Results 81 to 90 of about 524,166 (313)
Journal of Applied Clinical Medical Physics, EarlyView.Abstract Current radiotherapy practices rely on manual contouring of CT scans, which is time‐consuming, prone to variability, and requires highly trained experts. There is a need for more efficient and consistent contouring methods. This study evaluated the performance of the Varian Ethos AI auto‐contouring tool to assess its potential integration into
Robert N. Finnegan +6 more
wiley +1 more source
Active Learning Embedded in Incremental Decision Trees
, 2020 As technology evolves and electronic devices become widespread, the amount of data produced in the form of stream increases in enormous proportions. Data streams are an online source of data, meaning that it keeps producing data continuously. This creates the need for fast and reliable methods to analyse and extract information from these sources ...
Vinicius Eiji Martins +2 more
openaire +2 more sources
Journal of Applied Clinical Medical Physics, EarlyView.Abstract Introduction Many artificial intelligence (AI) solutions have been proposed to enhance the radiotherapy (RT) workflow, but limited applications have been implemented to date, suggesting an implementation gap. One contributing factor to this gap is a misalignment between AI systems and their users.
Luca M. Heising +11 more
wiley +1 more source
Learning decision trees from random examples
Information and Computation, 1989 AbstractWe define the rank of a decision tree and show that for any fixed r, the class of all decision trees of rank at most r on n Boolean variables is learnable from random examples in time polynomial in n and linear in 1/ɛ and log(1/δ), where ɛ is the accuracy parameter and δ is the confidence parameter.
A. Ehrenfeucht +2 more
openaire +3 more sources
Annals of Clinical and Translational Neurology, EarlyView.Abstract Objectives This study sought to evaluate proteomic, metabolomic, and immune signatures in the cerebrospinal fluid of individuals with Down Syndrome Regression Disorder (DSRD). Methods A prospective case–control study comparing proteomic, metabolomic, and immune profiles in individuals with DSRD was performed.
Jonathan D. Santoro +12 more
wiley +1 more source
Lower bounds on learning decision lists and trees
Information and Computation, 1995 Abstractk-Decision lists and decision trees play important roles in learning theory as well as in practical learning systems.k-Decision lists generalize classes such as monomials,k-DNF, andk-CNF, and like these subclasses they are polynomially PAC-learnable [R. Rivest,Mach. Learning2(1987), 229–246].
Ming Li +3 more
openaire +3 more sources
Iterative Bounding MDPs: Learning Interpretable Policies via Non-Interpretable Methods [PDF]
arXiv, 2021 Current work in explainable reinforcement learning generally produces policies in the form of a decision tree over the state space. Such policies can be used for formal safety verification, agent behavior prediction, and manual inspection of important features.
arxiv
Bridging Nature and Technology: A Perspective on Role of Machine Learning in Bioinspired Ceramics
Advanced Engineering Materials, EarlyView.Machine learning (ML) is revolutionizing the development of bioinspired ceramics. This article investigates how ML can be used to design new ceramic materials with exceptional performance, inspired by the structures found in nature. The research highlights how ML can predict material properties, optimize designs, and create advanced models to unlock a ...
Hamidreza Yazdani Sarvestani +2 more
wiley +1 more source
Digital Methods for the Fatigue Assessment of Engineering Steels
Advanced Engineering Materials, EarlyView.The use of engineering steels is often limited by their fatigue strength. In the sake of a faster product development, the fatigue behavior can be predicted by machine learning (ML). In this work, ML is applied on a heterogeneous database, covering a wide range of steel types.
Sascha Fliegener +7 more
wiley +1 more source
Advanced Engineering Materials, EarlyView.This manuscript presents advances in digital transformation within materials science and engineering, emphasizing the role of the MaterialDigital Initiative. By testing and applying concepts such as ontologies, knowledge graphs, and integrated workflows, it promotes semantic interoperability and data‐driven innovation. The article reviews collaborative
Bernd Bayerlein +44 more
wiley +1 more source