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9 results for “Discrimination methods”
Figure 1 in Comparison of two morphometric methods for discriminating honey bee (Apis mellifera L.) populations in Turkey
Figure 1. Sampling locations in Turkey (Thrace: 1–2; Aegean: 3; Central Anatolia/ Mediterranean: 4–11; Southeastern Anatolia: 12–13; Northeastern Anatolia: 14–15).
Dataset and fitting methods for Kinetic Proofreading can Enhance Single Nucleotide Discrimination in a Non-enzymatic DNA Strand Displacement Network
<p>This upload contains raw experimental data and the fitting methods used for the article "Kinetic Proofreading can Enhance Single Nucleotide Discrimination in a Non-enzymatic DNA Strand Displacement Network".</p>
Prospective Validation and Comparison of Different Ultrasound Methods for Discrimination Between Benign and Malignant Ovarian/Tubal Masses Prior to Surgery
ClinicalTrials.gov study NCT02847832. IPD Sharing: Not stated. Countries: 1. Publications: 15.
Data for: Theropod dinosaur diversity of the lower English Wealden: analysis of a tooth-based fauna from the Wadhurst Clay Formation (Lower Cretaceous: Valanginian) via phylogenetic, discriminant and machine learning methods
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Stimulus classification with electrical potential and impedance of living plants: comparing discriminant analysis and deep-learning methods
<p>The physiology of living organisms, such as living plants, is complex and particularly difficult to understand on a macroscopic, organism-holistic level. Among the many options for studying plant physiology, electrical potential and tissue impedance are arguably simple measurement techniques that can be used to gather plant-level information. Despite the many possible uses, our research is exclusively driven by the idea of phytosensing, that is, interpreting living plants’ signals to gather information about surrounding environmental conditions. As ready-to-use plant-level physiological models are not available, we consider the plant as a blackbox and apply statistics and machine learning to automatically interpret measured signals. In simple plant experiments, we expose <em>Zamioculcas zamiifolia</em> and <em>Solanum lycopersicum</em> (tomato) to four different stimuli: wind, heat, red light and blue light. We measure electrical potential and tissue impedance signals. Given these signals, we evaluate a large variety of methods from statistical discriminant analysis and from deep learning, for the classification problem of determining the stimulus to which the plant was exposed. We identify a set of methods that successfully classify stimuli with good accuracy, without a clear winner. The statistical approach is competitive, partially depending on data availability for the machine learning approach. Our extensive results show the feasibility of the blackbox approach and can be used in future research to select appropriate classifier techniques for a given use case. In our own future research, we will exploit these methods to derive a phytosensing approach to monitoring air pollution in urban areas.</p> <p>Data repository for our paper " <em>Stimulus classification with electrical potential and impedance of living plants: comparing discriminant analysis and deep-learning methods</em> ", submitted to the journal Bioinspiration & Biomimetics . Please refer to the paper for more information.</p> <p> </p> <p><strong>Contents of this repository</strong></p> <ul> <li><em>mu_interface:</em> Code for our data collection plant experiments, based on Raspberry Pis and the <a href="http://cybertronica.co/?q=products/phytosensor">Cybertronica phytosensing and phytoactuating system</a>.</li> <li><em>SupplementaryCode</em>: Includes the discriminant analysis classifier, raw datasets, calculated features, test-train split and the corresponding code.</li> <li><em>dl-4-tsc:</em> Deep learning framework developed by <a href="https://doi.org/10.1007/s10618-019-00619-1">Fawaz et. al (Deep learning for time series classification: a review)</a> and adapted to our use case. </li> <li><em>DeepClassifier: </em>Trained deep learning time series classifier.</li> <li><em>classification_results.xlsx: </em>Overview of the results from the deep learning framework (accuracy, precision, recall, training time, confusion matrix) and the achieved accuracies using discriminant analysis with sequential forward section (further evaluation metrics of the discriminant analysis can be found in SupplementaryCode.</li> </ul>
Figure 3A in Comparison of two morphometric methods for discriminating honey bee (Apis mellifera L.) populations in Turkey
Figure 3A. Scatter plot of principle component analysis of honey bee populations from different geographic regions based on TM (Thrace = ✳; Aegean = ×; Central Anatolia/Mediterranean = ◆; Southeastern Anatolia = △; Northeastern Anatolia = □).
Development of Novel MRI Methods for Detecting, Discriminating, and Measuring Liver Fibrosis and Congestion in Fontan Patients
ClinicalTrials.gov study NCT03539757. IPD Sharing: NO. Countries: 1. Publications: 0.
Data from: Reliability and discriminative ability of a new method for soccer kicking evaluation
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Data from: Reliability and discriminative ability of a new method for soccer kicking evaluation
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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.