What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Database
- Algorithm
The scientist’s investigation covers issues in Data mining, Mobile computing, Computer network, Search algorithm and Spatial database.
His Data mining study incorporates themes from Object and Pruning.
His studies deal with areas such as Services computing, Database index, Search engine indexing and Cache as well as Mobile computing.
His research in the fields of Atomic broadcast overlaps with other disciplines such as Wireless network.
His Search algorithm research includes themes of Information retrieval, Web search query, Web query classification, Ranking and Document clustering.
In general Nearest neighbor search, his work in Nearest neighbor graph is often linked to Fixed-radius near neighbors linking many areas of study.
His most cited work include:
- IR-Tree: An Efficient Index for Geographic Document Search (199 citations)
- Approaching the skyline in Z order (176 citations)
- Data management in location-dependent information services (170 citations)
What are the main themes of his work throughout his whole career to date?
Baihua Zheng spends much of his time researching Data mining, Nearest neighbor search, k-nearest neighbors algorithm, Theoretical computer science and Algorithm.
Many of his research projects under Data mining are closely connected to Point with Point, tying the diverse disciplines of science together.
The various areas that he examines in his k-nearest neighbors algorithm study include Object and Line segment.
His Theoretical computer science research incorporates themes from Algorithm design, Scalability, Probabilistic logic and Graph.
Baihua Zheng works mostly in the field of Scalability, limiting it down to concerns involving Computer network and, occasionally, Distributed computing.
His Pruning research is multidisciplinary, incorporating elements of Heuristics and Skyline.
He most often published in these fields:
- Data mining (48.19%)
- Nearest neighbor search (16.87%)
- k-nearest neighbors algorithm (16.27%)
What were the highlights of his more recent work (between 2018-2021)?
- Artificial intelligence (10.24%)
- Data mining (48.19%)
- Scalability (12.65%)
In recent papers he was focusing on the following fields of study:
His primary scientific interests are in Artificial intelligence, Data mining, Scalability, Schedule and Algorithm.
His research integrates issues of Machine learning, Computer vision and Brute-force search in his study of Artificial intelligence.
He interconnects Crowdsourcing and Human intelligence in the investigation of issues within Machine learning.
The concepts of his Data mining study are interwoven with issues in Active learning, Contrast and Road networks.
His study in Scalability is interdisciplinary in nature, drawing from both Travel time, Real-time computing, Inference and Smart card.
Baihua Zheng combines subjects such as Feature transformation and Probabilistic method with his study of Algorithm.
Between 2018 and 2021, his most popular works were:
- A Survey on Modern Deep Neural Network for Traffic Prediction: Trends, Methods and Challenges (9 citations)
- Deepdualmapper: A gated fusion network for automatic map extraction using aerial images and trajectories (4 citations)
- Querying Continuous Recurrent Convoys of Interest (4 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Database
- Algorithm
Baihua Zheng mainly focuses on Scalability, Artificial intelligence, Computer vision, Bus network and Schedule.
He has included themes like Travel time, Inference, Real-time computing, Smart card and Task in his Scalability study.
His work on Sensor fusion and Convolutional neural network as part of his general Artificial intelligence study is frequently connected to Fusion and Aerial image, thereby bridging the divide between different branches of science.
His Computer vision research is mostly focused on the topic Object.
His Bus network study spans across into subjects like Greedy algorithm, Mathematical optimization, Optimization problem, Public transport and Partition.
His Schedule research incorporates Linear programming, Heuristic, Scheduling and Approximation algorithm.
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