Dakota S. Dale Source Confirmed

Affiliation confirmed via AI analysis of OpenAlex, ORCID, and web sources.

Researcher

University of Arkansas at Fayetteville

unknown

3 h-index 8 pubs 24 cited

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Biography and Research Information

OverviewAI-generated summary

Dakota S. Dale researches the application of artificial intelligence and deep learning techniques to address complex data analysis challenges. Recent work includes developing deep learning solutions for mapping rice production systems using high-resolution imagery and exploring AI-based open-source intelligence (OSINT) for cyber event analysis via Twitter data. Dale has also investigated the use of machine learning for predicting CVSS base scores and for diagnosing COVID-19 using chest X-rays and transfer learning.

Dale collaborates with researchers at the University of Arkansas at Fayetteville, including Benjamin R. K. Runkle and Kylie McClanahan, as well as Jennifer Fowler and Emily S. Bellis from Arkansas State University. With a total of eight publications and 24 citations, Dale's work demonstrates activity in the fields of remote sensing, cybersecurity, and medical imaging.

Metrics

  • h-index: 3
  • Publications: 8
  • Citations: 24

Selected Publications

  • Twitter-Based OSINT for Cyber Event Analytics (2025) DOI
  • Dataset for Deep learning solutions for mapping contour levee rice production systems from very high resolution imagery (2023) DOI
  • Dataset for Deep learning solutions for mapping contour levee rice production systems from very high resolution imagery (2023) DOI
  • CVSS Base Score Prediction Using an Optimized Machine Learning Scheme (2023) DOI
  • Deep learning solutions for mapping contour levee rice production systems from very high resolution imagery (2023) DOI
  • AI-based Cyber Event OSINT via Twitter Data (2023) DOI
  • COVID19 Diagnosis Using Chest X-rays and Transfer Learning (2022) DOI

Collaborators

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