Paul Rad

3.2k citations
67 papers · 1.7k · 1 hit paper · h-index 22

Impact in

Papers in

Paul Rad

66 papers receiving 1.6k citations

Paul Rad's Hit Papers

Detecting Internet of Things attacks using distributed deep learning 2020 · 219 citations
2190+2+4Years since publication50100150200

Peers

Paul Rad
Comparison fields: 5 of 124
  • Signal Processing 311
  • Computer Networks and Communications 526
  • Computer Vision and Pattern Recognition 373
  • Artificial Intelligence 480
  • Information Systems 327
Replace Teddy Surya Gunawan with:
Teddy Surya Gunawan Malaysia
Roobaea Alroobaea Saudi Arabia
Zahid Halim Pakistan
Manju Khari India
Kah Phooi Seng Malaysia
Fahd N. Al‐Wesabi Saudi Arabia
Khalil El‐Khatib Canada
Kamal Z. Zamli Malaysia
Anis Yazidi Norway
Hoshang Kolivand United Kingdom
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Citations per field
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Citations per year

Countries citing papers authored by Paul Rad

Since Specialization
Citations

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

Fields of papers citing papers by Paul Rad

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

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

All Works

20 of 20 papers shown

Showing the 20 most-cited of 67 papers — load more, or switch the sort, to bring in the rest.

#Work
1
Detecting Internet of Things attacks using distributed deep learning
Hit paper breakdown →
2020219
2 2017179
3 2018128
4 201981
5 201973
6 201857
7 202056
8 202055
9 201751
10 201745
11 201645
12 201542
13 201833
14 202131
15 201727
16 201826
17 201625
18 202024
19 201723
20 201923

About Paul Rad

Paul Rad is a scholar working on Computer Vision and Pattern Recognition, Artificial Intelligence, Information Systems, Computer Networks and Communications and Cognitive Neuroscience, having authored 67 papers that have together received 1.7k indexed citations. Recurring topics across this work include Cloud Computing and Resource Management (9 papers), Video Surveillance and Tracking Methods (7 papers), IoT and Edge/Fog Computing (6 papers), Autonomous Vehicle Technology and Safety (5 papers), Advanced Neural Network Applications (5 papers), Advanced Data Storage Technologies (5 papers), EEG and Brain-Computer Interfaces (4 papers) and Anomaly Detection Techniques and Applications (4 papers). The work is most often cited by research in Signal Processing (311 citations), Computer Networks and Communications (526 citations), Computer Vision and Pattern Recognition (373 citations), Artificial Intelligence (480 citations) and Information Systems (327 citations). Paul Rad has collaborated with scholars based in United States, Iran and Poland. Frequent co-authors include Kim‐Kwang Raymond Choo, Mehdi Roopaei, Gonzalo De La Torre Parra, Mo Jamshidi, Nicole Beebe, Gonzalo de la Torre, John J. Prevost, Yufei Huang, Patrick Benavidez and Arun Das. Their work appears in journals such as Journal of Network and Computer Applications, IEEE Access, IEEE Transactions on Big Data, Future Generation Computer Systems and Information Systems Frontiers.

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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