Erdal Başaran

468 total citations
20 papers, 313 citations indexed

About

Erdal Başaran is a scholar working on Signal Processing, Artificial Intelligence and Plant Science. According to data from OpenAlex, Erdal Başaran has authored 20 papers receiving a total of 313 indexed citations (citations by other indexed papers that have themselves been cited), including 8 papers in Signal Processing, 7 papers in Artificial Intelligence and 6 papers in Plant Science. Recurrent topics in Erdal Başaran's work include Speech and Audio Processing (6 papers), Smart Agriculture and AI (6 papers) and AI in cancer detection (5 papers). Erdal Başaran is often cited by papers focused on Speech and Audio Processing (6 papers), Smart Agriculture and AI (6 papers) and AI in cancer detection (5 papers). Erdal Başaran collaborates with scholars based in Türkiye, United States and India. Erdal Başaran's co-authors include Zafer Cömert, Yüksel Çelik, Hüseyin Polat, Ümit Budak, Abdulkadir Şengür, Mesut Toğaçar and Sanjay Goel and has published in prestigious journals such as Neural Computing and Applications, Measurement and Computers in Biology and Medicine.

In The Last Decade

Erdal Başaran

18 papers receiving 301 citations

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Erdal Başaran Türkiye	9	128	69	67	65	59	20	313
Omar Hisham Alsadoon Iraq	11	149 1.2×	42 0.6×	39 0.6×	129 2.0×	83 1.4×	37	375
Sheeraz Akram Pakistan	17	222 1.7×	84 1.2×	16 0.2×	176 2.7×	237 4.0×	52	635
Pooja Rani India	12	199 1.6×	32 0.5×	48 0.7×	19 0.3×	59 1.0×	32	497
Asghar Ali Shah Pakistan	11	93 0.7×	31 0.4×	80 1.2×	48 0.7×	93 1.6×	47	403
Canghong Shi China	9	56 0.4×	34 0.5×	21 0.3×	164 2.5×	31 0.5×	39	246
Balajee Maram India	9	109 0.9×	25 0.4×	49 0.7×	108 1.7×	78 1.3×	65	334
Chun‐Nan Chou Taiwan	8	52 0.4×	10 0.1×	10 0.1×	98 1.5×	48 0.8×	18	307
Nikhat Parveen India	9	148 1.2×	35 0.5×	74 1.1×	70 1.1×	70 1.2×	46	323
Kwabena Adu Ghana	9	110 0.9×	17 0.2×	32 0.5×	77 1.2×	77 1.3×	24	287

Countries citing papers authored by Erdal Başaran

Since Specialization

Citations

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

Fields of papers citing papers by Erdal Başaran

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Erdal Başaran

This figure shows the co-authorship network connecting the top 25 collaborators of Erdal Başaran. A scholar is included among the top collaborators of Erdal Başaran 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 Erdal Başaran. Erdal Başaran 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

Başaran, Erdal, et al.. (2025). Regionally focused neural-coder model designed for the diagnosis of acute lymphoblastic leukemia disease. Measurement. 256. 118176–118176. 1 indexed citations

Cömert, Zafer, et al.. (2025). Classification of Maize Images Enhanced with Slot Attention Mechanism in Deep Learning Architectures. Electronics. 14(13). 2635–2635.

Çelik, Yüksel, Erdal Başaran, & Sanjay Goel. (2025). Deep Learning Methods for Intrusion Detection Systems on the CSE-CIC-IDS2018 Dataset: A Review. Lecture notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering. 38–65.

Başaran, Erdal, et al.. (2024). Classification of walnut dataset by selecting CNN features with whale optimization algorithm. Multimedia Tools and Applications. 83(31). 77061–77076. 5 indexed citations

Başaran, Erdal, et al.. (2024). Enhancing Anomaly Detection in Large-Scale Log Data Using Machine Learning: A Comparative Study of SVM and KNN Algorithms with HDFS Dataset. 1(1). 14–18. 2 indexed citations

Başaran, Erdal & Yüksel Çelik. (2024). Skin cancer diagnosis using CNN features with Genetic Algorithm and Particle Swarm Optimization methods. Transactions of the Institute of Measurement and Control. 46(14). 2706–2713. 1 indexed citations

Başaran, Erdal, et al.. (2023). Proposing a new approach based on convolutional neural networks and random forest for the diagnosis of Parkinson's disease from speech signals. Applied Acoustics. 211. 109476–109476. 24 indexed citations

Başaran, Erdal, et al.. (2023). Identification Of Walnut Variety From The Leaves Using Deep Learning Algorithms. Bitlis Eren Üniversitesi Fen Bilimleri Dergisi. 12(2). 531–543. 3 indexed citations

Başaran, Erdal, Zafer Cömert, & Yüksel Çelik. (2022). Neighbourhood component analysis and deep feature-based diagnosis model for middle ear otoscope images. Neural Computing and Applications. 34(8). 6027–6038. 9 indexed citations

10.

Çelik, Yüksel, et al.. (2022). Classification of walnut varieties obtained from walnut leaf images by the recommended residual block based CNN model. European Food Research and Technology. 249(3). 727–738. 8 indexed citations

11.

Başaran, Erdal. (2022). Classification of white blood cells with SVM by selecting SqueezeNet and LIME properties by mRMR method. Signal Image and Video Processing. 16(7). 1821–1829. 29 indexed citations

12.

Başaran, Erdal. (2022). A new brain tumor diagnostic model: Selection of textural feature extraction algorithms and convolution neural network features with optimization algorithms. Computers in Biology and Medicine. 148. 105857–105857. 44 indexed citations

13.

Çelik, Yüksel, et al.. (2022). Identification of durum wheat grains by using hybrid convolution neural network and deep features. Signal Image and Video Processing. 16(4). 1135–1142. 8 indexed citations

14.

Polat, Hüseyin, et al.. (2021). Machine Learning Approach Equipped with Neighbourhood Component Analysis for DDoS Attack Detection in Software-Defined Networking. Electronics. 10(11). 1227–1227. 69 indexed citations

15.

Başaran, Erdal, Zafer Cömert, & Yüksel Çelik. (2021). Timpanik Membran Görüntü Özellikleri Kullanılarak Sınıflandırılması. Fırat Üniversitesi Mühendislik Bilimleri Dergisi. 33(2). 441–453. 2 indexed citations

16.

Başaran, Erdal, et al.. (2020). Konvolüsyonel Sinir Ağı Tabanlı YOLO Yöntemi ile Orta Kulak Otoskop Görüntülerinde Timpanik Membran Bölgesinin Belirlenmesi. Deu Muhendislik Fakultesi Fen ve Muhendislik. 22(66). 919–928. 2 indexed citations

17.

Başaran, Erdal, Zafer Cömert, Abdulkadir Şengür, et al.. (2019). Chronic Tympanic Membrane Diagnosis based on Deep Convolutional Neural Network. 1–4. 34 indexed citations

18.

Başaran, Erdal, et al.. (2019). Determination of Tympanic Membrane Region in the Middle Ear Otoscope Images with Convolutional Neural Network Based YOLO Method. DergiPark (Istanbul University). 1 indexed citations

19.

Başaran, Erdal, Zafer Cömert, & Yüksel Çelik. (2019). Convolutional neural network approach for automatic tympanic membrane detection and classification. Biomedical Signal Processing and Control. 56. 101734–101734. 59 indexed citations

20.

Başaran, Erdal, et al.. (2019). Normal and Acute Tympanic Membrane Diagnosis based on Gray Level Co-Occurrence Matrix and Artificial Neural Networks. 1–6. 12 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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