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179 results for “Classification systems”
Architecturally Significant Requirements and Software Architecture for AI-Based Systems: A Case Study with Document Classification
Open the record for dataset details and reuse information.
Deep Learning-Based Recommendation System: Systematic Review and Classification - Outputs
<p>The datasets provided are the outputs of the paper titled "Deep Learning-Based Recommendation System: Systematic Review and Classification." They encompass multiple outputs, including primary articles, domain-focused articles, technique mapping, and domain mapping for each category.</p>
Urdu Translation of Bimanaual Fine Motor Functional Classification System 2
ClinicalTrials.gov study NCT06484465. IPD Sharing: NO. Countries: 1. Publications: 1.
General Tissue Response Classification System After Chemotherapy
ClinicalTrials.gov study NCT03791268. IPD Sharing: UNDECIDED. Countries: 1. Publications: 1.
Analysis of Caesarean Section Rate According to the Robson Classification System
ClinicalTrials.gov study NCT03794063. IPD Sharing: YES. Countries: 1. Publications: 7.
Movement System Impairment Based Classification Versus General Exercise for Chronic Non-specific Low Back Pain: a Randomised Controlled Trial
ClinicalTrials.gov study NCT02221609. IPD Sharing: Not stated. Countries: 1. Publications: 5.
American College of Rheumatology/European League Against Rheumatism (ACR/EULAR) Diagnostic and Classification Criteria for Primary Systemic Vasculitis
ClinicalTrials.gov study NCT01066208. IPD Sharing: Not stated. Countries: 33. Publications: 8.
Using A Novel Classification System in Intravenous GCs Therapy of TAO: A Multi-central, Randomized, Open, Superior Trial
ClinicalTrials.gov study NCT03107078. IPD Sharing: NO. Countries: 1. Publications: 1.
Effect of Core Muscles Training On Patients With Chronic Mechanical Low Back Pain According To SALIBA'S Postural Classification System
ClinicalTrials.gov study NCT06296667. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Establishment of Molecular Classification Models for Early Diagnosis of Digestive System Cancers
ClinicalTrials.gov study NCT05431621. IPD Sharing: NO. Countries: 1. Publications: 4.
Turkish Validity and Reliability of the Visual Function Classification System (VFCS)
ClinicalTrials.gov study NCT05102955. IPD Sharing: NO. Countries: 1. Publications: 1.
A Warning Red Flag Classification System to Predict Risk of Vasovagal Syncope During Office Hysteroscopy
ClinicalTrials.gov study NCT07030218. IPD Sharing: YES. Countries: 1. Publications: 4.
Effect of Tailored Preventive Program on Caires Incidence Using International Caries Classification and Management System (ICCMS) A Randomized Clinical Trial
ClinicalTrials.gov study NCT03189797. IPD Sharing: UNDECIDED. Countries: 1. Publications: 1.
Figure 2 from: Emmrich M, Vences M, Ernst R, Köhler J, Barej MF, Glaw F, Jansen M, Rödel M-O (2020) A guild classification system proposed for anuran advertisement calls. Zoosystematics and Evolution 96(2): 515-525. https://doi.org/10.3897/zse.96.38770
Figure 2 Key to anuran advertisement call guilds (compare text); each guild illustrated by schematic waveform and spectrogram.
Figure 3 from: Emmrich M, Vences M, Ernst R, Köhler J, Barej MF, Glaw F, Jansen M, Rödel M-O (2020) A guild classification system proposed for anuran advertisement calls. Zoosystematics and Evolution 96(2): 515-525. https://doi.org/10.3897/zse.96.38770
Figure 3 Examples for all different anuran advertisement call guilds (A–H), with a time scale of 0 to 1 s on x-axis and frequency scale of 0 to 8 kHz on y-axis (compare text). Guild A) non-frequency modulated, non-pulsed simple call (Bombina bombina; Bombinatoridae; dfrq/ms = 0.00 Hz/ms) (based on Schneider 2005); Guild B) frequency modulated, non-pulsed simple call (Leptodactylus fuscus; Leptodactylidae; dfrq/ms = 7.22 Hz/ms) (based on Márquez et al. 2002); Guild C) non-frequency modulated pulsed call (Hyla meridionalis; Hylidae; dfrq/ms = 0.67 Hz/ms) (based on Masó and Pijoan 2011); Guild D) frequency modulated pulsed call (Hyperolius pickersgilli; Hyperoliidae; dfrq/ms = 2.32 Hz/ms) (based on Du Preez and Carruthers 2009); Guild E) non-frequency modulated call with uniform notes (Sclerophrys mauritanica; Bufonidae; dfrq/ms = 0.31 Hz/ms) (based on Masó and Pijoan 2011); Guild F) frequency modulated call, with uniform notes (Pseudopaludicola boliviana; Leptodactylidae; dfrq/ms = 2.38 Hz/ms) (based on Márquez et al. 2002); Guild G) non-frequency modulated complex call (Smilisca sila; Hylidae; dfrq/m = 0.43 Hz/ms) (based on Ibanéz 1999); Guild H) frequency modulated complex call (Hyperolius nasutus; Hyperoliidae; dfrq/ms = 1.66 Hz/ms) (based on Du Preez and Carruthers 2009).
