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.
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