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1,291 results for “Artificial intelligence”
Rapid species discrimination of similar insects using hyperspectral imaging and lightweight edge artificial intelligence
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Data from: Artificial intelligence enabled multi-purpose smart detection in active-matrix electrowetting-on-dielectric digital microfluidics
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Data from: The performance of drones and artificial intelligence for monitoring sage-grouse at leks
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AIMMX: Artificial Intelligence Model Metadata Extractor
<p>Replication set that contains evaluation dataset and samples for MSR2020 submitted paper: "AIMMX: Artificial Intelligence Model Metadata Extractor."</p>
Wikidata Dump Artificial Intelligence
<p> RDF dump of wikidata produced with <a href="https://tools.wmflabs.org/wdumps/">wdumps</a>. </p> <p> All entities part of or sub class of artificial intelligence<br> <a href="https://tools.wmflabs.org/wdumps/dump/199">View on wdumper</a> </p> <p> <b>entity count</b>: 0, <b>statement count</b>: 0, <b>triple count</b>: 0 </p>
Artificial Intelligence (AI), in the Breast Cancer Screening Programme Questionnaire and Data
<p>The goal of this study is to gain insight into the level of trust of women in the Netherlands, in the decisions made by radiologists with the support of different applications of Artificial Intelligence (AI), in the Breast Cancer Screening Programme. Gaining insight into your level of trust in the decisions made by radiologists and AI regarding whether you have breast cancer or not, is of high importance, as this will help to anticipate which steps can be taken by hospitals and software developers, in the near future. The survey consists of 10 introductory questions and 42 statements and takes approximately 10 minutes to complete.</p> <p> </p>
Additional materials used in the paper "Towards Continuous Scientific Data Analysis and Hypothesis Evolution" on the Proceedings of the Thirty-First AAAI Conference on Artificial Intelligence (AAAI-17)
<p>This bundle contains a web page describing the materials used in the evaluation of the paper, along with references to the software and datasets, provenance metadata and workflows used. All the scripts and descriptions are included as well.</p>
Using Artificial Intelligence in Research: Academic Staff of Ukrainian HEIs
<p>The survey was conducted by the researchers of the Institute of Higher Education of NAES of Ukraine (https://ihed.org.ua/). Empirical data was collected from March 21 to April 3, 2023. The Register of Education Institutions of the Unified State Electronic Database on Education (https://registry.edbo.gov.ua/) was the source for the targeted sample.</p>
Use of artificial intelligence techniques for the recognition of human emotions: a bibliometric analysis
<p>Human emotion recognition with AI uses physiological, audiovisual, and linguistic signals. Despite its importance and great progress in emotion recognition, several challenges remain in generalization and evaluation through standards and shared data, as well as other research gaps. Therefore, the objective is to analyze the scientific production on the use of artificial intelligence techniques for the recognition of human emotions. This study uses bibliometric analysis following the guidelines of the PRISMA-2020 statement for literature reviews. Based on the results of the bibliometrics on the use of artificial intelligence techniques in for the recognition of human emotions, significant conclusions are obtained that improve the understanding of the current panorama in this field of research. A growing interest in the subject is observed during the years 2023, 2022, 2021 and 2020, which demonstrates the relevance and potential of artificial intelligence in the recognition of human emotions. A cubic polynomial growth in the number of scientific articles is observed, demonstrating a constant expansion of knowledge and support for future trends. Leading authors and journals are identified, highlighting global collaboration in China and India. The thematic evolution shows maturity and progressive specialization, with emerging concepts that promise future research and innovative applications.</p>
The Russian Leadership's Artificial Intelligence Narrative
