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356 results for “Machine learning models”
Unlabeled AnuraSet: A dataset for leveraging unlabeled data in machine learning models for passive acoustic monitoring
<p>The Unlabeled AnuraSet (U-AnuraSet) is an extension of the original AnuraSet dataset. It consists of soundscape recordings from passive acoustic monitoring conducted in Brazil. The recording sites are identical to those in the original AnuraSet. Each site comprises 2,666 one-minute raw audio files of unlabeled data. The U-AnuraSet is publicly available to encourage machine learning researchers to explore innovative methods for leveraging unlabeled data in the training of models aimed at solving problems such as anuran call identification.</p> <p>If you find the Unlabeled AnuraSet useful for your research, please consider citing it as follows:</p> <p>Cañas, J.S., Toro-Gómez, M.P., Sugai, L.S.M., et al. A dataset for benchmarking Neotropical anuran calls identification in passive acoustic monitoring. Sci Data 10, 771 (2023). https://doi.org/10.1038/s41597-023-02666-2</p>
Integrated Machine Learning model in Early Urban Flooding Warning System - Data
<p>AI_DATA.npy - Inundation data (mm) generated from MIKE+ model that has been converted to numpy array</p> <p>INDEX.npy - The index where inundation is > 0 </p> <p>source.tif - Source tif image for creating map from ML models</p>
The state-of-the-art machine learning model for Plasma Protein Binding Prediction: computational modeling with OCHEM and experimental validation
<p><span>Institute of Materia Medica, Chinese Academy of Medical Sciences purchased 10,000 ChemDiv databases.</span></p>
Data from: Performance of unmarked abundance models with data from machine-learning classification of passive acoustic recordings
<p>The ability to conduct cost-effective wildlife monitoring at scale is rapidly increasing due to availability of inexpensive autonomous recording units (ARUs) and automated species recognition, presenting a variety of advantages over human-based surveys. However, estimating abundance with such data collection techniques remains challenging because most abundance models require data that are difficult for low-cost monoaural ARUs to gather (e.g., counts of individuals, distance to individuals), especially when using the output of automated species recognition. Statistical models that do not require counting or measuring distances to target individuals in combination with low-cost ARUs provide a promising way of obtaining abundance estimates for large-scale wildlife monitoring projects but remain untested. We present a case study using avian field data collected in forests of Pennsylvania during the Spring of 2020 and 2021 using both traditional point counts and passive acoustic monitoring at the same locations. We tested the ability of the Royle-Nichols and time-to-detection models to estimate abundance of two species from detection histories generated by applying a machine-learning classifier to ARU-gathered data. We compared abundance estimates from these models to estimates from the same models fit using point-count data and to two additional models appropriate for point counts, the N-mixture model and distance models. We found that the Royle-Nichols and time-to-detection models can be used with ARU data to produce abundance estimates similar to those generated by a point-count based study but with greater precision. ARU-based models produced confidence or credible intervals that were on average 31.9% ( 11.9 SE) smaller than their point-count counterpart. Our findings were consistent across two species with differing relative abundance and habitat use patterns. The higher precision of models fit using ARU data is likely due to higher cumulative detection probability, which itself may be the result of greater survey effort using ARUs and machine-learning classifiers to sample significantly more time for focal species at any given point. Our results provide preliminary support the use of ARUs in abundance-based study applications, and thus may afford researchers a better understanding of habitat quality and population trends, while allowing them to make more informed conservation actions and recommendations.</p>
Dataset for "Machine Learning Driven Sensitivity Analysis of E3SM Land Model Parameters for Wetland Methane Emissions"
