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29 results for “Automated Machine Learning”
Released Experimental Dataset for Sampled Automated Machine Learning
<p>Released Experimental Dataset of "Doing More with Less: Characterizing Dataset Downsampling for AutoML"</p> <p> </p> <p>Experiments were run for 5 and 60 minutes on 16 datasets:<br> 4 small: < 10.000<br> 5 medium: < 100.000<br> 7 large: > 100.000<br> </p>
Covid-19 automated diagnosis and risk assessment through Metabolomics and Machine Learning
<p>COVID-19 plasma samples spectrometry datasets for machine learning input. Used in the work of article Covid-19 automated diagnosis and risk assessment through Metabolomics and Machine Learning, currently under submittion.</p> <p>Abstract:</p> <p>COVID-19 is still placing a heavy health and financial burden worldwide. Impairments in patient screening and risk management play a fundamental role on how governments and authorities are directing resources, planning reopening, as well as sanitary countermeasures, especially in regions where poverty is a major component in the equation. An efficient diagnostic method must be highly accurate, while having a cost-effective profile. We combined a machine learning-based algorithm with mass spectrometry to create an expeditious platform that discriminate COVID-19 in plasma samples within minutes, while also providing tools for risk assessment, to assist healthcare professionals in patient management and decision-making. A cross-sectional study with 815 patients (442 COVID-19, 350 controls and 23 COVID-19 suspicious) was enrolled from three Brazilian epicenters from April to July 2020. We were able to elect and identify 19 molecules that are related to the disease’s pathophysiology and several discriminating features to patient’s health-related outcomes. The method applied for COVID-19 diagnosis showed specificity >96% and sensitivity >83%, and specificity >80% and sensitivity >85% during risk assessment, both from blinded data. Our method introduced a new approach for COVID-19 screening, providing the indirect detection of infection through metabolites and contextualizing the findings the disease’s pathophysiology. The pairwise analysis of biomarkers brought robustness to the model developed using Machine Learning algorithms, transforming this screening approach in a tool with great potential for real-world application. </p>
The Dataset of Quantifying Alignment Deviations for Uniaxial Material Mechanical Testing via Automated Machine Learning
<p>The dataset consists of 4 alignment deviations of the uniaxial testing machine as well as 12 strain measurement points on cruciform specimens. A deep learning model is trained on the dataset to quantify 4 alignment deviations using 12 strain values on a thin plate specimen. The design of experiments includes Optimal Latin Hypercube, numerical modelling of Finite Element Methods. Using the Optimal Latin Hypercube, 12496 distinct groups of DOE simulation tests are constructed. Under the boundary conditions of 4 distinct deviations, 12 strain values at the required location on the cruciform specimen are obtained using Python scripts.</p> <p>The nine CSV files correspond to the nine analysis steps. The only difference among the nine analysis steps is the pretension force acting on RP1. Each CSV file contains 24 columns of data, and the corresponding contents of each column of data are as follows:</p> <ul> <li>Columns 1-6 are the freedoms of RP1 reference point, which are U1, U2, U3, ur1, UR2 and UR3 respectively;</li> <li>Columns 7-12 are the freedoms of RP2 reference points, which are U1, U2, U3, ur1, UR2 and UR3 respectively;</li> <li>Columns 13-24 are the strain values of the last 12 strain measurements of the thin plate rectangular specimen。</li> </ul>
Initial dataset used in SIMON, an automated machine learning approach
<p>The 7-Zip file contains raw data in the CSV file downloaded from Stanford Data Miner and used for further analysis using mulset algorithm and SIMON, as described in the publication:</p> <p>Tomic A, Tomic I, Rosenberg-Hasson Y, Dekker CL, Maecker HT, and Davis MM. SIMON, an automated machine learning system reveals immune signatures of influenza vaccine responses. <em>JImmunol</em>, doi: 10.4049/jimmunol.1900033, 2019.</p> <p>File was compressed using 7-Zip available at https://www.7-zip.org/.</p>
Data from: Automated workflow for the cell cycle analysis of (non-)adherent cells using a machine learning approach