<p>This list of online resources provides an overview of Russian official statements and developments in relation to artificial intelligence (AI) technologies from 2018 to 2023. It is based on a keyword search across five open-access sources, namely the websites of the President of Russia, the Government of Russia, and three main Russian news agencies: Interfax, RIA Novosti, and TASS. This compilation can be used to describe or analyse, among others, policy developments in the sphere of AI in Russia; the Russian leadership's statements, announcements, and discourses about AI technologies; as well as how these technologies are presented in official settings involving Russian policymakers.</p><p>This dataset forms the main empirical data for the article "Technology in the Quest for Status: The Russian Leadership's Artificial Intelligence Narrative" by Anna Nadibaidze (accepted in <i>Journal of International Relations and Development</i>, December 2023).</p>
Profiling of pancreatic adenocarcinoma using artificial intelligence-based integration of multi-omic and computational pathology features - Validation Data Sets
<p>Two public validation cohorts were utilized in the MT-Pilot study, the Cancer Genome Atlas (TCGA) and cohort-1 Johns Hopkins University (JHU). These datasets included DNA, RNA, clinical data, and tissue protein analytes analyzed for survival outcome prediction using AI/Machine Learning modeling. </p>
Anthropology and Artificial Intelligence - WAU Webinar
<p><strong>Anthropology and Artificial Intelligence</strong></p> <p>WAU - World Anthropological Union Webinar</p> <p>Organized by the IUAES & WCAA</p> <p> </p> <p><strong>Clara Saraiva</strong></p> <p>WCAA/ ICS-Universidade de Lisboa</p> <p>Convenor</p> <p> </p> <p><strong>Felipe Fernandes</strong></p> <p>IUAES/ Universidade Federal da Bahia</p> <p>Convenor</p> <p> </p> <p><strong>Michel Bouchard</strong></p> <p>WCAA/ University of Northern British Columbia<span> </span></p> <p>Web Mediator</p> <p> </p> <p>Participants:</p> <p><strong>Leah Junck</strong>, Journal Anthropology Southern Africa (editor in chief)</p> <p><strong>Maja Hojer Bruun</strong>, Aarhus University, Denmark</p> <p><strong>Amine Alaoui Soulimani</strong>, HUMA-Institute for Humanities in Africa</p> <p><strong>Divine Fuh</strong>, University of Cape Town, South Africa</p> <p><strong>Natalia Orrego</strong>, Latin American Network of Digital Anthropology</p> <p>A<strong>zza M.B. Ahmed</strong>, HUMA-Institute for Humanities in Africa</p>
Figure:1 Fish classification and taxonomy in Artificial Intelligence Applications in Fish Classification and Taxonomy: Advancing Our Understanding of Aquatic Biodiversity
Figure:1 Fish classification and taxonomy
Forest disturbance detection by using remote sensing and artificial intelligence in Africa
<p>The dataset arises from the "Forest Disturbance Detection Using Remote Sensing and Artificial Intelligence in Africa" (EO4Forest) project, a collaboration funded by the European Space Agency and conducted by Wrocław University of Environmental and Life Sciences (Poland) and Lagos State University (Nigeria). Designed for forest monitoring in the Ogun and Lagos States, the dataset includes detailed land cover classification maps for the years 2015, 2019, 2022, and 2023, all at a 20-meter spatial resolution to ensure accurate representation of land cover. A legend file accompanies the maps, clarifying six defined land cover classes: water, urbanized areas, soil, cropland, grasslands, and forest.</p> <p>The dataset includes over 112,000 training and 11,900 validation samples, which are essential for the accurate development and evaluation of the Random Forest classification models used. Special emphasis was placed on the forest class, ensuring a diverse representation of forest types, including tropical humid forests, mangroves, and dry woodlands.</p> <p>In addition to land cover maps, the dataset also contains forest gain and loss maps, with a particular focus on recent updates for selected subareas in 2022 and 2023, available in the NTR_ForestUpdate folder.</p> <p>Based on optical satellite imagery from Sentinel-2 and Landsat-8, the dataset leverages spectral indices and the Random Forest algorithm to classify land cover types. It provides valuable insights for environmental research related to deforestation, reforestation, and afforestation. Forest change maps are included to highlight areas of forest loss and gain, capturing the dynamic shifts in Nigeria's forest cover and offering detailed geographic context.</p> <p>The methodology, including data acquisition, preprocessing, feature extraction (spectral index calculation), classification, and accuracy assessment, is fully documented in Python scripts available in the associated GitHub repository. This ensures transparency and reproducibility, offering users both the processed outputs and the tools necessary for custom analyses and advanced forest monitoring.</p>
