Hong Xiao

915 citations
71 papers · 632 indexed · h-index 15

Hong Xiao

63 papers receiving 611 citations

Peers

Hong Xiao
Comparison fields: 5 of 100
  • Artificial Intelligence 331
  • Computational Theory and Mathematics 87
  • Computer Vision and Pattern Recognition 103
  • Industrial and Manufacturing Engineering 45
  • Computer Networks and Communications 81
Replace Michele Lombardi with:
Michele Lombardi Italy
Nailah Al–Madi Jordan
Lixin Ding China
Marcin Korytkowski Poland
Waleed Alomoush United Arab Emirates
Mohamed H. Haggag Egypt
Khaled Mellouli Tunisia
Krzysztof Michalak Poland
Preyas Popat United States
Hong Xiao relative to Michele Lombardi Italy Michele Lombardi's profile →
Citations per field
00.5×4.0×
Michele Lombardi · 1×
Citations per year

Countries citing papers authored by Hong Xiao

Since Specialization
Citations

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

Fields of papers citing papers by Hong Xiao

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Hong Xiao. 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 Hong Xiao. The network helps show where Hong Xiao may publish in the future.

Co-authorship network

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

All Works

20 of 20 papers shown
#Work
1 20250
2 20254
3 20250
4 20250
5 20242
6 20232
7 20230
8 20233
9 20221
10 202016
11 20194
12 20192
13 201817
14 201825
15 20187
16 201813
17 201716
18 201720
19 20175
20 201312

About Hong Xiao

Hong Xiao is a scholar working on Artificial Intelligence, Industrial and Manufacturing Engineering, Computer Vision and Pattern Recognition, Medical Laboratory Technology and Computer Networks and Communications, having authored 71 papers that have together received 632 indexed citations. Recurring topics across this work include Quantum Computing Algorithms and Architecture (12 papers), Neural Networks and Applications (6 papers), Quantum Information and Cryptography (6 papers), Neural Networks and Reservoir Computing (5 papers), Quantum-Dot Cellular Automata (5 papers), IoT and Edge/Fog Computing (4 papers), Digital Transformation in Industry (4 papers) and Industrial Vision Systems and Defect Detection (4 papers). The work is most often cited by research in Artificial Intelligence (331 citations), Computational Theory and Mathematics (87 citations), Computer Vision and Pattern Recognition (103 citations), Industrial and Manufacturing Engineering (45 citations) and Computer Networks and Communications (81 citations). Hong Xiao has collaborated with scholars based in China, Norway and Macao. Frequent co-authors include Panchi Li, Hao Wang, Hong‐Ning Dai, Junhao Zhou, Xiande Liu, Ya Zhao, Xuran Li, Wenchao Jiang, Suisheng Li and Qiu Wang. Their work appears in journals such as International Journal of Intelligent Systems, Sensors, Quantum Information Processing, IEEE Access and Optics Express.

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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