<p>This dataset is a part of the paper "Machine Learning Driven Sensitivity Analysis of E3SM Land Model Parameters for Wetland Methane Emissions", accepted for publication in the Journal of Advances in Modeling Earth Systems (JAMES).</p> <h2>Contents</h2> <p>This dataset includes:</p> <ul> <li><strong>lhs-gen-190.csv</strong>: Training input LHS samples generated by <code>lhsgen.py</code>.</li> <li><strong>lhs-gen-50-test.csv</strong>: Test input LHS samples generated by <code>lhsgen.py</code>.</li> <li><strong>190-elm-samples.csv</strong>: Training input perturbed parameter samples for performing ELM simulations.</li> <li><strong>50-elm-test-samples.csv</strong>: Test input perturbed parameter samples for performing ELM simulations.</li> <li><strong>train_CH-CHA.csv</strong>: Contains the five ELM simulation output flux values for 240 samples (190 train + 50 test).</li> <li><strong>lhsgen.py</strong>: Script for generating Latin Hypercube Samples.</li> <li><strong>gpr-fit-new.py</strong>: Script for fitting Gaussian Process Regression (GPR) models.</li> <li><strong>sobol-new.py</strong>: Script for performing Sobol sensitivity analysis.</li> </ul> <h2>Usage</h2> <ol> <li><strong>lhsgen.py</strong>: <ul> <li>Use this script to generate the Latin Hypercube Samples for parameter sampling.</li> </ul> </li> <li><strong>gpr-fit-new.py</strong>: <ul> <li>This script fits GPR models using the training samples provided in <code>lhs-gen-190.csv</code>.</li> <li>It tests the models using the input testing samples in <code>lhs-gen-50-test.csv</code>.</li> <li>The fitted GPR models are stored as <code>.joblib</code> files in the <code>gpr_models</code> directory.</li> <li>Corresponding cross-validation and R-squared values are stored in <code>.xlsx</code> files.</li> </ul> </li> <li><strong>sobol-new.py</strong>: <ul> <li>This script performs Sobol sensitivity analysis using the fitted GPR models by reading the .joblib files.</li> <li>The Sobol indices are written to <code>.xlsx</code> files in the <code>results</code> directory.</li> </ul> </li> </ol>
BRAIN Journal-Automatic Anthropometric System Development Using Machine Learning-Figure7. Model 3D of women body.
<p>In two cases, using the SVM classifier and Random Forest with trees 100, 200, 300, 400, 500 datasets before and after optimization commented as follows: the running time of Random Forest is greater comparing with SVM, because more trees are generated, many cases will be considered. In particular, increasing the number of trees, while labeling is long, but Random Forest provides higher accuracy SVM. Based on anthropometric features and machine learning algorithms, we have built an Android app in the smartphone environment. This app can automatically extrac tanthropometric features (12 features). The user must stand in front of the smartphone camera and takes 2 pictures. Then input their height (centimeters) for calibration. The application automatically extracts human parameters to enable adequate 3D models reconstruction. The results of the Android application are demonstrated in figure 7 .</p>
BRAIN Journal-Automatic Anthropometric System Development Using Machine Learning-Figure 6. The result of building a 3D model based on RF and SVM classification with "Important features".
<p>From the chart of figure 6, we found that "Important Features" gave the best 3D model, which fits with the object in the image. The pattern is close to 90% compared with the true size. Apply classification algorithm RF increases the accuracy of the results and reduces computing time for the program. There are many methods for data classifying. One of them is the method of the support vector machine (SVM). The SVM method is represented by Vladimir N. Vapnik (1995) in Support Vector Machines (SVM) - a set of learning algorithms similar with the supervisor has two main tasks: the classification and the regression analysis. In this article we use the method of the SVM classification problem for the size of the human body with 5 classes to compare the performance between SVM methods and Random Forest algorithm. </p>
BRAIN Journal-Automatic Anthropometric System Development Using Machine Learning-Figure 8. Model 3D of man body
<p>In two cases, using the SVM classifier and Random Forest with trees 100, 200, 300, 400, 500 datasets before and after optimization commented as follows: the running time of Random Forest is greater comparing with SVM, because more trees are generated, many cases will be considered. In particular, increasing the number of trees, while labeling is long, but Random Forest provides higher accuracy SVM. Based on anthropometric features and machine learning algorithms, we have built an Android app in the smartphone environment. This app can automatically extrac tanthropometric features (12 features). The user must stand in front of the smartphone camera and takes 2 pictures. Then input their height (centimeters) for calibration. The application automatically extracts human parameters to enable adequate 3D models reconstruction. The results of the Android application are demonstrated in figure 8 .</p>
Hyperparameter tuning and performance assessment of statistical and machine-learning models using spatial data.
