Hua Ming

2.6k citations
73 papers · 1.8k · h-index 19

Impact in

  • Pollution top 1%
    • Heavy metals in environment
    • Data Management and Algorithms
    • Advanced Malware Detection Techniques

Papers in

Hua Ming

67 papers receiving 1.7k citations

Peers

Hua Ming
Comparison fields: 5 of 129
  • Pollution 631
  • Signal Processing 510
  • Radiological and Ultrasound Technology 154
  • Computer Networks and Communications 548
  • Health, Toxicology and Mutagenesis 284
Replace Xiaoyong Zhou with:
Xiaoyong Zhou China
Yanjiao Chen China
Roberto Morabito Italy
Jesús S. Aguilar–Ruiz Spain
Quanyuan Wu China
Joaquı́n Sánchez-Soriano Spain
Hongjie Jia China
Yufan Yang China
Dimitrios Lekkas Greece
Hua Ming relative to Xiaoyong Zhou China Xiaoyong Zhou's profile →
Citations per field
00.5×1.6×
Xiaoyong Zhou · 1×
Citations per year

Countries citing papers authored by Hua Ming

Since Specialization
Citations

This map shows the geographic impact of Hua Ming's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Hua Ming with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Hua Ming more than expected).

Fields of papers citing papers by Hua Ming

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Hua Ming. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Hua Ming. The network helps show where Hua Ming may publish in the future.

Co-authors

The 25 scholars most cited alongside Hua Ming, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with Hua Ming Line = papers co-authored together Hua Ming links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown

Showing the 20 most-cited of 73 papers — load more, or switch the sort, to bring in the rest.

#Work
1 2007428
2 2019196
3 2008171
4 2009121
5 201089
6 201571
7 201452
8 200852
9 200951
10 200951
11 201450
12 201945
13 200837
14 201934
15 202129
16 200924
17 200722
18
Efficiently answering top-k typicality queries on large databases
200721
19 201018
20 202217

About Hua Ming

Hua Ming is a scholar working on Artificial Intelligence, Computer Networks and Communications, Information Systems, Signal Processing and Computer Vision and Pattern Recognition, having authored 73 papers that have together received 1.8k indexed citations. Recurring topics across this work include Software Engineering Research (12 papers), Data Management and Algorithms (11 papers), Advanced Software Engineering Methodologies (10 papers), Heavy metals in environment (8 papers), Advanced Database Systems and Queries (7 papers), Context-Aware Activity Recognition Systems (7 papers), Data Mining Algorithms and Applications (6 papers) and Software System Performance and Reliability (6 papers). The work is most often cited by research in Pollution (631 citations), Signal Processing (510 citations), Radiological and Ultrasound Technology (154 citations), Computer Networks and Communications (548 citations) and Health, Toxicology and Mutagenesis (284 citations). Hua Ming has collaborated with scholars based in United States, China and Canada. Frequent co-authors include Jian Pei, Xuemin Lin, Qilin Liao, Wenjie Zhang, Xinmin Wu, Ali Alqazzaz, Mohamed Zohdy, Ibrahim Alrashdi, Esam Aloufi and Raed Alharthi. Their work appears in journals such as IEEE Transactions on Software Engineering, ACM Transactions on Software Engineering and Methodology, IEEE Access, IT Professional and The VLDB Journal.

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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