D-Index & Metrics Best Publications

D-Index & Metrics

Discipline name D-index D-index (Discipline H-index) only includes papers and citation values for an examined discipline in contrast to General H-index which accounts for publications across all disciplines. Citations Publications World Ranking National Ranking
Computer Science D-index 48 Citations 7,984 179 World Ranking 3140 National Ranking 292

Overview

What is he best known for?

The fields of study he is best known for:

  • Statistics
  • Artificial intelligence
  • Machine learning

His main research concerns Data mining, Soft sensor, Artificial intelligence, Probabilistic logic and Algorithm. He integrates many fields, such as Data mining and Data modeling, in his works. His research in Soft sensor intersects with topics in Kriging and Benchmark.

His work carried out in the field of Artificial intelligence brings together such families of science as Machine learning and Pattern recognition. His research integrates issues of Principal component regression, Latent variable and Curse of dimensionality in his study of Probabilistic logic. His Algorithm research is multidisciplinary, relying on both Kernel, Fault detection and identification and Nonlinear system.

His most cited work include:

  • Review of Recent Research on Data-Based Process Monitoring (534 citations)
  • Data Mining and Analytics in the Process Industry: The Role of Machine Learning (325 citations)
  • Review on data-driven modeling and monitoring for plant-wide industrial processes (210 citations)

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

His primary areas of study are Data mining, Artificial intelligence, Soft sensor, Probabilistic logic and Pattern recognition. His Data mining research includes elements of Fault detection and isolation, Nonlinear system, Principal component analysis and Benchmark. He has included themes like Independent component analysis and Support vector machine in his Principal component analysis study.

Zhiqiang Ge has researched Artificial intelligence in several fields, including Machine learning and Partial least squares regression. His Soft sensor research also works with subjects such as

  • Algorithm which is related to area like Mean squared error and Mode,
  • Feature extraction that connect with fields like Dimensionality reduction. His work in Probabilistic logic covers topics such as Bayesian probability which are related to areas like Missing data.

He most often published in these fields:

  • Data mining (50.23%)
  • Artificial intelligence (36.62%)
  • Soft sensor (26.29%)

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

  • Data mining (50.23%)
  • Soft sensor (26.29%)
  • Artificial intelligence (36.62%)

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

His scientific interests lie mostly in Data mining, Soft sensor, Artificial intelligence, Data modeling and Feature extraction. His Data mining research incorporates elements of Fault detection and isolation, Probabilistic logic, Latent variable and Nonlinear system. His research in Latent variable tackles topics such as Principal component analysis which are related to areas like Latent variable model.

His Soft sensor study combines topics from a wide range of disciplines, such as Algorithm, Regression analysis, Data-driven and Overfitting. His Artificial intelligence research is multidisciplinary, incorporating perspectives in Sample, Machine learning and Pattern recognition. His research on Feature extraction also deals with topics like

  • Autoencoder together with Benchmark,
  • Feature, which have a strong connection to Bayesian network, Knowledge extraction, Analytics and Data science.

Between 2019 and 2021, his most popular works were:

  • Nonlinear probabilistic latent variable regression models for soft sensor application: From shallow to deep structure (25 citations)
  • Deep Learning for Industrial KPI Prediction: When Ensemble Learning Meets Semi-Supervised Data (11 citations)
  • Semisupervised Robust Modeling of Multimode Industrial Processes for Quality Variable Prediction Based on Student's t Mixture Model (11 citations)

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

  • Statistics
  • Artificial intelligence
  • Machine learning

Zhiqiang Ge mostly deals with Soft sensor, Data mining, Feature extraction, Artificial intelligence and Probabilistic logic. His Soft sensor research incorporates themes from Data-driven and Algorithm. His Algorithm research incorporates themes from Prior probability, Bayesian probability, Bayesian inference and Divergence.

Zhiqiang Ge integrates Data mining and Process modeling in his studies. His research on Artificial intelligence frequently links to adjacent areas such as Pattern recognition. His research integrates issues of Principal component analysis, Latent variable model, Latent variable and Nonlinear system in his study of Probabilistic logic.

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

Review of Recent Research on Data-Based Process Monitoring

Zhiqiang Ge;Zhihuan Song;Furong Gao.
Industrial & Engineering Chemistry Research (2013)

763 Citations

Data Mining and Analytics in the Process Industry: The Role of Machine Learning

Zhiqiang Ge;Zhihuan Song;Steven X. Ding;Biao Huang.
IEEE Access (2017)

493 Citations

Review on data-driven modeling and monitoring for plant-wide industrial processes

Zhiqiang Ge.
Chemometrics and Intelligent Laboratory Systems (2017)

313 Citations

Process Monitoring Based on Independent Component Analysis - Principal Component Analysis ( ICA - PCA ) and Similarity Factors

Zhiqiang Ge;Zhihuan Song.
Industrial & Engineering Chemistry Research (2007)

286 Citations

Improved kernel PCA-based monitoring approach for nonlinear processes

Zhiqiang Ge;Chunjie Yang;Zhihuan Song.
Chemical Engineering Science (2009)

198 Citations

Online monitoring of nonlinear multiple mode processes based on adaptive local model approach

Zhiqiang Ge;Zhihuan Song.
Control Engineering Practice (2008)

182 Citations

Distributed PCA Model for Plant-Wide Process Monitoring

Zhiqiang Ge;Zhihuan Song.
Industrial & Engineering Chemistry Research (2013)

175 Citations

A comparative study of just-in-time-learning based methods for online soft sensor modeling

Zhiqiang Ge;Zhihuan Song.
Chemometrics and Intelligent Laboratory Systems (2010)

163 Citations

Distributed Parallel PCA for Modeling and Monitoring of Large-Scale Plant-Wide Processes With Big Data

Jinlin Zhu;Zhiqiang Ge;Zhihuan Song.
IEEE Transactions on Industrial Informatics (2017)

159 Citations

Mixture Bayesian regularization method of PPCA for multimode process monitoring

Zhiqiang Ge;Zhihuan Song.
Aiche Journal (2010)

157 Citations

Best Scientists Citing Zhiqiang Ge

Biao Huang

Biao Huang

University of Alberta

Publications: 61

Zhihuan Song

Zhihuan Song

Zhejiang University

Publications: 49

Weihua Gui

Weihua Gui

Central South University

Publications: 40

Chunhua Yang

Chunhua Yang

Central South University

Publications: 36

Donghua Zhou

Donghua Zhou

Shandong University of Science and Technology

Publications: 36

Qunxiong Zhu

Qunxiong Zhu

Beijing University of Chemical Technology

Publications: 25

Steven X. Ding

Steven X. Ding

University of Duisburg-Essen

Publications: 19

Manabu Kano

Manabu Kano

Kyoto University

Publications: 18

Sheng Chen

Sheng Chen

University of Southampton

Publications: 16

S. Joe Qin

S. Joe Qin

City University of Hong Kong

Publications: 16

Uwe Kruger

Uwe Kruger

Rensselaer Polytechnic Institute

Publications: 15

Feng Qian

Feng Qian

Fudan University

Publications: 14

Furong Gao

Furong Gao

Hong Kong University of Science and Technology

Publications: 14

Zhiqiang Geng

Zhiqiang Geng

Beijing University of Chemical Technology

Publications: 13

ChangKyoo Yoo

ChangKyoo Yoo

Kyung Hee University

Publications: 12

Profile was last updated on December 6th, 2021.
Research.com Ranking is based on data retrieved from the Microsoft Academic Graph (MAG).
The ranking d-index is inferred from publications deemed to belong to the considered discipline.

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