A. Sheik Abdullah

567 citations
50 papers · 231 · h-index 8

Impact in

Papers in

    • Imbalanced Data Classification Techniques 8
    • Sentiment Analysis and Opinion Mining 3
    • Machine Learning and Data Classification 3
    • Anomaly Detection Techniques and Applications 2
    • Artificial Intelligence in Healthcare 14

A. Sheik Abdullah

45 papers receiving 215 citations

Peers

A. Sheik Abdullah
Comparison fields: 5 of 71
  • Health Information Management 87
  • Computer Science Applications 25
  • Medical Laboratory Technology 6
  • Health Informatics 5
  • Artificial Intelligence 99
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Citations per field
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Citations per year

Countries citing papers authored by A. Sheik Abdullah

Since Specialization
Citations

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

Fields of papers citing papers by A. Sheik Abdullah

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by A. Sheik Abdullah. 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 A. Sheik Abdullah. The network helps show where A. Sheik Abdullah may publish in the future.

Co-authors

The 24 scholars most cited alongside A. Sheik Abdullah, 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 A. Sheik Abdullah Line = papers co-authored together A. Sheik Abdullah links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown

Showing the 20 most-cited of 50 papers — load more, or switch the sort, to bring in the rest.

#Work
1
A Data mining Model for predicting the Coronary Heart Disease using Random Forest Classifier
201241
2 201721
3 201818
4 202412
5 202112
6 202311
7 201711
8 201211
9 20187
10 20226
11 20186
12 20215
13 20245
14 20224
15 20184
16 20214
17 20243
18
RANDOMIZED CLINICAL TRIAL OF SINGLE VERSUS THREE DOSES OF CEFAZOLIN AS PROPHYLAXIS FOR NONPERFORATED ACUTE APPENDICITIS
20123
19 20243
20 20183

About A. Sheik Abdullah

A. Sheik Abdullah is a scholar working on Artificial Intelligence, Health Information Management, Computer Vision and Pattern Recognition, Computer Science Applications and Computer Networks and Communications, having authored 50 papers that have together received 231 indexed citations. Recurring topics across this work include Artificial Intelligence in Healthcare (14 papers), Imbalanced Data Classification Techniques (8 papers), Online Learning and Analytics (5 papers), Stock Market Forecasting Methods (3 papers), Diabetes Management and Research (3 papers), Sentiment Analysis and Opinion Mining (3 papers), Machine Learning and Data Classification (3 papers) and Anomaly Detection Techniques and Applications (2 papers). The work is most often cited by research in Health Information Management (87 citations), Computer Science Applications (25 citations), Medical Laboratory Technology (6 citations), Health Informatics (5 citations) and Artificial Intelligence (99 citations). A. Sheik Abdullah has collaborated with scholars based in India, Malaysia and Türkiye. Frequent co-authors include P. Karthikeyan, R. Suganya, Murali Venkatesh, S. Rajaram, S. Geetha, Vinod Kumar, S. Geetha, S. Selvakumar, A. M. Abirami and V. Gomathi. Their work appears in journals such as Soft Computing, Multimedia Tools and Applications, Annals of Operations Research, Alexandria Engineering Journal and Results in Engineering.

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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