What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Computer network
- The Internet
His primary areas of study are Artificial intelligence, Visualization, Multimedia, Gesture and Distributed computing.
Zhihan Lv combines topics linked to Pattern recognition with his work on Artificial intelligence.
His research in Visualization intersects with topics in User experience design, Rendering and Graphics.
His study in Human–computer interaction extends to Multimedia with its themes.
His study in Human–computer interaction is interdisciplinary in nature, drawing from both Interactive visualization, Video game and Computer graphics.
His studies in Gesture integrate themes in fields like Multimodal interaction and Usability.
His most cited work include:
- Game on, science - how video game technology may help biologists tackle visualization challenges. (211 citations)
- Game on, science - how video game technology may help biologists tackle visualization challenges. (211 citations)
- Touch-less interactive augmented reality game on vision-based wearable device (195 citations)
What are the main themes of his work throughout his whole career to date?
Zhihan Lv mostly deals with Artificial intelligence, Virtual reality, Big data, Multimedia and Human–computer interaction.
Within one scientific family, Zhihan Lv focuses on topics pertaining to Computer vision under Artificial intelligence, and may sometimes address concerns connected to Convolutional neural network.
His Virtual reality research incorporates themes from Transport engineering, Traffic analysis, Preprint, Visualization and Information system.
In his works, Zhihan Lv performs multidisciplinary study on Visualization and Seabed.
He interconnects Computer security, Database and Data science in the investigation of issues within Big data.
In his study, which falls under the umbrella issue of Human–computer interaction, Usability is strongly linked to Gesture.
He most often published in these fields:
- Artificial intelligence (23.39%)
- Virtual reality (14.58%)
- Big data (13.90%)
What were the highlights of his more recent work (between 2019-2021)?
- Artificial intelligence (23.39%)
- Distributed computing (8.81%)
- Computer network (7.12%)
In recent papers he was focusing on the following fields of study:
Artificial intelligence, Distributed computing, Computer network, The Internet and Big data are his primary areas of study.
His biological study spans a wide range of topics, including Machine learning and Computer vision.
His Computer vision study integrates concerns from other disciplines, such as Visualization and Robustness.
His research in Distributed computing intersects with topics in Enhanced Data Rates for GSM Evolution, Service and Information system.
His Computer network research is multidisciplinary, relying on both Energy consumption, Wireless network and Smart city.
He interconnects Data modeling, Information privacy and Data science in the investigation of issues within Big data.
Between 2019 and 2021, his most popular works were:
- Deep belief network and linear perceptron based cognitive computing for collaborative robots (75 citations)
- Multiobjective Evolution of Fuzzy Rough Neural Network via Distributed Parallelism for Stock Prediction (65 citations)
- Hybrid Microgrid Many-Objective Sizing Optimization With Fuzzy Decision (64 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Computer network
- The Internet
His primary scientific interests are in Distributed computing, Computer network, Artificial intelligence, Energy consumption and Big data.
His studies deal with areas such as Service, Enhanced Data Rates for GSM Evolution, The Internet, Information system and Process as well as Distributed computing.
His studies in Computer network integrate themes in fields like Order and Information privacy.
His work carried out in the field of Energy consumption brings together such families of science as Secrecy, Efficient energy use, Parallel processing and Access control.
His study in Big data is interdisciplinary in nature, drawing from both Data modeling and Actuarial science.
His Fuzzy logic study typically links adjacent topics like Visualization.
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