1 more source