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Automated bio-AFM generation of large mechanome data set and their analysis by machine learning to classify prostatic cell lines_Training base 100 PC3-GFP
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An automated system for inspecting rock faces and detecting potential rock falls using machine learning
<p>Rockfall is a hazard in mountainous areas threatening infrastructure and human lives. Rockfall hazards are often mitigated by manual inspections using pry bars. The inspector must access the rock face, hit the rock surface, detect, and remove the loose rocks. This method is very labor demanding, unsafe, and challenging. This research presents a method that automatize the inspection of rock blocks that are prone to rockfall events. A robot is developed to replace the manual hammer tap process and collect the sound data remotely; subsequently, the sound signal is used to identify different types of the discontinuity in rocks in controlled laboratory environment. Machine learning is used to train the method to discriminate between intact rock and rock that may be prone to fall. This methodology was successfully applied to laboratory tests on rock. Finally, the research involves the implementation of this system in field to understand the potential and limitations of the proposing system in automatizing the rock inspections. This research enables the inspectors to collect data remotely, detect loose rocks, and save data for future references.</p>
Automated Trustworthiness Testing for Machine Learning Classifiers
<p>This repository includes data for the paper <em>Automated Trustworthiness Testing for Machine Learning Classifiers</em>.</p>
Automated Model Discovery for Tensional Homeostasis: Constitutive Machine Learning in Growth and Remodeling (Source code and data)
<p>This dataset contains</p> <ul> <li>the source code</li> <li>the data and examples</li> <li>the material subroutine with examples of uniaxial strain and stress</li> </ul> <p>of the inelastic Constitutive Artificial Neural Network (iCANN) enhanced by the concept of homeostatic surfaces to discover tensional homeostasis.</p> <p>The corresponding publication is:</p> <p>Holthusen, H., Brepols, T., Linka, K., & Kuhl, E..<em> </em></p> <p><em>Automated Model Discovery for Tensional Homeostasis: Constitutive Machine Learning in Growth and Remodeling.</em></p> <p> </p> <p><strong>Standalone_Materialroutine</strong></p> <ul> <li>00_Materialroutine: Contains the material subroutine implemented in FORTRAN</li> <li>01_uniaxial_strain: Example of the material subroutine in a uniaxial strain driven manner</li> <li>02_uniaxial_stress: Example of the material subroutine in a uniaxial stress driven manner</li> </ul> <p> </p> <p><strong>TensorFlow</strong></p> <ul> <li> <p>iCANN:</p> <ul> <li> <p>01_Biax/biax_l1: Keras/TensorFlow implementation of the iCANN. Example of the cross specimen with L1 (Lasso) regularization</p> </li> <li> <p>01_Biax/biax_l2: Keras/TensorFlow implementation of the iCANN. Example of the cross specimen with L2 (ridge) regularization</p> </li> <li> <p>02_Uniax/uniax_l1: Keras/TensorFlow implementation of the iCANN. Example of the stripe specimen with L1 (Lasso) regularization</p> </li> <li>02_Uniax/uniax_l1: Keras/TensorFlow implementation of the iCANN. Example of the stripe specimen with L2 (ridge) regularization</li> </ul> </li> <li> <p>iCANN_ABS_activation: Same four examples as above, however, with the absolute value activation function</p> </li> <li> <p>installed_packages: File containing a list of installed Python modules used to implement the iCANN</p> </li> </ul> <p>The TensorFlow implementations in all 01_Biax/ and 02_Uniax/ sub-directories are the same.</p> <p>The implementation in iCANN_ABS_activation is different with respect to the activation functions of the pseudo potential.</p> <p> </p> <p>The experimental data for the cross and stripe specimen are taken from the literature:</p> <p>Eichinger, J. F., Paukner, D., Szafron, J. M., Aydin, R. C., Humphrey, J. D., & Cyron, C. J. (2020).</p> <p>Computer-controlled biaxial bioreactor for investigating cell-mediated homeostasis in tissue equivalents. <em>Journal of biomechanical engineering</em>, <em>142</em>(7), 071011.</p> <p><a href="https://doi.org/10.1115/1.4046201">https://doi.org/10.1115/1.4046201</a></p>
Investigating automated bird detection from webcams using machine learning
