Results 301 to 310 of about 5,579,561 (370)

The Random Buffer Tree : A Randomized Technique for I/O-efficient Algorithms

, 2004
Saju Jude Dominic, G. Sajith
openalex   +2 more sources

Deep Learning Methods in Soft Robotics: Architectures and Applications

Advanced Intelligent Systems, EarlyView.
Soft robotics has seen intense research over the past two decades and offers a promising approach for future robotic applications. However, standard industrial methods may be challenging to apply to soft robots. Recent advances in deep learning provide powerful tools to analyze and design complex soft machines that can operate in unstructured ...
Tomáš Čakurda, Monika Trojanová, Pavlo Pomin, Alexander Hošovský   +3 more
wiley   +1 more source

Feature graphs for interpretable unsupervised tree ensembles: centrality, interaction, and application in disease subtyping. [PDF]

BioData Min
Sirocchi C, Urschler M, Pfeifer B.
europepmc   +1 more source

Generative Inverse Design of Metamaterials with Functional Responses by Interpretable Learning

Advanced Intelligent Systems, EarlyView.
This work introduces random‐forest‐based interpretable generative inverse design (RIGID), a new single‐shot inverse design method for metamaterials using interpretable machine learning and Markov chain Monte Carlo sampling. Once trained on a small dataset, RIGID can estimate the likelihood of designs achieving target behaviors (e.g., wave‐based ...
Wei (Wayne) Chen, Rachel Sun, Doksoo Lee, Carlos M. Portela, Wei Chen   +4 more
wiley   +1 more source

Leveraging mixed-effects regression trees for the analysis of high-dimensional longitudinal data to identify the low and high-risk subgroups: simulation study with application to genetic study. [PDF]

BioData Min
Jahangiri M, Kazemnejad A, Goldfeld KS, Daneshpour MS, Momen M, Mostafaei S, Khalili D, Akbarzadeh M.   +7 more
europepmc   +1 more source

Machine Learning‐Assisted Simulations and Predictions for Battery Interfaces

Advanced Intelligent Systems, EarlyView.
This review summarizes machine learning (ML)‐assisted simulations and predictions at battery interfaces. It highlights how employing ML algorithms with machine vision, enables the lithium dendrite growth simulation, the solid–electrolyte interphase formation, and other interfacial dynamics.
Zhaojun Sun, Xin Li, Yiming Wu, Qilin Gu, Shiyou Zheng   +4 more
wiley   +1 more source

"A mathematical theory of evolution": phylogenetic models dating back 100 years. [PDF]

Philos Trans R Soc Lond B Biol Sci
Rosenberg NA, Stadler T, Steel M.
europepmc   +1 more source

Probabilistic Analysis for Randomized Game Tree Evaluation

, 2004
Tämur Ali Khan, Ralph Neininger
openalex   +2 more sources

π‐PhenoDrug: A Comprehensive Deep Learning‐Based Pipeline for Phenotypic Drug Screening in High‐Content Analysis

Advanced Intelligent Systems, EarlyView.
This study develops a deep learning‐based pipeline named π‐PhenoDrug for cell phenotype‐driven drug activity screening. π‐PhenoDrug integrates cell segmentation, morphological profile construction, and phenotype analysis modules, and it can assess drug effects on living cells in both supervised and unsupervised modes.
Xiao Li, Qinxue Ouyang, Mingfei Han, Xiaoqing Liu, Fuchu He, Yunping Zhu, Ling Leng, Jie Ma   +7 more
wiley   +1 more source

Finding high posterior density phylogenies by systematically extending a directed acyclic graph. [PDF]

Algorithms Mol Biol
Jennings-Shaffer C, Rich DH, Macaulay M, Karcher MD, Ganapathy T, Kiami S, Kooperberg A, Zhang C, Suchard MA, Matsen FA.   +9 more
europepmc   +1 more source