What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Machine learning
- Statistics
The scientist’s investigation covers issues in Artificial intelligence, Pattern recognition, Support vector machine, Feature extraction and Computer-aided diagnosis.
His Artificial intelligence study incorporates themes from Machine learning and Single-photon emission computed tomography.
His Pattern recognition study combines topics in areas such as Speech recognition, Partial least squares regression and Divergence.
His Support vector machine study combines topics from a wide range of disciplines, such as Confusion matrix, Decision tree, Sensitivity, Magnetic resonance imaging and Emission computed tomography.
His research in Feature extraction intersects with topics in Mixture model, Linear discriminant analysis and Curse of dimensionality.
His biological study spans a wide range of topics, including Dementia, Disease, Neuroimaging, Dimensionality reduction and Discriminative model.
His most cited work include:
- Efficient voice activity detection algorithms using long-term speech information (298 citations)
- Ensembles of Deep Learning Architectures for the Early Diagnosis of the Alzheimer’s Disease (163 citations)
- Statistical voice activity detection using a multiple observation likelihood ratio test (160 citations)
What are the main themes of his work throughout his whole career to date?
His scientific interests lie mostly in Artificial intelligence, Pattern recognition, Support vector machine, Computer-aided diagnosis and Feature extraction.
In his work, Disease is strongly intertwined with Machine learning, which is a subfield of Artificial intelligence.
His Pattern recognition study integrates concerns from other disciplines, such as Speech recognition, Magnetic resonance imaging and Single-photon emission computed tomography.
He has researched Support vector machine in several fields, including Classifier, Contextual image classification, Alzheimer's disease, Supervised learning and Discriminative model.
The various areas that he examines in his Computer-aided diagnosis study include Independent component analysis, Deep learning and Emission computed tomography.
His Feature extraction research is multidisciplinary, incorporating perspectives in Image processing, Partial least squares regression and Mixture model.
He most often published in these fields:
- Artificial intelligence (64.25%)
- Pattern recognition (46.77%)
- Support vector machine (28.23%)
What were the highlights of his more recent work (between 2016-2021)?
- Artificial intelligence (64.25%)
- Pattern recognition (46.77%)
- Support vector machine (28.23%)
In recent papers he was focusing on the following fields of study:
Javier Ramírez focuses on Artificial intelligence, Pattern recognition, Support vector machine, Computer-aided diagnosis and Neuroimaging.
His research brings together the fields of Machine learning and Artificial intelligence.
His Pattern recognition study combines topics in areas such as Histogram, Voxel and Parkinson's disease.
His Support vector machine study combines topics from a wide range of disciplines, such as Contextual image classification, Supervised learning, Classifier and Alzheimer's disease.
His study in Computer-aided diagnosis is interdisciplinary in nature, drawing from both Independent component analysis and Magnetic resonance imaging.
The Neuroimaging study combines topics in areas such as Orientation, Cognitive psychology, Pattern recognition and Univariate.
Between 2016 and 2021, his most popular works were:
- Independent Component Analysis-Support Vector Machine-Based Computer-Aided Diagnosis System for Alzheimer's with Visual Support. (51 citations)
- Convolutional Neural Networks for Neuroimaging in Parkinson’s Disease: Is Preprocessing Needed? (36 citations)
- Studying the Manifold Structure of Alzheimer's Disease: A Deep Learning Approach Using Convolutional Autoencoders (30 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Machine learning
- Statistics
His scientific interests lie mostly in Artificial intelligence, Pattern recognition, Neuroimaging, Computer-aided diagnosis and Feature extraction.
His work investigates the relationship between Artificial intelligence and topics such as Machine learning that intersect with problems in Discriminant.
His Pattern recognition research includes elements of Voxel, Parkinson's disease, Regression and Identification.
His Neuroimaging research is multidisciplinary, incorporating elements of Cognitive psychology, Pattern recognition, Principal component analysis and Interpretability.
His Computer-aided diagnosis research incorporates themes from Disease progression, Disease, Relevance, Developmental psychology and Knowledge extraction.
He combines subjects such as Ensemble learning and Contextual image classification with his study of Support vector machine.
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