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921 results for “neural networks”
Convolutional Neural Networks in action
<p>https://x.com/Hamptonism/status/1794469409999786451</p>
Knowledge Graph Neural Network with Spatial-Aware Capsule for Drug-Drug Interaction Prediction
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Data and Sourcecode from: Neural Network-based Occupancy Detection on the Edge
<div> <h2>Environmental Data Collected for Data-Driven Occupancy Detection</h2> <h3>Version Information</h3> <p>v1.0: Holds repository alongside the dataset inside<br>v2.0: Holds dataset in a seperate zip folder</p> <h3>Dataset Information</h3> <div>The following data is collected from LoRa sensors of two rooms for a period of three months in an office building on the ground floor in Graz, Austria:</div> <div> <ul> <li>Open status of windows/doors</li> <li>Relative humidity</li> <li>CO2 concentration</li> <li>Ambient temperature</li> <li>PIR-based motion counter</li> <li>Light level</li> <li>IR-based occupancy (only room A)</li> <li>Average/peak sound level</li> <li>Radar-based people counter (left-to-right and right-to-left; only room A; <em><strong>no</strong> trustworthy ground truth!</em>)</li> </ul> </div> <div> </div> <div>Folder Organization in occupancy-detection-dataset.zip</div> <br> <div> ├── data</div> <div> │ ├── interim <- Intermediate data of room A and B that has been transformed.</div> <div> │ └── raw <- The original, immutable sensor data dump of room A and B.</div> <br><br> <div><strong>Raw Data</strong></div> <div>Raw sensor data of room A and B consisting of six and two work places respectively. Data is gathered in an interval of five minutes.</div> <br> <div>Note:</div> <ul> <li>Timezone ist UTC+00:00.</li> <li>Column "occupancy" in df_features.csv refers to IR based occupancy sensor from Elsys ERS Eye (Possible values 0-2).</li> <li>Column "motion" in df_features.csv refers to a PIR based motion counter.</li> <li>IR-based occupancy is not measured in room B.</li> </ul> <br> <div><strong>Intermediate Data</strong></div> <div>Event-based (door and window sensors) and interval based (humidity, CO2, temperature, ....) data is synchronized to retrieve a homogenous data set.</div> <div>Window columns are merged to represent the number of open windows. Nothing else was applied to the data.</div> <br> <div><strong>Ground Truth</strong></div> <div>Image-based occupancy ground truth data is separated in a file (df_occ.csv).</div> <div>It describes the number of occupants at a certain time stamp provided from images (manually labelled).</div> <h3>References</h3> <p>Coming soon.</p> </div>
Advanced neural network-based model for predicting court decisions on child custody
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ChEMBL Data for 'Achieving Well-Informed Decision-Making in Drug Discovery: A Comprehensive Calibration Study using Neural Network-Based Structure-Activity Models'
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Datasets collected for Masked adversarial neural network for cell type deconvolution in spatial transcriptomics
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Power Quality State Estimation for Distribution Grids based on Physics-Aware Neural Networks - Harmonic State Estimation
<p>Data set for the paper "Power Quality State Estimation for Distribution Grids based on Physics-Aware Neural Networks - Harmonic State Estimation"</p> <p>This upload contains</p> <ul> <li>Training set</li> <li>Validation set</li> <li>Test set</li> <li>Admittance matrices per frequency</li> </ul> <p>used for the paper as pickle files and weights of trained models as zip files.</p> <p>Weights represent the model with the best validation loss recorded within the first 3000 Epochs of training.</p> <p>Code for reading in the data sets, preprocessing and state estimation is available in the linked repository.</p> <p>To replicate the results of the paper follow these steps:</p> <ol> <li>clone the linked repository</li> <li>save the provided pickle files in the data folder of the linked repository</li> <li>optional: unzip weights and save them in the data folder, otherwise train a model yourself instead</li> </ol> <p><strong>Version 1.1:</strong></p> <p>Added data and model weights for the IEEE33 grid to improve comparability.