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1,943 results for “machine learning”
F I G U R E 2 in Machine learning for image based species identification
F I G U R E 2 Comparison between biological and artificial neuron and networks
Combining Virtual Reality and Machine Learning for Enhancing the Resiliency of Transportation Infrastructure in Extreme Events
<p>Corresponding data set for Tran-SET Project No. 18ITSLSU09. Abstract of the final report is stated below for reference:</p> <p>"Traffic management models that include route choice form the basis of traffic management systems. High-fidelity models that are based on rapidly evolving contextual conditions can have significant impact on smart and energy efficient transportation. Existing traffic/route choice models are generic and are calibrated on static contextual conditions. These models do not consider dynamic contextual conditions such as the location, failure of certain portions of the road network, the social network structure of population inhabiting the region, route choices made by other drivers, extreme conditions, etc. As a result, the model’s predictions are made at an aggregate level and for a fixed set of contextual factors. There is a clear need to develop traffic models that take into account local contexts and are closer to ground reality to provide government agencies the ability to make well-informed model-based decisions/policies.</p> <p>In this project: (1) used Immersive Virtual Environment (IVE) tools for generating context-aware and high-fidelity data related to drivers’ route choice behavior, (2) developed a novel approach for developing high-fidelity route choice models with increased predictive power by augmenting existing aggregate level baseline models with information on drivers' responses to contextual factors obtained from stated choice experiments carried out in an IVE through the use of knowledge distillation. To this end, the study used a virtual driving environment designed based on I-10 in Baton Rouge, LA. Five alternate routes were introduced to the participant. Ten experimental scenarios were conducted to produce initial data about drivers’ dynamic route choice behavior, given emerging contextual factors. Experimental results have demonstrated that the predictions of the augmented models produced by our approach are much closer to reality than that of the baseline. Our study demonstrates that existing route choice models based on econometric theories cannot accurately predict behavior in real world scenarios. For high-fidelity route choice models, one needs to combine existing route choice models with information about contextual factors gleaned from SCEs."</p>
A dataset for establishing a machine learning-based QSAR model to screen beta-lactamase inhibitors using the FARM -BIOMOL chemical library
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Machine Learning based Inference System for Diagnosing Chronic Kidney Disease
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Machine Learning on the Impacts of Mutations in the SARS-CoV-2 Spike RBD on Binding Affinity to Human ACE2 based on Deep Mutational Scanning Data
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Forecasting Cryptocurrency Markets: Predictive Modelling Using Statistical and Machine Learning Approaches
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Data Tabels (One-dimensional N-layer thermal modelling as a basis for effective machine learning training data generation for nondestructive testing of composite parts.)
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Microplastic deposit predictions on sandy beaches by geotech-nologies and machine learning models
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Identification of Key Genes for seed germination of Astragalus Mongolicus using WGCNA and machine Learning methods
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Probabilistic Emulation of the Community Radiative Transfer Model Using Machine Learning
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Predicting gut microbial behavior in human diseases via community metabolic modeling and machine learning
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Dataset of "Machine Learning Uncovers Provenance of Source Rocks for Volcano-Sedimentary Lithium Mineralizations in South China"
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Skillful Subseasonal Forecasts of Marine Heat Waves using Machine Learning
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Replication Data for: Interpretable machine learning prediction of fire emission and comparison with FireMIP process-based models
<p>The target and predictor variables used in the developed ML model.</p>
Image dataset from Automatic analysis of Trypanosoma cruzi: A machine learning approach for the detection of blood trypomastigotes in low resolution images
<p>Image files of mice blood smear containing <em>T. cruzi</em> trypomastigotes. The images were used to train and test the classification model of the objects (nuclei and/or kinetoplasts) present in the images.</p>
Detection, quantification and classification of ripened tomatoes: a comparative analysis of image processing and machine learning
<p>This is an open dataset.</p>
Detection, quantification and classification of ripened tomatoes: a comparative analysis of image processing and machine learning
<p>In this study, specifically for the detection of ripe/unripe tomatoes with/without defects in the crop field, two distinct methods are described and compared from captured images by a camera mounted on a mobile robot. One is a machine learning approach, known as 'Cascaded Object Detector' (COD) and the other is a composition of traditional customised methods, individually known as 'Colour Transformation': 'Colour Segmentation' and 'Circular Hough Transformation'. The (Viola-Jones) COD generates 'histogram of oriented gradient' (HOG) features to detect tomatoes. For ripeness checking, the RGB mean is calculated with a set of rules. However, for traditional methods, colour thresholding is applied to detect tomatoes either from natural or solid background and RGB colour is adjusted to identify ripened tomatoes. This algorithm is shown to be optimally feasible for any micro-controller based miniature electronic devices in terms of its run time complexity of <i>O</i>(<i>n</i><sup>3</sup>) for a traditional method in best and average cases. Comparisons show that the accuracy of the machine learning method is 95%, better than that of the Colour Segmentation Method using MATLAB.</p>
Machine learning techniques for mortality prediction in emergency departments: a systematic review
<p class="MDPI17abstract">This systematic review aimed to assess the performance and clinical feasibility of ML algorithms in prediction of in-hospital mortality for medical patients using vital signs at emergency departments.</p> <p class="MDPI17abstract"><b>Design: </b>A systematic review was performed.</p> <p class="MDPI17abstract"><b>Setting: </b>The databases including<b> </b>Medline (PubMed), Scopus, and Embase (Ovid) were searched between 2010 and 2021, to extract published articles in English, describing ML-based models utilizing vital signs variables to predict in-hospital mortality for patients admitted at emergency departments. CHARMS checklist was used for study planning and data extraction. The risk of bias for included papers was assessed using the PROBAST tool.</p> <p class="MDPI17abstract"><b>Participants: </b>Admitted patients to the ED</p> <p class="MDPI17abstract"><b>Main outcome measure: </b>In-hospital mortality.</p> <p class="MDPI17abstract"><b>Results: </b>Fifteen articles were included in the final review. We found that eight models including logistic regression, decision tree, K-nearest neighbors, support vector machine, gradient boosting, random forest, artificial neural networks, and deep neural networks have been applied in this domain. Most studies failed to report essential main analysis steps such as data preprocessing and handling missing values. Fourteen included studies had a high risk of bias in the statistical analysis part, which could lead to poor performance in practice. Although the main aim of all studies was developing a predictive model for mortality, nine articles did not provide a time horizon for the prediction.</p> <p class="MDPI17abstract"><b>Conclusion: </b>This review provided an updated overview of the state-of-the-art and revealed research gaps; based on these, we provide eight recommendations for future studies to make the use of ML more feasible in practice. By following these recommendations, we expect to see more robust ML models applied in the future to help clinicians identify patient deterioration earlier.</p>
Data of "Chemistrees: Data-Driven Identification of Reaction Pathways via Machine Learning"
<p>This is the data and assosiated in-house code for the paper:</p> <p>Chemistrees: Data-Driven Identification of Reaction Pathways via Machine Learning</p> <p>Sander Roet, Christopher D. Daub, and Enrico Riccardi</p> <p>Journal of Chemical Theory and Computation <strong>2021</strong> <em>17</em> (10), 6193-6202</p> <p>DOI: 10.1021/acs.jctc.1c00458</p>
Prediction of olivine in distinct forming-environments using machine learning and implications for magmatic sulfide prospectivity
<p>Olivine compositions from global volcanic and plutonic samples.</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.