John V. Monaco

996 citations
41 papers · 630 · h-index 14

Impact in

Papers in

    • User Authentication and Security Systems 28
    • Spam and Phishing Detection 4
    • Biometric Identification and Security 17
    • Advanced Malware Detection Techniques 11

John V. Monaco

39 papers receiving 590 citations

Peers

John V. Monaco
Comparison fields: 5 of 80
  • Signal Processing 331
  • Human-Computer Interaction 124
  • Information Systems 447
  • Artificial Intelligence 171
  • Computer Vision and Pattern Recognition 89
Replace Daniele Gunetti with:
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Citations per field
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Citations per year

Countries citing papers authored by John V. Monaco

Since Specialization
Citations

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

Fields of papers citing papers by John V. Monaco

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by John V. Monaco. 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 John V. Monaco. The network helps show where John V. Monaco may publish in the future.

Co-authors

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

All Works

20 of 20 papers shown

Showing the 20 most-cited of 41 papers — load more, or switch the sort, to bring in the rest.

#Work
1 201682
2 201355
3 201151
4 202143
5 201737
6 201535
7 201830
8 201230
9 201624
10 202022
11 201318
12 201618
13 201316
14 201514
15 201813
16 201813
17 201612
18 201511
19 202010
20 201710

About John V. Monaco

John V. Monaco is a scholar working on Information Systems, Signal Processing, Artificial Intelligence, Human-Computer Interaction and Computer Vision and Pattern Recognition, having authored 41 papers that have together received 630 indexed citations. Recurring topics across this work include User Authentication and Security Systems (28 papers), Biometric Identification and Security (17 papers), Advanced Malware Detection Techniques (11 papers), Spam and Phishing Detection (4 papers), Authorship Attribution and Profiling (4 papers), Advanced Memory and Neural Computing (4 papers), Interactive and Immersive Displays (3 papers) and Hand Gesture Recognition Systems (3 papers). The work is most often cited by research in Signal Processing (331 citations), Human-Computer Interaction (124 citations), Information Systems (447 citations), Artificial Intelligence (171 citations) and Computer Vision and Pattern Recognition (89 citations). John V. Monaco has collaborated with scholars based in United States, Spain and India. Frequent co-authors include Charles C. Tappert, Sung-Hyuk Cha, John Stewart, Md Liakat Ali, Meikang Qiu, Aythami Morales, Alejandro Acien, Rubén Vera-Rodríguez, Julián Fiérrez and Md Liakat Ali. Their work appears in journals such as Journal of Statistical Software, IEEE Transactions on Circuits and Systems I Regular Papers, Pattern Recognition, Journal of Signal Processing Systems and Concurrency and Computation Practice and Experience.

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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