H-Index & Metrics Top Publications

H-Index & Metrics

Discipline name H-index Citations Publications World Ranking National Ranking
Computer Science H-index 30 Citations 3,367 179 World Ranking 8493 National Ranking 122

Overview

What is he best known for?

The fields of study he is best known for:

  • Artificial intelligence
  • Machine learning
  • Statistics

Carlos G. Puntonet mainly focuses on Artificial intelligence, Pattern recognition, Support vector machine, Algorithm and Computer-aided diagnosis. His Artificial intelligence research incorporates themes from Machine learning, Likelihood-ratio test, Blind signal separation and Estimator. His Pattern recognition research integrates issues from Bispectrum and Voice activity detection.

He combines subjects such as Feature extraction, Feature selection and Voxel with his study of Support vector machine. His studies deal with areas such as Fixed point, Artificial neural network, Linear independence, Infomax and Mathematical optimization as well as Algorithm. His research in Computer-aided diagnosis tackles topics such as Disease which are related to areas like Neuroimaging.

His most cited work include:

  • 18F-FDG PET imaging analysis for computer aided Alzheimer's diagnosis (111 citations)
  • A Geometric Algorithm for Overcomplete Linear ICA (93 citations)
  • A Novel LMS Algorithm Applied to Adaptive Noise Cancellation (85 citations)

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

Artificial intelligence, Independent component analysis, Pattern recognition, Algorithm and Blind signal separation are his primary areas of study. The study incorporates disciplines such as Machine learning and Computer vision in addition to Artificial intelligence. The Independent component analysis study combines topics in areas such as Simulated annealing, Higher-order statistics, Preprocessor and Acoustic emission.

His Pattern recognition research includes elements of Speech recognition and Voxel. His study explores the link between Algorithm and topics such as Series that cross with problems in Hilbert–Huang transform. Maxima and minima is closely connected to Genetic algorithm in his research, which is encompassed under the umbrella topic of Blind signal separation.

He most often published in these fields:

  • Artificial intelligence (45.05%)
  • Independent component analysis (34.47%)
  • Pattern recognition (33.79%)

What were the highlights of his more recent work (between 2010-2021)?

  • Artificial intelligence (45.05%)
  • Pattern recognition (33.79%)
  • Support vector machine (11.60%)

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

His scientific interests lie mostly in Artificial intelligence, Pattern recognition, Support vector machine, Computer-aided diagnosis and Algorithm. His research in Artificial intelligence is mostly concerned with Voxel. Carlos G. Puntonet has researched Pattern recognition in several fields, including Kullback–Leibler divergence and Data set.

His work deals with themes such as Neuroimaging, Clinical psychology, Positron emission tomography, Random forest and Feature selection, which intersect with Support vector machine. Carlos G. Puntonet interconnects Supervised learning and Feature vector in the investigation of issues within Computer-aided diagnosis. His Algorithm study integrates concerns from other disciplines, such as Basis, Hilbert–Huang transform, Series and Signal processing.

Between 2010 and 2021, his most popular works were:

  • 18F-FDG PET imaging analysis for computer aided Alzheimer's diagnosis (111 citations)
  • Brain connectivity analysis: a short survey (69 citations)
  • Computer Aided Diagnosis tool for Alzheimer's Disease based on Mann-Whitney-Wilcoxon U-Test (61 citations)

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

  • Artificial intelligence
  • Machine learning
  • Statistics

The scientist’s investigation covers issues in Artificial intelligence, Support vector machine, Pattern recognition, Feature selection and Computer-aided diagnosis. His Artificial intelligence study frequently links to other fields, such as Machine learning. His Support vector machine research includes themes of Multivariate statistics, Neuroimaging, Clinical psychology and Pairwise comparison.

His Pattern recognition study combines topics from a wide range of disciplines, such as Rendering and Test set. His Feature selection research focuses on subjects like Voxel, which are linked to Positron emission tomography, Principal component analysis, Association rule learning and Training set. His Computer-aided diagnosis research focuses on Disease and how it connects with Feature vector and Supervised learning.

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.

Top Publications

18F-FDG PET imaging analysis for computer aided Alzheimer's diagnosis

I. A. Illán;J. M. Górriz;J. Ramírez;D. Salas-Gonzalez.
Information Sciences (2011)

177 Citations

A Geometric Algorithm for Overcomplete Linear ICA

Fabian J. Theis;Elmar Wolfgang Lang;Carlos García Puntonet.
Neurocomputing (2004)

140 Citations

A Novel LMS Algorithm Applied to Adaptive Noise Cancellation

J.M. Gorriz;J. Ramirez;S. Cruces-Alvarez;C.G. Puntonet.
IEEE Signal Processing Letters (2009)

126 Citations

Brain connectivity analysis: a short survey

E. W. Lang;A. M. Tomé;I. R. Keck;J. M. Górriz-Sáez.
Computational Intelligence and Neuroscience (2012)

121 Citations

Automatic tool for Alzheimer's disease diagnosis using PCA and Bayesian classification rules

M. López;J. Ramírez;J.M. Górriz;D. Salas-Gonzalez.
Electronics Letters (2009)

110 Citations

Alzheimer's diagnosis using eigenbrains and support vector machines

I. Álvarez;J.M. Górriz;J. Ramírez;D. Salas-Gonzalez.
Electronics Letters (2009)

109 Citations

Linear geometric ICA: fundamentals and algorithms

Fabian J. Theis;Andreas Jung;Carlos G. Puntonet;Elmar W. Lang.
Neural Computation (2003)

98 Citations

Computer Aided Diagnosis tool for Alzheimer's Disease based on Mann-Whitney-Wilcoxon U-Test

F.J. Martínez-Murcia;J.M. Górriz;J. Ramírez;C.G. Puntonet.
Expert Systems With Applications (2012)

92 Citations

Separation of sources: a geometry-based procedure for reconstruction of n-valued signals

C. G. Puntonet;A. Prieto;C. Jutten;M. Rodríguez-Alvarez.
Signal Processing (1995)

86 Citations

Digital image analysis for automatic enumeration of malaria parasites using morphological operations

J.E. Arco;J.M. Górriz;J. Ramírez;I. Álvarez.
Expert Systems With Applications (2015)

80 Citations

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

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