H-Index & Metrics Best Publications

H-Index & Metrics

Discipline name H-index Citations Publications World Ranking National Ranking
Computer Science D-index 32 Citations 4,924 198 World Ranking 7021 National Ranking 665

Overview

What is he best known for?

The fields of study he is best known for:

  • Artificial intelligence
  • Statistics
  • Machine learning

Quan Pan focuses on Artificial intelligence, Pattern recognition, Machine learning, Object and Classifier. He focuses mostly in the field of Artificial intelligence, narrowing it down to topics relating to Computer vision and, in certain cases, Sequence. His Pattern recognition research is multidisciplinary, relying on both Weighting, Energy and Protein structure.

His research in Object intersects with topics in Pixel, Cluster analysis, Class, Fuzzy logic and Uncertain data. His work on Classifier fusion as part of general Classifier research is often related to Evidential reasoning approach, Protein quaternary structure and Conserved sequence, thus linking different fields of science. His Classification methods research incorporates themes from Basic belief and Missing data.

His most cited work include:

  • Real-time multiple objects tracking with occlusion handling in dynamic scenes (191 citations)
  • Adaptive imputation of missing values for incomplete pattern classification (125 citations)
  • Using the concept of Chou’s pseudo amino acid composition to predict protein subcellular localization: an approach by incorporating evolutionary information and von Neumann entropies (116 citations)

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

His primary areas of study are Artificial intelligence, Algorithm, Pattern recognition, Computer vision and Control theory. His study in Artificial intelligence is interdisciplinary in nature, drawing from both Machine learning and Data mining. His biological study spans a wide range of topics, including Tracking, Radar tracker, Nonlinear system, Kalman filter and Multipath propagation.

His research on Pattern recognition often connects related topics like Data set. As part of his studies on Computer vision, he frequently links adjacent subjects like Robustness. His Control theory study combines topics in areas such as Minimum mean square error, Estimator, Filter and State.

He most often published in these fields:

  • Artificial intelligence (41.13%)
  • Algorithm (25.07%)
  • Pattern recognition (20.28%)

What were the highlights of his more recent work (between 2018-2021)?

  • Artificial intelligence (41.13%)
  • Algorithm (25.07%)
  • Motion planning (3.38%)

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

His primary scientific interests are in Artificial intelligence, Algorithm, Motion planning, Computer vision and Pattern recognition. Artificial intelligence connects with themes related to Machine learning in his study. His work deals with themes such as Multipath propagation, Inference and Filter, which intersect with Algorithm.

His research in Motion planning intersects with topics in Mathematical optimization, Collision avoidance and Reinforcement learning. His Computer vision study integrates concerns from other disciplines, such as Inertial navigation system and Attitude indicator. His Pattern recognition research is multidisciplinary, incorporating perspectives in Feature and Data set.

Between 2018 and 2021, his most popular works were:

  • Learning binary codes with neural collaborative filtering for efficient recommendation systems (34 citations)
  • A survey on multi-sensor fusion based obstacle detection for intelligent ground vehicles in off-road environments (27 citations)
  • Disentangled Variational Auto-Encoder for semi-supervised learning (25 citations)

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

  • Artificial intelligence
  • Statistics
  • Machine learning

His primary areas of investigation include Artificial intelligence, Control theory, Computer vision, Classifier and Collision avoidance. His Artificial intelligence research includes elements of Machine learning and Pattern recognition. His Control theory research is multidisciplinary, relying on both Collision, Multi-agent system and Task.

His Computer vision research incorporates themes from Transformation, Robot and Self localization. His Classifier research incorporates elements of Labeled data and Data set. His work carried out in the field of Collision avoidance brings together such families of science as Local optimum, General motion control, Control and Obstacle avoidance.

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

Real-time multiple objects tracking with occlusion handling in dynamic scenes

Tao Yang;Quan Pan;Jing Li;S.Z. Li.
computer vision and pattern recognition (2005)

328 Citations

Using the concept of Chou’s pseudo amino acid composition to predict protein subcellular localization: an approach by incorporating evolutionary information and von Neumann entropies

Shao-Wu Zhang;Yun-Long Zhang;Hui-Fang Yang;Chun-Hui Zhao.
Amino Acids (2008)

175 Citations

Combination of Classifiers With Optimal Weight Based on Evidential Reasoning

Zhun-Ga Liu;Quan Pan;Jean Dezert;Arnaud Martin.
IEEE Transactions on Fuzzy Systems (2018)

164 Citations

Adaptive imputation of missing values for incomplete pattern classification

Zhun-ga Liu;Quan Pan;Jean Dezert;Arnaud Martin.
Pattern Recognition (2016)

157 Citations

Classifier Fusion With Contextual Reliability Evaluation

Zhunga Liu;Quan Pan;Jean Dezert;Jun-Wei Han.
IEEE Transactions on Systems, Man, and Cybernetics (2018)

151 Citations

A new belief-based K-nearest neighbor classification method

Zhun-Ga Liu;Quan Pan;Jean Dezert.
Pattern Recognition (2013)

148 Citations

Prediction of protein subcellular localization by support vector machines using multi-scale energy and pseudo amino acid composition.

J.-Y. Shi;S.-W. Zhang;Q. Pan;Y.-M. Cheng.
Amino Acids (2007)

144 Citations

Combination of sources of evidence with different discounting factors based on a new dissimilarity measure

Zhun-ga Liu;Jean Dezert;Quan Pan;Grégoire Mercier.
decision support systems (2011)

138 Citations

Studies on Hyperspectral Face Recognition in Visible Spectrum With Feature Band Selection

Wei Di;Lei Zhang;David Zhang;Quan Pan.
systems man and cybernetics (2010)

135 Citations

Brief paper: Multi-rate stochastic H∞ filtering for networked multi-sensor fusion

Yan Liang;Tongwen Chen;Quan Pan.
Automatica (2010)

125 Citations

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Best Scientists Citing Quan Pan

Yong Deng

Yong Deng

Southwest University

Publications: 79

Kuo-Chen Chou

Kuo-Chen Chou

The Gordon Life Science Institute

Publications: 45

Zidong Wang

Zidong Wang

Brunel University London

Publications: 23

Erik Cambria

Erik Cambria

Nanyang Technological University

Publications: 17

Li Yu

Li Yu

Zhejiang University of Technology

Publications: 14

Wen-An Zhang

Wen-An Zhang

Zhejiang University of Technology

Publications: 12

Jean Dezert

Jean Dezert

Office National d'Études et de Recherches Aérospatiales

Publications: 12

Gabriele Moser

Gabriele Moser

University of Genoa

Publications: 11

Stan Z. Li

Stan Z. Li

Chinese Academy of Sciences

Publications: 11

Bo Chen

Bo Chen

Zhejiang University of Technology

Publications: 10

Hong-Bin Shen

Hong-Bin Shen

Shanghai Jiao Tong University

Publications: 9

Hao Lin

Hao Lin

University of Electronic Science and Technology of China

Publications: 8

Amauri Garcia

Amauri Garcia

State University of Campinas

Publications: 8

Edwin K. P. Chong

Edwin K. P. Chong

Colorado State University

Publications: 8

Bin Liu

Bin Liu

Nanjing University

Publications: 8

Sankaran Mahadevan

Sankaran Mahadevan

Vanderbilt University

Publications: 8

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