<p>We provide a dataset of images(.jpeg) with their corresponding annotations files(.xml) used to train a bird detection deep learning model. These images were collected from the live stream feeds of Cornell Lab of Ornithology (https://www.allaboutbirds.org/cams/) situated in 6 unique locations around the world as follows:</p> <ul> <li>Treman bird feeding garden at the Cornell Ornithology Laboratory in Ithaca, New York. At this station, Axis P11448-LE cameras are used to capture the recordings from feeders perched on the edge of both Sapsucker Woods and its 10-acre ponds. This site mainly attracts forest species like chickadees (Poecile atricapillus), red-winged blackbirds (Agelaius phoeniceus), and woodpeckers (Picidae). A total of 2065 images were captured from this location.</li> <li> Fort Davis in Western Texas, USA. At this site, a total of 30 hummingbird feeder cams are hosted at an elevation of over 5500 feet. From this site, 1440 images were captured.</li> <li>Sachatamia Lodge in Mindo, Ecuador. This site has a live hummingbird feed watcher that attracts over 132 species of hummingbirds including: Fawn-breasted Brilliant, White-necked Jacobin, Purple-bibbed Whitetip, Violet-tailed Sylph, Velvet-purple Coronet, and many others. A total of 2063 images were captured from this location.</li> <li>Morris County, New Jersey, USA. Feeders at this location attract over 39 species including Red-bellied Woodpecker, Red-winged Blackbird, Purple Finch, Blue Jay, Pine Siskin, Hairy Woodpecker, and others. Footage at this site is captured by an Axis P1448-LE Camera and Axis T8351 Microphone. A total of 1876 images were recorded from this site.</li> <li>Canopy Lodge in El Valle de Anton, Panama. Over 158 bird species visit this location annually and these include Gray-headed Chachalaca, Ruddy Ground-Dove, White-tipped Dove, Green Hermit, and others. A total of 1600 images were captured.</li> <li>Southeast tip of South Island, New Zealand. At this site, nearly 10000 seabirds visit this location annually and a total of 1548 images were captured.</li> </ul> <p> The Cornell Lab of Ornithology is an institute dedicated to biodiversity conversation with the main focus on birds through research, citizen science, and education. The autoscreen software was used to capture the images from the live feeds and images of approximately 1 Megapixel (Joint Photographic Experts Group) JPEG-coloured images of resolution 1366 X 768 X 3 pixels were collected (https://sourceforge.net/projects/autoscreen/). The software took a new image every 30 seconds and was captured during different times of the day in order to avoid a sample-biased dataset. In total, 10592 images were collected for this study.</p> <p><strong>Files provided</strong></p> <p>Train.zip – contains 6779 image files(.jpeg) and 6779 annotation files (.xml)</p> <p>Validation.zip – contains 1695 image files(.jpeg) and 1695 annotation files (.xml)</p> <p>Test.zip –contains 2118 image files(.jpeg)</p> <p>Scripts.zip - Contains scripts needed in manipulating the dataset like dataset partitioning, and creation of CSV and tfrecords files. </p> <p>This dataset was used in the MSc thesis titled “Investigating automated bird detection from webcams using machine learning” by Alex Mirugwe, University of Cape Town – South Africa.</p> <p> </p>
Data from: Automation and machine learning drive rapid optimization of isoprenol production in Pseudomonas putida
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Data from: Automating field based floral surveys with machine learning
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Seek and learn: automated identification of microevents in animal behaviour using envelopes of acceleration data and machine learning
<p>1. Animal-borne accelerometers have been used across more than 120 species to infer biologically significant information such as energy expenditure and broad behavioural categories. While the accelerometer's high sensitivity to movement and fast response times present the unprecedented opportunity to resolve fine-scale behaviour, leveraging this opportunity will require overcoming the challenge of developing general, automated methods to analyse the nonstationary signals generated by nonlinear processes governing erratic, impulsive movement characteristic of fine-scale behaviour. 