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114 results for “artificial neural networks”
Ecological Niche Models, in 2019 and across RCP 2.6, 4.5, and 8.5 scenarios in 2050 and 2100, of 1508 European Marine Species, developed with Artificial Neural Networks, Maximum Entropy, Support Vector Machines, and AquaMaps at 0.5° Resolution
<p>Native ecological niche models of 1508 European species (894 fish and 614 non fish) developed with AquaMaps, Artificial Neural Networks, Maximum Entropy, and Support Vector Machines, for 2019 and under RCP 2.6, 4.5, and 8.5 scenarios in 2050 and 2100, at 0.5° spatial resolution.</p>
Biodiversity Index, in 2019 and across RCP 4.5, and 8.5 scenarios in 2050 and 2100 of 1508 European Marine Species based on ensemble Ecological Niche Models developed with Artificial Neural Networks, Maximum Entropy, Support Vector Machines, and AquaMaps at 0.5° Resolution
<p>Biodiversity Index in 2019 and across RCP 4.5, and 8.5 scenarios in 2050 and 2100 of 1508 European marine species based on ensemble Ecological Niche Models developed with Artificial Neural Networks, Maximum Entropy, Support Vector Machines, and AquaMaps at 0.5° Resolution. The Index counts the number of species (among the 1508) potentially present in each 0.5° cell according to the ensemble models. For each ensemble model, a threshold of at least 3 models agreeing on species presence in the cell was used to indicate species presence.</p>
Ecological Niche Models of 96 European Marine Species, for 2019, developed with AquaMaps, Artificial Neural Networks, Maximum Entropy, and Support Vector Machines at 0.1° Resolution
<p>Native ecological niche models of 96 European marine species of particular commercial and conservation interest developed with AquaMaps, Artificial Neural Networks, Maximum Entropy, and Support Vector Machines, for 2019 at 0.1° spatial resolution.</p>
Ensemble Ecological Niche Models and Biodiversity Index for 2019 of 96 European Marine Species based on Ecological Niche Models developed with Artificial Neural Networks, Maximum Entropy, AquaMaps, and Support Vector Machines at 0.1° Resolution
<p>Ensemble Ecological Niche Models for 2019 of 96 European marine species of particular commercial and conservation interest, based on Ecological Niche Models developed with (i) Artificial Neural Networks, (ii) Maximum Entropy, (iii) Support Vector Machines, and (iv) AquaMaps at 0.1° Resolution. The data report, for each 0.1° cell, how many models (from 0 to 4) overcome a model-specific decision threshold to assess species presence in the cell. A Biodiversity Index is also provided as the count of the number of species (among the 96) potentially present in each 0.1° cell according to the ensemble models. For each ensemble model, a threshold of at least 3 models agreeing on species presence in the cell was used to indicate species presence.</p>
Data release for "OrchID: a Generalized Framework for Taxonomic Classification of Images Using Evolved Artificial Neural Networks"
<p><strong>Abstract</strong></p> <p>Taxonomic expertise for the identification of species is rare and costly. On-going advances in computer vision and machine learning have led to the development of numerous semi- and fully automated species identification systems. However, these systems are rarely agnostic to specific morphology, rarely can perform taxonomic “approximation” (by which we mean partial identification at least to higher taxonomic level if not to species), and frequently rely on costly scientific imaging technologies.</p> <p>We present a generic, hierarchical identification system for automated taxonomic approximation of organisms from images. We assessed the effectiveness of this system using photographs of slipper orchids (Cypripedioideae), for which we implemented image pre-processing, segmentation, and colour and shape feature extraction algorithms to obtain digital phenotypes for 116 species. The identification system trained on these digital phenotypes uses a nested hierarchy of artificial neural networks for pattern recognition and automated classification that mirrors the Linnean taxonomy, such that user-submitted photos can be assigned a genus, section, and species classification by traversing this hierarchy.