What is he best known for?
The fields of study Zhong-Ke Gao is best known for:
- Two-phase flow
- Multiphase flow
- Wavelet
His Quantum mechanics study frequently draws connections between adjacent fields such as Nonlinear system and Phase (matter).
His research on Geotechnical engineering often connects related areas such as Water flow and Hydrology (agriculture).
His study in Geotechnical engineering extends to Hydrology (agriculture) with its themes.
His studies link Pattern recognition (psychology) with Artificial intelligence.
Pattern recognition (psychology) is often connected to Artificial intelligence in his work.
In most of his Flow (mathematics) studies, his work intersects topics such as Geometry.
He frequently studies issues relating to Flow (mathematics) and Geometry.
His study deals with a combination of Machine learning and Time series.
He undertakes interdisciplinary study in the fields of Time series and Machine learning through his research.
His most cited work include:
- Complex network analysis of time series (214 citations)
- EEG-Based Spatio–Temporal Convolutional Neural Network for Driver Fatigue Evaluation (208 citations)
- Multivariate weighted complex network analysis for characterizing nonlinear dynamic behavior in two-phase flow (165 citations)
What are the main themes of his work throughout his whole career to date
In his works, Zhong-Ke Gao conducts interdisciplinary research on Artificial intelligence and Algorithm.
Zhong-Ke Gao performs multidisciplinary study in the fields of Algorithm and Artificial intelligence via his papers.
Flow (mathematics) is closely attributed to Geometry in his work.
As part of his studies on Geometry, he frequently links adjacent subjects like Flow (mathematics).
Many of his studies on Quantum mechanics apply to Nonlinear system as well.
His Nonlinear system study frequently links to adjacent areas such as Quantum mechanics.
His research on Mechanics frequently connects to adjacent areas such as Two-phase flow.
His work in Two-phase flow is not limited to one particular discipline; it also encompasses Mechanics.
His Machine learning study typically links adjacent topics like Multivariate statistics.
Zhong-Ke Gao most often published in these fields:
- Artificial intelligence (61.00%)
- Flow (mathematics) (39.00%)
- Quantum mechanics (39.00%)
What were the highlights of his more recent work (between 2020-2022)?
- Artificial intelligence (100.00%)
- Electroencephalography (85.71%)
- Pattern recognition (psychology) (85.71%)
In recent works Zhong-Ke Gao was focusing on the following fields of study:
His study looks at the intersection of Decoding methods and topics like Algorithm with State (computer science).
State (computer science) is closely attributed to Algorithm in his work.
Many of his studies on Artificial intelligence involve topics that are commonly interrelated, such as Emotion classification.
Zhong-Ke Gao conducted interdisciplinary study in his works that combined Electroencephalography and Evoked potential.
Zhong-Ke Gao regularly links together related areas like Artificial intelligence in his Pattern recognition (psychology) studies.
He integrates Convolutional neural network with Softmax function in his study.
Zhong-Ke Gao undertakes interdisciplinary study in the fields of Softmax function and Convolutional neural network through his works.
In most of his Neuroscience studies, his work intersects topics such as Visual evoked potentials.
His research is interdisciplinary, bridging the disciplines of Neuroscience and Visual evoked potentials.
Between 2020 and 2022, his most popular works were:
- Complex networks and deep learning for EEG signal analysis (49 citations)
- A Complex Network-Based Broad Learning System for Detecting Driver Fatigue From EEG Signals (33 citations)
- Classification of EEG Signals on VEP-Based BCI Systems With Broad Learning (20 citations)
In his most recent research, the most cited works focused on:
- Neuroscience
- Machine learning
- Deep learning
Zhong-Ke Gao merges Artificial intelligence with Convolutional neural network in his research.
Convolutional neural network and Deep learning are two areas of study in which he engages in interdisciplinary research.
Deep learning and Feature extraction are two areas of study in which Zhong-Ke Gao engages in interdisciplinary research.
His Electroencephalography study frequently links to related topics such as Psychiatry.
Much of his study explores Psychiatry relationship to Electroencephalography.
His research links Artificial intelligence with Pattern recognition (psychology).
His research ties Evoked potential and Neuroscience together.
Zhong-Ke Gao regularly ties together related areas like Neuroscience in his Evoked potential studies.
In his study, he carries out multidisciplinary Machine learning and Feature extraction research.
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