Abdullah M. Albarrak

463 citations
22 papers · 194 indexed · 1 hit paper · h-index 7
Topics
Artificial Intelligence in Healthcare (5 papers)Data Management and Algorithms (4 papers)Advanced Database Systems and Queries (3 papers)

In The Last Decade

Abdullah M. Albarrak

18 papers receiving 188 citations

Hit Papers

Machine Learning-Based Predictive Models for Detection of...2024202620252024255075

Peers

Abdullah M. Albarrak
Comparison fields: 5 of 68
  • Artificial Intelligence 71
  • Health Information Management 50
  • Computer Networks and Communications 30
  • Computer Vision and Pattern Recognition 26
  • Signal Processing 26
Replace Junfeng Hu with:
Junfeng Hu China
Sakyajit Bhattacharya India
Md Tabrez Nafis India
Nahed Sharmen Bangladesh
Rajib Kumar Halder Bangladesh
Sarina Aminizadeh Iran
Ali Raad Lebanon
Mohammad Moshawrab Canada
Sallauddin Mohmmad India
Aswathy Ravikumar India
Abdullah M. Albarrak relative to Junfeng Hu China Junfeng Hu's profile →
Citations per field
00.5×2.5×
Junfeng Hu · 1×
Citations per year

Countries citing papers authored by Abdullah M. Albarrak

Since Specialization
Citations

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

Fields of papers citing papers by Abdullah M. Albarrak

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Abdullah M. Albarrak

This figure shows the co-authorship network connecting the top 25 collaborators of Abdullah M. Albarrak. A scholar is included among the top collaborators of Abdullah M. Albarrak 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 Abdullah M. Albarrak. Abdullah M. Albarrak 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
#WorkIndexed citations
1 0
2 0
3 1
4 0
5 2
6
Machine Learning-Based Predictive Models for Detection of Cardiovascular Diseasesbreakdown →
81
7 2
8 3
9 10
10 37
11 1
12 2
13 2
14 1
15 11
16 9
17 3
18 6
19 16
20 3

About Abdullah M. Albarrak

Abdullah M. Albarrak is a scholar working on Health Information Management, Health Informatics and Signal Processing, having authored 22 papers that have together received 194 indexed citations. Recurring topics across this work include Artificial Intelligence in Healthcare (5 papers), Data Management and Algorithms (4 papers) and Advanced Database Systems and Queries (3 papers). The work is most often cited by research in Health Information Management (50 citations), Health Informatics (14 citations) and Signal Processing (26 citations). Abdullah M. Albarrak has collaborated with scholars based in Saudi Arabia, Malaysia and United Kingdom. Frequent co-authors include Sultan Noman Qasem, Faisal Saeed, Shadi Basurra, Fuad A. Ghaleb, Mohamed A. Sharaf, H. Khan, Haza Nuzly Abdull Hamed, Habibollah Haron, Bander Ali Saleh Al‐rimy and Mohammed Al-Sarem. Their work appears in journals such as Scientific Reports, IEEE Access and Sensors.

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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