Nuh Alpaslan

521 total citations
29 papers, 383 citations indexed

About

Nuh Alpaslan is a scholar working on Computer Vision and Pattern Recognition, Artificial Intelligence and Oncology. According to data from OpenAlex, Nuh Alpaslan has authored 29 papers receiving a total of 383 indexed citations (citations by other indexed papers that have themselves been cited), including 10 papers in Computer Vision and Pattern Recognition, 7 papers in Artificial Intelligence and 6 papers in Oncology. Recurrent topics in Nuh Alpaslan's work include Image Retrieval and Classification Techniques (7 papers), AI in cancer detection (6 papers) and Medical Image Segmentation Techniques (5 papers). Nuh Alpaslan is often cited by papers focused on Image Retrieval and Classification Techniques (7 papers), AI in cancer detection (6 papers) and Medical Image Segmentation Techniques (5 papers). Nuh Alpaslan collaborates with scholars based in Türkiye and United States. Nuh Alpaslan's co-authors include Ahu Dikilitaş, Alihan Bozoğlan, Hacer Sahin, Kazım Hanbay, Abdullah Seçkin Ertuğrul, Davut Hanbay, Muhammed Fatih Talu, Hüseyin Fırat, Prabir Bhattacharya and Mahmudur Rahman and has published in prestigious journals such as SHILAP Revista de lepidopterología, Expert Systems with Applications and IEEE Access.

In The Last Decade

Nuh Alpaslan

28 papers receiving 373 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Nuh Alpaslan Türkiye 12 124 94 58 48 43 29 383
Rashmi Mukherjee India 15 23 0.2× 65 0.7× 67 1.2× 24 0.5× 92 2.1× 24 641
Yen‐Chun Chen Taiwan 9 23 0.2× 278 3.0× 149 2.6× 13 0.3× 136 3.2× 28 610
Marı́a J. Carreira Spain 14 41 0.3× 226 2.4× 132 2.3× 10 0.2× 90 2.1× 39 802
Aditya Saxena India 7 39 0.3× 17 0.2× 44 0.8× 4 0.1× 33 0.8× 35 243
Yuqi Zhu China 10 25 0.2× 21 0.2× 44 0.8× 31 0.6× 59 1.4× 25 281
Hirose Japan 11 19 0.2× 16 0.2× 42 0.7× 25 0.5× 36 0.8× 29 338
Cheng‐Han Lin Taiwan 17 6 0.0× 96 1.0× 19 0.3× 85 1.8× 291 6.8× 49 753
Evangelos Kazakos Greece 12 6 0.0× 274 2.9× 137 2.4× 5 0.1× 51 1.2× 28 596
Miao Cheng China 6 27 0.2× 27 0.3× 28 0.5× 23 0.5× 35 0.8× 31 194
Rongchao Zhang China 11 13 0.1× 19 0.2× 6 0.1× 19 0.4× 58 1.3× 32 374