</p> <p>For the IEEE33 grid, all data (train, test, validation) is saved in one pickle file; see the release tag 1.1.0 in the accompanying GitHub repository for details on the data format. Moreover, the training set size of the new grid was increased from 35040 to 131400 samples to incorporate simulation results that capture a broader range of system states. </p> <p>The code was slightly updated to account for inclusion of the IEEE33 grid. Therefore, model weights and input data are now expected in either `cigrelv` or `ieee33` subfolder.</p> <p>Added Transformer and CNN model weights for IEEE33 and CNN weights for the CIGRE grid. The Transformer model is trained with a smaller batch size since the model did not fit into GPU memory using the same batch size as in other models. This change results in more gradient updates and significantly longer training times, thus the amount of epochs was reduced to achieve a fairer comparison (batch sized reduced from 16384 to 1024, epochs reduced from 3000 to 375, total amount of gradient updates increased from 27000<em> </em>to 48375). The training of the PANN model over 3000 Epochs is significantly faster than that of the Transformer model trained over 375 epochs (approximately 2.5 hours vs 12.5 hours).</p>
DeepFruits: A Fruit Detection System Using Deep Neural Networks
<p>This is the dataset associated with MDPI Sensors paper entitled "DeepFruits: A Fruit Detection System Using Deep Neural Networks".</p> <p>This paper presents a novel approach to fruit detection using deep convolutional neural networks. The aim is to build an accurate, fast and reliable fruit detection system, which is a vital element of an autonomous agricultural robotic platform; it is a key element for fruit yield estimation and automated harvesting. Recent work in deep neural networks has led to the development of a state-of-the-art object detector termed Faster Region-based CNN (Faster R-CNN). We adapt this model, through transfer learning, for the task of fruit detection using imagery obtained from two modalities: colour (RGB) and Near-Infrared (NIR). Early and late fusion methods are explored for combining the multi-modal (RGB and NIR) information. This leads to a novel multi-modal Faster R-CNN model, which achieves state-of-the-art results compared to prior work with the F1 score, which takes into account both precision and recall performances improving from 0.807 to 0.838 for the detection of sweet pepper. In addition to improved accuracy, this approach is also much quicker to deploy for new fruits, as it requires bounding box annotation rather than pixel-level annotation (annotating bounding boxes is approximately an order of magnitude quicker to perform). The model is retrained to perform the detection of seven fruits, with the entire process taking four hours to annotate and train the new model per fruit.</p>
Data: Imitation learning of fish and swarm behavior with Recurrent Neural Networks
<p>Data for the Master's Thesis <em>Imitation learning of fish and swarm behavior with Recurrent Neural Networks</em></p>
Traning set for "Accurate De Novo Peptide Sequencing Using Fully Convolutional Neural Networks"
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HiPHD: Hierarchical Classification for Protein Remote Homology Detection using Graph Neural Networks and Language Models
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Physics-informed Partitioned Coupled Neural Operator for Complex Networks Datasets
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Global Prediction Of Total Organic Carbon In Marine Sediments Using Deep Neural Networks (nn-toc) v2
<p>This is the second version that was uploaded to make the code available for the paper submission <strong><span>NN-TOC v1: global prediction of total organic carbon in marine sediments using deep neural networks</span></strong> to the Geoscientific Model Development journal. Here we create a deep neural network based approach for the geospatial predicition of total organic carbon percentages in marine sediments.</p> <p><span>The data folder contains "raw" features and labels, "interim" data for preprocessed features and labels and "output"s produced from the model. While the preprocessed folder contain all the other files that can be produced by running the code. The features are in .nc or .grd file format. The other files are in .xyz or .csv file format.</span></p> <p> </p>
Dataset used in "Estimating Dispersion Coefficient in Flow Through Heterogeneous Porous Media by a Deep Convolutional Neural Network" by Kamrava et al. in Geophysical Research Letters.