</p> <p>Performance of the identification system varied depending on photo quality, number of species included for training, and desired taxonomic level for identification. High quality photos were scarce for some taxa and were under-represented in the training set, resulting in imbalanced network training. The image features used for training were sufficient to reliably identify photos to the correct genus but less so to the correct section and species.</p> <p>The outcomes of this project include a library of feature extraction algorithms called <em>ImgPheno</em>, a collection of scripts for neural network training called <em>NBClassify</em>, a library for evolutionary optimization of artificial neural network construction called <em>AI::FANN::Evolving</em> and a planned web application called <em>OrchID</em> for identification of user-submitted images. All project outcomes are open source and freely available.</p> <p><strong>About this release</strong></p> <p>This release corresponds belongs with our response to the reviewers of PLoS One. At this stage of the review cycle the manuscript is assessed as 'minor revision'. Consequently, we don't anticipate making more releases until publication.</p>
Artificial Neural Networks-generated Dataset: pH, Total Alkalinity, and Hydrogen Ion Concentration in Ría de Vigo (NW Spain), 1995–2020
<p>This dataset comprises input data from INTECMAR and the predicted outcomes. The variables and their units are as follows:</p> <p>station: 'Station ID [1-6]'</p> <p>year: 'Year [1995-2020]'</p> <p>month: 'Month [1-12]'</p> <p>day: 'Day'</p> <p>latitude: 'Latitude (decimal degrees)'</p> <p>longitude: 'Longitude (decimal degrees)'</p> <p>depth: 'Depth (meters)'</p> <p>temperature: 'Temperature (degrees Celsius)'</p> <p>salinity: 'Salinity (psu)'</p> <p>phosphate: 'Phosphate (umol/kg)'</p> <p>nitrate: 'Nitrate (umol/kg)'</p> <p>silicate: 'Silicate (umol/kg)'</p> <p>cweek: 'Cosine week'</p> <p>sweek: 'Sine week'</p> <p>TA: 'Total Alkalinity predicted (umol/kg)'</p> <p>NTA: 'Normalized Total Alkalinity (umol/kg)'</p> <p>NAT_st: 'Normalized per station Total Alkalinity (umol/kg)'</p> <p>NTA_gl: 'Normalized globally Total Alkalinity (umol/kg)'</p> <p>pHTS_insitu: 'pH insitu (pH units)'</p> <p>HT: 'Hydrogen ion concentration predicted (nmol/kg)'</p> <p> </p> <p>The authors gratefully acknowledge the financial support by the Programa de axudas á etapa predoutoral da Xunta de Galicia (Axencia Galega de Innovación) (Grant nº IN606A-2022/025). F.F.P. and A.V. were supported by REDEIRA (TED2021-132188B-I00) project, funded by MCIN/AEI/10.13039/501100011033. The authors also express their gratitude to the Instituto Tecnolóxico para o Control do Medio Mariño de Galicia (INTECMAR), for the analyses and production of the database used to make predictions.</p>
Robust Method for Property Prediction via Artificial Neural Networks: Incorporating Key Structural Features for Carbon Dioxide – Ionic Liquid Mixtures
<p>This Dataset comprises two sub-sets of information:</p> <ul> <li>Database and Results of the work present in the paper "Robust Method for Property Prediction via Artificial Neural Networks: Incorporating Key Structural Features for Carbon Dioxide – Ionic Liquid Mixtures" published in The Journal of Physical Chemistry B (https://doi.org/10.1021/acs.jpcb.4c04432).</li> <li>Sample of the code used, in order to reproduce any of the results presented above. This can be found in the previous version of this Dataset (v1.0 https://zenodo.org/records/11216901)</li> </ul> <p> </p> <p>Regarding the sample code, an example for all ANN Models used in this work is provided. This includes the three models used:</p> <ol> <li>One based only on Critical Properties of Ionic Liquids (CRT Model)</li> <li>One based only on Structural Properties of Ionic Liquids (STR Model)</li> <li>One combination of the previous models, taking into account both Critical and Structural Properties (COMB Model)</li> </ol> <p>In this manner, it is possible to observe the differences between the performance of the different models, either through statiscal analysis or using graphical representation. This allows for the benchmarking to be done in a more concise way.</p>