Figure 1 from: Emmrich M, Vences M, Ernst R, Köhler J, Barej MF, Glaw F, Jansen M, Rödel M-O (2020) A guild classification system proposed for anuran advertisement calls. Zoosystematics and Evolution 96(2): 515-525. https://doi.org/10.3897/zse.96.38770
Figure 1 Basic types of anuran vocalizations based on their temporal structure, shown as schematic waveforms, modified after Littlejohn (2001): (a) non-pulsed call, (b) pulsed call, (c) call with uniform pulsed notes, (d) complex call containing different note types, and (e) two complex calls in a call series. Black arrows mark inter-note intervals and red arrow marks inter-call interval.
Supplementary material 1 from: Emmrich M, Vences M, Ernst R, Köhler J, Barej MF, Glaw F, Jansen M, Rödel M-O (2020) A guild classification system proposed for anuran advertisement calls. Zoosystematics and Evolution 96(2): 515-525. https://doi.org/10.3897/zse.96.38770
Database for the definition of anuran call guilds
Supplementary material 2 from: Bustamante RO, Alves L, Goncalves E, Duarte M, Herrera I (2020) A classification system for predicting invasiveness using climatic niche traits and global distribution models: application to alien plant species in Chile. NeoBiota 63: 127-146. https://doi.org/10.3897/neobiota.63.50049
Table S2. Basic information obtained for 49 exotic plants in Chile
Supplementary material 3 from: Bustamante RO, Alves L, Goncalves E, Duarte M, Herrera I (2020) A classification system for predicting invasiveness using climatic niche traits and global distribution models: application to alien plant species in Chile. NeoBiota 63: 127-146. https://doi.org/10.3897/neobiota.63.50049
Map of the species
Data from: Census parcels cropping system classification from multitemporal remote imagery: a proposed universal methodology
A procedure named CROPCLASS was developed to semi-automate census parcel crop assessment in any agricultural area using multitemporal remote images. For each area, CROPCLASS consists of a) a definition of census parcels through vector files in all of the images; b) the extraction of spectral bands (SB) and key vegetation index (VI) average values for each parcel and image; c) the conformation of a matrix data (MD) of the extracted information; d) the classification of MD decision trees (DT) and Structured Query Language (SQL) crop predictive model definition also based on preliminary land-use ground-truth work in a reduced number of parcels; and e) the implementation of predictive models to classify unidentified parcels land uses. The software named CROPCLASS-2.0 was developed to semi-automatically perform the described procedure in an economically feasible manner. The CROPCLASS methodology was validated using seven GeoEye-1 satellite images that were taken over the LaVentilla area (Southern Spain) from April to October 2010 at 3- to 4-week intervals. The studied region was visited every 3 weeks, identifying 12 crops and others land uses in 311 parcels. The DT training models for each cropping system were assessed at a 95% to 100% overall accuracy (OA) for each crop within its corresponding cropping systems. The DT training models that were used to directly identify the individual crops were assessed with 80.7% OA, with a user accuracy of approximately 80% or higher for most crops. Generally, the DT model accuracy was similar using the seven images that were taken at approximately one-month intervals or a set of three images that were taken during early spring, summer and autumn, or set of two images that were taken at about 2 to 3 months interval. The classification of the unidentified parcels for the individual crops was achieved with an OA of 79.5%.
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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.