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Dataset results
114 results for “artificial neural networks”
Artificial neural networks enable genome-scale simulations of intracellular signaling
GEO Series GSE202515. Homo sapiens. 190 samples. Type: Expression profiling by high throughput sequencing.
Identification of a selective G1-phase benzimidazolone inhibitor by a senescence-targeted virtual screen using artificial neural networks
GEO Series GSE72621. Homo sapiens. 38 samples. Type: Expression profiling by array.
Global ocean dimethyl sulfide climatology estimated from observations and an artificial neural network
<p>Surface ocean DMS concentrations and sea-to-air flux estimated using an Artificial Neural Network model. </p> <p><a href="https://zenodo.org/api/files/3da6a1cf-5a4c-4186-984e-20a3298facfc/DMS_Concentration_monthly_mean.csv?versionId=4dabc517-22c9-4829-84e3-5df97ef8aa74">DMS_Concentration_monthly_mean.csv</a> and <a href="https://zenodo.org/api/files/3da6a1cf-5a4c-4186-984e-20a3298facfc/DMS_Flux_Wang2020.mat?versionId=a5e8de9f-5962-4940-974b-e14731a0c466">DMS_Flux_Wang2020.mat</a> are previous versions based on BGD paper.</p> <p><a href="https://zenodo.org/api/files/3da6a1cf-5a4c-4186-984e-20a3298facfc/DMS_clim_Wang20_v1.mat?versionId=02ca48ed-306e-44da-a406-3ac48f75e811">DMS_clim_Wang20_v1</a>.mat and <a href="https://zenodo.org/api/files/3da6a1cf-5a4c-4186-984e-20a3298facfc/Sea2Air_Flux_Wang20_v1.mat?versionId=6244f1bc-26d1-4f5e-b6ea-93be132bf6d3">Sea2Air_Flux_Wang20_v1.mat</a> are newer versions based on BGD paper revision.</p> <p>PMEL_NAAMES_* data are raw DMS data along with environmental parameters, so interested user can play with the models.</p> <p>The ANN models can be found in the the following repository: <a href="https://github.com/weileiw/ANN-DMS-code">https://github.com/weileiw/ANN-DMS-code</a></p> <p> </p> <p> </p> <p> </p>
Geomagnetic datasets of BJI station reconstructed through Artificial Neural Network improved by Genetic Algorithm in 2021
<p>Beijing station established in 1954 is one of the oldest geomagnetic observatories in China, which plays an important role in data exchange, and further provide data or standardization for satellite observation and geomagnetic model construction. With the development of urbanization, the observed data are greatly disturbed by subways, and data disturbed are almost unavailable. The dataset was reconstructed through Artificial Neural Network improved by Genetic Algorithm, including minutely data of three components (D, H and Z) in 2021. This reconstruction method has been proved to be effective.</p>
Med-ReLU: A Hybrid Activation Function Tailored for Deep Artificial Neural Networks in Medical Image Segmentation without Parameter Tuning
<p>Background: Deep learning (DL) is derived from the domain of Artificial Neural Network (ANN). It makes one of the most important elements of deep learning algorithms. Deep learning segmentation models are based on layer-by-layer convolution learning attribute representation directed by forward and backward propagation. Throughout the process vital role is played by appropriately chosen activation function (AF) in order to guarantee the robustness of the model learning. However, the existing activation functions are either ineffective in addressing the vanishing gradient problem or get burdened with multiple parameters that need to be manually tuned. Moreover, the current research on activation function design mainly focuses on classification tasks using natural images from the MNIST, CIFAR-10 and CIFAR-100 datasets. Therefore,Med-ReLU as a novel activation function for medical image segmentation, is proposed. The proposed activation function avoids deep learning models from the attacks of dead neurons or from the vanishing gradient problems. Method: Med-ReLU is a hybrid activation function that combines the property of two activation functions of ReLU and Softsign. For positive inputs, Med-ReLU utilizes the linear property just like ReLU to produce an output without vanishing gradient. The negative inputs converge in polynomial ways towards their asymptotes as property of the softsign AF that ensures robust training processing without the problem of dead neurons that rarely activate across the entire training dataset. Results: The training performance and segmentation accuracy of Med-ReLU have been investigated. The proposed function has demonstrated stable training and does not suffer from over-fitting. Hence, Med-ReLU has consistently outperformed the existing state-of-art activation functions in medical image segmentation tasks. Conclusion: Med-ReLU has been designed as a parameter-free activation function for DL image segmentation tasks. This activation function is easy-to-implement on complex and deep learning models. The utility of this research lies in affirming the impact of Med-ReLU on different Artificial Neural Network architectures and for various kinds of anomaly addressing tasks.</p>
An Observational Clinical Study on the Construction of an Artificial Neural Network Model for ICU Pneumonia
ClinicalTrials.gov study NCT06661499. IPD Sharing: NO. Countries: 1. Publications: 0.
Artificial Neural Network Directed Therapy of Severe Obstructive Sleep Apnea
ClinicalTrials.gov study NCT01286636. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Language Acquisition in the Brain and Algorithms: Towards Systematic Monitoring of the Evolution of Semantic Representations in Biological and Artificial Neural Networks
ClinicalTrials.gov study NCT05217043. IPD Sharing: NO. Countries: 1. Publications: 0.
Data from: Identification of any structure-specific hepatotoxic potential of different pyrrolizidine alkaloids using Random Forest and artificial Neural Network
Open the record for dataset details and reuse information.
GPM Ground Validation Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks Cloud Classification System (PERSIANN-CCS) IFloodS
The GPM Ground Validation Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks Cloud Classification System (PERSIANN-CCS) IFloodS dataset is a subset from the global 30-minute PERSIANN-CCS files generated in near-real time selected for the time period of the GPM Ground Validation Iowa Flood Studies (IFloodS) field campaign. The main goal of IFloodS were to collect detailed measurements of precipitation at the Earth’s surface using ground instruments and advanced weather radars and to simultaneously collect data from satellites passing overhead. This PERSIANN-CCS data product is available in ASCII and netCDF-4 formats from April 1, 2013 thru July 1, 2013.
Osteoporotic Precisely Screening Using Chest Radiograph and Artificial Neural Network (OPSCAN)
ClinicalTrials.gov study NCT05721157. IPD Sharing: NO. Countries: 1. Publications: 0.
GPM Ground Validation Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks Cloud Classification System (PERSIANN-CCS) IFloodS V1
The GPM Ground Validation Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks Cloud Classification System (PERSIANN-CCS) IFloodS dataset is a subset from the global 30-minute PERSIANN-CCS files generated in near-real time selected for the time period of the GPM Ground Validation Iowa Flood Studies (IFloodS) field campaign. The main goal of IFloodS were to collect detailed measurements of precipitation at the Earth’s surface using ground instruments and advanced weather radars and to simultaneously collect data from satellites passing overhead. This PERSIANN-CCS data product is available in ASCII and netCDF-4 formats from April 1, 2013 thru July 1, 2013.
Simple Feedforward Artificial Neural Network
<p><br /> Simple Feedforward Artificial Neural Network</p>
Trained Artificial Neural Network for Detecting Cut-off low related Vb-Cyclones in a large single-model ensemble
<p>Trained network data accompanying the research letter "Detecting Climate Change Effects on Vb-Cyclones in a 50-Member Single-Model Ensemble Using Machine Learning" submitted to Geophysical Research Letters.</p> <p>Licence: Creative Commons Attribution-NonCommercial-No Derivatives 4.0 International (CC BY-NC-ND 4.0)</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.