2. We address this issue by conceptualising fine-scale behaviour in terms of characteristic microevents: impulsive movements producing brief (<1 s) shock signals in accelerometer data. We propose a 'seek-and-learn' approach: a novel microevent detection step first locates where shock signals occur ('seek') by searching for peaks in envelopes of acceleration data. Robust machine learning ('learn') employing meaningful features then separates microevents. We showcase the application of our method on tri-axial accelerometer data collected on ten free-living meerkats (Suricata suricatta) for four fine-scale foraging behaviours – searching for digging sites, one-armed digging, two-armed digging, and head jerks during prey ingestion. Annotated videos served as groundtruth, and performance was benchmarked against that of a variety of classical machine-learning approaches. 3. Microevent identification (μEvId) with eight features in a three-node hierarchical classification scheme employing logistic regression at each node achieved a mean overall accuracy of >85% during leave-one-individual-out cross-validation, and exceeded that of the best classical machine learning approach by 9%. μEvId was found to be robust not only to inter-individual variation but also to large changes in model parameters. 4. Our results show that microevents can be modelled as impulse responses of the animal body-and-sensor system. The microevent detection step retains only informative regions of the signal, which results in the selection of discriminative features that reflect biomechanical differences between microevents. Moving-window-based classical machine learning approaches lack this prefiltering step, and were found to be suboptimal for capturing the nonstationary dynamics of the recorded signals. The general, automated technique of μEvId, together with existing models that can identify broad behavioural categories, provides future studies with a powerful toolkit to exploit the full potential of accelerometers for animal behaviour recognition.</p>
Automated detection of lameness in sheep using machine learning approaches: novel insights into behavioural differences among lame and non-lame sheep
Lameness in sheep is the biggest cause of concern regarding poor health and welfare among sheep producing countries. Best practice for lameness relies on rapid treatment, yet there are no objective measures of lameness detection. Use of accelerometers and gyroscopes have been widely used in human activity studies and their use is becoming increasingly common in livestock. In this study, we used 23 datasets (10 non-lame and 13 lame sheep) from an accelerometer and gyroscope-based ear sensor with a sampling frequency of 16 Hz to develop and compare algorithms that can differentiate lameness within three different activities (walking, standing and lying). We show for the first time that features extracted from accelerometer and gyroscope signals can differentiate between lame and non-lame sheep while standing, walking and lying. The random forest algorithm performed best for classifying lameness with accuracy of 84.91% within lying, 81.15% within standing and 76.83% within walking and overall correctly classified over 80% sheep within activities. Both accelerometer and gyroscope-based features ranked among the top 10 features for classification. Our results suggest that novel behavioural differences between lame and non-lame sheep across all three activities could be used to develop an automated system for lameness detection.
Automated cell type annotation and exploration of single cell signalling dynamics using mass cytometry and machine learning
<p>In this repository we share processed data that were generated using the bioinformatics framework we developed in publication "Automated cell type annotation and exploration of single cell signalling dynamics using mass cytometry and machine learning".</p> <p>These datasets accompany the source codes provided in our GitHub page https://github.com/dkleftogi/singleCellClassification. </p> <p>The datasets are as follows:</p> <ol> <li>cofactors_v2.RDa : antibody-specific co-factors used to harmonise fcs files from different batches</li> <li>ctrl_annotated.RDa : the annotated cohort of seven healthy donors</li> <li>data_umap.RDa : UMAP representation of the data used to generate the figures in our paper</li> <li>DREMI_feature_matrix.RDa : the DREMI feature matrix used for ML-based modelling presented in our paper</li> <li>median_feature_matrix.RDa : the baseline feature matrix based on medians used for ML-bases modelling in the paper</li> <li>patient_annotated.RDa : the annotated cohort of leukemia patients (n=43)</li> </ol> <p>We note that the raw files of the leukemia cohort can be found in http://flowrepository.org/id/RvFr0LLv9McDJ89jgK50G4lwnfDFRTrcMelxYgnSIcE2Cymrpf2qh2NaWybtWDNH</p> <p> </p>
