Y. Lan

23 papers receiving 535 citations

Hit Papers

A survey on federated learning: challenges and applications 2022 · 304 citations
3042022202620232024100200300

Peers

Y. Lan
Comparison fields: 5 of 106
  • Health Informatics 26
  • Artificial Intelligence 276
  • Computer Science Applications 23
  • Computer Networks and Communications 80
  • Information Systems 75
Replace Chin Soon Ku with:
Chin Soon Ku Malaysia
Young-Seob Jeong South Korea
Amit Kumar Das India
Michael R. Smith United States
Fawaz Khaled Alarfaj Saudi Arabia
Akhilesh Tiwari India
Bilal Alhayani Türkiye
Songnian Zhang Canada
Gautam Kunapuli United States
Y. Lan relative to Chin Soon Ku Malaysia Chin Soon Ku's profile →
Citations per field
00.5×
Chin Soon Ku · 1×
Citations per year

Countries citing papers authored by Y. Lan

Since Specialization
Citations

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

Fields of papers citing papers by Y. Lan

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

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

All Works

20 of 20 papers shown

Showing the 20 most-cited of 27 papers — load more, or switch the sort, to bring in the rest.

#Work
1
A survey on federated learning: challenges and applications
Hit paper breakdown →
2022304
2 202152
3 202140
4 201724
5 202221
6 200219
7 200718
8 199018
9 202414
10
Measuring Ionic Liquids Content in Water by UV-Vis Spectra
20087
11 20157
12 20196
13 20226
14 20253
15 20213
16 19913
17 20162
18 20251
19 20151
20 20221

About Y. Lan

Y. Lan is a scholar working on Nuclear and High Energy Physics, Computer Graphics and Computer-Aided Design, Industrial and Manufacturing Engineering, Modeling and Simulation and Signal Processing, having authored 27 papers that have together received 553 indexed citations. Recurring topics across this work include Atomic and Subatomic Physics Research (3 papers), Neutrino Physics Research (3 papers), Laser Design and Applications (2 papers), Music Technology and Sound Studies (2 papers), Particle accelerators and beam dynamics (2 papers), Spectroscopy and Laser Applications (2 papers), Advanced Battery Materials and Technologies (2 papers) and Advancements in Battery Materials (2 papers). The work is most often cited by research in Health Informatics (26 citations), Artificial Intelligence (276 citations), Computer Science Applications (23 citations), Computer Networks and Communications (80 citations) and Information Systems (75 citations). Y. Lan has collaborated with scholars based in China, Canada and United Kingdom. Frequent co-authors include Zhixia Zhang, Jie Wen, Zhihua Cui, Jianghui Cai, Wensheng Zhang, Savas Konur, Daniel Neagu, Xingjuan Cai, Dhavalkumar Thakker and Wensheng Zhang. Their work appears in journals such as International Journal of Machine Learning and Cybernetics, Energy storage materials, International Journal of Food Properties, Journal of Applied Physics and IEEE Transactions on Industrial Informatics.

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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