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Dataset results
17 results for “Machine Behavior”
Investigating Online Art Search through Quantitative Behavioral Data and Machine Learning Techniques - Dataset
<p>This dataset includes the detailed values and scripts used to study behavioral aspects of users searching online for Art and Culture by analyzing quantitative data collected by the Art Boulevard search engine using machine learning techniques. This dataset is part of the core methodology, results and discussion sections of the research paper entitled "<strong>Investigating Online Art Search through Quantitative Behavioral Data and Machine Learning Techniques</strong>"</p>
On the Understandability of Language Constructs to Structure the State and Behavior in Abstract State Machine Specifications: A Controlled Experiment
<p>Data-Set and Artifacts: Documents, Forms, and R Scripts for Reproducibility of the Empirical Study</p>
The supplementary materials for "Roughness prediction of end milling surface for behavior mapping of digital twined machine tools".
<p>This is the supplementary materials for a paper named "Roughness prediction of end milling surface for behavior mapping of digital twined machine tools" published on the Digital Twin journal.</p>
Data from: Olfactory testing in Parkinson's disease & REM behavior disorder: a machine learning approach
<p><span><span><span><span><span><span><span><span><span><span><span><b>Objective: </b></span></span></span></span></span></span></span></span></span></span></span><span><span><span><span><span><span><span><span><span><span><span>We sought to identify an abbreviated test of impaired olfaction, amenable for use in busy clinical environments in prodromal (isolated REM sleep Behavior Disorder (iRBD)) and manifest Parkinson's.</span></span></span></span></span></span></span></span></span></span></span></p> <p><span><span><span><span><span><span><span><span><span><span><span><b>Methods: </b></span></span></span></span></span></span></span></span></span></span></span><span><span><span><span><span><span><span><span><span><span><span>890 PD and 313 control participants in the Discovery cohort study underwent Sniffin' stick odour identification assessment. Random forests were initially trained to distinguish individuals with poor (functional anosmia/hyposmia) and good (normosmia/super-smeller) smell ability using all 16 Sniffin' sticks. Models were retrained using the top 3 sticks ranked by order of predictor importance. One randomly selected 3-stick model was tested in a second independent Parkinson's dataset (n=452) and in two iRBD datasets (Discovery n=241; Marburg n=37) before being compared to previously described abbreviated Sniffin' stick combinations.</span></span></span></span></span></span></span></span></span></span></span></p> <p><span><span><span><span><span><span><span><span><span><span><span><b>Results: </b></span></span></span></span></span></span></span></span></span></span></span><span><span><span><span><span><span><span><span><span><span><span>In differentiating poor from good smell ability, the overall area under the curve (AUC) value associated with the top 3 sticks (Anise, Licorice and Banana) was 0.95 in the development dataset (sensitivity:90%, specificity:92%, PPV:92%, NPV:90%). Internal and external validation confirmed AUCs≥0.90. The combination of 3-stick model determined poor smell and an RBD screening questionnaire score of ≥5, separated iRBD from controls with a sensitivity, specificity, PPV and NPV of 65%, 100%, 100% and 30%. </span></span></span></span></span></span></span></span></span></span></span></p> <p><span><span><span><span><span><span><span><span><span><span><span><b>Conclusions: </b></span></span></span></span></span></span></span></span></span></span></span><span><span><span><span><span><span><span><span><span><span><span>Our 3-Sniffin'-stick model holds potential utility as a brief screening test in the stratification of individuals with Parkinson's and iRBD according to olfactory dysfunction.</span></span></span></span></span></span></span></span></span></span></span></p> <p><span><span><span><span><span><span><span><span><span><span><span><b>Classification of Evidence: </b></span></span></span></span></span></span></span></span></span></span></span><span><span><span><span><span><span><span><span><span><span><span>This study provides Class III evidence that a 3-Sniffin'-stick model distinguishes individuals with poor and good smell ability and can be used to screen for individuals with iRBD.</span></span></span></span></span></span></span></span></span></span></span></p>
Validating Machine -Learned Classifiers of Sedentary Behavior and Physical Activity
ClinicalTrials.gov study NCT01775826. IPD Sharing: Not stated. Countries: 1. Publications: 2.
Data from: Olfactory testing in Parkinson’s disease & REM behavior disorder: a machine learning approach
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Predicting gut microbial behavior in human diseases via community metabolic modeling and machine learning
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Live programming controlled experiment on state machines for robotic behaviors
<p>All the information about the controlled experiment performed on the Live Robot Programming language (LRP) vs SMACH (Python API), both for program comprehension and program writing</p> <ul> <li>Programs for both SMACH and LRP</li> <li>Introductory material for both SMACH and LRP</li> <li>Questionnaires</li> <li>Raw and processed data</li> </ul>
Data from: Comprehensive machine learning analysis of Hydra behavior reveals a stable basal behavioral repertoire
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Auditing Machine Behaviors: Does a diverse crowd result in different social norms?