Surrogate-based optimization using an artificial neural network for a parameter identification in a 3D marine ecosystem model
<p><strong>Abstract:</strong></p> <p>Parameter identification for marine ecosystem models is important for the assessment and validation of marine ecosystem models against observational data. The surrogate-based optimization (SBO) is a computationally efficient method to optimize complex models. SBO replaces the computationally expensive (high-fidelity) model by a surrogate constructed from a less accurate but computationally cheaper (low-fidelity) model in combination with an appropriate correction approach, which improves the accuracy of the low-fidelity model. To construct a computationally cheap low-fidelity model, we tested three different approaches to compute an approximation of the annually periodic solution (i.e., a steady annual cycle) of a marine ecosystem model: firstly, a reduced number of spin-up iterations (several decades instead of millennia), secondly, an artificial neural network (ANN) approximating the steady annual cycle and, finally, a combination of the both approaches. Except for the low-fidelity model using only the ANN, the SBO yielded a solution close to the target and reduced the computational effort significantly. If an ANN approximating appropriately a marine ecosystem model is available, the SBO using this ANN as low-fidelity model presents a promising and computational efficient method for the validation.</p> <p> </p> <p><strong>Content:</strong></p> <ul> <li>SQLite database including the data of the different optimization runs</li> <li>Structure and weights of the used artificial neural network</li> <li>Tracer concentrations obtain from the high-fidelity model for the different optimization runs</li> </ul>
Landslide Susceptibility and 3-day Antecedent Rainfall generated by Artificial Neural Networks
<p>[ENGLISH]</p> <p>In this dataset, you can find:</p> <p>- Landslide susceptibility indexes for the Serra Geral geomorphic unit, from 0 (low susceptibility) to 1 (high susceptibility). Files starting in map_susc</p> <p>- 3-day Antecedent Rainfall Thresholds for rainfall-induced landslides in the Serra Geral geomorphic unit. Files starting in map_3day</p> <p>The figure tiles_location.png shows the locations of each tile within the states of Rio Grande do Sul and Santa Catarina, Brazil. Background map: OpenStreetMap contributors (2022)</p> <p>This dataset was produced within the research conducted for the PhD Thesis of Luísa Vieira Lucchese. The link to the Thesis will be added here when it is available. Reference:</p> <p>LUCCHESE, Luísa Vieira. Modelagem de Suscetibilidade e de Limiares de Precipitação para Deslizamentos de Terra utilizando métodos de Aprendizagem de Máquina. 2022. PhD Thesis (Water Resources and Environmental Sanitation) — Instituto de Pesquisas Hidráulicas, Universidade Federal do Rio Grande do Sul, Porto Alegre, 2022.</p> <p> </p> <p>[PORTUGUÊS DO BRASIL]</p> <p>Neste conjunto de dados, você encontra:</p> <p>- Índices de suscetibilidade a deslizamentos de terra para a unidade geomorfológica da Serra Geral, de 0 (baixa suscetibilidade) até 1 (alta suscetibilidade). Os arquivos têm o prefixo map_susc</p> <p>- Precipitação antecedente de 3 dias para a ocorrência de deslizamentos de terra na unidade geomorfológica da Serra Geral. Os arquivos têm o prefixo map_3day</p> <p>A figura tiles_location.png mostra a localização de cada bloco dentro dos estados do Rio Grande do Sul e de Santa Catarina. Mapa de fundo: OpenStreetMap contributors (2022)</p> <p>Este conjunto de dados é produto da Tese de Doutorado de Luísa Vieira Lucchese. O link para a Tese será adicionado aqui, quando estiver disponível. Referência:</p> <p>LUCCHESE, Luísa Vieira. Modelagem de Suscetibilidade e de Limiares de Precipitação para Deslizamentos de Terra utilizando métodos de Aprendizagem de Máquina. 2022. Tese (Doutorado em Recursos Hídricos e Saneamento Ambiental) — Instituto de Pesquisas Hidráulicas, Universidade Federal do Rio Grande Sul, Porto Alegre, 2022.</p>
