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Dataset results
55 results for “knowledge modeling”
Knowledge-based Iterative Model Reconstruction at Low-kilovoltage (kV) Cardiac Computed Tomography (CT)
ClinicalTrials.gov study NCT01896674. IPD Sharing: Not stated. Countries: 0. Publications: 1.
Models of Disordered Knowledge and Memory Systems in Dementia and Related Disorders
ClinicalTrials.gov study NCT00004557. IPD Sharing: Not stated. Countries: 1. Publications: 3.
The Effect of a Transtheoretical Model-Based Educational Intervention on First-Year Nursing Students' HPV Knowledge, Health Beliefs, and Vaccination Behavior Intentions
ClinicalTrials.gov study NCT07248904. IPD Sharing: NO. Countries: 1. Publications: 0.
Embeddings of KG-COVID-19 knowledge graph (Aug 12 build), produced using node2vec, skipgram model, p=q=1, walk length = 100, num walks = 20
<p>Embeddings of KG-COVID-19 knowledge graph (Aug 12 build), produced using Embiggen, node2vec, skipgram model, p=q=1, walk length = 100, num walks = 20</p>
Embeddings of KG-COVID-19 knowledge graph (Aug 12 build), 80/20 training/test split, produced using node2vec, skipgram model, p=q=1, walk length = 100, num walks = 20
<p>KG-COVID-19 embedding data from Sep 8, 2020 experiment, for training/test split of 80/20: </p> <p>These embeddings and weights were produced from this notebook on or around Sep 8, 2020:</p> <p>https://github.com/justaddcoffee/kg_covid_19_drug_analyses/blob/master/Graph%20embedding%20using%20SkipGram%20homogeneous%20graph.ipynb</p> <p>SkipGram_80_20_training_test_epoch_500_delta_0.0001_embedding.npy<br> SkipGram_80_20_training_test_epoch_500_delta_0.0001_weights.h5</p> <p>I'm also including two runs just before this, with different epoch number and delta values:</p> <p>SkipGram_80_20_training_test_embedding_sep_6_2020_epoch_200_delta_0.001.npy</p> <p>SkipGram_80_20_training_test_weights_sep_6_2020_epoch_200_delta_0.001.h5</p> <p>SkipGram_80_20_training_test_embedding_sep_7_2020_epoch_200_delta_0.0001.npy<br> SkipGram_80_20_training_test_weights_sep_7_2020_epoch_200_delta_0.0001.h5</p>
Explore the Sharing Model Intervene to Improve the Knowledge, Attitudes, Service Intentions and Service Start-up Effects of the Eight Major Non-cancer Disease End-stage Caregivers on Well-being and Pa
ClinicalTrials.gov study NCT05407272. IPD Sharing: NO. Countries: 1. Publications: 0.
The Effect of Health Belief Model-Based Education and Motivational Interviewing on HPV (Human Papilloma Virus) Knowledge and Vaccine Awareness in Women
ClinicalTrials.gov study NCT07097766. IPD Sharing: YES. Countries: 1. Publications: 0.
Development and Application of a Diagnosis and Treatment System for Children's Brain Diseases Based on Knowledge Graphs and Large Models
ClinicalTrials.gov study NCT06848959. IPD Sharing: UNDECIDED. Countries: 1. Publications: 0.
The Effect of Health Belief Model-Based Cancer Education on Cancer Information Load, Knowledge and Attitudes Towards Cancer Screenings
ClinicalTrials.gov study NCT06579625. IPD Sharing: NO. Countries: 1. Publications: 0.
Health Promotion Model, Fertility Awareness, Preconception Knowledge and Attitude
ClinicalTrials.gov study NCT06690554. IPD Sharing: NO. Countries: 1. Publications: 0.
Data from: Using expert knowledge to incorporate uncertainty in cause-of-death assignments for modeling of cause-specific mortality
Open the record for dataset details and reuse information.
HER2 data used in the article entitled "MSclassifier: Median-Supplement model-based Classification tool for automated knowledge discovery"
<p>This repository contains HER2 training and test sets used for evaluating MSclassifier and other packages in the software article entitled "MSclassifier: median-supplement model-based classification tool for automated knowledge discovery." The training set is comprised of 100 instances and 74 attributes while the test set is comprised of 62 instances and 74 attributes. The training samples were used to obtain results from a 10-fold cross-validation testing of how MSclassifier and other packages accurately predicted HER2-receptor status phenotypes in breast cancer in the article. The data used in the software article was obtained from the supplementary data of "Adabor ES, Acquaah-Mensah GK, Machine learning approaches to decipher hormone and HER2 receptor status phenotypes in breast cancer, Briefings in Bioinformatics 2019; 20 (2): 504–514, https://doi.org/10.1093/bib/bbx138" by permission of Oxford University Press. Here, it is reproduced by permission of Oxford University Press.</p> <p> </p>
Why should software companies use models to improve knowledge management? A survey in Brazil
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Exploring the Knowledge, Attitudes, Behaviors and Perceived Barriers of Hepatologists Towards Non-invasive Hepatocellular Carcinoma Prediction Models in Hepatitis C Patients Who Achieved Sustained Vir
ClinicalTrials.gov study NCT06872047. IPD Sharing: Not stated. Countries: 0. Publications: 0.
A knowledge-based system approach for sensor fault modeling, detection and mitigation
Sensors are vital components for control and advanced health management techniques. However, sensors continue to be considered the weak link in many engineering applications since often they are less reli- able than the system they are observing. This is in part due to the sensors’ operating principles and their susceptibility to interference from the environment. Detecting and mitigating sensor failure modes are becoming increasingly important in more complex and safety-critical applications. This paper reports on different techniques for sensor fault detection, disambiguation, and mitigation. It presents an expert system that uses a combination of object-oriented modeling, rules, and semantic networks to deal with the most common sensor faults, such as bias, drift, scaling, and dropout, as well as system faults. The paper also describes a sensor correction module that is based on fault parameters extraction (for bias, drift, and scaling fault modes) as well as utilizing partial redundancy for dropout sensor fault modes). The knowledge-based system was derived from the results obtained in a previously deployed Neural Network (NN) application for fault detection and disambiguation. Results are illustrated on an electromechanical actuator application where the system faults are jam and spalling. In addition to the functions implemented in the previous work, system fault detection under sensor failure was also modeled. The paper includes a sensitivity analysis that compares the results previously obtained with the NN. It concludes with a discussion of similarities and differences between the two approaches and how the knowledge based system provides additional functionality compared to the NN implementation.
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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.