Ke Yang Data-verified

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

Assistant Professor

Last publication 2025 Last refreshed 2026-05-16

faculty

11 h-index 26 pubs 1,815 cited

Biography and Research Information

OverviewAI-generated summary

Ke Yang's research focuses on the application of advanced machine learning techniques, particularly deep convolutional neural networks, for intelligent fault diagnosis and predictive modeling. Yang has investigated novel principal component analysis methods integrated with long short-term memory networks for productivity prediction in areas such as cutter suction dredgers. Recent work includes the development of efficient convolutional neural networks for intelligent fault diagnosis and studies on chemical kinetic models for specific compounds like 1-methylnaphthalene. Yang's scholarly contributions are reflected in an h-index of 11 and over 1,800 citations across 26 publications. Collaborations include shared publications with Richard G. Ham and Richard J. Ham from the Arkansas Agricultural Experiment Station and Meijing Tan from the University of Arkansas at Fayetteville.

Metrics

  • h-index: 11
  • Publications: 26
  • Citations: 1,815

Selected Publications

  • An Attention-Enhanced YOLOv8 Architecture for Leakage Detection in Building Systems (2025)
  • Analysis of Censored Aggregate Failure-time Data Using Phase-type Distributions (2025)
  • Development of a Detailed Chemical Kinetic Model for 1-Methylnaphthalene (2024)
    2 citations DOI OpenAlex
  • Molecular Characterization and Expression Analysis of a Gene Encoding 3-Hydroxy-3-Methylglutaryl-CoA Reductase (HMGR) from Bipolaris eleusines, an Ophiobolin A-Producing Fungus (2024)
    1 citation DOI OpenAlex

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Collaboration Network

45 Collaborators 14 Institutions 3 Countries

Top Collaborators

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