H-Index & Metrics Best Publications

H-Index & Metrics

Discipline name H-index Citations Publications World Ranking National Ranking
Computer Science D-index 30 Citations 3,897 148 World Ranking 8780 National Ranking 818

Overview

What is he best known for?

The fields of study he is best known for:

  • Artificial intelligence
  • Artificial neural network
  • Machine learning

Junfei Qiao spends much of his time researching Artificial neural network, Control theory, Artificial intelligence, Nonlinear system and Radial basis function. His Artificial neural network research incorporates elements of Algorithm, Mathematical optimization, Fuzzy logic and Pruning. Within one scientific family, Junfei Qiao focuses on topics pertaining to Key under Algorithm, and may sometimes address concerns connected to System identification and Probabilistic neural network.

The study incorporates disciplines such as Model predictive control and Benchmark in addition to Control theory. His biological study spans a wide range of topics, including Data mining and Computer vision. His Nonlinear system research is mostly focused on the topic Nonlinear modelling.

His most cited work include:

  • Learning a No-Reference Quality Assessment Model of Enhanced Images With Big Data (174 citations)
  • An efficient self-organizing RBF neural network for water quality prediction (144 citations)
  • A Self-Organizing Fuzzy Neural Network Based on a Growing-and-Pruning Algorithm (120 citations)

What are the main themes of his work throughout his whole career to date?

His scientific interests lie mostly in Artificial neural network, Artificial intelligence, Nonlinear system, Algorithm and Benchmark. His study in Artificial neural network is interdisciplinary in nature, drawing from both Mathematical optimization, Control theory and Sewage treatment. The Artificial intelligence study combines topics in areas such as Machine learning and Pattern recognition.

His work deals with themes such as Control engineering, Gradient method and Robustness, which intersect with Nonlinear system. His work carried out in the field of Algorithm brings together such families of science as Echo state network, Feature selection and Time series. His research in Benchmark intersects with topics in Structure and Fuzzy logic.

He most often published in these fields:

  • Artificial neural network (48.39%)
  • Artificial intelligence (31.85%)
  • Nonlinear system (22.98%)

What were the highlights of his more recent work (between 2019-2021)?

  • Artificial neural network (48.39%)
  • Artificial intelligence (31.85%)
  • Benchmark (18.15%)

In recent papers he was focusing on the following fields of study:

The scientist’s investigation covers issues in Artificial neural network, Artificial intelligence, Benchmark, Algorithm and Nonlinear system. His studies deal with areas such as Data-driven, Mathematical optimization, Feature vector and Sewage treatment as well as Artificial neural network. He has included themes like Machine learning and Pattern recognition in his Artificial intelligence study.

His studies in Benchmark integrate themes in fields like Data mining, Stability, Particle swarm optimization, Pareto principle and Fuzzy logic. His Algorithm study combines topics from a wide range of disciplines, such as Hidden layer, Time series and Sensitivity. In his research, Chaotic and Radial basis function is intimately related to Modular neural network, which falls under the overarching field of Nonlinear system.

Between 2019 and 2021, his most popular works were:

  • Deep Dual-Channel Neural Network for Image-Based Smoke Detection (28 citations)
  • Stacked Selective Ensemble for PM 2.5 Forecast (20 citations)
  • A sparse deep belief network with efficient fuzzy learning framework. (12 citations)

In his most recent research, the most cited papers focused on:

  • Artificial intelligence
  • Machine learning
  • Artificial neural network

Artificial intelligence, Artificial neural network, Algorithm, Nonlinear system and Pattern recognition are his primary areas of study. Junfei Qiao combines subjects such as Machine learning and Statistical regularity with his study of Artificial intelligence. In his works, Junfei Qiao undertakes multidisciplinary study on Artificial neural network and Approximation error.

His Algorithm research is multidisciplinary, incorporating perspectives in Multi-source and Mill. His Nonlinear system study combines topics in areas such as Effluent, Sparse approximation, Mathematical optimization, Benchmark and Robustness. The Benchmark study which covers Optimal control that intersects with Sewage treatment.

