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2 results for “super learner”
Occupations on the map: Using a super learner algorithm to downscale labor statistics, data
<p>This repository contains all the input and output data (including maps) related to <a href="https://doi.org/10.1371/journal.pone.0278120">Van Dijk et al. (2022), Occupations on the map: Using a super learner algorithm to downscale labor statistics</a>. It does not contain several large (> 4GB) intermediate files, which summarize the results of the large number of machine learning models that were trained and tuned as part of the super learner algorithm. These files can be created by running the scripts in the supplementary GitHub repository: https://github.com/michielvandijk/occupations_on_the_map. All input and output maps produced as part of this study can also be accessed by means of an interactive web application: https://shiny.wur.nl/occupation-map-vnm.</p> <p>In this paper, we demonstrated an approach to create fine-scale gridded occupation maps by means of downscaling district-level labor statistics informed by remote sensing and other spatial information. We applied a super-learner algorithm that combined the results of different machine learning models to predict the shares of six major occupation categories and the labor force participation rate at a resolution of 30 arc seconds (~1x1 km) in Vietnam. The results were subsequently combined with gridded information on the working-age population to produce maps of the number of workers per occupation. The proposed approach can also be applied to produce maps of other (labor) statistics, which are only available at aggregated levels.</p>
Study On Hybrid Model Combining Super Learner And Physics-based Models For SHM In Bridges Using Low-cost BWIM
<p>The main objective of this project is to study and develop hybrid models of BWIM physics-based models incorporates with SHM inspection used by artificial intelligence (AI) techniques to predict structural damage. This study presents a comprehensive assessment of FE simulations leveraging contact method verified by vehicle-bridge interaction(VBI) theory and uses machine learning (ML) techniques to identify and predict structural damages from the structural response automatically. Bridges are a fundamental part of infrastructure management. The main challenge that we face is the aging of these transportation infrastructures without a tool to perform accurate structural assessments in a real-time manner. Unfortunately, this topic is still not completely developed due to the lack of study upon BWIM simulations designed with several different severity damages. The Contact method, a new approach to simulating moving-vehicle motion in BWIM simulation, is to carry out actual structural response verified by the VBI with comprehensive parameter studies. In order to simulation the reality complex condition of the bridge, the FE model is designed by four different classes of damages with three different damage locations applied with two different load conditions (e.g., static load and moving load). The responses collected from FE simulation are used for structural damage prediction leveraging the ML damage prediction model. Among ML methods, the XGBoost with assembly decision tree shows the most reliable results. The results in this project indicate that structural damage prediction can be achieved by using the ML technique and BWIM structural response, which provides high accuracy of damage prediction.</p>
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Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.