Mona Jamjoom

1.8k citations
73 papers · 1.0k indexed · h-index 18

Impact in

Papers in

Mona Jamjoom

64 papers receiving 970 citations

Peers

Mona Jamjoom
Comparison fields: 5 of 134
  • Neurology 140
  • Computer Science Applications 86
  • Health Information Management 53
  • Artificial Intelligence 363
  • Computer Vision and Pattern Recognition 204
Replace Sushruta Mishra with:
Sushruta Mishra India
Parminder Singh India
K. Vijayakumar India
Khan Md. Hasib Bangladesh
Dharmender Kumar India
Sultan Alfarhood Saudi Arabia
Karim Afdel Morocco
Shakir Khan Saudi Arabia
Mejdl Safran Saudi Arabia
Sunil L. Bangare India
Mona Jamjoom relative to Sushruta Mishra India Sushruta Mishra's profile →
Citations per field
00.5×2.6×
Sushruta Mishra · 1×
Citations per year

Countries citing papers authored by Mona Jamjoom

Since Specialization
Citations

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

Fields of papers citing papers by Mona Jamjoom

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

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

All Works

20 of 20 papers shown

Showing the 20 most-cited of 73 papers — load more, or switch the sort, to bring in the rest.

#Work
1 202282
2 202358
3 202352
4 201851
5 202047
6 202346
7 202345
8 202241
9 202237
10 202334
11 202232
12 202031
13 202126
14 202326
15 202126
16 201923
17 202220
18 202219
19 202317
20 202317

About Mona Jamjoom

Mona Jamjoom is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition, Information Systems, Radiology, Nuclear Medicine and Imaging and Computer Networks and Communications, having authored 73 papers that have together received 1.0k indexed citations. Recurring topics across this work include AI in cancer detection (7 papers), Imbalanced Data Classification Techniques (7 papers), Sentiment Analysis and Opinion Mining (6 papers), Brain Tumor Detection and Classification (6 papers), COVID-19 diagnosis using AI (4 papers), Hate Speech and Cyberbullying Detection (4 papers), Text and Document Classification Technologies (4 papers) and Vehicle License Plate Recognition (4 papers). The work is most often cited by research in Neurology (140 citations), Computer Science Applications (86 citations), Health Information Management (53 citations), Artificial Intelligence (363 citations) and Computer Vision and Pattern Recognition (204 citations). Mona Jamjoom has collaborated with scholars based in Saudi Arabia, Pakistan and Egypt. Frequent co-authors include Zahid Ullah, Nagwan Abdel Samee, Farrukh Saleem, Amel Ksibi, Essam H. Houssein, Ben Othman Soufiene, Abdelaziz A. Abdelhamid, El‐Sayed M. El‐kenawy, Manel Ayadi and Abdelhameed Ibrahim‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬. Their work appears in journals such as IEEE Access, Computers, materials & continua/Computers, materials & continua (Print), Scientific Reports, PeerJ Computer Science and Computers in Biology and Medicine.

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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