Yaman Akbulut

1.1k total citations
32 papers, 741 citations indexed

About

Yaman Akbulut is a scholar working on Computer Vision and Pattern Recognition, Biomedical Engineering and Artificial Intelligence. According to data from OpenAlex, Yaman Akbulut has authored 32 papers receiving a total of 741 indexed citations (citations by other indexed papers that have themselves been cited), including 17 papers in Computer Vision and Pattern Recognition, 9 papers in Biomedical Engineering and 8 papers in Artificial Intelligence. Recurrent topics in Yaman Akbulut's work include Face and Expression Recognition (8 papers), Retinal Imaging and Analysis (7 papers) and Machine Learning and ELM (5 papers). Yaman Akbulut is often cited by papers focused on Face and Expression Recognition (8 papers), Retinal Imaging and Analysis (7 papers) and Machine Learning and ELM (5 papers). Yaman Akbulut collaborates with scholars based in Türkiye, United States and India. Yaman Akbulut's co-authors include Abdulkadir Şengür, Yanhui Guo, Ümit Budak, Varun Bajaj, Florentín Smarandache, Fatih Demir, Orhan Atıla, Sami Ekici, Burak Taşçı and Zafer Cömert and has published in prestigious journals such as Applied Soft Computing, Physica A Statistical Mechanics and its Applications and IEEE Sensors Journal.

In The Last Decade

Yaman Akbulut

31 papers receiving 709 citations

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name	h						Papers	Cites
Yaman Akbulut Türkiye	15	208	153	152	128	118	32	741
Cataldo Guaragnella Italy	15	294 1.4×	119 0.8×	134 0.9×	143 1.1×	70 0.6×	86	1.0k
Türker Tuncer Türkiye	18	339 1.6×	230 1.5×	336 2.2×	191 1.5×	223 1.9×	89	1.2k
Burak Taşçı Türkiye	16	126 0.6×	191 1.2×	171 1.1×	52 0.4×	127 1.1×	48	713
Fatih Demir Türkiye	18	195 0.9×	113 0.7×	244 1.6×	95 0.7×	222 1.9×	37	1.1k
Su Yang China	12	182 0.9×	249 1.6×	180 1.2×	48 0.4×	33 0.3×	44	752
Ali Farzamnia Malaysia	15	83 0.4×	321 2.1×	125 0.8×	71 0.6×	92 0.8×	88	880
Adil Deniz Duru Türkiye	13	111 0.5×	186 1.2×	95 0.6×	51 0.4×	79 0.7×	72	580
Ali Hassan Pakistan	19	268 1.3×	313 2.0×	279 1.8×	125 1.0×	80 0.7×	91	1.1k
Chee‐Ming Ting Malaysia	14	153 0.7×	424 2.8×	117 0.8×	62 0.5×	87 0.7×	67	799
Muhammad Awais United Kingdom	14	515 2.5×	75 0.5×	292 1.9×	84 0.7×	114 1.0×	56	972

Countries citing papers authored by Yaman Akbulut

Since Specialization

Citations

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

Fields of papers citing papers by Yaman Akbulut

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Yaman Akbulut

This figure shows the co-authorship network connecting the top 25 collaborators of Yaman Akbulut. A scholar is included among the top collaborators of Yaman Akbulut based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Yaman Akbulut. Yaman Akbulut is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

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20 of 20 papers shown

Çakmak, Tolga, Fatih Demir, Mehmet Ali Kobat, et al.. (2025). Automatic Detection of Occluded Main Coronary Arteries of NSTEMI Patients with MI-MS ConvMixer + WSSE Without CAG. Diagnostics. 15(3). 347–347. 1 indexed citations

Akbulut, Yaman. (2023). Automated Pneumonia Based Lung Diseases Classification with Robust Technique Based on a Customized Deep Learning Approach. Diagnostics. 13(2). 260–260. 14 indexed citations

Demir, Fatih & Yaman Akbulut. (2022). A new deep technique using R-CNN model and L1NSR feature selection for brain MRI classification. Biomedical Signal Processing and Control. 75. 103625–103625. 22 indexed citations

