Adi Akavia

1.1k citations
20 papers · 200 · h-index 9

Impact in

Papers in

Adi Akavia

19 papers receiving 186 citations

Peers

Adi Akavia
Comparison fields: 5 of 26
  • Artificial Intelligence 131
  • Computational Theory and Mathematics 56
  • Computational Mechanics 52
  • Computer Vision and Pattern Recognition 47
  • Numerical Analysis 11
Replace Daniel S. Roche with:
Daniel S. Roche United States
Franz‐Josef Pfreundt Germany
Takeshi Shimoyama Japan
Dror Irony Israel
Baofeng Wu China
Toshiyasu Matsushima Japan
Kadir Akbudak Saudi Arabia
Farbod Roosta-Khorasani United States
Brian Bullins United States
John Voight United States
Adi Akavia relative to Daniel S. Roche United States Daniel S. Roche's profile →
Citations per field
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Daniel S. Roche · 1×
Citations per year

Countries citing papers authored by Adi Akavia

Since Specialization
Citations

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

Fields of papers citing papers by Adi Akavia

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

The 23 scholars most cited alongside Adi Akavia, 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 Adi Akavia Line = papers co-authored together Adi Akavia links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown
#Work
1 200439
2 200632
3
Deterministic Sparse Fourier Approximation via Fooling Arithmetic Progressions.
201021
4 202220
5 201818
6 201213
7 201911
8 201310
9 202210
10 20147
11 20223
12 20233
13 20193
14 20193
15 20232
16 20082
17 20241
18 20241
19
Finding Significant Fourier Transform Coefficients Deterministically and Locally
20081
20 20200

About Adi Akavia

Adi Akavia is a scholar working on Artificial Intelligence, Computational Theory and Mathematics, Information Systems, Computer Networks and Communications and Computer Vision and Pattern Recognition, having authored 20 papers that have together received 200 indexed citations. Recurring topics across this work include Cryptography and Data Security (15 papers), Privacy-Preserving Technologies in Data (7 papers), Complexity and Algorithms in Graphs (7 papers), Coding theory and cryptography (4 papers), Chaos-based Image/Signal Encryption (3 papers), Cryptography and Residue Arithmetic (2 papers), Mathematical Approximation and Integration (2 papers) and Mathematical Analysis and Transform Methods (2 papers). The work is most often cited by research in Artificial Intelligence (131 citations), Computational Theory and Mathematics (56 citations), Computational Mechanics (52 citations), Computer Vision and Pattern Recognition (47 citations) and Numerical Analysis (11 citations). Adi Akavia has collaborated with scholars based in Israel, India and Hong Kong. Frequent co-authors include Muli Safra, S. Goldwasser, Shafi Goldwasser, Dana Moshkovitz, Dan Feldman, Oded Goldreich, Yacov Manevich, Carmit Hazay, Shai Halevi and Craig Gentry. Their work appears in journals such as ACM Transactions on Privacy and Security, Genome Research, IACR Transactions on Cryptographic Hardware and Embedded Systems, Journal of Cryptology and IEEE Transactions on Information Theory.

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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