<p>This is a research compendium (RC) for the publication "Hyperparameter tuning and performance assessment of statistical and machine-learning algorithms using spatial data".</p> <p>The code (including figures, appendices and the manuscript) is packed in <strong>pathogen-modeling-3.zip </strong>or can be found directly in the <a href="https://github.com/pat-s/pathogen-modeling">Github repository</a>.</p> <ul> <li><strong>Publication figures</strong>: analysis/paper/submission/3/latex-source-files/</li> <li><strong>Appendices</strong>: analysis/paper/submission/3/</li> </ul> <p>This RC represents a static snapshot at the time of submission. The Github repository will receive changes after the publication was published.</p> <p><strong>Data sources</strong></p> <ul> <li>Atlas Climatico: <a href="http://opengis.uab.es/wms/iberia/index.htm">http://opengis.uab.es/wms/iberia/index.htm</a></li> <li>DEM: ftp://ftp.geo.euskadi.eus/lidar/MDE_LIDAR_2016_ETRS89/</li> <li>Lithology: <a href="http://www.geo.euskadi.eus/geonetwork/srv/spa/main.home">http://www.geo.euskadi.eus/geonetwork/srv/spa/main.home</a></li> <li>pH: <a href="https://esdac.jrc.ec.europa.eu/content/soil-ph-europe#tabs-0-description=0">https://esdac.jrc.ec.europa.eu/content/soil-ph-europe#tabs-0-description=0</a></li> <li>soil: <a href="https://www.isric.org/explore/soilgrids">https://www.isric.org/explore/soilgrids</a></li> </ul> <p><strong>Licenses</strong></p> <p>All files are shared via the given license with the exception of "soil.tif" which is shared via the <strong>ODbL </strong>license<strong>.</strong></p>
Datasets, trained models and supporting results for machine learning tensorial properties of atomic systems via XPaiNN model.
Open the record for dataset details and reuse information.
UNSUPERVISED MACHINE LEARNING AND VECTOR MODELS IN DESIGNING AND OPTIMIZATION OF TELECOM RETAIL CHANNELS
<p>This paper examines the use of unsupervised machine learning and vector models in the design and optimization of retail channels for telecommunications services. Unsupervised machine learning allows you to analyze and identify hidden patterns in large volumes of untagged data, which is especially important in a dynamically changing consumer market. Vector models, in turn, provide high accuracy of demand forecasting and inventory management, contributing to an increase in the efficiency of trading channels. The synergy of these technologies allows companies to improve customer experience, optimize operational processes and increase competitiveness in the market. The main focus of the work is on data processing methods, including correlation analysis, the use of the support vector machine (SVM) method and its adaptation to solve problems related to predicting customer behavior and optimizing logistics processes.</p>
SENTIMENT ANALYSIS OF CUSTOMER FEEDBACK IN THE BANKING SECTOR: A COMPARATIVE STUDY OF MACHINE LEARNING MODELS
<p><span>This study investigates the application of sentiment analysis to customer feedback in the banking sector, utilizing natural language processing (NLP) techniques and machine learning models to classify customer sentiments into positive, neutral, and negative categories. Feedback was sourced from online platforms, including bank websites, social media, and third-party review sites. Data preprocessing steps, such as tokenization, stemming, and feature extraction using TF-IDF, were employed to prepare the text for analysis. Various machine learning algorithms, including Logistic Regression, Random Forest, Support Vector Machine (SVM), Long Short-Term Memory (LSTM), and Naïve Bayes, were implemented and evaluated using metrics such as accuracy, precision, recall, and F1-score. The results show that LSTM outperformed all models with a 91% accuracy, followed closely by SVM at 89%. These findings demonstrate the potential of advanced machine learning techniques in accurately classifying sentiments and provide valuable insights into customer satisfaction and areas for improvement within the banking sector. Future work aims to further optimize models for better classification of neutral feedback and explore more advanced deep learning models, such as BERT.</span></p>
