Gias Uddin

35 papers receiving 441 citations

Gias Uddin's Hit Papers

A benchmark study of machine learning models for online fake news detection 2021 · 137 citations
1370+1+3Years since publication4080120

Peers

Gias Uddin
Comparison fields: 5 of 72
  • Software 53
  • Information Systems 306
  • Computer Science Applications 39
  • Health Informatics 9
  • Signal Processing 71
Replace Maliheh Izadi with:
Maliheh Izadi Netherlands
Marcelo de Almeida Maia Brazil
Inah Omoronyia United Kingdom
Zhipeng Gao China
Sarah Chasins United States
Sallam Abualhaija Luxembourg
Razieh Nokhbeh Zaeem United States
Edison Marrese-Taylor Japan
Wafaa S. El-Kassas Egypt
Jorge Morato Spain
Gias Uddin relative to Maliheh Izadi Netherlands Maliheh Izadi's profile →
Citations per field
00.5×2.9×
Maliheh Izadi · 1×
Citations per year

Countries citing papers authored by Gias Uddin

Since Specialization
Citations

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

Fields of papers citing papers by Gias Uddin

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

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

All Works

20 of 20 papers shown

Showing the 20 most-cited of 39 papers — load more, or switch the sort, to bring in the rest.

#Work
1
A benchmark study of machine learning models for online fake news detection
Hit paper breakdown →
2021137
2 202248
3 201936
4 202228
5 202126
6 202223
7 202119
8 202216
9 201912
10 202112
11 202211
12 202311
13 20229
14 20227
15 20247
16 20227
17 20245
18 20225
19 20224
20 20154

About Gias Uddin

Gias Uddin is a scholar working on Information Systems, Artificial Intelligence, Computer Science Applications, Computer Networks and Communications and Software, having authored 39 papers that have together received 459 indexed citations. Recurring topics across this work include Software Engineering Research (25 papers), Open Source Software Innovations (8 papers), Software Engineering Techniques and Practices (8 papers), Topic Modeling (5 papers), Software Testing and Debugging Techniques (4 papers), Software Reliability and Analysis Research (4 papers), Software System Performance and Reliability (4 papers) and Mobile Crowdsensing and Crowdsourcing (3 papers). The work is most often cited by research in Software (53 citations), Information Systems (306 citations), Computer Science Applications (39 citations), Health Informatics (9 citations) and Signal Processing (71 citations). Gias Uddin has collaborated with scholars based in Canada, Bangladesh and United States. Frequent co-authors include Junaed Younus Khan, Anindya Iqbal, Sadia Afroz, Foutse Khomh, Chanchal K. Roy, Rifat Shahriyar, Omar Alam, Mohammed Abdou Janati Idrissi, Partha Chakraborty and Shaiful Chowdhury. Their work appears in journals such as Empirical Software Engineering, ACM Transactions on Software Engineering and Methodology, Information and Software Technology, Journal of Systems and Software and Journal of Applied Pharmaceutical 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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