Ke Yang Source Confirmed

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

Assistant Professor

University of Arkansas at Fayetteville

faculty

11 h-index 26 pubs 1,801 cited

Research Areas

Links

ORCID Lab Website

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OverviewAI-generated summary

Ke Yang's research primarily investigates the application of advanced machine learning techniques, particularly deep convolutional neural networks, for complex problem-solving. His work includes developing intelligent fault diagnosis methods, such as the ECNN model and an improved convolutional neural network based on ACCC. Yang has also explored transfer learning for spare parts classification in industrial settings and utilized principal component analysis integrated with Long Short-Term Memory networks for productivity prediction in sectors like dredging.

His recent publications also extend to fundamental scientific research, including the development of a detailed chemical kinetic model for 1-methylnaphthalene. Yang has a research network including collaborators Meijing Tan and Richard J. Ham from the University of Arkansas at Fayetteville. His academic contributions are reflected in an h-index of 11 and over 1,800 citations across 26 publications.

Metrics

h-index: 11
Publications: 26
Citations: 1,801

Selected Publications

An Attention-Enhanced YOLOv8 Architecture for Leakage Detection in Building Systems (2025) DOI
Analysis of Censored Aggregate Failure-time Data Using Phase-type Distributions (2025) DOI
Development of a Detailed Chemical Kinetic Model for 1-Methylnaphthalene (2024) DOI
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) DOI