Hammad Afzal

77 papers receiving 1.2k citations

Peers

Hammad Afzal
Comparison fields: 5 of 117
  • Health Informatics 32
  • General Dentistry 23
  • Signal Processing 159
  • Information Systems 297
  • Oral Surgery 83
Replace Sushruta Mishra with:
Sushruta Mishra India
Lewis Nkenyereye South Korea
Rashid Amin Pakistan
Antoni Ligęza Poland
Suliman Mohamed Fati Saudi Arabia
Noor Akhmad Setiawan Indonesia
Sachin Ahuja India
Hrudaya Kumar Tripathy India
Mohsen Kahani Iran
Muhammad Fermi Pasha Malaysia
Hammad Afzal relative to Sushruta Mishra India Sushruta Mishra's profile →
Citations per field
00.5×10×15×20×23.7×
Sushruta Mishra · 1×
Citations per year

Countries citing papers authored by Hammad Afzal

Since Specialization
Citations

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

Fields of papers citing papers by Hammad Afzal

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

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

All Works

20 of 20 papers shown

Showing the 20 most-cited of 84 papers — load more, or switch the sort, to bring in the rest.

#Work
1 2020145
2 201872
3 201762
4 202261
5 201758
6 202244
7 202143
8 202343
9 201841
10 201238
11 201935
12 201934
13 202133
14 202332
15 201827
16 201926
17 201926
18 202123
19 201819
20 202019

About Hammad Afzal

Hammad Afzal is a scholar working on Artificial Intelligence, Information Systems, Computer Networks and Communications, Computer Vision and Pattern Recognition and Signal Processing, having authored 84 papers that have together received 1.2k indexed citations. Recurring topics across this work include Topic Modeling (12 papers), Advanced Malware Detection Techniques (11 papers), Network Security and Intrusion Detection (9 papers), Internet Traffic Analysis and Secure E-voting (8 papers), Spam and Phishing Detection (8 papers), Sentiment Analysis and Opinion Mining (7 papers), IoT and Edge/Fog Computing (6 papers) and Software Engineering Research (5 papers). The work is most often cited by research in Health Informatics (32 citations), General Dentistry (23 citations), Signal Processing (159 citations), Information Systems (297 citations) and Oral Surgery (83 citations). Hammad Afzal has collaborated with scholars based in Pakistan, United Kingdom and South Korea. Frequent co-authors include Haider Abbas, Naima Iltaf, Muhammad Faisal Amjad, Raheel Nawaz, Waseem Iqbal, Yawar Abbas Bangash, Imran Siddiqi, Imran Rashid, Mehreen Ahmed and Imran Shafi. Their work appears in journals such as IEEE Access, Neural Computing and Applications, Sensors, Neurocomputing and Journal of Information Security and Applications.

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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