Md Abul Bashar

486 citations
31 papers · 275 · h-index 9

Impact in

Papers in

    • Topic Modeling 12
    • Hate Speech and Cyberbullying Detection 6
    • Advanced Text Analysis Techniques 5
    • Domain Adaptation and Few-Shot Learning 3
    • Text and Document Classification Technologies 3
    • Machine Learning in Healthcare 2
    • Data Mining Algorithms and Applications 2

Md Abul Bashar

28 papers receiving 268 citations

Peers

Md Abul Bashar
Comparison fields: 5 of 71
  • Computational Mathematics 6
  • Artificial Intelligence 162
  • Computer Vision and Pattern Recognition 70
  • General Social Sciences 6
  • Urban Studies 11
Replace Soheila Molaei with:
Soheila Molaei Iran
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Massih-Reza Amini France
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Citations per field
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Citations per year

Countries citing papers authored by Md Abul Bashar

Since Specialization
Citations

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

Fields of papers citing papers by Md Abul Bashar

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Md Abul Bashar. 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 Md Abul Bashar. The network helps show where Md Abul Bashar may publish in the future.

Co-authors

The 21 scholars most cited alongside Md Abul Bashar, 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 Md Abul Bashar Line = papers co-authored together Md Abul Bashar links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown

Showing the 20 most-cited of 31 papers — load more, or switch the sort, to bring in the rest.

#Work
1 202267
2 202026
3 202120
4 201919
5 202116
6 202115
7 201713
8 202213
9 202110
10 20227
11 20207
12 20237
13 20186
14 20096
15 20235
16 20245
17 20165
18 20235
19 20244
20 20174

About Md Abul Bashar

Md Abul Bashar is a scholar working on Artificial Intelligence, Information Systems, Computer Vision and Pattern Recognition, Computer Networks and Communications and Signal Processing, having authored 31 papers that have together received 275 indexed citations. Recurring topics across this work include Topic Modeling (12 papers), Hate Speech and Cyberbullying Detection (6 papers), Advanced Text Analysis Techniques (5 papers), Domain Adaptation and Few-Shot Learning (3 papers), Advanced Image and Video Retrieval Techniques (3 papers), Text and Document Classification Technologies (3 papers), Data Mining Algorithms and Applications (2 papers) and Machine Learning in Healthcare (2 papers). The work is most often cited by research in Computational Mathematics (6 citations), Artificial Intelligence (162 citations), Computer Vision and Pattern Recognition (70 citations), General Social Sciences (6 citations) and Urban Studies (11 citations). Md Abul Bashar has collaborated with scholars based in Australia, China and Bangladesh. Frequent co-authors include Richi Nayak, Thirunavukarasu Balasubramaniam, Nicolas Suzor, Yuefeng Li, Yang Gao, Sam Cunningham, Abby Cathcart, Yue Xu, Raymond Y.K. Lau and Mahinthan Chandramohan. Their work appears in journals such as Social Network Analysis and Mining, ACM Transactions on Intelligent Systems and Technology, Pattern Recognition, Intelligent Systems with Applications and Knowledge-Based Systems.

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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