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921 results for “neural networks”
MATLAB Codes for: A Neural Network Weights Initialization Approach for Diagnosing Real Aircraft Engine Inter-Shaft Bearing Faults
<p><strong>Description:</strong></p> <p>This repository contains the MATLAB codes used in our paper [1] on fault diagnosis of inter-shaft aircraft bearings, published by MDPI Machines. The codes encompass all the necessary materials to reproduce the findings outlined in the paper. </p> <p><strong>Dataset Access:</strong></p> <p>The dataset utilized in this study is available under request from the authors of reference [8] in our paper. To obtain the dataset, please follow the instructions provided by the respective authors.</p> <p><strong>Data Format:</strong></p> <p>The dataset is saved in '*.npy' 3D variable format. To reproduce this study, it is necessary to transform these variables to '.mat' format since the codes are implemented in MATLAB. You can find the codes for transferring the 3D '*.npy' files to '*.mat' files here [<a href="../records/10184606">here</a>]</p> <p>We appreciate your interest in our work.</p> <p>[1] Berghout, Tarek, Toufik Bentrcia, Wei Hong Lim, and Mohamed Benbouzid. 2023. "A Neural Network Weights Initialization Approach for Diagnosing Real Aircraft Engine Inter-Shaft Bearing Faults" <em>Machines</em> 11, no. 12: 1089. https://doi.org/10.3390/machines11121089</p>
Dataset for "Explainable Offline-Online Training of Neural Networks for Parameterizations: A 1D Gravity Wave-QBO Testbed in the Small-data Regime" by Pahlavan et al. (2023)
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multi class dataset_Dipper Throated Optimization with Deep Convolutional Neural Network-based Crop Classification on Remote Sensing Image Analysis
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Dataset and codes: Abundance of trace fossil Phycosiphon incertum in core sections measured using a convolutional neural network
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Supplementary data (CC BY-NC-SA 4.0): Migration of Zeolite-Encapsulated Subnanometre Platinum Clusters via Reactive Neural Network Potentials
<p><strong>Content (Creative Commons Attribution Non Commercial Share Alike 4.0 International):</strong></p> <ul> <li>Trajectory files containing structures, energies and forces of CHA, MWW (including MWW*), TON, MFI (Pt1, Pt3, Pt5 at 750, 1000, 1250 K) as (extended) xyz files readable by the <a href="https://wiki.fysik.dtu.dk/ase/index.html">Atomic Simulation Environment </a>(ASE)</li> <li>Animated gif files of Pt1 migration between double-six rings in CHA, Pt3 jump through an eight-ring in CHA, and insertion of Pt1 into a t-pen unit in MFI</li> <li>Neural Network Potential (NNP) files readable by <a href="https://github.com/atomistic-machine-learning/schnetpack/tree/schnetpack1.0">SchNetPack version 1.0</a></li> </ul>
Dataset of the paper "Modeling the Flow and Geomorphic Heterogeneity Induced by Salt Marsh Vegetation Patches Based on Convolutional Neural Network UNet-Flow"
<p>Modeling the Flow and Geomorphic Heterogeneity Induced by Salt Marsh Vegetation Patches Based on Convolutional Neural Network UNet-Flow</p>
Neural network kinetics
<p>The folder contains the data for creating main figures and supplementary figures in the original paper.</p>
Indoor climate projections at 90 workplaces in the Upper Rhine Valley modelled by artificial neural networks
<p>The uploaded files contain the modelled indoor temperature (Ti) and physiologically equivalent temperature (PETi) data at 90 different workplaces in the Upper Rhine Valley, presented in the article "Climate projections of human thermal comfort for indoor workplaces" by Sulzer and Christen (2024), <a href="https://doi.org/10.1007/s10584-024-03685-7">https://doi.org/10.1007/s10584-024-03685-7</a>. The different csv files contain metadata to the different workplaces, the training data recorded in 2021 and 2022, the modelled data for the historical time period 1970-1999 using ERA5-Land data as input data, and for the future time period 2070-2099 using 22 different climate projections as input data. </p> <p>In the file <a href="../api/records/8229253/draft/files/Workplaces_training_2021-2022.csv/content" target="_blank" rel="noopener noreferrer">Workplaces_training_2021-2022.csv</a> you can find the measured data at the workplaces used for training of the models and in <a href="../api/records/8229253/draft/files/Workplaces_metadata.csv/content" target="_blank" rel="noopener noreferrer">Workplaces_metadata.csv</a> you can find some metadata about each workplace.</p> <p> </p>
Emergence of Emotion Selectivity in Deep Neural Networks Trained to Recognize Visual Objects
<h1>Datasets and analysis code of the following publication:</h1> <p>Peng Liu, Ke Bo, Mingzhou Ding and Ruogu Fang (2024). Emergence of Emotion Selectivity in Deep Neural Networks Trained to Recognize Visual Objects. <em>PLOS Computational Biology. </em>DOI: 10.1371/journal.pcbi.1011943</p> <p>For any questions please contact the first author at mail pliu1 [at] ufl [dot] edu</p> <h2><strong>Contents:</strong></h2> <p><strong> Code_DataAnalysis</strong><br> - Extracted Selectivity</p> <p> -- IAPS and NAPS datasets</p> <p> -- Neurons In Alexnet and VGG networks</p> <p> --Networks are pre-trained on ImageNet and randomly initialized</p> <p> - Extracted Overlapped Selectivity across IAPS and NAPS.</p> <p> - Extracted tuning performance changes from two datasets and the VGG network</p> <p> -Code to replicate the key results including </p> <p> --Tuning quality </p> <p> -- Number of overlapped neurons</p> <p> -- Enhance neuron activity</p> <p> -- Lesion neurons</p> <p> <strong>TrainedNetworks</strong></p> <p> --Pre-trained VGG network on ImageNet </p> <p> --Pre-trained Alexnet network on ImageNet</p> <p>After pre-training these networks on ImageNet, we fixed their weights and trained them to classify pleasant, neutral, and unpleasant images into three emotion categories using both IAPS and NAPS datasets.<br> </p> <p><strong>Image datasets</strong></p> <p>Access image datasets by request from https://csea.phhp.ufl.edu/media/iapsmessage.html for IAPS and https://lobi.nencki.edu.pl/research/8/ for NAPS.</p>
