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Rong Qu
PeerJ Author
1,075 Points

Contributions by role

Editor 1,075

Contributions by subject area

Artificial Intelligence
Computer Vision
Data Mining and Machine Learning
Neural Networks
Algorithms and Analysis of Algorithms
Optimization Theory and Computation
Computer Networks and Communications
Emerging Technologies
Data Science
Cryptography
Graphics
Programming Languages
Real-Time and Embedded Systems
Natural Language and Speech
Theory and Formal Methods

Rong Qu

PeerJ Author

Summary

A Professor at the University of Nottingham. An IEEE Senior Member since 2012. Main research interests: modelling and automated design of optimisation algorithms in transport scheduling in logistics, personnel scheduling, telecommunication network routing, portfolio optimisation, and timetabling problems, etc. by integrating evolutionary algorithms and mathematical programming with machine learning in operational research and artificial intelligence.

Artificial Intelligence Optimization Theory & Computation

Past or current institution affiliations

Nottingham University

Work details

Professor

University of Nottingham
October 1998
Computer Science

Websites

  • Google Scholar
  • LinkedIn

PeerJ Contributions

  • Edited 6

Academic Editor on

November 29, 2023
Understanding the black-box: towards interpretable and reliable deep learning models
Tehreem Qamar, Narmeen Zakaria Bawany
https://doi.org/10.7717/peerj-cs.1629
September 1, 2023
Benchmarking a fast, satisficing vehicle routing algorithm for public health emergency planning and response: “Good Enough for Jazz”
Emma L. McDaniel, Sampson Akwafuo, Joshua Urbanovsky, Armin R. Mikler
https://doi.org/10.7717/peerj-cs.1541
August 17, 2023
Multi-supervised bidirectional fusion network for road-surface condition recognition
Hongbin Zhang, Zhijie Li, Wengang Wang, Lang Hu, Jiayue Xu, Meng Yuan, Zelin Wang, Yafeng Ren, Yiyuan Ye
https://doi.org/10.7717/peerj-cs.1446
July 26, 2023
An automatic system for extracting figure-caption pair from medical documents: a six-fold approach
Jyotismita Chaki
https://doi.org/10.7717/peerj-cs.1452
April 9, 2021
Optimal 1-NN prototypes for pathological geometries
Ilia Sucholutsky, Matthias Schonlau
https://doi.org/10.7717/peerj-cs.464
August 12, 2019
Pay attention and you won’t lose it: a deep learning approach to sequence imputation
Ilia Sucholutsky, Apurva Narayan, Matthias Schonlau, Sebastian Fischmeister
https://doi.org/10.7717/peerj-cs.210