Four spatial prediction datasets of susceptibility to gully erosion, comparing machine learning models, in the Piraí drainage basin, southeastern Brazil
<p>The data in this repository refer to the article published in the journal Land, entitled: Machine Learning Models for the Spatial Prediction of Gully Erosion Susceptibility in the Piraí Drainage Basin, Paraíba do Sul Middle Valley, Southeast Brazil.</p>
Data for publication of "Determining the sensitive parameters of WRF model for the prediction of tropical cyclones in the Bay of Bengal using Global Sensitivity Analysis and Machine Learning"
<p>The data are made available as part of the paper "Determining the sensitive parameters of WRF model for the prediction of tropical cyclones in the Bay of Bengal using Global Sensitivity Analysis and Machine Learning", submitted to Geoscientific Model Development. This data set incorporates selected post-processed files needed to reproduce the results presented in the paper.</p> <p>The data contains six zip files, that are:</p> <ul> <li>Namelist.input files for the WRF model simulations of ten tropical cyclones</li> <li>WRF model simulation outputs using the default parameter values</li> <li>WRF model simulation outputs using the optimal parameter values (which give minimum RMSE value)</li> <li>IMDAA surface observations and IMERG precipitation data</li> <li>IMD observed tracks of ten tropical cyclones</li> <li>Ipython notebooks of sensitivity analysis and machine learning codes</li> </ul> <p>The remaining files are the ncl scripts that were used to obtain the figures. The ncl scripts used the data in the zip files.</p>
Machine learning models, and training, validation and test datasets for: "Sequence determinants of human gene regulatory elements"
<p>This record contains the training, test and validation datasets used to train and evaluate the machine learning models in manuscript:</p> <p><strong>Sahu, Biswajyoti, et al. "Sequence determinants of human gene regulatory elements." (2021).</strong></p> <p><br> This record contains also the final hyperparameter-optimized models for each training dataset/task combination described in the manuscript. The README-files provided with the record describe the datasets and models in more detail. The datasets deposited here are derived from the original raw data (GEO accession: GSE180158) as described in the Methods of the manuscript.</p>
Data Models for Dataset Drift Controls in Machine Learning With Optical Images - Datasets
<p>This dataset accompanies the paper titled</p> <p><em>Data Models for Dataset Drift Controls in Machine Learning with Images</em><br> <br> that appeared in the Transactions on Machine Learning Research<br> <br> <a href="https://openreview.net/forum?id=I4IkGmgFJz">https://openreview.net/forum?id=I4IkGmgFJz</a><br> </p> <pre><code>@article{ oala2023data, title={Data Models for Dataset Drift Controls in Machine Learning With Optical Images}, author={Luis Oala and Marco Aversa and Gabriel Nobis and Kurt Willis and Yoan Neuenschwander and Mich{\`e}le Buck and Christian Matek and Jerome Extermann and Enrico Pomarico and Wojciech Samek and Roderick Murray-Smith and Christoph Clausen and Bruno Sanguinetti}, journal={Transactions on Machine Learning Research}, issn={2835-8856}, year={2023}, url={https://openreview.net/forum?id=I4IkGmgFJz}, note={} }</code></pre> <p>We make available two datasets.</p> <p><strong>Raw-Microscopy:</strong></p> <ul> <li><strong>940 raw bright-field microscopy images</strong> of human blood smear slides for leukocyte classification (microscopy/images/raw_scale100) with corresponding labels (microscopy/labels).