What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Statistics
- Machine learning
Hamid Soltanian-Zadeh mainly focuses on Artificial intelligence, Magnetic resonance imaging, Segmentation, Computer vision and Image processing.
His study in Artificial intelligence is interdisciplinary in nature, drawing from both Machine learning, Electroencephalography and Pattern recognition.
The various areas that Hamid Soltanian-Zadeh examines in his Pattern recognition study include Artificial neural network and Receiver operating characteristic.
His Magnetic resonance imaging research incorporates themes from Lesion, Pathology, Resting state fMRI and Nuclear medicine.
His Segmentation research includes elements of Stability, Thresholding, Region of interest and Temporal lobe, Epilepsy.
Hamid Soltanian-Zadeh has researched Image processing in several fields, including Visualization and Feature.
His most cited work include:
- Correlation of VEGF and angiopoietin expression with disruption of blood-brain barrier and angiogenesis after focal cerebral ischemia. (343 citations)
- Radon transform orientation estimation for rotation invariant texture analysis (247 citations)
- Multiwavelet grading of pathological images of prostate (163 citations)
What are the main themes of his work throughout his whole career to date?
Hamid Soltanian-Zadeh focuses on Artificial intelligence, Pattern recognition, Computer vision, Segmentation and Magnetic resonance imaging.
His Artificial intelligence study focuses mostly on Feature extraction, Image segmentation, Wavelet, Image processing and Feature vector.
Hamid Soltanian-Zadeh works mostly in the field of Pattern recognition, limiting it down to topics relating to Electroencephalography and, in certain cases, Epilepsy and Functional magnetic resonance imaging.
His biological study spans a wide range of topics, including Temporal lobe and Medical imaging.
Hamid Soltanian-Zadeh combines subjects such as Lateralization of brain function, Lesion, Pathology and Nuclear medicine with his study of Magnetic resonance imaging.
His Lesion study incorporates themes from Stroke and Ischemia.
He most often published in these fields:
- Artificial intelligence (73.86%)
- Pattern recognition (46.86%)
- Computer vision (34.57%)
What were the highlights of his more recent work (between 2016-2021)?
- Artificial intelligence (73.86%)
- Pattern recognition (46.86%)
- Epilepsy (15.43%)
In recent papers he was focusing on the following fields of study:
Hamid Soltanian-Zadeh mostly deals with Artificial intelligence, Pattern recognition, Epilepsy, Neuroscience and Electroencephalography.
His Artificial intelligence study frequently draws connections to adjacent fields such as Machine learning.
He works on Pattern recognition which deals in particular with Segmentation.
His research integrates issues of White matter, Neuroimaging and Disease in his study of Epilepsy.
His Feature selection research incorporates elements of Magnetic resonance imaging and Multilayer perceptron.
His work carried out in the field of Magnetic resonance imaging brings together such families of science as Functional neuroimaging and Data mining.
Between 2016 and 2021, his most popular works were:
- Optimized Deep Learning for EEG Big Data and Seizure Prediction BCI via Internet of Things (68 citations)
- Improved prediction of outcome in Parkinson's disease using radiomics analysis of longitudinal DAT SPECT images. (42 citations)
- Improved prediction of outcome in Parkinson's disease using radiomics analysis of longitudinal DAT SPECT images. (42 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Statistics
- Machine learning
The scientist’s investigation covers issues in Artificial intelligence, Pattern recognition, Epilepsy, Electroencephalography and Neuroscience.
The concepts of his Artificial intelligence study are interwoven with issues in Machine learning and Nonlinear system.
Hamid Soltanian-Zadeh studies Pattern recognition, focusing on Feature extraction in particular.
His Epilepsy research integrates issues from Focus and Cardiology.
His work in the fields of Functional connectivity and Resting state fMRI overlaps with other areas such as Modalities.
His research in Feature selection intersects with topics in Spatial analysis, Data mining, Correlation, Magnetic resonance imaging and Multilayer perceptron.
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