Albert No

514 citations
28 papers · 200 · h-index 8

Impact in

Papers in

Albert No

24 papers receiving 195 citations

Peers

Albert No
Comparison fields: 5 of 49
  • Computer Networks and Communications 70
  • Artificial Intelligence 68
  • Computer Vision and Pattern Recognition 41
  • General Decision Sciences 3
  • Computational Theory and Mathematics 20
Replace M. Vanitha with:
M. Vanitha India
Antony Joseph United States
Himanshu Asnani United States
David Jaz Myers United Kingdom
S. K. Setua India
Moritz Wiese Germany
Jüri Lember Estonia
Takashi Horiyama Japan
Prateek Yadav India
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Citations per field
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Citations per year

Countries citing papers authored by Albert No

Since Specialization
Citations

This map shows the geographic impact of Albert No'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 Albert No with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Albert No more than expected).

Fields of papers citing papers by Albert No

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Albert No. 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 Albert No. The network helps show where Albert No may publish in the future.

Co-authors

The 25 scholars most cited alongside Albert No, 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 Albert No Line = papers co-authored together Albert No links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown

Showing the 20 most-cited of 28 papers — load more, or switch the sort, to bring in the rest.

#Work
1 201941
2 202135
3 201621
4 201418
5 202012
6 20189
7 20169
8 20159
9 20237
10 20235
11 20195
12 20144
13 20224
14 20213
15 20183
16 20162
17 20202
18 20132
19 20192
20 20232

About Albert No

Albert No is a scholar working on Artificial Intelligence, Molecular Biology, Computer Networks and Communications, Computer Vision and Pattern Recognition and Computational Theory and Mathematics, having authored 28 papers that have together received 200 indexed citations. Recurring topics across this work include Algorithms and Data Compression (11 papers), DNA and Biological Computing (6 papers), Advanced biosensing and bioanalysis techniques (5 papers), Cellular Automata and Applications (5 papers), Wireless Communication Security Techniques (5 papers), Error Correcting Code Techniques (4 papers), Genomics and Phylogenetic Studies (4 papers) and Advanced Data Compression Techniques (3 papers). The work is most often cited by research in Computer Networks and Communications (70 citations), Artificial Intelligence (68 citations), Computer Vision and Pattern Recognition (41 citations), General Decision Sciences (3 citations) and Computational Theory and Mathematics (20 citations). Albert No has collaborated with scholars based in South Korea, United States and China. Frequent co-authors include Tsachy Weissman, Minseok Choi, Mingyue Ji, Joongheon Kim, Sunghwan Kim, Amir Ingber, Jaeho Jeong, Jong‐Seon No, Hosung Park and Seong‐Joon Park. Their work appears in journals such as IEEE Access, IEEE Transactions on Information Theory, Bioinformatics, BMC Bioinformatics and Applied Sciences.

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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