Ming‐Chao Chiang

2.0k citations
83 papers · 1.3k indexed · 1 hit paper · h-index 16

Ming‐Chao Chiang

78 papers receiving 1.2k citations

Hit Papers

Data Mining for Internet of Things: A Survey4242014202620182022100200300400

Peers

Ming‐Chao Chiang
Comparison fields: 5 of 101
  • Computer Networks and Communications 541
  • Information Systems 337
  • Artificial Intelligence 436
  • Computer Vision and Pattern Recognition 269
  • Hardware and Architecture 77
Replace P. Dhavachelvan with:
P. Dhavachelvan India
Homayun Motameni Iran
Benjamin Moseley United States
Daya Gupta India
Ahmad Sharieh Jordan
Quanwang Wu China
Hesam Izakian Canada
Marjan Kuchaki Rafsanjani Iran
Christian Prehofer Germany
Shingo Yamaguchi Japan
Ming‐Chao Chiang relative to P. Dhavachelvan India P. Dhavachelvan's profile →
Citations per field
00.5×2.6×
P. Dhavachelvan · 1×
Citations per year

Countries citing papers authored by Ming‐Chao Chiang

Since Specialization
Citations

This map shows the geographic impact of Ming‐Chao Chiang'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 Ming‐Chao Chiang with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Ming‐Chao Chiang more than expected).

Fields of papers citing papers by Ming‐Chao Chiang

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Ming‐Chao Chiang. 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 Ming‐Chao Chiang. The network helps show where Ming‐Chao Chiang may publish in the future.

Co-authorship network

The 25 scholars most cited alongside Ming‐Chao Chiang, 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 Ming‐Chao Chiang Line = papers co-authored together Ming‐Chao Chiang links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown
#Work
1 20234
2 20207
3 20201
4 20172
5 201646
6 20163
7 20156
8 201412
9 201326
10 20132
11
Social Networks-based Adaptive Pairing Strategy for Cooperative Learning
201212
12 20126
13 20117
14 201063
15 20103
16 201015
17 20095
18 20068
19
A Public Domain System for Camera Calibration and Distortion Correction
19966
20
The Integrating Resampler and EfficientImage Warping
19967

About Ming‐Chao Chiang

Ming‐Chao Chiang is a scholar working on Hardware and Architecture, Artificial Intelligence and Signal Processing, having authored 83 papers that have together received 1.3k indexed citations. Recurring topics across this work include Metaheuristic Optimization Algorithms Research (27 papers), Advanced Clustering Algorithms Research (14 papers), Advanced Multi-Objective Optimization Algorithms (11 papers), Data Management and Algorithms (10 papers), Data Mining Algorithms and Applications (7 papers), Embedded Systems Design Techniques (7 papers), Interconnection Networks and Systems (7 papers) and Evolutionary Algorithms and Applications (6 papers). The work is most often cited by research in Computer Networks and Communications (541 citations), Information Systems (337 citations) and Artificial Intelligence (436 citations). Ming‐Chao Chiang has collaborated with scholars based in Taiwan, United States and China. Frequent co-authors include Chun‐Wei Tsai, Laurence T. Yang, Chin‐Feng Lai, Chu‐Sing Yang, Terrance E. Boult, Weicheng Huang, Tzung‐Pei Hong, Shih-Pang Tseng, Min Chen and Jerry Chun‐Wei Lin. Their work appears in journals such as Computers in Human Behavior, IEEE Communications Surveys & Tutorials and IEEE Access.

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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