This overview was generated by a machine learning system which analysed the scientist’s body of work. If you have any feedback, you can contact us here.

Best Publications

An efficient self-organizing RBF neural network for water quality prediction

Hong-Gui Han;Qi-li Chen;Jun-Fei Qiao.
Neural Networks (2011)

225 Citations

Learning a No-Reference Quality Assessment Model of Enhanced Images With Big Data

Ke Gu;Dacheng Tao;Jun-Fei Qiao;Weisi Lin.
IEEE Transactions on Neural Networks (2018)

220 Citations

A Self-Organizing Fuzzy Neural Network Based on a Growing-and-Pruning Algorithm

Honggui Han;Junfei Qiao.
IEEE Transactions on Fuzzy Systems (2010)

187 Citations

Model predictive control of dissolved oxygen concentration based on a self-organizing RBF neural network

Hong-Gui Han;Jun-Fei Qiao;Qi-Li Chen.
Control Engineering Practice (2012)

182 Citations

Nonlinear Model-Predictive Control for Industrial Processes: An Application to Wastewater Treatment Process

Honggui Han;Junfei Qiao.
IEEE Transactions on Industrial Electronics (2014)

127 Citations

Adaptive Computation Algorithm for RBF Neural Network

Hong-Gui Han;Jun-Fei Qiao.
IEEE Transactions on Neural Networks (2012)

103 Citations

Nonlinear Model Predictive Control Based on a Self-Organizing Recurrent Neural Network

Hong-Gui Han;Lu Zhang;Ying Hou;Jun-Fei Qiao.
IEEE Transactions on Neural Networks (2016)

103 Citations

Model-Based Referenceless Quality Metric of 3D Synthesized Images Using Local Image Description

Ke Gu;Vinit Jakhetiya;Jun-Fei Qiao;Xiaoli Li.
IEEE Transactions on Image Processing (2018)

98 Citations

Nonlinear Systems Modeling Based on Self-Organizing Fuzzy-Neural-Network With Adaptive Computation Algorithm

Honggui Han;Xiao-Long Wu;Jun-Fei Qiao.
IEEE Transactions on Systems, Man, and Cybernetics (2014)

97 Citations

Evaluating Quality of Screen Content Images Via Structural Variation Analysis

Ke Gu;Junfei Qiao;Xiongkuo Min;Guanghui Yue.
IEEE Transactions on Visualization and Computer Graphics (2018)

91 Citations

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Best Scientists Citing Junfei Qiao

Ke Gu

Ke Gu

Beijing University of Technology

Publications: 36

Guangtao Zhai

Guangtao Zhai

Shanghai Jiao Tong University

Publications: 18

Weihua Gui

Weihua Gui

Central South University

Publications: 17

Witold Pedrycz

Witold Pedrycz

University of Alberta

Publications: 13

Weisi Lin

Weisi Lin

Nanyang Technological University

Publications: 13

Edwin Lughofer

Edwin Lughofer

Johannes Kepler University of Linz

Publications: 10

C. L. Philip Chen

C. L. Philip Chen

University of Macau

Publications: 10

Chunhua Yang

Chunhua Yang

Central South University

Publications: 9

Shiqi Wang

Shiqi Wang

City University of Hong Kong

Publications: 9

Meng Joo Er

Meng Joo Er

Dalian Maritime University

Publications: 9

Zhiqiang Geng

Zhiqiang Geng

Beijing University of Chemical Technology

Publications: 8

Qunxiong Zhu

Qunxiong Zhu

Beijing University of Chemical Technology

Publications: 8

Guoyin Wang

Guoyin Wang

Chongqing University of Posts and Telecommunications

Publications: 7

Yaochu Jin

Yaochu Jin

University of Surrey

Publications: 7

Ding Wang

Ding Wang

Beijing University of Technology

Publications: 7

Yuming Fang

Yuming Fang

Jiangxi University of Finance and Economics

Publications: 6

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