Data from: Large-scale photonic chiplet Taichi empowers 160-TOPS/W artificial general intelligence
<p>The everlasting pursuit for a more powerful artificial general intelligence (AGI) continuously demands for higher computing performance. Breaking the restrictions from the Moore's Law, integrated photonic neural networks have shown great potential in terms of superior processing speed and high energy-efficiency. However, the capacity and scalability are restricted by the unavoidable and time-varying on-chip errors that only simple tasks and shallow models (e.g., 10-category digits classification with thousand-parameter-level networks) can be on-chip realized optically. To support modern AGIs with optical computing, we innovate Taichi, large-scale photonic chiplets owning millions-of-neuron on-chip computing capability with 160 TOPS/W energy efficiency. For the first time, Taichi experimentally realized on-chip optical neural networks (ONNs) with over 13.96M neurons for thousand-category-level classification (tested 91.89% of accuracy for 1623-category Omniglot dataset) and high-fidelity artificial intelligence-generated content (AIGC) with state-of-the-art performances, while achieving up to 2 orders of magnitude improvement in computing efficiency than state-of-the-art AI chips. Based on an integrated diffractive-interference-hybrid design and a general distributed computing architecture, Taichi paves its own way for large-scale on-chip ONN models and advanced tasks, further exploiting the flexibility and potential of optical devices, which will lead to a giant leap in scalability, accuracy and efficiency to support modern AGI.</p>
Successful and timely uptake of artificial intelligence in science in the EU: references
<p>This reference list accompanies the SAPEA evidence review report on the same topic.</p>
Optimizing Deep Learning Models for Aflatoxin Detection: A Case of Artificial Intelligence-Driven Classified Groundnut Image Datasets for Postharvest Management
<p><strong>DATASET DESCRIPTION </strong><br>This dataset comprises a curated collection of classified groundnut images, specifically designed for deep learning applications in aflatoxin detection. The dataset is organized into four distinct categories: Healthy, Moldy, Insect-Infested, and Physiological Disorder, making it a vital resource for training AI and machine learning models aimed at advancing agricultural research. These classifications are crucial for the development of AI-driven solutions addressing aflatoxin contamination, enhancing crop quality assessments, and improving postharvest management practices.<br>The dataset has been developed to support research in agricultural Artificial Intelligence (AI), machine learning (ML), and food safety, with a focus on aiding resource-constrained regions in combating postharvest losses due to contamination. By leveraging this dataset, researchers can contribute to safeguarding public health, promoting food security, and supporting smallholder farmers.</p> <p><strong>POTENTIAL APPLICATIONS</strong><br>This dataset provides numerous opportunities for innovation in agriculture through AI and deep learning technologies. Its key applications include:<br><strong>Early Aflatoxin Detection</strong>: Facilitates the development of AI-powered models for prompt identification of aflatoxins in groundnuts, helping mitigate associated health risks.<br><strong>Postharvest Management Improvement</strong>: Enables the creation of innovative solutions to enhance storage, handling, and processing, reducing contamination and losses.<br><strong>Food Safety and Quality Assurance</strong>: Strengthens agricultural value chains by supporting the production of safe and high-quality food products.</p> <p><strong>BROADER IMPACT</strong><br>This resource is invaluable for fostering AI innovation in agriculture, particularly in resource-limited environments. It addresses critical challenges such as postharvest losses and food contamination while contributing to global efforts in sustainable agricultural development. By utilizing this dataset, researchers can improve food security, support smallholder farmers, and drive advancements in agricultural practices that benefit both local and global communities.</p>
Artificial Intelligence and Educational Quality in Higher Education: A Systematic Review