Countries citing papers authored by Nuh Alpaslan

Since Specialization
Citations

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

Fields of papers citing papers by Nuh Alpaslan

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Nuh Alpaslan

This figure shows the co-authorship network connecting the top 25 collaborators of Nuh Alpaslan. A scholar is included among the top collaborators of Nuh Alpaslan 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 Nuh Alpaslan. Nuh Alpaslan is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
1.
Alpaslan, Nuh, et al.. (2023). Evrişimsel Sinir Ağı Mimarileri ve Öğrenim Aktarma ile Bitki Zararlısı Çekirge Türlerinin Sınıflandırması. Fırat Üniversitesi Mühendislik Bilimleri Dergisi. 35(1). 321–331. 3 indexed citations
2.
Alpaslan, Nuh, et al.. (2023). Wind Speed Prediction Using Meteorological Measurements for Elazığ Province. DergiPark (Istanbul University).
3.
Alpaslan, Nuh. (2022). Neutrosophic set based local binary pattern for texture classification. Expert Systems with Applications. 209. 118350–118350. 9 indexed citations
4.
Alpaslan, Nuh, et al.. (2021). Robust optimization of SegNet hyperparameters for skin lesion segmentation. Multimedia Tools and Applications. 81(25). 36031–36051. 19 indexed citations
5.
Alpaslan, Nuh, et al.. (2021). Robust optimization of SVM hyper-parameters for spillway type selection. Ain Shams Engineering Journal. 12(3). 2413–2423. 18 indexed citations
6.
Alpaslan, Nuh. (2020). A Novel Texture Classification Method Based on Neutrosophic Truth. SHILAP Revista de lepidopterología. 3(1). 28–39. 1 indexed citations
7.
Fırat, Hüseyin & Nuh Alpaslan. (2020). An effective approach to the two-dimensional rectangular packing problem in the manufacturing industry. Computers & Industrial Engineering. 148. 106687–106687. 19 indexed citations
8.
Alpaslan, Nuh, et al.. (2020). Cilt Lezyon Bölütlemesi için Metasezgisel Temelli Otsu Eşikleme Yöntemi. Türk doğa ve fen dergisi :. 9(1). 42–48. 1 indexed citations
9.
Alpaslan, Nuh & Kazım Hanbay. (2020). Multi-Scale Shape Index-Based Local Binary Patterns for Texture Classification. IEEE Signal Processing Letters. 27. 660–664. 16 indexed citations
10.
Alpaslan, Nuh, et al.. (2020). SegNet Mimarisi Kullanılarak Cilt Lezyon Bölütleme Performansının İyileştirilmesi. European Journal of Science and Technology. 40–45. 5 indexed citations
11.
Alpaslan, Nuh & Kazım Hanbay. (2020). Multi-Resolution Intrinsic Texture Geometry-Based Local Binary Pattern for Texture Classification. IEEE Access. 8. 54415–54430. 29 indexed citations
12.
Fırat, Hüseyin & Nuh Alpaslan. (2019). Sezgisel Algoritmalar Kullanılarak İki Boyutlu Dikdörtgen Şerit Paketleme Probleminin Çözümü. European Journal of Science and Technology. 315–322. 1 indexed citations
13.
Alpaslan, Nuh. (2019). MEME KANSERİ TANISI İÇİN DERİN ÖZNİTELİK TABANLI KARAR DESTEK SİSTEMİ. 7(1). 213–227. 3 indexed citations
14.
Rahman, Md Mahmudur & Nuh Alpaslan. (2017). Automated melanoma recognition in dermoscopic images based on extreme learning machine (ELM). Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE. 10134. 1013414–1013414. 3 indexed citations
15.
Rahman, Mahmudur, Nuh Alpaslan, & Prabir Bhattacharya. (2016). Developing a retrieval based diagnostic aid for automated melanoma recognition of dermoscopic images. 1–7. 21 indexed citations
16.
Talu, Muhammed Fatih, et al.. (2013). Calculation of melatonin and resveratrol effects on steatosis hepatis using soft computing methods. Computer Methods and Programs in Biomedicine. 111(2). 498–506. 6 indexed citations
17.
18.
Alpaslan, Nuh, et al.. (2012). Calculation of drug effectiveness on treatment of steatosis hepatis using HOG based ANN.. DergiPark (Istanbul University). 2 indexed citations
19.
Ertuğrul, Abdullah Seçkin, Hacer Sahin, Ahu Dikilitaş, Nuh Alpaslan, & Alihan Bozoğlan. (2012). Comparison of CCL28, interleukin‐8, interleukin‐1β and tumor necrosis factor‐alpha in subjects with gingivitis, chronic periodontitis and generalized aggressive periodontitis. Journal of Periodontal Research. 48(1). 44–51. 102 indexed citations
20.
Ertuğrul, Abdullah Seçkin, Ahu Dikilitaş, Hacer Sahin, Nuh Alpaslan, & Alihan Bozoğlan. (2012). Gingival crevicular fluid adrenomedullin level in individuals with and without diabetes mellitus type 2. Journal of Periodontal Research. 48(3). 342–349. 6 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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