Muhammad Azeem Abbas

439 citations
37 papers · 278 indexed · h-index 9

Muhammad Azeem Abbas

35 papers receiving 264 citations

Peers

Muhammad Azeem Abbas
Comparison fields: 5 of 80
  • Computer Science Applications 73
  • Health Informatics 6
  • Information Systems 94
  • Health Information Management 14
  • Artificial Intelligence 83
Replace Rukshan Athauda with:
Rukshan Athauda Australia
Md. Alimul Haque India
Rosa Alarcón Chile
Abdul Wahid India
Monica Ciolacu Germany
Lidia Stanganelli Italy
Sujan Kumar Saha India
Kamal Uddin Sarker Malaysia
Thomas Way United States
Jun‐Ming Su Taiwan
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Citations per year

Countries citing papers authored by Muhammad Azeem Abbas

Since Specialization
Citations

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

Fields of papers citing papers by Muhammad Azeem Abbas

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network

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

All Works

20 of 20 papers shown
#Work
1 20242
2 20222
3 20216
4 20214
5 202123
6 20212
7 20208
8
An Empirical Throughput Analysis of Multimedia Applications with OpenFlow-based Dynamic Load Balancing Approach in WLAN
20191
9
Determining Influential Factors and Challenges in Automatic Taxonomy Generation: A Systematic Literature Review of Techniques 1999-2016.
20191
10 20194
11 20181
12
Enabling profound hearing impaired children to articulate words using lip-reading through software application.
20181
13 201825
14 20171
15 201742
16 20152
17 20145
18 20122
19 20123
20 20093

About Muhammad Azeem Abbas

Muhammad Azeem Abbas is a scholar working on Computer Science Applications, Information Systems and Artificial Intelligence, having authored 37 papers that have together received 278 indexed citations. Recurring topics across this work include Mobile Learning in Education (4 papers), Intelligent Tutoring Systems and Adaptive Learning (4 papers), Online Learning and Analytics (3 papers), Software Engineering Research (3 papers), Topic Modeling (3 papers), Recommender Systems and Techniques (3 papers), Natural Language Processing Techniques (3 papers) and Advanced Text Analysis Techniques (3 papers). The work is most often cited by research in Computer Science Applications (73 citations), Health Informatics (6 citations) and Information Systems (94 citations). Muhammad Azeem Abbas has collaborated with scholars based in Pakistan, United Kingdom and Malaysia. Frequent co-authors include Sharifullah Khan, Yaser Hafeez, Asif Nawaz, Gwo‐Jen Hwang, Bushra Hamid, Muhammad Taha Jilani, Arshia Rehman, Muhammad Aqib, Sadia Ali and Saheed Ajayi. Their work appears in journals such as Interactive Learning Environments, International Journal of Mobile Learning and Organisation, British Journal of Educational Technology, Education and Information Technologies and Tehnicki vjesnik - Technical Gazette.

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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