Muhammad Umar Aftab

1.6k citations
55 papers · 941 · h-index 17

Impact in

Papers in

Muhammad Umar Aftab

52 papers receiving 900 citations

Peers

Muhammad Umar Aftab
Comparison fields: 5 of 92
  • Computer Networks and Communications 234
  • Artificial Intelligence 329
  • Building and Construction 135
  • Computer Vision and Pattern Recognition 202
  • Information Systems 158
Replace Shen Wang with:
Shen Wang Ireland
Furqan Alam Saudi Arabia
Usha Devi Gandhi India
Petrus Mursanto Indonesia
François Carrez United Kingdom
Rajalakshmi Krishnamurthi India
Andreas P. Plageras Greece
Hanan Abdullah Mengash Saudi Arabia
Héctor Quintián Spain
Guiyang Luo China
Muhammad Umar Aftab relative to Shen Wang Ireland Shen Wang's profile →
Citations per field
00.5×2.6×
Shen Wang · 1×
Citations per year

Countries citing papers authored by Muhammad Umar Aftab

Since Specialization
Citations

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

Fields of papers citing papers by Muhammad Umar Aftab

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Muhammad Umar Aftab. 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 Muhammad Umar Aftab. The network helps show where Muhammad Umar Aftab may publish in the future.

Co-authors

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

All Works

20 of 20 papers shown

Showing the 20 most-cited of 55 papers — load more, or switch the sort, to bring in the rest.

#Work
1 2017140
2 202175
3 202164
4 201964
5 201651
6 202346
7 202032
8 202131
9 201927
10 201924
11 202122
12 201922
13 201521
14 201920
15 202118
16 201818
17 201818
18 202114
19 202114
20 201513

About Muhammad Umar Aftab

Muhammad Umar Aftab is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition, Information Systems, Sociology and Political Science and Computer Networks and Communications, having authored 55 papers that have together received 941 indexed citations. Recurring topics across this work include Cryptography and Data Security (11 papers), Video Surveillance and Tracking Methods (8 papers), Privacy-Preserving Technologies in Data (8 papers), Access Control and Trust (8 papers), Advanced Neural Network Applications (7 papers), Cloud Data Security Solutions (6 papers), Anomaly Detection Techniques and Applications (5 papers) and Blockchain Technology Applications and Security (5 papers). The work is most often cited by research in Computer Networks and Communications (234 citations), Artificial Intelligence (329 citations), Building and Construction (135 citations), Computer Vision and Pattern Recognition (202 citations) and Information Systems (158 citations). Muhammad Umar Aftab has collaborated with scholars based in Pakistan, China and Saudi Arabia. Frequent co-authors include Chi-Kin Chau, Chien Chen, Talal Rahwan, Zhiguang Qin, Ariyo Oluwasanmi, Zakria Zakria, Ngo Tung Son, Muhammad Shahzad Sarfraz, Xuyun Nie and Jianhua Deng. Their work appears in journals such as IEEE Access, Sensors, IEEE Transactions on Industrial Informatics, Symmetry 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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