Data from: Supervised classification of plant communities with artificial neural networks
<p>This dataset was used to test the performance of artificial neural networks for supervised classification of plant communities, published in:</p><p>Černá L. & Chytrý M. (2005) Supervised classification of plant communities with artificial neural networks. <i>Journal of Vegetation Science</i> 16, 407-414. https://doi.org/10.1111/j.1654-1103.2005.tb02380.x</p><p>The meaning of the individual columns (separated by semicolons) in the file is as follows (for details see the above-mentioned article):</p><ul><li>Plot no - unique number of the vegetation plot</li><li>Group expert - plot membership in classes 1-11 of the expert classification</li><li>Subset expert random B - assignment of the plot to the training, selection, test or ignored data subset, using the random selection of the training (and selection) subset, for the expert classification</li><li>Subset expert dg species B - assignment of the plot to the training, selection, test or ignored data subset, using the selection of the training (and selection) subset by diagnostic species, for the expert classification</li><li>Assignment expert random - a class assignment of the plot by the MLP classifier, when trained with the randomly selected training (and selection) subset, for expert classification</li><li>Assignment expert dg-sp - a class assignment of the plot by the MLP classifier, when trained with the plots rich in diagnostic species contained in the training (and selection) subset, for expert classification</li><li>Group cluster - plot membership in classes 1-11 of the numerical classification</li><li>Subset cluster random - assignment of the plot to the training, selection, test or ignored data subset, using the random selection of the training (and selection) subset, for numerical classification</li><li>Subset cluster dg species - assignment of the plot to the training, selection, test or ignored data subset, using the selection of the training (and selection) subset by diagnostic species, for expert classification, for numerical classification</li><li>Assignment cluster random - class assignment of the plot by the MLP classifier, when trained with the randomly selected training (and selection) subset, for expert classification, for numerical classification</li><li>Assignment cluster dg-sp - class assignment of the plot by the MLP classifier, when trained with the plots rich in diagnostic species contained in the training (and selection) subset, for expert classification, for numerical classification </li><li>598 species, with cover/abundance estimates on an ordinal scale of 1-9</li></ul>
Archival Datasets for SuperNova Artificial Inference by Lstm neural networks (SNAIL)
<p>The spectral-observation dataset (enclosed in the file archival_spec_observations.tar.gz) is comprised of 3091 observed spectra from 361 SNe Ia, largely contributed from CfA (Blondin et al. 2012), BSNIP (Silverman et al. 2012), CSP (Folatelli et al. 2013) and Supernova Polarimetry Program (Wang & Wheeler 2008; Cikota et al. 2019a; Yang et al. 2020).</p> <p>The spectral-template dataset (enclosed in the file archival_spec_templates.tar.gz) includes 361 spectral templates, each of them (covering -15 to +33d with wavelength from 3800 to 7200 A) was generated from the available spectroscopic observations of an individual SN via a LSTM neural network model.</p> <p>The auxiliary photometry dataset (enclosed in the file archival_phot_observations.tar.gz) provides the B & V light curves of these SNe (in total, 196 available SNe Ia), that were used to calibrate the synthetic B-V color of the observed spectra.</p> <p>In additional, the two master catalogs give the detailed information about the 361 SNe and their spectroscopic observations, respectively. </p> <p>These datasets are associated to the paper "Spectroscopic Studies of Type Ia Supernovae Using LSTM Neural Networks" (Hu et al. 2022, ApJ, accepted).</p>
Data set for "Low-Power Artificial Neural Network Perceptron Based on Monolayer MoS2"