Data used in "Biologically informed deep neural network for prostate cancer discovery" publication
<p>Data used in the publication titled "<strong>Biologically informed deep neural network for prostate cancer discovery </strong>" </p> <p>Elmarakeby, Haitham A., et al. "Biologically informed deep neural network for prostate cancer discovery." <em>Nature</em> 598.7880 (2021): 348-352.</p> <p>These datasets were derived from the following public domain resources:</p> <ol> <li>Armenia J, Wankowicz SAM, Liu D, Gao J, Kundra R, Reznik E, et al. The long tail of oncogenic drivers in prostate cancer. Nat Genet. 2018;50: 645–651. DOI: <a href="https://doi.org/10.1038/s41588-018-0078-z">10.1038/s41588-018-0078-z</a></li> <li>Fraser M, Sabelnykova VY, Yamaguchi TN, Heisler LE, Livingstone J, Huang V, et al. Genomic hallmarks of localized, non-indolent prostate cancer. Nature. 2017;541: 359–364. https://doi.org/10.1038/nature20788</li> <li>Robinson DR, Wu Y-M, Lonigro RJ, Vats P, Cobain E, Everett J, et al. Integrative clinical genomics of metastatic cancer. Nature. 2017;548: 297–303. https://doi.org/10.1038/nature23306</li> <li>Fabregat A, Jupe S, Matthews L, Sidiropoulos K, Gillespie M, Garapati P, et al. The Reactome Pathway Knowledgebase. Nucleic Acids Res. 2018;46: D649–D655. DOI: <a href="https://doi.org/10.1093/nar/gkv1351">10.1093/nar/gkv1351</a></li> </ol> <p> </p>
Learning Useful Representations of Recurrent Neural Network Weight Matrices
<p>Dataset of RNN weights for the ICML 2024 paper "Learning Useful Representations of Recurrent Neural Network Weight Matrices". See <a title="GitHub repository" href="https://github.com/vincentherrmann/rnn-weights-representation-learning">https://github.com/vincentherrmann/rnn-weights-representation-learning</a>.</p> <h1> </h1>
Data for "Neutron scattering and neural-network quantum molecular dynamics investigation of the vibrations of ammonia along the solid-to-liquid transition"
<p>Data for "Neutron scattering and neural-network quantum molecular dynamics investigation of the vibrations of ammonia along the solid-to-liquid transition".</p> <p>neutron_data.zip --> neutron data in .nxspe form. S(Q,E) calculated using the DAVE software. Includes logbook spreadsheet. </p> <p>Training_Data.xyz --> xyz file containing training data used to generate Allegro machine learning forcefield in the paper</p> <p>nh3_pimd.deploy --> Trained Allegro model to that can be used in LAMMPS and RXMD software a ML forcefield </p> <p>POSCAR_UNIT_CELL_AMMONIA --> NH3 unit cell in solid phase in POSCAR format that can be read by the VASP software used to perform the DFT simmulations.</p>
Formation and Retrieval of Cell Assemblies in a Biologically Realistic Spiking Neural Network Model of Area CA3 in the Mouse Hippocampus
<p>Dataset accompanying the manuscript "Formation and Retrieval of Cell Assemblies in a Biologically Realistic Spiking Neural Network Model of Area CA3 in the Mouse Hippocampus". This dataset is used to re-create all figure panels with underlying data in the manuscript.</p>
State of Health Estimation of Lithium-Ion Batteries Based on Electrochemical Impedance Spectroscopy and Backpropagation Neural Network
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Graph Neural Network vs. Large Language Model: A Comparative Analysis for Bug Report Priority and Severity Prediction
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Figure for "A quick battery charging curve prediction by artificial neural network"
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Datasets for "Physicochemical graph neural network for learning protein-ligand interaction fingerprints from sequence data"
<div> <p>Datasets used for implementing the <a href="https://github.com/huankoh/PSICHIC">PSICHIC</a> experiments shown in the <a href="https://doi.org/10.1101/2023.09.17.558145">manuscript</a>.</p> <p> </p> </div>
Finite element dataset and Artificial Neural Networks algorithms to predict the mechanical properties of innovative CLT
<p>This folder includes the data collected from the finite element simulations of the innovative CLT to compute its mechanical properties, the error of the closed-form solutions predicting the bending stiffness in the minor direction D22, the variation of the distance between the Reissner Mindlin and Bending Gradient theory in terms of spacing between lateral lamellas, the hyperparameters tuning of several Artificial Neural Networks algorithms with or without prior knowledge, the ML evaluations, the saved artificial neural network algorithms to predict each mechanical property of innovative CLT, and the ML application to use it.</p>
Imbalanced regressive neural network model for whistler-mode hiss waves: spatial and temporal evolution
<p>This dataset contains the whistler-mode hiss waves obtained from the Van Allen Probes. It is accompanied by the manuscript "<span>Imbalanced regressive neural network model for whistler-mode hiss waves: spatial and temporal evolution". </span></p>
An Empirical Comparative Study of Convolutional Neural Network and Support Vector Machine in Digital Signature for Digital Document Authentication
<p>Dataset dan figure of the research</p>
ScienceDex guides
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
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.