<p>Dataset of the paper:</p> <p>Christoforou, E., Orphanou, K., Nicolaou, N., Stavrakis, E., & Otterbacher, J. (2023, October). Auditing Machine Behaviors: Does a diverse crowd result in different social norms?. In 2023 10th International Conference on Behavioural and Social Computing (BESC) (pp. 1-8). IEEE.</p> <p>Abstract: </p> <p>Many intelligent systems depend on black-box services for achieving goals. For example, Decision Support Systems (DSS) incorporate image tagging services for translating the content of an image into useful information that serves the purpose of the system under development (e.g., facilitating hiring decisions). Auditing the results of DSS for fairness can thus become a cumbersome process as they are correlated with the output of the black-box image tagger. In this work, we assume that tags generated by an image tagger are part of the DSS and in terms of fairness can be considered socially acceptable or unacceptable conforming to the varying goals of a DSS. For example, the tag "good-looking" can be considered appropriate in a dating context but inappropriate in a hiring context. We propose a methodology for leveraging the use of crowdsourcing as an approach for auditing what is socially acceptable and unacceptable for tags generated by black-box image taggers. As a first step, we attempt to understand how the crowdworkers perceive (un)acceptable tags in two scenarios: (i) a hiring DSS, and (ii) a dating DSS. Through our analysis, we investigate how answers (in our case tags) received by crowdworkers representing different demographic groups (i.e., country of residence and gender) impact the auditing process. Results indicate that varying the DSS scenario, and the demographics of the crowdworkers, yields a different fairness perspective concurrent to the "acceptable" and "unacceptable" social norms.</p>
The dataset for paper Roughness prediction of end milling surface for behavior mapping of digital twined machine tools
<p>The original real measured surface roughness is in the "Ra.xlsx". The origigital sensor data is named as "ACF-x-x". In each excel document, three directions of vibration, current and force sensor data is included. <br> Naming rules is as follows: In“ACF-1-2", "1-2"represents the second slot in first layler. <br> The corresponding cutting parameters for each slot are illustraed in "Ra.xlsx" document. </p>
Numerical analysis and machine learning techniques on the behavior of FRP confined circular reinforced concrete columns
<p>This study presents a comprehensive nonlinear finite element study on the behavior of circular fibre reinforced polymer (FRP) confined reinforced and plain concrete columns under concentric loads. For this investigation, 65 test models with a combination of spiral hoop reinforced concrete, concrete with longitudinal and circular hoop reinforcements, and FRP confined plain concrete were designed . Four different machine learning (ML) techniques were developed to predict the ultimate axial load and strain at the tensile rupture of FRP. The accuracy of the proposed finite element model (FEM) was verified by comparing it with the existing experimental test results. The impact of unconfined concrete strength, hoop reinforcement ratio, thickness of FRP, and spiral hoop spacing on the confinement effectiveness, load-carrying capacity, and ductility behavior of circular FRP confined concrete columns were demonstrated. The parametric analysis found that axial load capacity of FRP-confined concrete columns increased when unconfined concrete strength increased, while low-strength confined concrete achieved a larger strength improvement ratio than high-grade concrete. The investigation also revealed that the thickness of the confining FRP has a significant impact on the confinement effectiveness of hoop reinforcement. The correlation between the FEM and experimental tests yielded 99.60% R<sup>2</sup> for ultimate axial load and 93.40% R<sup>2</sup> for ultimate strain. Extra tree regressor (ETR) and gradient boosting of ML yielded accurate predictions of ultimate axial load and strain at the tensile rupture of FRP compared to other approaches, but ETR has the best comprehensive prediction performance using the comprehensive ranking system. Overall, ETR can be applied in the ultimate axial load and strain prediction of circular FRP confined reinforced and plain concrete columns under concentric loads, conserving resources, time, and cost through laboratory testing.</p> <p> </p> <p> </p> <p> </p> <p> </p>
Prediction of Violent Behavior in Patients With Schizophrenia by Multimodal Machine Learning
ClinicalTrials.gov study NCT04520399. IPD Sharing: NO. Countries: 1. Publications: 0.
Improving Diagnosis and Clinical Management of Familial Hypercholesterolemia Through Integrated Machine Learning, Implementation Science, and Behavioral Economics
ClinicalTrials.gov study NCT05746247. IPD Sharing: NO. Countries: 1. Publications: 0.
Using Machine Learning to Detect Risky Behavior in Psychiatric Clinics
ClinicalTrials.gov study NCT06421480. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Novel Multimodal Neural, Physiological, and Behavioral Sensing and Machine Learning for Mental States
ClinicalTrials.gov study NCT07110688. IPD Sharing: YES. Countries: 1. Publications: 0.
Identification of Clinically Occult Glioma Cells and Characterization of Glioma Behavior Through Machine Learning Analysis of Advanced Imaging Technology
ClinicalTrials.gov study NCT00330109. IPD Sharing: Not stated. Countries: 1. Publications: 0.
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.