Results 61 to 70 of about 2,917,023 (283)
Engage D5.6 Thematic challenge briefing notes (1st and 2nd releases) [PDF]
, 2022 Engage identified four thematic challenges to address research topics not contemporaneously (sufficiently) addressed by SESAR. This deliverable serves primarily as a record of the two sets of released thematic challenge briefing ...
Cook, A.J., Tanner, G.
core
Arthritis Care &Research, EarlyView.Objective Reproductive‐age women with systemic autoimmune and rheumatic diseases (SARDs) have unique information needs related to their SARDs and reproductive health. We sought to understand their use of and receptivity to current and hypothetical generative artificial intelligence (AI) tools for health information‐seeking. Methods We conducted a cross‐
Mariam Arif +5 more
wiley +1 more source
GIScience & Remote SensingThe MultiResolution Land Characteristics (MRLC) consortium’s National Land Cover Database (NLCD) was established as an operational, stakeholder-oriented land cover monitoring program with the release of the NLCD2001 database.
James Wickham +5 more
doaj +1 more source
Digital classification of cloud masses from weather imagery using machine learning algorithms
Revista Facultad de Ingeniería Universidad de Antioquia, 2014 Accurate identification of precipitating clouds is a challenging task. In the present work, Support Vector Machines, Decision Trees and Random Forests algorithms were applied to discriminate between precipitating clouds and non-precipitating clouds from
Salomón Einstein Ramírez-Fernández +1 more
doaj +1 more source
Evaluating the classification accuracy of fuzzy thematic maps with a simple parametric measure
, 2004 In thematic maps, information is traditionally represented in a onepixel– one-class method, which assumes each pixel in the map can be assigned unambiguously to a single class.
RICOTTA, Carlo
core +1 more source
Physics‐Grounded Materials Artificial Intelligence for Reliable Materials Discovery
Advanced Functional Materials, EarlyView.Physics‐Grounded Materials AI (PhysMat AI) integrates physical priors, descriptors, constraints, verification, and data infrastructure into a unified full‐stack framework, enabling reliable, interpretable, and autonomous AI‐driven materials discovery.
Yuhang Wang +3 more
wiley +1 more source
Thematic mapper design parameter investigation [PDF]
, 1978 This study simulated the multispectral data sets to be expected from three different Thematic Mapper configurations, and the ground processing of these data sets by three different resampling techniques.
Wheeler, S. G., Colby, C. P., Jr.
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Advanced Robotics Research, EarlyView.This study explores how information processing is distributed between brains and bodies through a codesign approach. Using the “backpropagation through soft body” framework, brain–body coupling agents are developed and analyzed across several tasks in which output is generated through the agents’ physical dynamics.
Hiroki Tomioka +3 more
wiley +1 more source
Qubahan Academic JournalCreating question text using a cognitive approach based on Bloom’s Taxonomy (BT) is essential for maintaining question quality in learning assessment.
Sucipto Sucipto +2 more
doaj +1 more source
The effect of spatial, spectral and radiometric factors on classification accuracy using thematic mapper data [PDF]
, 1984 An experiment of a factorial design was conducted to test the effects on classification accuracy of land cover types due to the improved spatial, spectral and radiometric characteristics of the Thematic Mapper (TM) in comparison to the Multispectral ...
Alexander, D. +4 more
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