Mohammed Eshtay

509 citations
15 papers · 394 · h-index 9

Impact in

  • Software top 10%
    • Software Reliability and Analysis Research
    • Metaheuristic Optimization Algorithms Research
    • Machine Learning and ELM
    • Evolutionary Algorithms and Applications

Papers in

    • Machine Learning and ELM 5
    • Neural Networks and Applications 2
    • Imbalanced Data Classification Techniques 2
    • Metaheuristic Optimization Algorithms Research 2
    • Software Engineering Research 2

Mohammed Eshtay

14 papers receiving 377 citations

Peers

Mohammed Eshtay
Comparison fields: 5 of 77
  • Software 47
  • Artificial Intelligence 262
  • Health Information Management 16
  • Signal Processing 35
  • Information Systems 72
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Shang Zheng China
Saihua Cai China
Lucija Brezočnik Slovenia
N. Ramaraj India
Feng Tan China
Ayaz Isazadeh Iran
Prabhjot Kaur India
Ruliang Xiao China
Mingxing Duan China
Dejun Mu China
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Citations per field
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Citations per year

Countries citing papers authored by Mohammed Eshtay

Since Specialization
Citations

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

Fields of papers citing papers by Mohammed Eshtay

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

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

All Works

15 of 15 papers shown
#Work
1 201994
2 201890
3 201844
4 202036
5 202035
6 202227
7 202322
8 202015
9 20239
10 20218
11 20208
12 20193
13 20162
14 20201
15 20220

About Mohammed Eshtay

Mohammed Eshtay is a scholar working on Artificial Intelligence, Information Systems, Computer Vision and Pattern Recognition, Computer Networks and Communications and Signal Processing, having authored 15 papers that have together received 394 indexed citations. Recurring topics across this work include Machine Learning and ELM (5 papers), Face and Expression Recognition (4 papers), MicroRNA in disease regulation (2 papers), Neural Networks and Applications (2 papers), Software Reliability and Analysis Research (2 papers), Imbalanced Data Classification Techniques (2 papers), Software Engineering Research (2 papers) and Metaheuristic Optimization Algorithms Research (2 papers). The work is most often cited by research in Software (47 citations), Artificial Intelligence (262 citations), Health Information Management (16 citations), Signal Processing (35 citations) and Information Systems (72 citations). Mohammed Eshtay has collaborated with scholars based in Jordan, Saudi Arabia and Iran. Frequent co-authors include Hossam Faris, Nadim Obeid, Ibrahim Aljarah, Ala’ M. Al-Zoubi, Ali Asghar Heidari, Seyedali Mirjalili, Majdi Mafarja, Iman Almomani, Maria Habib and Keshav Dahal. Their work appears in journals such as International Journal of Machine Learning and Cybernetics, Applied Sciences, Neural Computing and Applications, Expert Systems with Applications and Egyptian Informatics Journal.

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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