What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Machine learning
- Statistics
His primary areas of study are Artificial intelligence, Electroencephalography, Pattern recognition, Wavelet and Wavelet transform.
His Artificial intelligence research incorporates elements of Machine learning and Cognition.
His Electroencephalography research integrates issues from Entropy, Speech recognition, Disease and Audiology.
His Pattern recognition study integrates concerns from other disciplines, such as Hierarchical clustering, Hilbert–Huang transform, Coherence and Sensitivity.
His Wavelet research focuses on subjects like Artificial neural network, which are linked to Discrete wavelet transform, Algorithm and Magnetic flux.
The study incorporates disciplines such as Synthetic aperture radar, Independent component analysis, Thematic Mapper and Image fusion in addition to Wavelet transform.
His most cited work include:
- Image fusion techniques for remote sensing applications (319 citations)
- Multivariate Multi-Scale Permutation Entropy for Complexity Analysis of Alzheimer’s Disease EEG (168 citations)
- Automatic Artifact Rejection From Multichannel Scalp EEG by Wavelet ICA (155 citations)
What are the main themes of his work throughout his whole career to date?
Francesco Carlo Morabito mostly deals with Artificial intelligence, Electroencephalography, Artificial neural network, Pattern recognition and Machine learning.
Independent component analysis, Wavelet transform, Deep learning, Wavelet and Fuzzy logic are the core of his Artificial intelligence study.
His Independent component analysis research incorporates themes from Speech recognition, Principal component analysis, Kurtosis and Signal processing.
Within one scientific family, he focuses on topics pertaining to Audiology under Electroencephalography, and may sometimes address concerns connected to Cognition.
As a member of one scientific family, Francesco Carlo Morabito mostly works in the field of Artificial neural network, focusing on Inverse problem and, on occasion, Eddy current.
His Pattern recognition research includes themes of Entropy and Signal.
He most often published in these fields:
- Artificial intelligence (47.99%)
- Electroencephalography (26.32%)
- Artificial neural network (26.01%)
What were the highlights of his more recent work (between 2015-2021)?
- Artificial intelligence (47.99%)
- Electroencephalography (26.32%)
- Pattern recognition (24.15%)
In recent papers he was focusing on the following fields of study:
His main research concerns Artificial intelligence, Electroencephalography, Pattern recognition, Deep learning and Machine learning.
Francesco Carlo Morabito connects Artificial intelligence with Permutation in his study.
Francesco Carlo Morabito has included themes like Dementia, Cognitive impairment, Disease and Audiology in his Electroencephalography study.
He studied Pattern recognition and Fuzzy logic that intersect with Steel plates, Image and Algorithm.
His biological study spans a wide range of topics, including Supervised learning, Anomaly detection and Unsupervised learning.
His Perceptron study in the realm of Machine learning connects with subjects such as Data structure.
Between 2015 and 2021, his most popular works were:
- Deep Learning Representation from Electroencephalography of Early-Stage Creutzfeldt-Jakob Disease and Features for Differentiation from Rapidly Progressive Dementia (72 citations)
- A Convolutional Neural Network approach for classification of dementia stages based on 2D-spectral representation of EEG recordings (68 citations)
- PERMUTATION DISALIGNMENT INDEX AS AN INDIRECT, EEG-BASED, MEASURE OF BRAIN CONNECTIVITY IN MCI AND AD PATIENTS (45 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Machine learning
- Statistics
Francesco Carlo Morabito focuses on Artificial intelligence, Electroencephalography, Pattern recognition, Deep learning and Machine learning.
All of his Artificial intelligence and Artificial neural network, Computational intelligence, Fuzzy set, Fuzzy logic and Probabilistic neural network investigations are sub-components of the entire Artificial intelligence study.
He interconnects Feature engineering, Entropy, Information theory and Data science in the investigation of issues within Artificial neural network.
When carried out as part of a general Electroencephalography research project, his work on Eeg data is frequently linked to work in Permutation, therefore connecting diverse disciplines of study.
A large part of his Pattern recognition studies is devoted to Wavelet transform.
His Deep learning study integrates concerns from other disciplines, such as Supervised learning, Unsupervised learning, Convolutional neural network and Support vector machine.
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