İbrahim Türkoğlu

95 papers receiving 2.5k citations

İbrahim Türkoğlu's Hit Papers

Comparison of deep learning approaches to predict COVID-19 infection 2020 · 270 citations
2700+6+12Years since publication100200300400500

Peers

İbrahim Türkoğlu
Comparison fields: 5 of 158
  • Health Information Management 661
  • Health Informatics 73
  • Medical Laboratory Technology 46
  • Artificial Intelligence 1.0k
  • Radiology, Nuclear Medicine and Imaging 511
Replace Liaqat Ali with:
Liaqat Ali Pakistan
Paweł Pławiak Poland
Giovanna Sannino Italy
Roohallah Alizadehsani Australia
Asif Karim Australia
Mirjam Jonkman Australia
Muhammad Umer Pakistan
Friso De Boer Australia
Luca Romeo Italy
Salih Güneş Türkiye
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Citations per field
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Citations per year

Countries citing papers authored by İbrahim Türkoğlu

Since Specialization
Citations

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

Fields of papers citing papers by İbrahim Türkoğlu

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

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

All Works

20 of 20 papers shown

Showing the 20 most-cited of 111 papers — load more, or switch the sort, to bring in the rest.

#Work
1
Effective diagnosis of heart disease through neural networks ensembles
Hit paper breakdown →
2008513
2
Comparison of deep learning approaches to predict COVID-19 infection
Hit paper breakdown →
2020270
3 2020255
4 2020137
5 2006136
6 2008100
7 200298
8 200689
9 200388
10 200672
11 200570
12 200863
13 200658
14 200853
15 200744
16 201939
17 200538
18 201935
19 202032
20 200530

About İbrahim Türkoğlu

İbrahim Türkoğlu is a scholar working on Computer Vision and Pattern Recognition, Artificial Intelligence, Control and Systems Engineering, Signal Processing and Analytical Chemistry, having authored 111 papers that have together received 2.7k indexed citations. Recurring topics across this work include Neural Networks and Applications (13 papers), Machine Learning in Bioinformatics (11 papers), EEG and Brain-Computer Interfaces (8 papers), Spectroscopy and Chemometric Analyses (7 papers), Image and Signal Denoising Methods (7 papers), Anomaly Detection Techniques and Applications (6 papers), Fractal and DNA sequence analysis (6 papers) and Phonocardiography and Auscultation Techniques (5 papers). The work is most often cited by research in Health Information Management (661 citations), Health Informatics (73 citations), Medical Laboratory Technology (46 citations), Artificial Intelligence (1.0k citations) and Radiology, Nuclear Medicine and Imaging (511 citations). İbrahim Türkoğlu has collaborated with scholars based in Türkiye, United States and Nepal. Frequent co-authors include Talha Burak Alakuş, Abdulkadir Şengür, Resul Daş, Ahmet Hamdi Arslan, Suat Toraman, Engin Avcı, Erdoğan İlkay, Davut Hanbay, Murat Gönen and Mustafa Poyraz. Their work appears in journals such as Expert Systems with Applications, Gazi Üniversitesi Mühendislik-Mimarlık Fakültesi Dergisi, Ain Shams Engineering Journal, Biomedical Signal Processing and Control and Chemometrics and Intelligent Laboratory Systems.

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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