Muhammad Ehatisham-ul-Haq

1.7k citations
46 papers · 1.2k · h-index 19

Impact in

Papers in

Muhammad Ehatisham-ul-Haq

46 papers receiving 1.2k citations

Peers

Muhammad Ehatisham-ul-Haq
Comparison fields: 5 of 91
  • Computer Vision and Pattern Recognition 779
  • Human-Computer Interaction 103
  • Signal Processing 173
  • Artificial Intelligence 364
  • Information Systems 208
Replace Sozo Inoue with:
Sozo Inoue Japan
Le T. Nguyen United States
Jang‐Hee Yoo South Korea
Gwenn Englebienne Netherlands
Daniela Micucci Italy
Bogdan Ionescu Romania
Yufei Chen China
Pedro C. Diniz United States
Tanır Özçelebi Netherlands
Petko Georgiev United Kingdom
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Citations per field
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Citations per year

Countries citing papers authored by Muhammad Ehatisham-ul-Haq

Since Specialization
Citations

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

Fields of papers citing papers by Muhammad Ehatisham-ul-Haq

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Muhammad Ehatisham-ul-Haq. 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 Ehatisham-ul-Haq. The network helps show where Muhammad Ehatisham-ul-Haq may publish in the future.

Co-authors

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

All Works

20 of 20 papers shown

Showing the 20 most-cited of 46 papers — load more, or switch the sort, to bring in the rest.

#Work
1 2019140
2 2019126
3 2021117
4 201896
5 201788
6 202061
7 202059
8 202052
9 202043
10 202042
11 202233
12 202033
13 202029
14 201929
15 202029
16 202025
17 202023
18 202020
19 201918
20 201817

About Muhammad Ehatisham-ul-Haq

Muhammad Ehatisham-ul-Haq is a scholar working on Computer Vision and Pattern Recognition, Artificial Intelligence, Computer Networks and Communications, Information Systems and Human-Computer Interaction, having authored 46 papers that have together received 1.2k indexed citations. Recurring topics across this work include Context-Aware Activity Recognition Systems (21 papers), IoT and Edge/Fog Computing (9 papers), User Authentication and Security Systems (7 papers), Chaos-based Image/Signal Encryption (7 papers), Coding theory and cryptography (6 papers), Cryptographic Implementations and Security (5 papers), EEG and Brain-Computer Interfaces (5 papers) and Non-Invasive Vital Sign Monitoring (4 papers). The work is most often cited by research in Computer Vision and Pattern Recognition (779 citations), Human-Computer Interaction (103 citations), Signal Processing (173 citations), Artificial Intelligence (364 citations) and Information Systems (208 citations). Muhammad Ehatisham-ul-Haq has collaborated with scholars based in Pakistan, United Kingdom and Saudi Arabia. Frequent co-authors include Muhammad Awais Azam, Usman Naeem, Faisal Hussain, Fawad Hussain, Nasir Siddiqui, Yasar Amin, Asra Khalid, Sadaqat Ur Rehman, Shanshan Tu and Raja Majid Mehmood. Their work appears in journals such as IEEE Access, IEEE Sensors Journal, Electronics, Sensors and PLoS ONE.

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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