Results 171 to 180 of about 2,068,010 (305)

Quantifying Structure in Random Forests [PDF]

, 2022
Sutton, Hannah
core  

Pathway to the future for northwest Oregon state forests

, 2001
This archived document is maintained by the Oregon State Library as part of the Oregon Documents Depository Program. It is for informational purposes and may not be suitable for legal purposes.Title from cover.Mode of access: Internet from the Oregon ...

core  

eXplainable Random Forest

Advanced machine learning models have become widely adopted in various domains due to theirexceptional performance. However, their complexity often renders them difficult to interpret, whichcan be a significant limitation in high-stakes decision-making scenarios where explainability is crucial.In this study we propose eXplainable Random Forest (XRF ...
Amit, Guy, Gur, Shlomit
openaire   +3 more sources

Intelligent Orthopedics: Machine Learning in Diagnosis of Bone Disease, Implants, and Bone Health Monitoring

Advanced Healthcare Materials, EarlyView.
Efficient recovery from traumatic or degenerative diseases is a great challenge, even after all the advancements in bone and cartilage regeneration. Machine learning (ML) algorithms have presented opportunities to enhance these aspects by accurately analyzing imaging data.
Maryam Kamaei, Hesam Mohammadi, Shamim Golafshan, Mojtaba Alehosseini, Mahsa Mohammadzadeh, Atefeh Golshirazi, Nasim Golafshan, Tatiane Eufrásio‐da‐Silva, Alireza Dolatshahi‐Pirouz, Gorka Orive   +9 more
wiley   +1 more source

Human iPSC‐Derived Tissue‐Engineered Nigrostriatal Pathway for Chronic Restoration of Dopaminergic Axon Tracts in a Rat Parkinson's Model

Advanced Healthcare Materials, EarlyView.
Inspired by conventional cell therapy and emerging bioengineering approaches, the tissue‐engineered nigrostriatal pathway (TE‐NSP) has an anatomically‐inspired design comprising a population of human dopaminergic neurons with unidirectional, long‐projecting axon tracts.
Wisberty J. Gordián‐Vélez, Dimple Chouhan, Kevin D. Browne, James Lim, Saarang P. Karandikar, Rodrigo A. España, John E. Duda, H. Isaac Chen, D. Kacy Cullen   +8 more
wiley   +1 more source

Self‐Assembled Monolayers in p–i–n Perovskite Solar Cells: Molecular Design, Interfacial Engineering, and Machine Learning–Accelerated Material Discovery

Advanced Materials, EarlyView.
This review highlights the role of self‐assembled monolayers (SAMs) in perovskite solar cells, covering molecular engineering, multifunctional interface regulation, machine learning (ML) accelerated discovery, advanced device architectures, and pathways toward scalable fabrication and commercialization for high‐efficiency and stable single‐junction and
Asmat Ullah, Ying Luo, Stefaan De Wolf
wiley   +1 more source

Organic Materials of Tomorrow: Horizons of Artificial Intelligence

Advanced Materials, EarlyView.
This review examines machine learning techniques accelerating the discovery of organic semiconductors by linking molecular structure to properties. Key methods include graph neural networks, generative models, and active learning. Applications to organic photovoltaics demonstrate practical impact.
Harold Mena, J. Terence Blaskovits, Kun‐Han Lin, Denis Andrienko   +3 more
wiley   +1 more source

Rforce: Random Forests for Composite Endpoints. [PDF]

Stat Med
Wang Y, Kim S, Lin CW, Ahn KW.
europepmc   +1 more source

“Smelltronics”—From Gas to Smell Sensing

Advanced Materials, EarlyView.
The emerging field of smelltronics, encompassing sensing technologies for complex volatile organic compounds, holds significant potential for extracting valuable chemical information. It facilitates the noninvasive, real‐time monitoring of humans, food, and the environment.
Takeshi Ono, Tsunaki Takahashi, Jiangyang Liu, Haruka Honda, Zeng Jing, Wataru Tanaka, Takuro Hosomi, Takeshi Yanagida   +7 more
wiley   +1 more source

FeatureForest: the power of foundation models, the usability of random forests. [PDF]

Npj Imaging
Seifi M, Dalle Nogare D, Battagliotti JM, Galinova V, Rao AK, Jouneau PH, Archit A, AI4Life Horizon Europe Programme Consortium, Pape C, Decelle J, Jug F, Deschamps J.   +11 more
europepmc   +2 more sources