İbrahim Türkoğlu

88 papers receiving 2.2k citations

İbrahim Türkoğlu's Hit Papers

Comparison of deep learning approaches to predict COVID-19 infection 2020 · 245 citations
2450+6+12Years since publication100200300400

Peers

İbrahim Türkoğlu
Comparison fields: 5 of 163
  • Health Information Management 572
  • Health Informatics 64
  • Artificial Intelligence 913
  • Medical Laboratory Technology 40
  • Radiology, Nuclear Medicine and Imaging 483
Replace Liaqat Ali with:
Liaqat Ali Pakistan
Amin Ul Haq China
Friso De Boer Australia
Paweł Pławiak Poland
Asif Karim Australia
Giovanna Sannino Italy
F. M. Javed Mehedi Shamrat Bangladesh
Mohd Khanapi Abd Ghani Malaysia
Salih Güneş Türkiye
Sonali Agarwal India
İbrahim Türkoğlu relative to Liaqat Ali Pakistan Liaqat Ali's profile →
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 104 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 →
2008436
2
Comparison of deep learning approaches to predict COVID-19 infection
Hit paper breakdown →
2020245
3 2020232
4 2006127
5 2020117
6 200289
7 200888
8 200687
9 200382
10 200667
11 200566
12 200858
13 200654
14 200847
15 200740
16 201934
17 201933
18 202030
19 200725
20 200724

About İbrahim Türkoğlu

İbrahim Türkoğlu is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition, Control and Systems Engineering, Molecular Biology and Information Systems, having authored 104 papers that have together received 2.4k indexed citations. Recurring topics across this work include Neural Networks and Applications (12 papers), Machine Learning in Bioinformatics (10 papers), EEG and Brain-Computer Interfaces (8 papers), Spectroscopy and Chemometric Analyses (7 papers), Fractal and DNA sequence analysis (6 papers), Image and Signal Denoising Methods (6 papers), Emotion and Mood Recognition (5 papers) and RNA and protein synthesis mechanisms (5 papers). The work is most often cited by research in Health Information Management (572 citations), Health Informatics (64 citations), Artificial Intelligence (913 citations), Medical Laboratory Technology (40 citations) and Radiology, Nuclear Medicine and Imaging (483 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 Arslan, Suat Toraman, Erdoğan İlkay, Engin Avcı, Davut Hanbay, Murat Gönen and Mustafa Poyraz. Their work appears in journals such as Expert Systems with Applications, Ain Shams Engineering Journal, Gazi Üniversitesi Mühendislik-Mimarlık Fakültesi Dergisi, Chaos Solitons & Fractals 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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