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4 results for “Machine Learning Security”
Replication Data for the paper "Predicting Food-Security Crises in the Horn of Africa Using Machine Learning"
<p>This folder contains all input data necessary to run the machine learning model as described in the paper "Predicting Food Security Crises in the Horn of Africa Using Machine Learning". </p><p>This model is developed at the Institute for Environmental Studies, Vrije Universiteit Amsterdam. </p><p>Questions or remarks can be send to tim.busker@vu.nl</p>
Artifact: "If security is required": Engineering and Security Practices for Machine Learning-based IoT Devices
<p>Artifact for "If security is required": Engineering and Security Practices for Machine Learning-based IoT Devices</p>
ADVANCEMENTS IN AIRLINE SECURITY: EVALUATING MACHINE LEARNING MODELS FOR THREAT DETECTION
<p>This study assessed the performance of four machine learning algorithms—Decision Tree (DT), Random Forest (RF), Support Vector Machine (SVM), and Neural Network (NN)—for predicting airline security threats using a dataset of 100,000 entries with 30 features. The models were evaluated based on accuracy, precision, recall, F1-Score, and AUC-ROC. The Neural Network achieved the highest performance, with an accuracy of 88%, precision of 86%, recall of 85%, F1-Score of 85.5%, and AUC-ROC of 0.90, demonstrating superior capability in capturing complex, non-linear patterns. The Random Forest model followed, with an accuracy of 85%, precision of 83%, recall of 82%, F1-Score of 82.5%, and AUC-ROC of 0.87, offering a robust and generalizable solution. The SVM model attained an accuracy of 81%, precision of 80%, recall of 78%, F1-Score of 79%, and AUC-ROC of 0.84, showing effective binary classification but with higher computational costs. The Decision Tree model, while interpretable, had the lowest performance metrics: accuracy of 78%, precision of 76%, recall of 72%, F1-Score of 74%, and AUC-ROC of 0.79. The results indicate that Neural Networks and Random Forests are the most effective models for airline security threat detection, with Neural Networks providing the highest overall accuracy and AUC-ROC.</p>
Data and code for "Machine learning can guide food security efforts when primary data is not available"
<p>Data and code repository for the paper "Machine learning can guide food security efforts when primary data is not available" by Giulia Martini, Alberto Bracci, Lorenzo Riches, Sejal Jaiswal, Matteo Corea, Jonathan Rivers, Arif Husain, and Elisa Omodei.</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.