D-Index & Metrics Best Publications

D-Index & Metrics 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.

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 34 Citations 4,562 143 World Ranking 8209 National Ranking 3808

Overview

What is he best known for?

The fields of study he is best known for:

  • Statistics
  • Artificial intelligence
  • Machine learning

Uwe Kruger mostly deals with Data mining, Artificial intelligence, Algorithm, Principal component analysis and Fault detection and isolation. Uwe Kruger usually deals with Data mining and limits it to topics linked to Condition monitoring and Multivariate kernel density estimation, Variable kernel density estimation, Density estimation, Kernel and Probability density function. Many of his studies involve connections with topics such as Pattern recognition and Artificial intelligence.

His work on Covariance matrix as part of general Algorithm study is frequently linked to Decomposition, therefore connecting diverse disciplines of science. The study incorporates disciplines such as Multivariate statistical process control, Econometrics and Benchmark in addition to Principal component analysis. His biological study spans a wide range of topics, including Partial least squares regression and Statistical process control.

His most cited work include:

  • Process monitoring approach using fast moving window PCA (235 citations)
  • Recursive partial least squares algorithms for monitoring complex industrial processes (159 citations)
  • Moving window kernel PCA for adaptive monitoring of nonlinear processes (159 citations)

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

Artificial intelligence, Data mining, Pattern recognition, Algorithm and Principal component analysis are his primary areas of study. His Artificial intelligence study combines topics from a wide range of disciplines, such as Machine learning and Multivariate statistics. His Data mining research is multidisciplinary, relying on both Fault, Multivariate statistical process control, Fault detection and isolation and Regression.

His biological study deals with issues like Statistical process control, which deal with fields such as Normal distribution. His Algorithm research includes themes of Nonparametric statistics, Kernel, Feature vector, Graphical model and Partial least squares regression. His Principal component analysis study combines topics from a wide range of disciplines, such as Subspace topology and Multivariate analysis.

He most often published in these fields:

  • Artificial intelligence (33.52%)
  • Data mining (18.75%)
  • Pattern recognition (18.75%)

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

  • Artificial intelligence (33.52%)
  • Deep learning (5.68%)
  • Pattern recognition (18.75%)

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

Uwe Kruger focuses on Artificial intelligence, Deep learning, Pattern recognition, Machine learning and Algorithm. His study in Artificial neural network, Feature extraction, Convolutional neural network, Noise reduction and Image registration falls within the category of Artificial intelligence. His Deep learning study combines topics in areas such as Bayesian probability and Medical imaging.

His Pattern recognition research is multidisciplinary, incorporating perspectives in Categorization, Kernel and Histogram. His work on Unsupervised learning as part of general Machine learning study is frequently linked to Set, Learning curve and Factor, bridging the gap between disciplines. His Algorithm study integrates concerns from other disciplines, such as Subspace topology, Probability density function, Event, State space and State-space representation.

Between 2018 and 2021, his most popular works were:

  • Deep learning in medical image registration: a survey (111 citations)
  • Competitive performance of a modularized deep neural network compared to commercial algorithms for low-dose CT image reconstruction. (105 citations)
  • Learning deep similarity metric for 3D MR-TRUS image registration. (34 citations)

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

  • Statistics
  • Artificial intelligence
  • Machine learning

His primary areas of study are Artificial intelligence, Deep learning, Artificial neural network, Image registration and Variable. In his research on the topic of Artificial intelligence, Control is strongly related with Machine learning. Uwe Kruger has researched Artificial neural network in several fields, including Feature, Metric, Noise reduction, Iterative reconstruction and Pattern recognition.

He has included themes like Algorithm, Image and Fidelity in his Noise reduction study. Uwe Kruger combines subjects such as Image fusion, Data science and Medical imaging with his study of Image registration. Variable combines with fields such as Data mining, Cointegration, Basis, Multivariate normal distribution and Fault in his work.

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

Process monitoring approach using fast moving window PCA

Xun Wang;Uwe Kruger;George W. Irwin.
Industrial & Engineering Chemistry Research (2005)

343 Citations

Deep learning in medical image registration: a survey

Grant Haskins;Uwe Kruger;Pingkun Yan.
machine vision applications (2020)

294 Citations

3-D Convolutional Encoder-Decoder Network for Low-Dose CT via Transfer Learning From a 2-D Trained Network

Hongming Shan;Yi Zhang;Qingsong Yang;Uwe Kruger.
IEEE Transactions on Medical Imaging (2018)

278 Citations

Moving window kernel PCA for adaptive monitoring of nonlinear processes

Xueqin Liu;Xueqin Liu;Uwe Kruger;Tim Littler;Lei Xie.
Chemometrics and Intelligent Laboratory Systems (2009)

228 Citations

Recursive partial least squares algorithms for monitoring complex industrial processes

Xun Wang;Uwe Kruger;Barry Lennox.
Control Engineering Practice (2003)

227 Citations

Competitive performance of a modularized deep neural network compared to commercial algorithms for low-dose CT image reconstruction.

Hongming Shan;Atul Padole;Fatemeh Homayounieh;Uwe Kruger.
Nature Machine Intelligence (2019)

206 Citations

3D Convolutional Encoder-Decoder Network for Low-Dose CT via Transfer Learning from a 2D Trained Network

Hongming Shan;Yi Zhang;Qingsong Yang;Uwe Kruger.
arXiv: Computer Vision and Pattern Recognition (2018)

140 Citations

DETECTION OF INCIPIENT TOOTH DEFECT IN HELICAL GEARS USING MULTIVARIATE STATISTICS

N. Baydar;Q. Chen;A. Ball;U. Kruger.
Mechanical Systems and Signal Processing (2001)

133 Citations

Can Deep Learning Outperform Modern Commercial CT Image Reconstruction Methods

Hongming Shan;Atul Padole;Fatemeh Homayounieh;Uwe Krüger.
arXiv: Computer Vision and Pattern Recognition (2018)

130 Citations

Statistical‐based monitoring of multivariate non‐Gaussian systems

Xueqin Liu;Lei Xie;Uwe Kruger;Tim Littler.
Aiche Journal (2008)

119 Citations

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