<p>Data sets for the publication "Low-Power Artificial Neural Network Perceptron Based on Monolayer MoS<sub>2</sub>", doi:10.1021/acsnano.1c07065</p>
Global sea surface dimethyl sulfide dataset simulated by artificial neural network
<p>This dataset contains (1) the matched and binned data used for constructing an artificial neural network (ANN) model to simulate the sea surface concentration of dimethyl sulfide (DMS); (2) the simulated global daily sea surface concentrations of DMS ranging from 2005 to 2014 by ANN model and the calculated total transfer velocities (Kt) and sea-to-air fluxes; (3) the simulated global monthly sea surface concentrations of DMS ranging from 2005 to 2100 by ANN model and CMIP6 ensemble and the calculated Kt and sea-to-air fluxes; (4) the yearly mean DMS concentration of each grid in different sensitivity experiments exploring the roles different variables play in driving DMS future changes. The input variables of this ANN model include chlorophyll <em>a</em>, sea surface temperature (SST), mixed layer depth (MLD), nitrate, phosphate, silicate, dissolved oxygen (DO), downward short-wave radiation (DSWF), and sea surface salinity (SSS). The future projections (2015-2100) are subjected into two Shared Socioeconomic Pathway scenarios SSP2-4.5 and SSP5-8.5. The spatial resolution of the simulated dataset is 1°×1°. The units of DMS concentration, Kt, and flux are nmol L<sup>–1</sup>, m s<sup>–1</sup>, and μmol S m<sup>–2</sup> d <sup>–1</sup>, respectively.</p> <p>Compared with the previous version (v1.0), this version is based on an updated ANN model after adjusting the data match-up between satellite and in-situ chlorophyll <em>a</em> for ANN training. In addition, the historical simulation based on CMIP6 only covers the time period from 2005 to 2014, which was from 1850 to 2014 for v1.0.</p>
Estimation of axial loads in tie-rods: Dataset generated from Finite Element simulations for training Artificial Neural Network
<p>Dataset employed for training the Artificial Neural Networks (ANNs) presented in the cited journal article. The trained ANNs were used to estimate the tensile force in tie-rods installed in a historical structure (the church of the monastery of Sant Cugat close to Barcelona) from dynamic parameters obtained from vibration testing.</p> <p>The dataset consists of input-otput data generated using finite element (FE) simulations. A blank column has been used to separate input data from output data.</p> <p>More details on the nature of the data and how it was employed can be found in the following journal article, which is supplemented by this upload:<br> <em><strong>Makoond N, Pelà L, Molins C. Robust estimation of axial loads sustained by tie-rods in historical structures using Artificial Neural Networks. Structural Health Monitoring. 2022;0(0). doi:</strong></em><strong><a href="https://doi.org/10.1177/14759217221123326">10.1177/14759217221123326</a></strong></p> <p><a href="https://www.researchgate.net/publication/364098652_Robust_estimation_of_axial_loads_sustained_by_tie-rods_in_historical_structures_using_Artificial_Neural_Networks">Link to author's version of accepted manuscript</a></p> <p>This work was supported by the Servei del Patrimoni Arquitectònic of the Generalitat de Catalunya through a project (managed by the City Council of Sant Cugat) aimed at monitoring the church of the Monastery of Sant Cugat (grant number C-10764). Financial support is also acknowledged from the Ministry of Science, Innovation and Universities of the Spanish Government and the ERDF (European Regional Development Fund) through the SEVERUS project (Multilevel evaluation of seismic vulnerability and risk mitigation of masonry buildings in resilient historical urban centres) (grant number RTI2018-099589-B-I00).</p>
Figure 5 in Evaluation of geostatistical method and hybrid Artificial Neural Network with imperialist competitive algorithm for predicting distribution pattern of Tetranychus urticae (Acari: Tetranychidae) in cucumber field of Behbahan, Iran
Figure 5. Moving colonies to imperialist in culture and language axes (AtashpazGargari et al. 2008).
