D-Index & Metrics Best Publications

D-Index & Metrics

Discipline name D-index D-index (Discipline H-index) only includes papers and citation values for an examined discipline in contrast to General H-index which accounts for publications across all disciplines. Citations Publications World Ranking National Ranking
Computer Science D-index 39 Citations 6,824 258 World Ranking 4839 National Ranking 459

Overview

What is he best known for?

The fields of study he is best known for:

  • Operating system
  • Computer network
  • Artificial intelligence

Computer network, Vehicular ad hoc network, Real-time computing, Distributed computing and Simulation are his primary areas of study. A large part of his Computer network studies is devoted to Wireless sensor network. Yanmin Zhu interconnects Mobile computing, Global Positioning System, Mobile radio and Network packet in the investigation of issues within Vehicular ad hoc network.

His Real-time computing research includes elements of Wireless ad hoc network, Mobility model and Compressed sensing. His Distributed computing study combines topics from a wide range of disciplines, such as Crowdsourcing, Scheduling, Rationality and Fair-share scheduling. His Simulation study integrates concerns from other disciplines, such as Estimation theory, Assisted GPS, Location-based service and Accelerometer.

His most cited work include:

  • TRAC: Truthful Auction for Location-Aware Collaborative Sensing in Mobile Crowdsourcing (280 citations)
  • CDC : Compressive Data Collection for Wireless Sensor Networks (225 citations)
  • Recognizing Exponential Inter-Contact Time in VANETs (214 citations)

What are the main themes of his work throughout his whole career to date?

Yanmin Zhu focuses on Computer network, Distributed computing, Real-time computing, Wireless sensor network and Vehicular ad hoc network. The various areas that he examines in his Computer network study include Wireless, Wireless ad hoc network and Relay. In the subject of general Distributed computing, his work in Distributed algorithm is often linked to Resource management, thereby combining diverse domains of study.

His work deals with themes such as Event, Simulation and Compressed sensing, which intersect with Real-time computing. His Wireless sensor network research integrates issues from Sensor node and Key distribution in wireless sensor networks. In his study, which falls under the umbrella issue of Vehicular ad hoc network, Data mining is strongly linked to Global Positioning System.

He most often published in these fields:

  • Computer network (27.60%)
  • Distributed computing (19.58%)
  • Real-time computing (17.21%)

What were the highlights of his more recent work (between 2017-2021)?

  • Artificial intelligence (10.09%)
  • Machine learning (5.93%)
  • Artificial neural network (5.34%)

In recent papers he was focusing on the following fields of study:

Yanmin Zhu mainly investigates Artificial intelligence, Machine learning, Artificial neural network, Distributed computing and Cloud computing. His Artificial intelligence research incorporates themes from Mobile device and Time series. His Artificial neural network course of study focuses on Data mining and Graph and Recurrent neural network.

His Distributed computing research includes elements of Incentive, Payment and Latency. In general Cloud computing, his work in Mobile edge computing is often linked to Lyapunov optimization and Flexibility linking many areas of study. The Smart device study which covers Reinforcement learning that intersects with Real-time computing.

Between 2017 and 2021, his most popular works were:

  • Predicting Multi-step Citywide Passenger Demands Using Attention-based Neural Networks (63 citations)
  • A Neural Attention Model for Urban Air Quality Inference: Learning the Weights of Monitoring Stations. (35 citations)
  • CoLight: Learning Network-level Cooperation for Traffic Signal Control (32 citations)

In his most recent research, the most cited papers focused on:

  • Operating system
  • Computer network
  • Artificial intelligence

His primary scientific interests are in Artificial intelligence, Artificial neural network, Speech recognition, Machine learning and Data mining. His work on Deep learning as part of general Artificial intelligence study is frequently linked to Background subtraction, bridging the gap between disciplines. His Deep learning research is multidisciplinary, relying on both Waveform and Simulation.

His work in Artificial neural network tackles topics such as Convolutional neural network which are related to areas like TRIPS architecture, Scheduling and Data science. Yanmin Zhu has included themes like Point of interest, Prosperity, Preference and Hidden Markov model in his Machine learning study. His work in the fields of Data mining, such as Big data, overlaps with other areas such as Coherence.

