Suzaimah Ramli

422 total citations
40 papers, 257 citations indexed

About

Suzaimah Ramli is a scholar working on Computer Vision and Pattern Recognition, Artificial Intelligence and Information Systems. According to data from OpenAlex, Suzaimah Ramli has authored 40 papers receiving a total of 257 indexed citations (citations by other indexed papers that have themselves been cited), including 12 papers in Computer Vision and Pattern Recognition, 9 papers in Artificial Intelligence and 7 papers in Information Systems. Recurrent topics in Suzaimah Ramli's work include Industrial Vision Systems and Defect Detection (6 papers), Sentiment Analysis and Opinion Mining (4 papers) and Video Surveillance and Tracking Methods (4 papers). Suzaimah Ramli is often cited by papers focused on Industrial Vision Systems and Defect Detection (6 papers), Sentiment Analysis and Opinion Mining (4 papers) and Video Surveillance and Tracking Methods (4 papers). Suzaimah Ramli collaborates with scholars based in Malaysia, Indonesia and Morocco. Suzaimah Ramli's co-authors include Mohd Marzuki Mustafa, Aini Hussain, Aouache Mustapha, Mohd Asyraf Zulkifley, Slamet Riyadi, Mohd Dzulkhairi Mohd Rani, Nurul Azmawati Mohamed, Tengku Mohd Tengku Sembok, Siti Zulaikha Ngah Demon and Yessi Jusman and has published in prestigious journals such as SHILAP Revista de lepidopterología, PLoS ONE and IEEE Access.

In The Last Decade

Suzaimah Ramli

29 papers receiving 248 citations

Peers

Suzaimah Ramli
Comparison fields: 5 of 85
  • Artificial Intelligence 59
  • Building and Construction 41
  • Information Systems 39
  • Control and Systems Engineering 35
  • Computer Vision and Pattern Recognition 34
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Citations per field, relative to Suzaimah Ramli
Suzaimah Ramli · 1×
Citations per year, relative to Suzaimah Ramli
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Countries citing papers authored by Suzaimah Ramli

Since Specialization
Citations

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

Fields of papers citing papers by Suzaimah Ramli

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Suzaimah Ramli

This figure shows the co-authorship network connecting the top 25 collaborators of Suzaimah Ramli. A scholar is included among the top collaborators of Suzaimah Ramli based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Suzaimah Ramli. Suzaimah Ramli is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
# Work Indexed citations
1 0
2 0
3 0
4 13
5 4
6 0
7 2
8 1
9 1
10 1
11 1
12 3
13 13
14 0
15
Aggressive movement detection using optical flow features base on digital & thermal camera
2
16 0
17 34
18 2
19 0
20 6

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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