What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Computer network
- Operating system
His primary areas of investigation include Computer network, Distributed computing, Multicast, Real-time computing and Bandwidth.
Kien A. Hua combines topics linked to Vehicular ad hoc network with his work on Computer network.
His Distributed computing study integrates concerns from other disciplines, such as Distributed memory, Protocol processing, Tuple and Adapter.
His Multicast research is multidisciplinary, incorporating elements of Software deployment and The Internet.
Kien A. Hua interconnects Node, Tree and Overhead in the investigation of issues within The Internet.
As a part of the same scientific study, he usually deals with the Bandwidth, concentrating on Download and frequently concerns with Bandwidth sharing, Session, Multimedia servers, Broadcast communication network and Video on demand.
His most cited work include:
- ZIGZAG: an efficient peer-to-peer scheme for media streaming (636 citations)
- Patching: a multicast technique for true video-on-demand services (594 citations)
- Skyscraper broadcasting: a new broadcasting scheme for metropolitan video-on-demand systems (520 citations)
What are the main themes of his work throughout his whole career to date?
Kien A. Hua focuses on Computer network, Distributed computing, Artificial intelligence, Data mining and Scalability.
His work carried out in the field of Computer network brings together such families of science as Wireless ad hoc network and Wireless network.
His study in the field of Load balancing also crosses realms of Bottleneck.
The concepts of his Load balancing study are interwoven with issues in Tuple and Parallel computing.
His Artificial intelligence research integrates issues from Machine learning, Computer vision and Pattern recognition.
The various areas that he examines in his Data mining study include Query expansion, Theoretical computer science, Search engine indexing and Image retrieval.
He most often published in these fields:
- Computer network (31.63%)
- Distributed computing (23.00%)
- Artificial intelligence (20.77%)
What were the highlights of his more recent work (between 2015-2021)?
- Artificial intelligence (20.77%)
- Machine learning (6.39%)
- Feature extraction (4.79%)
In recent papers he was focusing on the following fields of study:
His primary areas of study are Artificial intelligence, Machine learning, Feature extraction, Hash function and Recurrent neural network.
His studies deal with areas such as Pattern recognition and Natural language processing as well as Artificial intelligence.
His studies in Machine learning integrate themes in fields like Sentence and Data mining.
Kien A. Hua has included themes like Probabilistic logic, Graph, Joint and Human–computer interaction in his Feature extraction study.
The study incorporates disciplines such as Salient, Activity recognition and State in addition to Recurrent neural network.
His Redundancy research includes elements of Network performance, Computer network and Thread.
Between 2015 and 2021, his most popular works were:
- Linear Subspace Ranking Hashing for Cross-Modal Retrieval (80 citations)
- Learning Compact Features for Human Activity Recognition Via Probabilistic First-Take-All (19 citations)
- Learning Label Preserving Binary Codes for Multimedia Retrieval: A General Approach (17 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Computer network
- Operating system
Kien A. Hua spends much of his time researching Artificial intelligence, Feature extraction, Machine learning, Data mining and Hash function.
His study ties his expertise on Computer vision together with the subject of Artificial intelligence.
His Feature extraction study also includes
- Activity recognition which intersects with area such as Wearable computer, Recurrent neural network, Object and Benchmark,
- Probabilistic logic that intertwine with fields like User-generated content, Sequential Pattern Mining and Social media,
- Graph and related Semantic feature, Visualization, Information retrieval and Closed captioning.
His research in Machine learning intersects with topics in Semantics, Nearest neighbor search and Subspace topology.
His Data mining study combines topics in areas such as Abstraction, Perspective, Bilinear interpolation and Microblogging.
His studies deal with areas such as Video post-processing, Video compression picture types, Motion compensation and Block-matching algorithm as well as Pattern recognition.
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