Ming‐Chao Chiang
- Computer Networks and Communications top 2%
- Artificial Intelligence top 5%
- Information Systems top 2%
- Computer Vision and Pattern Recognition top 5%
- Electrical and Electronic Engineering
- Co-authors
- Chun‐Wei TsaiLaurence T. YangChin‐Feng LaiChu‐Sing YangTerrance E. BoultWeicheng HuangTzung‐Pei HongShih-Pang Tseng
- Topics
- Metaheuristic Optimization Algorithms Research (27 papers)Advanced Clustering Algorithms Research (14 papers)Advanced Multi-Objective Optimization Algorithms (11 papers)
- Partner nations
- TaiwanUnited StatesChina
In The Last Decade
Ming‐Chao Chiang
78 papers receiving 1.2k citations
Hit Papers
Peers
Comparison fields: 5 of 101
- Computer Networks and Communications 541
- Artificial Intelligence 436
- Information Systems 337
- Computer Vision and Pattern Recognition 269
- Electrical and Electronic Engineering 157
Countries citing papers authored by Ming‐Chao Chiang
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
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 of co-authors of Ming‐Chao Chiang
This figure shows the co-authorship network connecting the top 25 collaborators of Ming‐Chao Chiang. A scholar is included among the top collaborators of Ming‐Chao Chiang based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Ming‐Chao Chiang. Ming‐Chao Chiang is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 4 | |
| 2 | 7 | |
| 3 | 1 | |
| 4 | 2 | |
| 5 | 46 | |
| 6 | 3 | |
| 7 | 6 | |
| 8 | 12 | |
| 9 | 26 | |
| 10 | 2 | |
| 11 | Social Networks-based Adaptive Pairing Strategy for Cooperative Learning | 12 |
| 12 | 6 | |
| 13 | 7 | |
| 14 | 63 | |
| 15 | 3 | |
| 16 | 15 | |
| 17 | 5 | |
| 18 | 8 | |
| 19 | A Public Domain System for Camera Calibration and Distortion Correction | 6 |
| 20 | The Integrating Resampler and EfficientImage Warping | 7 |
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) and Advanced Multi-Objective Optimization Algorithms (11 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.