<p><span><span lang="EN-US">This study examines the impact of artificial intelligence (AI) on educational quality in higher education. The objective was to evaluate how AI optimizes teaching-learning processes and to identify challenges and opportunities for its implementation. A systematic review was conducted using the PRISMA protocol, selecting 22 documents from databases such as Scopus, Web of Science, EBSCO, and ProQuest (2002–2024). Results indicate that AI enhances education through personalized learning, administrative automation, and automated assessment. However, challenges like technological infrastructure needs, data privacy concerns, and resistance to change remain. AI’s transformative potential depends on strategic implementation that considers ethical and social factors.</span></span></p>
Ethical Requirements in the Age of Artificial Intelligence - Supplementary material
<p><strong>Context:</strong> Software developers and users are growing concerned about the ethical use of software, especially with Artificial Intelligence (AI). In this context, we investigated how ethical requirements can be elicited and incorporated into software development. <strong>Problem</strong>: The challenge is identifying and defining effective methods for eliciting and managing ethical requirements in software development. <strong>Solution</strong>: We conducted a Systematic Literature Review (SLR) to identify techniques, methods, processes, frameworks, and tools for eliciting, analyzing, and specifying ethical requirements. <strong>IS Theory</strong>: We explore the application of theories related to requirements engineering, ethics in technology, and data governance. It focuses, in particular, on ensuring that information systems comply with ethical and legal principles from the beginning of the development cycle. <strong>Method</strong>: Following the Kitchenham and Charters protocol, we conducted an SLR with stages of planning, conducting, and reporting the results. <strong>Summarization of Results</strong>: We have identified 47 primary studies. These studies address different approaches to eliciting ethical requirements, including techniques based on user stories, analysis of ethical guidelines, specific frameworks such as ECCOLA, and methods such as interviews and modeling. <strong>Contributions and Impact on the IS area</strong>: The report contributes to the field by consolidating existing practices in the literature regarding ethical requirements. It provides a comprehensive overview of the techniques and tools available for integrating ethical considerations into software systems and identifies gaps and opportunities for future research. The study significantly impacts the IS field by providing practical and theoretical guidelines for eliciting ethical requirements in information systems.</p> <p>Supplementary material of SLR.</p>
COVID-19 evidence syntheses with artificial intelligence: an empirical study of systematic reviews
<p><strong>Objectives</strong>: A rapidly developing scenario like a pandemic requires the prompt production of high-quality systematic reviews, which can be automated using artificial intelligence (AI) techniques. We evaluated the application of AI tools in COVID-19 evidence syntheses.</p> <p><strong>Study design</strong>: After prospective registration of the review protocol, we automated the download of all open-access COVID-19 systematic reviews in the COVID-19 Living Overview of Evidence database, indexed them for AI-related keywords, and located those that used AI tools. We compared their journals' JCR Impact Factor, citations per month, screening workloads, completion times (from pre-registration to preprint or submission to a journal) and AMSTAR-2 methodology assessments (maximum score 13 points) with a set of publication date matched control reviews without AI.</p> <p><strong>Results</strong>: Of the 3999 COVID-19 reviews, 28 (0.7%, 95% CI 0.47-1.03%) made use of AI. On average, compared to controls (n=64), AI reviews were published in journals with higher Impact Factors (median 8.9 vs 3.5, P<0.001), and screened more abstracts per author (302.2 vs 140.3, P=0.009) and per included study (189.0 vs 365.8, P<0.001) while inspecting less full texts per author (5.3 vs 14.0, P=0.005). No differences were found in citation counts (0.5 vs 0.6, P=0.600), inspected full texts per included study (3.8 vs 3.4, P=0.481), completion times (74.0 vs 123.0, P=0.205) or AMSTAR-2 (7.5 vs 6.3, P=0.119).</p> <p><strong>Conclusion</strong>: AI was an underutilized tool in COVID-19 systematic reviews. Its usage, compared to reviews without AI, was associated with more efficient screening of literature and higher publication impact. There is scope for the application of AI in automating systematic reviews.</p>
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.