</li> <li><strong>5,640 variations measured at six additional different intensities </strong>(microscopy/images/raw_scale001-raw_scale0075)</li> <li><strong>11,280 images of the raw sensor data processed through twelve different pipelines</strong> (microscopy/images/processed_views)</li> </ul> <p><strong>Raw-Drone:</strong></p> <ul> <li><strong>548 raw drone camera images for car segmentation</strong> (drone/images_tiles_256/raw_scale100) with corresponding binary segmentation mask (drone/masks_tiles_256). The images and the masks are cropped from 12 raw drone camera images (drone/images_full/raw_scale100) and 12 masks (drone/masks_full) of size 3648 by 5472.</li> <li><strong>3,288 variations measured at six additional different intensities</strong> (drone/images_tiles_256/raw_scale001-raw_scale075).</li> <li><strong>6,576 images of the raw sensor data processed through twelve different pipelines</strong> (drone/images_tiles_256/processed_views).</li> </ul> <p>Detailed datasheets for the two datasets can be found in the appendices of the TMLR paper.</p> <p>The code repository for this project can be found at <a href="https://github.com/aiaudit-org/raw2logit">https://github.com/aiaudit-org/raw2logit</a></p> <p> </p>
Data for: PerchPicker classifier model v7: A catalog of American silver perch (Bairdiella chrysoura) calls for machine learning
<p>This data repository contains labeled passive underwater acoustic data used to train and test the machine-learning model of Bohnenstiehl (in prep - 2023), <em>Automated cataloging of American silver perch (Bairdiella chrysoura) calls using machine learning</em>. The software accompanying this paper is known as PerchPicker (<a href="https://github.com/drbohnen/PerchPicker" rel="noopener">GitHub - drbohnen/PerchPicker)</a>, and the classifier model presented in the paper is v7. It consists of more than 6000 labeled perch and 6000 labeled other signals. Labeled scalogram images are provided, along with pressure-corrected waveforms (micro-Pascals) sampled at 24 kHz. Each waveform sample is 90 ms long. The center 30 ms of these waveform segments represent the portion of the signal used in training and testing the classifier model. Waveform data are provided in multiple formats: 1) MATLAB (.mat) files containing the 'perch' and 'other' waveforms stored in column format, and 2) individual .wav files, each containing a labeled waveform example. Codes are provided to demonstrate how these .wav files can be read into MATLAB and PYTHON. These labeled data can be used to re-train the PerchPicker model or develop alternative classifiers. </p>
Trained Models for "Segmenting functional tissue units across human organs using community-driven development of generalizable machine learning algorithms"
<p>This repository contains the trained model weights for the baseline model and the winning solutions in the Kaggle competition "HuBMAP+HPA - Hacking the Human Body", and is part of the paper "Segmenting functional tissue units across human organs using community-driven development of generalizable machine learning algorithms".</p> <p>The directory contains:</p> <p><strong>trained_model_1_weights.zip: </strong>Trained model weights for first place solution (Team 1).</p> <p><strong>trained_model_2_weights.zip:</strong> Trained model weights for second place solution (Team 2).</p> <p><strong>trained_model_3_weights.zip: </strong>Trained model weights for third place solution (Team 3).</p> <p><strong>trained_model_weights_baseline.zip:</strong> Trained model weights for the baseline model.</p>
Measuring Space-Time Accessibility: Hansen's Model Vs. Machine Learning
<p>This dataset consists of Hansen accessibility data in 2011 and 2020 in the municipalities of Lombardia and Emilia Romagna (Italy). Neural Network (NN) learns to predict Hansen accessibility based on the number of residents, employed people and travel time.</p>
A Database of Ultrastable MOFs Reassembled from Stable Fragments with Machine Learning Models
<p>Dataset of MOFs constructed from building blocks of stable MOFs.</p> <p>Note: the columns labeled "rho" in features_and_properties are actually cell volume and not density.</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.