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Dataset results
117 results for “AI system”
M-Stock: AI-Based Photography Assessment System using Convolutional Neural Networks
<p>M-Stock (Mae Fah Luang University Photo Stock), an AI-driven automated photo evaluation platform designed to support student learning in photography by providing real-time feedback on both technical and artistic elements of their work. Using Convolutional Neural Networks (CNNs)</p> <p>Author: Assistant Professor Surapol Vorapatratorn, Ph.D<br>Organization: Center of Excellence in Artificial Intelligence and Emerging Technologies <br>School of Applied Digital Technology, Mae Fah Luang University, Chiang Rai, Thailand</p> <p><br>To start web service<br>1.Run runStreamlit.bat</p> <p>File description<br>Home.py => Home page<br>web.config => Streamlit's Path<br>Train_800.ipynb => Training model in Jupyter notebook file</p> <p>Directory description<br>image => Website image<br>model => model location<br>pages => each python web page <br>temp_dir => user upload file location<br>train_dir => training set</p>
Dataset of the Paper "Architecture Decisions in AI-based Systems Development: An Empirical Study"
<p>This dataset collected from Stack Overflow (SO) and GitHub was used to conduct an empirical study on architecture decisions in AI-based systems development. We provide below a brief description of each file:</p> <p><strong>1. Dataset (SO).xlsx</strong></p> <p>contains the IDs and URLs of the labelled posts which are related to architecture decisions in AI-based systems development from SO, and the data extracted from these related SO posts.</p> <p><strong>2. Dataset (GitHub).xlsx</strong></p> <p>contains the project names, issue IDs, and issue URLs of AI-based projects selected from GitHub, and the data extracted from the relevant issues.</p> <p><strong>3. Extracted Data (SO+GitHub).xlsx</strong></p> <p>provides the final results of data extracted from SO posts and GitHub issues.</p>
Architecturally Significant Requirements and Software Architecture for AI-Based Systems: A Case Study with Document Classification
Open the record for dataset details and reuse information.
Perceived fairness and perceived transparency of AI systems according to system's characteristics, personality traits and demographic characteristics
<p>We collected data of 3197 users' fairness perception regarding various configurations of a AI-based system in the recruitment domain, as well as, the demographic and personally characteristics of the participants.</p> <p>The dataset includes the following columns:</p> <p><strong>:System characteristics</strong></p> <p> :Certification</p> <p>Uncertificated system (U)</p> <p>Certificated system (C)</p> <p>:Input data</p> <p>High quality input data (H)</p> <p>Low quality input data (L)</p> <p>:Output</p> <p>Positive outcome (P)</p> <p>Borderline outcome (B)</p> <p>Negative outcome (N)</p> <p>:Explanation style</p> <p>Control- no explanation (CON)</p> <p>Case-based (CAS)</p> <p>Certification-based (CER)</p> <p>Demographic-based (DEM)</p> <p>Input influence-based (INP)</p> <p>Sensitivity-based (SEN)</p> <p><strong>:Demographic characteristics</strong></p> <p>:Gender</p> <p>Female</p> <p>Male</p> <p>:Age</p> <p>18-34</p> <p>35-50</p> <p>50+</p> <p>:Residence</p> <p>Unites states of America</p> <p>India</p> <p>Other</p> <p>:Education level</p> <p>High school degree or less</p> <p>Bachelor's degree</p> <p>Master's or doctoral degree</p> <p>:Employment status</p> <p>Not employed</p> <p>Employed</p> <p>:Income level</p> <p>Above average</p> <p>Average</p> <p>Below average</p> <p><strong>:Personality characteristics</strong></p> <p>(TIPI questionnaire)</p> <p>Extraverted, enthusiastic</p> <p>1-7 (1= disagree strongly up to 7= agree strongly)</p> <p>Critical, quarrelsome</p> <p>1-7 (1= disagree strongly up to 7= agree strongly)</p> <p>Dependable, self-disciplined</p> <p>1-7 (1= disagree strongly up to 7= agree strongly)</p> <p>Anxious, easily upset</p> <p>1-7 (1= disagree strongly up to 7= agree strongly)</p> <p>Open to new experiences, complex</p> <p>1-7 (1= disagree strongly up to 7= agree strongly)</p> <p>Reserved, quiet</p> <p>1-7 (1= disagree strongly up to 7= agree strongly)</p> <p>Sympathetic, warm</p> <p>1-7 (1= disagree strongly up to 7= agree strongly)</p> <p>Disorganized, careless</p> <p>1-7 (1= disagree strongly up to 7= agree strongly)</p> <p>Calm, emotionally stable</p> <p>1-7 (1= disagree strongly up to 7= agree strongly)</p> <p>Conventional, uncreative</p> <p>1-7 (1= disagree strongly up to 7= agree strongly)</p> <p><strong>:Participants responses</strong></p> <p>:Fairness evaluation</p> <p>The participants were requested to report their level of perceived fairness (their view about the fairness of the system - at what level they consider the system as a fair system) on a 6-point Likert scale, from "Extremely fair" (represented as 3) to "Extremely unfair" (represented as -3). The option of "neither fair or unfair" (represented as 0) was excluded from the scale.