What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Machine learning
- Statistics
Xiaohui Liu mainly investigates Control theory, Linear matrix inequality, Exponential stability, Artificial neural network and Stochastic process.
His work in Control theory addresses issues such as Complex network, which are connected to fields such as Minimum mean square error.
His research on Linear matrix inequality concerns the broader Mathematical optimization.
The Exponential stability study combines topics in areas such as Recurrent neural network, Stability conditions and Stability theory.
His research in Artificial neural network intersects with topics in Stability and Markov process.
His Stochastic process research incorporates themes from Estimator and Conditional probability distribution.
His most cited work include:
- A survey of deep neural network architectures and their applications (1147 citations)
- Global exponential stability of generalized recurrent neural networks with discrete and distributed delays (601 citations)
- Robust H/sub /spl infin// filtering for stochastic time-delay systems with missing measurements (465 citations)
What are the main themes of his work throughout his whole career to date?
His primary scientific interests are in Artificial intelligence, Control theory, Data mining, Mathematical optimization and Stochastic process.
His Artificial intelligence study combines topics in areas such as Machine learning, Computer vision and Pattern recognition.
Many of his studies involve connections with topics such as Artificial neural network and Control theory.
His Data mining research is multidisciplinary, incorporating elements of Set, Outlier, Cluster analysis, Microarray and Domain knowledge.
His Stochastic process study which covers Upper and lower bounds that intersects with Bounded function and Applied mathematics.
Xiaohui Liu combines subjects such as Filtering problem and Filter with his study of Nonlinear system.
He most often published in these fields:
- Artificial intelligence (29.48%)
- Control theory (28.65%)
- Data mining (15.43%)
What were the highlights of his more recent work (between 2016-2021)?
- Artificial intelligence (29.48%)
- Algorithm (9.37%)
- Upper and lower bounds (8.26%)
In recent papers he was focusing on the following fields of study:
Xiaohui Liu mostly deals with Artificial intelligence, Algorithm, Upper and lower bounds, Computer vision and Particle swarm optimization.
His research in Artificial intelligence intersects with topics in Machine learning and Optical coherence tomography.
His studies in Algorithm integrate themes in fields like Probability distribution, Filter, Data collection, Bernoulli's principle and Filtering problem.
He has included themes like Stochastic process and Applied mathematics in his Upper and lower bounds study.
His study in Stochastic process is interdisciplinary in nature, drawing from both Estimator and Control theory.
In his papers, he integrates diverse fields, such as Control theory and Transmission time.
Between 2016 and 2021, his most popular works were:
- A survey of deep neural network architectures and their applications (1147 citations)
- State estimation under false data injection attacks: Security analysis and system protection (119 citations)
- Examining the moderating effect of individual-level cultural values on users' acceptance of E-learning in developing countries: a structural equation modeling of an extended technology acceptance model (113 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Machine learning
- Statistics
His primary areas of investigation include Upper and lower bounds, Estimator, Discrete time and continuous time, Artificial intelligence and Algorithm.
Xiaohui Liu works mostly in the field of Upper and lower bounds, limiting it down to concerns involving Filter and, occasionally, Kalman filter and Probabilistic logic.
The study incorporates disciplines such as Reliability, Control theory, Stochastic process, Bernoulli's principle and Scheme in addition to Estimator.
His Control theory study deals with Bounded function intersecting with Repressilator, Linear matrix inequality and Complex network.
His Discrete time and continuous time research is multidisciplinary, relying on both State, Stability theory, Lyapunov function, Nonlinear system and Applied mathematics.
His research integrates issues of Convergence and Operations research in his study of Artificial neural network.
This overview was generated by a machine learning system which analysed the scientist’s
body of work. If you have any feedback, you can
contact us
here.
