Mohib Ullah

686 citations
47 papers · 374 · h-index 11

Impact in

    • AI in cancer detection
    • Internet Traffic Analysis and Secure E-voting
    • Anomaly Detection Techniques and Applications
    • Privacy-Preserving Technologies in Data
    • Digital Imaging for Blood Diseases
    • Video Surveillance and Tracking Methods

Papers in

Mohib Ullah

46 papers receiving 362 citations

Peers

Mohib Ullah
Comparison fields: 5 of 94
  • Artificial Intelligence 175
  • Computer Vision and Pattern Recognition 107
  • Health Informatics 3
  • Water Science and Technology 24
  • Computer Networks and Communications 36
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Noelia Vállez Spain
Anjali Gautam India
Abdul Hannan Khan Pakistan
C. Karthikeyan India
Zulfikar Alom Bangladesh
Santosh Kumar Vishwakarma India
Fahd S. Alharithi Saudi Arabia
Syed Inthiyaz India
Neelamadhab Padhy India
Md. Anwar Hussen Wadud Bangladesh
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Citations per field
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Citations per year

Countries citing papers authored by Mohib Ullah

Since Specialization
Citations

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

Fields of papers citing papers by Mohib Ullah

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

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

All Works

20 of 20 papers shown

Showing the 20 most-cited of 47 papers — load more, or switch the sort, to bring in the rest.

#Work
1 202148
2 202139
3 202131
4 202026
5 202223
6 202016
7 201514
8 201412
9 202011
10 201910
11 202110
12 20238
13 20247
14 20237
15 20206
16 20176
17 20196
18 20236
19 20216
20 20235

About Mohib Ullah

Mohib Ullah is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition, Sociology and Political Science, Radiology, Nuclear Medicine and Imaging and Molecular Biology, having authored 47 papers that have together received 374 indexed citations. Recurring topics across this work include Privacy-Preserving Technologies in Data (9 papers), Privacy, Security, and Data Protection (7 papers), Video Surveillance and Tracking Methods (6 papers), Internet Traffic Analysis and Secure E-voting (6 papers), Anomaly Detection Techniques and Applications (5 papers), AI in cancer detection (5 papers), Spam and Phishing Detection (4 papers) and Sentiment Analysis and Opinion Mining (3 papers). The work is most often cited by research in Artificial Intelligence (175 citations), Computer Vision and Pattern Recognition (107 citations), Health Informatics (3 citations), Water Science and Technology (24 citations) and Computer Networks and Communications (36 citations). Mohib Ullah has collaborated with scholars based in Pakistan, Saudi Arabia and Norway. Frequent co-authors include Rafiullah Khan, Irfan Ullah Khan, Nida Aslam, Sumayh S. Aljameel, M. Irfan Uddin, Atif Khan, Nicola Conci, Habib Ullah, Muhammad Arshad Islam and Talha Anwar. Their work appears in journals such as Complexity, IEEE Access, Computers in Biology and Medicine, Frontiers in Bioengineering and Biotechnology and Frontiers in Plant Science.

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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