What is she best known for?
The fields of study she is best known for:
- Artificial intelligence
- Computer network
- Operating system
Wireless sensor network, Cluster analysis, Efficient energy use, Energy consumption and Computer network are her primary areas of study.
Her Wireless sensor network research is multidisciplinary, incorporating elements of Mobile sink, Network performance, Algorithm, Particle swarm optimization and Node.
While the research belongs to areas of Algorithm, Jin Wang spends her time largely on the problem of Mobile agent, intersecting her research to questions surrounding Adaptive routing, Greedy algorithm, Energy efficient routing, Base station and Network packet.
Her research integrates issues of Routing and Timer in her study of Cluster analysis.
Jin Wang has researched Efficient energy use in several fields, including Key distribution in wireless sensor networks, Energy and Distributed computing.
Her work on Asynchronous communication as part of general Computer network research is often related to Mechanism, thus linking different fields of science.
Her most cited work include:
- Exogenous plant MIR168a specifically targets mammalian LDLRAP1: Evidence of cross-kingdom regulation by microRNA (663 citations)
- Role of miR-143 targeting KRAS in colorectal tumorigenesis (436 citations)
- A novel routing protocol providing good transmission reliability in underwater sensor networks (343 citations)
What are the main themes of her work throughout her whole career to date?
Her scientific interests lie mostly in Computer network, Artificial intelligence, Wireless sensor network, Distributed computing and Cloud computing.
Her study ties her expertise on Wireless together with the subject of Computer network.
Her studies deal with areas such as Machine learning, Computer vision and Pattern recognition as well as Artificial intelligence.
Her Wireless sensor network research includes elements of Energy consumption, Cluster analysis, Key distribution in wireless sensor networks, Real-time computing and Efficient energy use.
Her research in Cloud computing intersects with topics in Computer security, Encryption, Server and Database.
Her work carried out in the field of Wireless Routing Protocol brings together such families of science as Link-state routing protocol and Dynamic Source Routing.
She most often published in these fields:
- Computer network (18.78%)
- Artificial intelligence (18.17%)
- Wireless sensor network (18.47%)
What were the highlights of her more recent work (between 2019-2021)?
- Artificial intelligence (18.17%)
- Optics (4.89%)
- Pattern recognition (7.94%)
In recent papers she was focusing on the following fields of study:
Her main research concerns Artificial intelligence, Optics, Pattern recognition, Edge computing and Distributed computing.
Her Artificial intelligence study combines topics in areas such as Machine learning and Natural language processing.
Her work focuses on many connections between Edge computing and other disciplines, such as Edge device, that overlap with her field of interest in Computation.
Her Distributed computing research incorporates themes from Block and Enhanced Data Rates for GSM Evolution, Mobile edge computing.
The study incorporates disciplines such as Algorithm and Base station in addition to Server.
The various areas that Jin Wang examines in her Energy consumption study include Wireless and Wireless sensor network.
Between 2019 and 2021, her most popular works were:
- Spatial and semantic convolutional features for robust visual object tracking (63 citations)
- Optimal coverage multi-path scheduling scheme with multiple mobile sinks for WSNs (62 citations)
- A Cascaded R-CNN With Multiscale Attention and Imbalanced Samples for Traffic Sign Detection (45 citations)
In her most recent research, the most cited papers focused on:
- Artificial intelligence
- Computer network
- The Internet
Her main research concerns Artificial intelligence, Pattern recognition, Convolutional neural network, Artificial neural network and Distributed computing.
Her work on Classifier is typically connected to Series as part of general Pattern recognition study, connecting several disciplines of science.
Her work investigates the relationship between Convolutional neural network and topics such as Field that intersect with problems in Mobile device, Machine learning, Feature and Normalization.
Her Artificial neural network research is multidisciplinary, incorporating perspectives in Feature extraction, Naive Bayes classifier, Sequence, Event and Random forest.
Her biological study spans a wide range of topics, including Access network, Wireless and Energy consumption, Enhanced Data Rates for GSM Evolution, Mobile edge computing.
Her studies in Server integrate themes in fields like Algorithm, Wireless sensor network and Upload.
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