What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Machine learning
- Statistics
Juan Manuel Górriz mainly focuses on Artificial intelligence, Support vector machine, Pattern recognition, Computer-aided diagnosis and Feature extraction.
His work in Artificial intelligence addresses subjects such as Neuroimaging, which are connected to disciplines such as Cognitive impairment.
Juan Manuel Górriz has researched Support vector machine in several fields, including Single-photon emission computed tomography, Sensitivity, Magnetic resonance imaging, Dementia and Emission computed tomography.
The study incorporates disciplines such as Contextual image classification, Partial least squares regression and Multivariate statistics in addition to Pattern recognition.
His Computer-aided diagnosis research integrates issues from Independent component analysis, Disease, Parkinsonism, Dimensionality reduction and Discriminative model.
Juan Manuel Górriz interconnects Linear discriminant analysis and Curse of dimensionality in the investigation of issues within Feature extraction.
His most cited work include:
- Ensembles of Deep Learning Architectures for the Early Diagnosis of the Alzheimer’s Disease (163 citations)
- Voice Activity Detection. Fundamentals and Speech Recognition System Robustness (153 citations)
- Early diagnosis of Alzheimer׳s disease based on partial least squares, principal component analysis and support vector machine using segmented MRI images (143 citations)
What are the main themes of his work throughout his whole career to date?
His primary areas of study are Artificial intelligence, Pattern recognition, Support vector machine, Computer-aided diagnosis and Feature extraction.
As a part of the same scientific study, Juan Manuel Górriz usually deals with the Artificial intelligence, concentrating on Machine learning and frequently concerns with Disease.
The concepts of his Pattern recognition study are interwoven with issues in Positron emission tomography, Single-photon emission computed tomography, Magnetic resonance imaging and Speech recognition.
His work deals with themes such as Contextual image classification, Alzheimer's disease, Supervised learning, Discriminative model and Feature selection, which intersect with Support vector machine.
He studied Computer-aided diagnosis and Independent component analysis that intersect with Time series.
His Feature extraction study incorporates themes from Image processing, Partial least squares regression and Mixture model.
He most often published in these fields:
- Artificial intelligence (71.27%)
- Pattern recognition (49.86%)
- Support vector machine (30.89%)
What were the highlights of his more recent work (between 2017-2021)?
- Artificial intelligence (71.27%)
- Pattern recognition (49.86%)
- Support vector machine (30.89%)
In recent papers he was focusing on the following fields of study:
Artificial intelligence, Pattern recognition, Support vector machine, Neuroimaging and Deep learning are his primary areas of study.
The Artificial intelligence study which covers Machine learning that intersects with Principal component analysis.
His Pattern recognition research includes themes of Artificial neural network, Overfitting, Voxel and Pooling.
His studies deal with areas such as Classifier, Parkinson's disease, Statistical power, Ensemble learning and Test set as well as Support vector machine.
Juan Manuel Górriz combines subjects such as Multivariate analysis and Disease with his study of Neuroimaging.
In his work, Normalization is strongly intertwined with Spatial normalization, which is a subfield of Deep learning.
Between 2017 and 2021, his most popular works were:
- Convolutional Neural Networks for Neuroimaging in Parkinson’s Disease: Is Preprocessing Needed? (36 citations)
- Convolutional Neural Networks for Neuroimaging in Parkinson’s Disease: Is Preprocessing Needed? (36 citations)
- Covid-19 Classification by FGCNet with Deep Feature Fusion from Graph Convolutional Network and Convolutional Neural Network. (34 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Machine learning
- Statistics
Juan Manuel Górriz focuses on Artificial intelligence, Pattern recognition, Neuroimaging, Convolutional neural network and Support vector machine.
The Artificial intelligence study combines topics in areas such as Multinomial logistic regression and Identification.
His research integrates issues of Voxel and Parkinson's disease in his study of Pattern recognition.
Juan Manuel Górriz has included themes like Principal component analysis and Pattern recognition in his Neuroimaging study.
His Support vector machine research is multidisciplinary, relying on both Ensemble learning, Magnetic resonance imaging, Brain activity and meditation and Learning disability.
His Computer-aided diagnosis course of study focuses on Feature extraction and Wavelet, Hilbert–Huang transform and Contextual image classification.
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