This overview was generated by a machine learning system which analysed the scientist’s body of work. If you have any feedback, you can contact us here.

Best Publications

TRAC: Truthful Auction for Location-Aware Collaborative Sensing in Mobile Crowdsourcing

Zhenni Feng;Yanmin Zhu;Qian Zhang;Lionel Ming-Shuan Ni.
international conference on computer communications (2014)

340 Citations

Recognizing Exponential Inter-Contact Time in VANETs

Hongzi Zhu;Luoyi Fu;Guangtao Xue;Yanmin Zhu.
international conference on computer communications (2010)

280 Citations

CDC : Compressive Data Collection for Wireless Sensor Networks

Xiao-Yang Liu;Yanmin Zhu;Linghe Kong;Cong Liu.
IEEE Transactions on Parallel and Distributed Systems (2015)

278 Citations

Diagnosing New York city's noises with ubiquitous data

Yu Zheng;Tong Liu;Yilun Wang;Yanmin Zhu.
ubiquitous computing (2014)

236 Citations

Toward Secure Multikeyword Top-k Retrieval over Encrypted Cloud Data

Jiadi Yu;Peng Lu;Yanmin Zhu;Guangtao Xue.
IEEE Transactions on Dependable and Secure Computing (2013)

183 Citations

A Survey on Trajectory Data Mining: Techniques and Applications

Zhenni Feng;Yanmin Zhu.
IEEE Access (2016)

182 Citations

Mining large-scale, sparse GPS traces for map inference: comparison of approaches

Xuemei Liu;James Biagioni;Jakob Eriksson;Yin Wang.
knowledge discovery and data mining (2012)

169 Citations

SenSpeed: Sensing driving conditions to estimate vehicle speed in urban environments

Haofu Han;Jiadi Yu;Hongzi Zhu;Yingying Chen.
international conference on computer communications (2014)

163 Citations

SenSpeed: Sensing Driving Conditions to Estimate Vehicle Speed in Urban Environments

Jiadi Yu;Hongzi Zhu;Haofu Han;Yingying Jennifer Chen.
IEEE Transactions on Mobile Computing (2016)

162 Citations

A Compressive Sensing Approach to Urban Traffic Estimation with Probe Vehicles

Yanmin Zhu;Zhi Li;Hongzi Zhu;Minglu Li.
IEEE Transactions on Mobile Computing (2013)

158 Citations

Best Scientists Citing Yanmin Zhu

Yong Li

Yong Li

Tsinghua University

Publications: 45

Daqing Zhang

Daqing Zhang

Télécom SudParis

Publications: 33

Jie Wu

Jie Wu

Temple University

Publications: 31

Depeng Jin

Depeng Jin

Tsinghua University

Publications: 30

Guihai Chen

Guihai Chen

Nanjing University

Publications: 27

Haiying Shen

Haiying Shen

University of Virginia

Publications: 23

Fan Wu

Fan Wu

Shanghai Jiao Tong University

Publications: 22

Yunhao Liu

Yunhao Liu

Tsinghua University

Publications: 21

Minglu Li

Minglu Li

Zhejiang Normal University

Publications: 21

Xinbing Wang

Xinbing Wang

Shanghai Jiao Tong University

Publications: 19

Xuemin Shen

Xuemin Shen

University of Waterloo

Publications: 19

Song Guo

Song Guo

Hong Kong Polytechnic University

Publications: 19

Yu Wang

Yu Wang

Temple University

Publications: 18

Mo Li

Mo Li

Nanyang Technological University

Publications: 17

Pietro Manzoni

Pietro Manzoni

Universitat Politècnica de València

Publications: 16

Pan Hui

Pan Hui

Hong Kong University of Science and Technology

Publications: 16

Profile was last updated on December 6th, 2021.
Research.com Ranking is based on data retrieved from the Microsoft Academic Graph (MAG).
The ranking d-index is inferred from publications deemed to belong to the considered discipline.

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