CORR Synthesis: Can Decision Tree Learning Advance Orthopaedic Surgery Research? [PDF]
Clin Orthop Relat Res, 2023 Wilson A.
europepmc +1 more source
Learning Invariants using Decision Trees
, 2015 15 pages, 2 ...
Krishna, Siddharth +2 more
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Learning Multiple Tasks with Boosted Decision Trees [PDF]
, 2012 We address the problem of multi-task learning with no label correspondence among tasks. Learning multiple related tasks simultane- ously, by exploiting their shared knowledge can improve the predictive performance on every task. We develop the multi-task Adaboost en- vironment with Multi-Task Decision Trees as weak classifiers.
Faddoul, Jean Baptiste +3 more
openaire +3 more sources
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
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Advanced Healthcare Materials, EarlyView.AIMSPec‐LoC is a novel lab‐on‐a‐chip platform integrating size‐based extracellular vesicle (EVs) separation with label‐free Raman spectroscopy and AI‐powered classification via SKiNET. This high‐throughput, portable system enables real‐time, multiplexed molecular fingerprinting of EVs from biofluids, offering transformative potential for early, non ...
Emma Buchan +3 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.
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Norbornene Homopolymerization Limits Cell Spreading in Thiol–Ene Photoclick Hydrogels
Advanced Healthcare Materials, EarlyView.Thiol–norbornene click reactions are often used in the development of cell‐permissive 3D hydrogels. However, ene–ene crosslinks in other thiol–ene systems are known to limit permissivity. This study demonstrates the negative effects of norbornene homopolymerization on 3D cell spreading and circumvents the issue by modulating polymer degree of ...
James L. Gentry, Steven R. Caliari
wiley +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
Hierarchically MOF‐Based Porous Monolith Composites for Atmospheric Water Harvesting
Advanced Materials, EarlyView.This review explores the design of hierarchical porous materials for atmospheric water harvesting, focusing on metal‐organic frameworks (MOFs) and porous monoliths. Emphasis is placed on integrating MOF nanoscale porosity with the microscale channels of monolithic scaffolds to enhance sorption‐desorption performance.
Mahyar Panahi‐Sarmad +7 more
wiley +1 more source