Md Abul Bashar

460 citations
23 papers · 203 · h-index 7

Impact in

Papers in

    • Topic Modeling 7
    • Advanced Text Analysis Techniques 4
    • Hate Speech and Cyberbullying Detection 4
    • Text and Document Classification Technologies 2
    • Quantum Information and Cryptography 2
    • Quantum Computing Algorithms and Architecture 2

Md Abul Bashar

21 papers receiving 198 citations

Peers

Md Abul Bashar
Comparison fields: 5 of 65
  • Computational Mathematics 6
  • Artificial Intelligence 112
  • Computer Vision and Pattern Recognition 58
  • Urban Studies 11
  • General Social Sciences 6
Replace Soheila Molaei with:
Soheila Molaei Iran
Massih-Reza Amini France
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Baoyu Jing United States
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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 19 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 23 papers — load more, or switch the sort, to bring in the rest.

#Work
1 202260
2 202023
3 202117
4 202113
5 202213
6 202112
7 201711
8 20226
9 20206
10 20236
11 20186
12 20096
13 20234
14 20244
15 20234
16 20243
17 20173
18 20162
19 20251
20 20231

About Md Abul Bashar

Md Abul Bashar is a scholar working on Artificial Intelligence, Information Systems, Computer Networks and Communications, Computer Vision and Pattern Recognition and Atomic and Molecular Physics, and Optics, having authored 23 papers that have together received 203 indexed citations. Recurring topics across this work include Topic Modeling (7 papers), Advanced Text Analysis Techniques (4 papers), Hate Speech and Cyberbullying Detection (4 papers), Text and Document Classification Technologies (2 papers), Quantum Mechanics and Applications (2 papers), Quantum Information and Cryptography (2 papers), Time Series Analysis and Forecasting (2 papers) and Quantum Computing Algorithms and Architecture (2 papers). The work is most often cited by research in Computational Mathematics (6 citations), Artificial Intelligence (112 citations), Computer Vision and Pattern Recognition (58 citations), Urban Studies (11 citations) and General Social Sciences (6 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, Abby Cathcart, Yang Gao, Mahinthan Chandramohan, Raymond Y.K. Lau, Yue Xu and Rajibul Islam. Their work appears in journals such as Social Network Analysis and Mining, Intelligent Systems with Applications, ACM Transactions on Intelligent Systems and Technology, Pattern Recognition and Data Mining and Knowledge Discovery.

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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