Figure 2 in Evaluation of geostatistical method and hybrid Artificial Neural Network with imperialist competitive algorithm for predicting distribution pattern of Tetranychus urticae (Acari: Tetranychidae) in cucumber field of Behbahan, Iran
Figure 2. Generalized semivariogram showing the range of spatial dependence, nugget effect (C0) variability associated with spatial dependence (C), and sill (C + C0).
Data from: Prediction of Pedestrian Speed with Artificial Neural Networks
<p>Corridor data are trajectories of pedestrians in a closed corridor of lenght 30m and width 1.8m. The trajectories are measured on a section of length 6m. Experiments are carried out with N=15, 30, 60, 85, 95, 110, 140 and 230 participants.</p> <p>Bottleneck data are trajectories of pedestrian in a bottleneck of lenght 8m and width 1.8m. Experiments are carried out with 150 participants for bottleneck widths w=0.7, 0.95 1.2 and 1.8m.</p> <p>See http://ped.fz-juelich.de/experiments/2009.05.12_Duesseldorf_Messe_Hermes/docu/VersuchsdokumentationHERMES.pdf page 20 and 24 for details (in German). The data are part of the online database http://ped.fz-juelich.de/database.</p> <p>Column names of the file are: ID FRAME X Y Z.</p> <ul> <li>ID is the pedestrian ID.</li> <li>FRAME is the frame number (frame rate is 1/16s).</li> <li>X Y and Z are pedestrian position in 3D.</li> </ul>
BRAIN Journal-Evolving Spiking Neural Networks for Control of Artificial Creatures-Figure 14. Number of artificial creatures that could survive in the external environment
<p>Artificial creatures and the virtual environment are designed and implemented in a C++ platform in which simulations are performed. In the GA algorithm, at First step a population of 100 complex artificial creatures that each of them had 150 neurons were tested and evaluated by GA; each neuron connected to 15 post-synaptic neurons with different axonal conduction delays between every two neurons. In every generation fitness function has been calculated for all population. The initial energy level for each creature is considered as 50. Figure 14 the number of survived chromosome increases with<br> generation progressing.</p>
BRAIN Journal-Evolving Spiking Neural Networks for Control of Artificial Creatures-Figure 11. Crossover with two cut points
<p>Then next generation are produced by combination of the elites (15%), crossover (55%) and mutation (30%) of the initial population. Elites are the best chromosomes which are directly transferred to the next generation. Because of long chromosome length, for crossover, five points are randomly chosen in each parent as cut points. Figure 11 shows a typical crossover with two cut points and Figure 12 illustrates a flowchart for the proposed evolutionary model. Selections are based on Roulette Wheel selection, more detailed information can be found in.</p>
BRAIN Journal-Evolving Spiking Neural Networks for Control of Artificial Creatures-Figure 10: A typical neuron and postsynaptic connections with weights and delays
<p>First, an initial random population of creatures is generated where the neural networks of the creatures are coded as chromosomes, as shown in Figure 10a and Figure 10b. Each chromosome consists of four parts: A1, A2, A3 and A4. Each part consists of N segments for N neurons of a typical neural network structure. The first part, A1, denotes a, b, c and d parameters of neurons Izhikevich model (discussed in (1) and (2)). Each segment of A2 shows postsynaptic weights and connections for corresponding neuron and each segment of A3 indicates postsynaptic delays of theconnections. Segment A4 shows postsynaptic neurons that are connected to corresponding neuron, as shown in Figure 10b.</p>
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
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Annotated Behaviour and Observability Dataset (ABODe)
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DANDI Archive for NWB datasets
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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.