What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Computer network
- Machine learning
His primary scientific interests are in Artificial intelligence, Distributed computing, Pattern recognition, Wireless and Convolutional neural network.
When carried out as part of a general Artificial intelligence research project, his work on Object and Deep learning is frequently linked to work in Class and Action, therefore connecting diverse disciplines of study.
As part of the same scientific family, he usually focuses on Distributed computing, concentrating on Wireless sensor network and intersecting with Range query and Cluster analysis.
His study on Image segmentation is often connected to Graph as part of broader study in Pattern recognition.
His Wireless research includes elements of Telecommunications network, Robot and Enhanced Data Rates for GSM Evolution.
His work in Convolutional neural network covers topics such as Segmentation which are related to areas like Residual neural network, Transfer of learning and Contextual image classification.
His most cited work include:
- An edge computing-enabled computation offloading method with privacy preservation for internet of connected vehicles (96 citations)
- Multi-dimensional data indexing and range query processing via Voronoi diagram for internet of things (94 citations)
- Finding All You Need: Web APIs Recommendation in Web of Things Through Keywords Search (83 citations)
What are the main themes of his work throughout his whole career to date?
Shaohua Wan mostly deals with Artificial intelligence, Computer network, Pattern recognition, Edge computing and Distributed computing.
His study in Artificial intelligence is interdisciplinary in nature, drawing from both Machine learning and Computer vision.
The Machine learning study combines topics in areas such as Classifier, Class and Contextual image classification.
Shaohua Wan combines subjects such as Object and Histogram with his study of Pattern recognition.
His work investigates the relationship between Edge computing and topics such as The Internet that intersect with problems in Load balancing.
His research in Distributed computing intersects with topics in Scheduling and Mobile edge computing.
He most often published in these fields:
- Artificial intelligence (40.60%)
- Computer network (17.29%)
- Pattern recognition (14.29%)
What were the highlights of his more recent work (between 2020-2021)?
- Artificial intelligence (40.60%)
- Enhanced Data Rates for GSM Evolution (9.02%)
- Edge computing (13.53%)
In recent papers he was focusing on the following fields of study:
Shaohua Wan focuses on Artificial intelligence, Enhanced Data Rates for GSM Evolution, Edge computing, Server and Computer network.
His study brings together the fields of Machine learning and Artificial intelligence.
His Enhanced Data Rates for GSM Evolution research includes themes of Learning based, Multimedia and Object detection.
His research in Edge computing is mostly concerned with Computation offloading.
His work in Computation offloading addresses issues such as Task, which are connected to fields such as Scheduling and Distributed computing.
The concepts of his Network packet study are interwoven with issues in LPWAN, Throughput and Data transmission.
Between 2020 and 2021, his most popular works were:
- Edge Server Quantification and Placement for Offloading Social Media Services in Industrial Cognitive IoV (32 citations)
- An Edge Traffic Flow Detection Scheme Based on Deep Learning in an Intelligent Transportation System (11 citations)
- Security-Aware Dynamic Scheduling for Real-Time Optimization in Cloud-Based Industrial Applications (6 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Computer network
- Machine learning
His primary areas of study are Enhanced Data Rates for GSM Evolution, Intelligent transportation system, Agricultural engineering, Precision agriculture and Irrigation.
Shaohua Wan interconnects Quality of service, Computer network, Server and The Internet in the investigation of issues within Enhanced Data Rates for GSM Evolution.
His Intelligent transportation system research encompasses a variety of disciplines, including Environmental pollution, Real-time computing, Vehicle tracking system, Object detection and Traffic flow.
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