</p> <p>:Transparency evaluation</p> <p>the participants were requested to report their level of perceived transparency (their understanding why the system produced the specific output - at what level they understand why this output was given) on a 6-point Likert scale, from " Thoroughly understand" (represented as 3) to " Thoroughly don't understand" (represented as -3). The option of "neither understand or don't understand" (represented as 0) was excluded from the scale.</p> <p>:Output Expectation</p> <p>The participants were requested to report their expectation for the specific output based on the input they received according to the system's scale, 5-point Likert scale from "Strongly recommended" (represented as 2) to "Strongly not recommended" (represented as -2).</p> <p> </p>
Understanding Explainability during System Design for Safety Critical AI Systems
<p>Our research aims to explore the perspective of internal stakeholders on explainability</p>
Evaluation of Effectiveness for Connected Network for EMS Comprehensive Technical-support Using Artificial Intelligence (CONNECT-AI) System by Community Intervention
ClinicalTrials.gov study NCT04829279. IPD Sharing: NO. Countries: 1. Publications: 1.
Validation of AI-ENDO System in Endoscopic Submucosal Dissection
ClinicalTrials.gov study NCT07392268. IPD Sharing: NO. Countries: 1. Publications: 18.
Validation of an AI-based Biliopancreatic EUS Navigation System for Real-time Quality Improvement: A Prospective, Single-center, Randomized Controlled Trial
ClinicalTrials.gov study NCT05457101. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Effect of Two Colonoscopy AI Systems for Colon Polyp Detection
ClinicalTrials.gov study NCT05089071. IPD Sharing: NO. Countries: 1. Publications: 1.
Effectiveness of an Edge AI-based Augmented Reality System for Hand Hygiene Training: a Multi-centre, Mixed-methods Cluster Randomised Controlled Trial
ClinicalTrials.gov study NCT07280026. IPD Sharing: UNDECIDED. Countries: 2. Publications: 4.
New Stent Retriever, VERSI System for AIS
ClinicalTrials.gov study NCT03366818. IPD Sharing: NO. Countries: 1. Publications: 1.
AI-Based Monitoring System for Chronic Heart Failure With Advanced Wearable and Mini-Invasive Devices
ClinicalTrials.gov study NCT06909682. IPD Sharing: NO. Countries: 0. Publications: 5.
AI-Assisted Analgesia Copilot System
ClinicalTrials.gov study NCT07253012. IPD Sharing: UNDECIDED. Countries: 1. Publications: 3.
Adolescent Idiopathic Scoliosis (AIS) Patient Positioning in EOS System®
ClinicalTrials.gov study NCT02269657. IPD Sharing: YES. Countries: 1. Publications: 0.
Comparison of Traditional Instructions Vs AI Based Instructions With Customized Chat GPT as Remote Support System on Education of Orthodontic Patients
ClinicalTrials.gov study NCT07183111. IPD Sharing: NO. Countries: 1. Publications: 2.
Evaluate the Effects of An AI System on Colonoscopy Quality of Novice Endoscopists
ClinicalTrials.gov study NCT05323279. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Clinical validatiOn of an AI-based DEcision Support System for Robotic Upper Limb Rehabilitation in Patients With Stroke. A CO-AIDER Study.
ClinicalTrials.gov study NCT07199322. IPD Sharing: Not stated. Countries: 1. Publications: 3.
Retrograde Cholangiopancreatography AI Assisted System Validation on Effectiveness and Safety
ClinicalTrials.gov study NCT04719117. IPD Sharing: Not stated. Countries: 1. Publications: 1.
AI-assisted Endoscopy Report System In Improving Reporting Quality
ClinicalTrials.gov study NCT05479253. IPD Sharing: NO. Countries: 1. Publications: 1.
AI-Assisted Insulin Titration System on Inpatients Glucose Control
ClinicalTrials.gov study NCT04517201. IPD Sharing: NO. Countries: 1. Publications: 5.
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.