Dataset for the research paper "Automated machine learning in research – a literature review"
<p>This repository contains the literature used in the research paper "Automated Machine Learning in Research – A Literature Review."</p> <p>The four BibTeX files contain the following collections of literature:</p> <table> <tbody> <tr> <td><strong>File</strong></td> <td><strong>Description</strong></td> <td><strong>Method</strong></td> </tr> <tr> <td><strong>01_Initial_papers.bib</strong></td> <td>385 Initial papers</td> <td>Keyword search in scientific databases Scopus and Web of Science</td> </tr> <tr> <td><strong>02_Primary_papers.bib</strong></td> <td>267 Primary papers</td> <td>Removing duplicate articles and filtering for full articles (i.e., conference and journal papers)</td> </tr> <tr> <td><strong>03_Possibly_relevant_papers.bib</strong></td> <td>54 Possibly relevant papers</td> <td>Identifying possibly relevant papers through abstract scan and application of inclusion criteria</td> </tr> <tr> <td><strong>04_Relevant_papers.bib</strong></td> <td>49 Relevant papers</td> <td>Inclusion of relevant papers after full-text analysis and backward and forward searches</td> </tr> </tbody> </table> <p>The following inclusion criteria were used to identify the 54 possibly relevant papers during the abstract scan:</p> <ul> <li>The abstract must mention autoML or related concepts such as low-code ML, meta-learning, or automated hyperparameter tuning and their potential use in research.</li> <li>The abstract must mention reproducibility or related concepts such as transparency or explainability in the context of autoML or the related concepts mentioned above</li> </ul>
Dataset for puplication: Machine Learning in Automated Monitoring of Metabolic Changes Accompanying the Differentiation of Adipose Tissue-Derived Human Mesenchymal Stem cells employing 1H-1H TOCSY NMR
<p>Data set used in the publication: <strong>Machine Learning in Automated Monitoring of Metabolic Changes Accompanying the Differentiation of Adipose Tissue-Derived Human Mesenchymal Stem cells employing <sup>1</sup>H-<sup>1</sup>H TOCSY NMR. </strong></p> <p>Abstract: In this work, the dynamic evolution of adipose tissue-derived human MSCs (AT-derived hMSCs) after fourteen days of cultivation, adiobocytes and osteocytes differentiation has been inspected based on 2D NMR TOCSY using machine learning techniques. Multi-class classification in addition to novelty detection of metabolites was established based on the profile of a control hMSCs sample at four days cultivation and successively detect the absence and the abundance of metabolites in differentiated MSCs following a set of <sup>1</sup>H-<sup>1</sup>H TOCSY profiles. The uploaded files are:</p> <p>File: metabolites_names.xlsx contain the names of the used metabolites.</p> <p>File: metabolites.xlsx</p> <p> column 1: metabolite abbreviation</p> <p>column 2: 2D NMR TOCSY horizontal and vertical frequencies of metabolites in the control group at 4 days cultivation (Ct d4)</p> <p>column 3: 2D NMR TOCSY horizontal and vertical frequencies of metabolites found after 14 days of cultivation (Ct d14)</p> <p>column 4: 2D NMR TOCSY horizontal and vertical frequencies of metabolites found after 14 days of differentiation into adipocytes (AT d14)</p> <p>column 5: 2D NMR TOCSY horizontal and vertical frequencies of metabolites found after14 days of differentiation into osteocytes (OS d14)</p> <p>column 6: 2D NMR TOCSY horizontal and vertical standard frequencies of all metabolites measured at broadband high resolution 600.13 MHz NMR</p>
AIDI - Research & Development of a Multisensor-Based Machine Learning Technology for Real-Time Automated Detection of COVID-19 Decompensation
ClinicalTrials.gov study NCT05220306. IPD Sharing: NO. Countries: 1. Publications: 1.
Seek and learn: automated identification of microevents in animal behaviour using envelopes of acceleration data and machine learning
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Automated detection of lameness in sheep using machine learning approaches: novel insights into behavioural differences among lame and non-lame sheep
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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.