Mücahit Çevik

1.3k citations
58 papers · 815 · h-index 14

Impact in

  • Oncology top 10%
    • Global Cancer Incidence and Screening
    • Colorectal Cancer Screening and Detection
    • Cancer Risks and Factors

Papers in

Mücahit Çevik

53 papers receiving 794 citations

Peers

Mücahit Çevik
Comparison fields: 5 of 102
  • Oncology 272
  • Health Informatics 12
  • Cancer Research 115
  • Artificial Intelligence 232
  • Industrial and Manufacturing Engineering 65
Replace Yishi Zhang with:
Yishi Zhang China
Stefano Forti Italy
Haipeng Chen China
Jianbin Li China
Stefano Mariani Italy
Belal Ahmad Canada
Heinz Schmidt Australia
Francesco Rundo Italy
Xiaohan Hao China
Mücahit Çevik relative to Yishi Zhang China Yishi Zhang's profile →
Citations per field
00.5×1.5×2.4×
Yishi Zhang · 1×
Citations per year

Countries citing papers authored by Mücahit Çevik

Since Specialization
Citations

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

Fields of papers citing papers by Mücahit Çevik

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

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

All Works

20 of 20 papers shown

Showing the 20 most-cited of 58 papers — load more, or switch the sort, to bring in the rest.

#Work
1 2014152
2 2014110
3 201493
4 201842
5 202233
6 202129
7 202128
8 202028
9 202125
10 201125
11 202125
12 201520
13 201820
14 202114
15 202213
16 201813
17 202111
18 202211
19 202110
20 20219

About Mücahit Çevik

Mücahit Çevik is a scholar working on Artificial Intelligence, Information Systems, Oncology, Management Science and Operations Research and Pulmonary and Respiratory Medicine, having authored 58 papers that have together received 815 indexed citations. Recurring topics across this work include Software Engineering Research (10 papers), Global Cancer Incidence and Screening (8 papers), Transportation and Mobility Innovations (6 papers), Software Engineering Techniques and Practices (5 papers), Stock Market Forecasting Methods (5 papers), Software Reliability and Analysis Research (5 papers), AI in cancer detection (5 papers) and Advanced Text Analysis Techniques (5 papers). The work is most often cited by research in Oncology (272 citations), Health Informatics (12 citations), Cancer Research (115 citations), Artificial Intelligence (232 citations) and Industrial and Manufacturing Engineering (65 citations). Mücahit Çevik has collaborated with scholars based in Canada, United States and Türkiye. Frequent co-authors include Oğuzhan Alagöz, Ayşe Bener, Natasha K. Stout, Brian L. Sprague, Karla Kerlikowske, Diana L. Miglioretti, Anna N.A. Tosteson, Constance D. Lehman, Christoph I. Lee and Harry J. de Koning. Their work appears in journals such as Applied Intelligence, Medical Decision Making, Information Technology and Management, Computers & Operations Research and Physics in Medicine and Biology.

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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