Tanyıldızı, Harun, et al.. (2020). Deep learning model for estimating the mechanical properties of concrete containing silica fume exposed to high temperatures. Frontiers of Structural and Civil Engineering. 14(6). 1316–1330. 30 indexed citations

Budak, Ümit, Varun Bajaj, Yaman Akbulut, Orhan Atıla, & Abdulkadir Şengür. (2019). An Effective Hybrid Model for EEG-Based Drowsiness Detection. IEEE Sensors Journal. 19(17). 7624–7631. 117 indexed citations

Şengür, Abdulkadir, Yaman Akbulut, & Ümit Budak. (2019). Food Image Classification with Deep Features. 1–6. 28 indexed citations

Cömert, Zafer, Abdulkadir Şengür, Yaman Akbulut, et al.. (2019). A Simple and Effective Approach for Digitization of the CTG Signals from CTG Traces. IRBM. 40(5). 286–296. 13 indexed citations

Şengür, Abdulkadir, et al.. (2019). Deep Features and Extreme Learning Machines based Apparel Classification. 5 indexed citations

Guo, Yanhui, et al.. (2019). An effective color image segmentation approach using neutrosophic adaptive mean shift clustering. Zenodo (CERN European Organization for Nuclear Research). 2 indexed citations

10.

Şengür, Abdulkadir, Zahid Akhtar, Yaman Akbulut, Sami Ekici, & Ümit Budak. (2018). Deep Feature Extraction for Face Liveness Detection. 1–4. 22 indexed citations

11.

Şengür, Abdulkadir, Ümit Budak, & Yaman Akbulut. (2018). CLASSIFICATION OF AMYOTROPHIC LATERAL SCLEROSIS AND HEALTHY ELECTROMYOGRAPHY SIGNALS BASED ON TRANSFER LEARNING. 8(2). 179–185. 4 indexed citations

12.

Guo, Yanhui, et al.. (2018). An effective color image segmentation approach using neutrosophic adaptive mean shift clustering. Measurement. 119. 28–40. 41 indexed citations

13.

Budak, Ümit, Abdulkadir Şengür, Yanhui Guo, & Yaman Akbulut. (2017). A novel microaneurysms detection approach based on convolutional neural networks with reinforcement sample learning algorithm. Health Information Science and Systems. 5(1). 14–14. 43 indexed citations

14.

Budak, Ümit, et al.. (2017). A novel approach based on image processing algorithms for microaneurysm candidate detection. 1–4. 1 indexed citations

15.

Şengür, Abdulkadir, Yaman Akbulut, Yanhui Guo, & Varun Bajaj. (2017). Classification of amyotrophic lateral sclerosis disease based on convolutional neural network and reinforcement sample learning algorithm. Health Information Science and Systems. 5(1). 9–9. 52 indexed citations

16.

Akbulut, Yaman, Abdulkadir Şengür, Ümit Budak, & Sami Ekici. (2017). Deep learning based face liveness detection in videos. 1–4. 24 indexed citations

17.

Aslan, Muzaffer, Yaman Akbulut, Abdulkadir Şengür, & M. Cevdet İnce. (2017). Eklem tabanlı etkili düşme tespiti. Gazi Üniversitesi Mühendislik-Mimarlık Fakültesi Dergisi. 32(4). 1025–1034. 7 indexed citations

18.

Akbulut, Yaman, Abdulkadir Şengür, Yanhui Guo, & Florentín Smarandache. (2017). NS-k-NN: Neutrosophic Set-Based k-Nearest Neighbors Classifier. Symmetry. 9(9). 179–179. 75 indexed citations

19.

Budak, Ümit, Abdulkadir Şengür, & Yaman Akbulut. (2017). Localization of macular edema region from color retinal images for detection of diabetic retinopathy. 1–4. 1 indexed citations

20.

Şengür, Abdulkadir, Yanhui Guo, & Yaman Akbulut. (2016). Time–frequency texture descriptors of EEG signals for efficient detection of epileptic seizure. Brain Informatics. 3(2). 101–108. 50 indexed citations

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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