<p>Morphology of Heterogeneous Porous Media</p>
Physics-Guided Architecture (PGA) of Neural Networks for Quantifying Uncertainty in Lake Temperature Modeling
<p><strong>Abstract:</strong><br> To simultaneously address the rising need of expressing uncertainties in deep learning models along with producing model outputs which are consistent with the known scientific knowledge, we propose a novel physics-guided architecture (PGA) of neural networks in the context of lake temperature modeling where the physical constraints are hard coded in the neural network architecture. This allows us to integrate such models with state of the art uncertainty estimation approaches such as Monte Carlo (MC) Dropout without sacrificing the physical consistency of our results. We demonstrate the effectiveness of our approach in ensuring better generalizability as well as physical consistency in MC estimates over data collected from Lake Mendota in Wisconsin and Falling Creek Reservoir in Virginia, even with limited training data. We further show that our MC estimates correctly match the distribution of ground-truth observations, thus making the PGA paradigm amenable to physically grounded uncertainty quantification.</p>
Trained deep neural networks for MSI/dMMR detection in colorectal cancer histology
<p>These are trained neural network models in PyTorch format to process tessellated images of colorectal cancer histology samples. The input is expected to be 224x224 px RGB image tiles normalized with the Macenko method. The output is a probability of the image tile for being MSI/dMMR or MSS/pMMR. </p> <p>The models have been trained on eight cohorts but not on the validation cohort. The validation cohorts are:</p> <p>Ex_0 : DACHS</p> <p>Ex_1 : DUSSEL</p> <p>Ex_2 : MECC</p> <p>Ex_3 : QUASAR</p> <p>Ex_4 : RAINBOW</p> <p>Ex_5 : TCGA</p> <p>Ex_6 : UMM</p> <p>Ex_7 : YORKSHIRE</p> <p>Ex_8 : MUNICH</p> <p>The models can be loaded in Python with </p> <p>>>> model = torch.load(path, map_location=torch.device('cpu'))</p> <p>Further details are given in the manuscript.</p>
MuGNN: API Misuse Detection using Graph Neural Networks and Clustering
<div> <div>This artifact presents `MuGNN`, a novel framework for efficiently detecting API misuse in Java code. The approach leverages a `Graph Neural Network (GNN)` model to generate embeddings of Java API usage code using a custom `API Flow Graph (AFG)` representation. This representation captures execution sequences, data flow, and control flow, enabling better understanding of API usage patterns. MuGNN employs self-supervised pre-training and clustering to analyze API usage and identify potential misuse.</div> </div>
Drones, automatic counting tools and artificial neural networks in wildlife population censusing
<p>1. The use of a drone to count the flock sizes of 33 species of waterbirds during the breeding and non-breeding periods was investigated.</p> <p>2. In 96% of 343 cases, drone counting was successful. 18.8% of non-breeding birds and 3.6% of breeding birds exhibited adverse reactions: the former birds were flushed, whereas the latter attempted to attack the drone.</p> <p>3. The automatic counting of birds was best done with ImageJ/Fiji microbiology software – the average counting rate was 100 birds in 64 seconds.</p> <p>4. Machine learning using neural network algorithms proved to be an effective and quick way of counting birds – 100 birds in 7 seconds. However, the preparation of images and machine learning time is time-consuming, so this method is recommended only for large data sets and large bird assemblages.</p> <p>5. The responsible study of wildlife using a drone should only be carried out by persons experienced in the biology and behaviour of the target animals.</p>
Screening for Chagas disease using a deep neural network
<p>Pre-trained models accompanying the source code at https://github.com/carji475/ecg-chagas</p>
EuroSAT processed with quantum convolutional neural network
<p>This dataset contains all the <a href="https://github.com/phelber/EuroSAT">EuroSAT</a> images processed with a quanvolutional neural network with rx rotation gates and a random layer.</p>
ScienceDex guides
Understand access before you commit
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.