Sapiah Sakri

595 citations
16 papers · 374 indexed · h-index 7
Topics
Artificial Intelligence in Healthcare (5 papers)AI in cancer detection (3 papers)Imbalanced Data Classification Techniques (3 papers)
Journals
SHILAP Revista de lepidopterologíaIEEE AccessInformation and Software Technology

In The Last Decade

Sapiah Sakri

15 papers receiving 342 citations

Peers

Sapiah Sakri
Comparison fields: 5 of 78
  • Artificial Intelligence 205
  • Health Information Management 75
  • Computer Vision and Pattern Recognition 59
  • Neurology 58
  • Information Systems 54
Replace Luping Liu with:
Luping Liu China
Ito Wasito Indonesia
Vibhav Prakash Singh India
Rita Chhikara India
Omar Hisham Alsadoon Iraq
Vandana Bhattacharjee India
Arfan Jaffar Pakistan
J. Arokia Renjit India
C. Kavitha India
Sang‐Woong Lee South Korea
Sapiah Sakri relative to Luping Liu China Luping Liu's profile →
Citations per field
00.5×5.8×
Luping Liu · 1×
Citations per year

Countries citing papers authored by Sapiah Sakri

Since Specialization
Citations

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

Fields of papers citing papers by Sapiah Sakri

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Sapiah Sakri

This figure shows the co-authorship network connecting the top 25 collaborators of Sapiah Sakri. A scholar is included among the top collaborators of Sapiah Sakri 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 Sapiah Sakri. Sapiah Sakri is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

16 of 16 papers shown
#WorkIndexed citations
1 7
2 2
3 1
4 7
5 1
6 1
7 38
8 32
9 6
10 8
11 4
12 2
13 5
14 76
15 4
16 180

About Sapiah Sakri

Sapiah Sakri is a scholar working on Health Information Management, Computer Science Applications and General Social Sciences, having authored 16 papers that have together received 374 indexed citations. Recurring topics across this work include Artificial Intelligence in Healthcare (5 papers), AI in cancer detection (3 papers) and Imbalanced Data Classification Techniques (3 papers). The work is most often cited by research in Health Information Management (75 citations), Neurology (58 citations) and Artificial Intelligence (205 citations). Sapiah Sakri has collaborated with scholars based in Saudi Arabia, Malaysia and India. Frequent co-authors include Zuhaira Muhammad Zain, Shakila Basheer, Surbhi Bhatia, Senthilkumar Mohan, Abdul Quadir, Ghadah Aldehim, Ala Saleh Alluhaidan, Mamoon Rashid, Jasni Mohamad Zain and Nazik Alturki. Their work appears in journals such as SHILAP Revista de lepidopterología, IEEE Access and Information and Software Technology.

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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