Hünkar Can Tunç

618 total citations · 1 hit paper
7 papers, 410 citations indexed

About

Hünkar Can Tunç is a scholar working on Hardware and Architecture, Computer Networks and Communications and Artificial Intelligence. According to data from OpenAlex, Hünkar Can Tunç has authored 7 papers receiving a total of 410 indexed citations (citations by other indexed papers that have themselves been cited), including 4 papers in Hardware and Architecture, 2 papers in Computer Networks and Communications and 2 papers in Artificial Intelligence. Recurrent topics in Hünkar Can Tunç's work include Parallel Computing and Optimization Techniques (4 papers), Speech Recognition and Synthesis (2 papers) and Embedded Systems Design Techniques (2 papers). Hünkar Can Tunç is often cited by papers focused on Parallel Computing and Optimization Techniques (4 papers), Speech Recognition and Synthesis (2 papers) and Embedded Systems Design Techniques (2 papers). Hünkar Can Tunç collaborates with scholars based in Denmark, Singapore and Germany. Hünkar Can Tunç's co-authors include Hülya Apaydın, Melih Tütüncü, Ayşegül Gündüz, C. Okan Sakar, Görkem Serbes, Betül Erdoğdu Şakar, Hatice Nizam Ozogur, Tarkan Aydın, M. Erdem Isenkul and Andreas Pavlogiannis and has published in prestigious journals such as Applied Soft Computing, Medical & Biological Engineering & Computing and Proceedings of the ACM on Programming Languages.

In The Last Decade

Hünkar Can Tunç

7 papers receiving 391 citations

Hit Papers

align trajectories

What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

A comparative analysis of speech signal processing algorithms for Parkinson’s disease classification and the use of the tunable Q-factor wavelet transform

2018 352 citations C. Okan Sakar, Görkem Serbes et al. Applied Soft Computing profile →

Peers

Hünkar Can Tunç

PHYSI

AI

SP

NEURO

CVPR

Hatice Nizam Ozogur Türkiye

Tarkan Aydın Türkiye

Biswajit Karan India

M. Erdem Isenkul Türkiye

Zehra Karapınar Şentürk Türkiye

Roberto Rosas-Romero Mexico

Betül Erdoğdu Şakar Türkiye

Tarun Gulati India

Rohit Lamba India

Moumita Pramanik India

Hatice Nizam Ozogur Türkiye

Hünkar Can Tunç

230 ×0.9

PHYSI

140 ×1.0

AI

125 ×0.9

SP

93 ×0.9

NEURO

43 ×1.0

CVPR

Citations per year, relative to Hünkar Can Tunç Hünkar Can Tunç (= 1×) peers Hatice Nizam Ozogur

Countries citing papers authored by Hünkar Can Tunç

Since Specialization

Citations

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

Fields of papers citing papers by Hünkar Can Tunç

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Hünkar Can Tunç. 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 Hünkar Can Tunç. The network helps show where Hünkar Can Tunç may publish in the future.

Co-authorship network of co-authors of Hünkar Can Tunç

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

All Works

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7 of 7 papers shown

1.

Tunç, Hünkar Can, et al.. (2024). CSSTs: A Dynamic Data Structure for Partial Orders in Concurrent Execution Analysis. Scopus (Elsevier). 223–238. 1 indexed citations

2.

Tunç, Hünkar Can, et al.. (2023). Optimal Reads-From Consistency Checking for C11-Style Memory Models. Proceedings of the ACM on Programming Languages. 7(PLDI). 761–785. 5 indexed citations

3.

Tunç, Hünkar Can, Umang Mathur, Andreas Pavlogiannis, & Mahesh Viswanathan. (2023). Sound Dynamic Deadlock Prediction in Linear Time. Proceedings of the ACM on Programming Languages. 7(PLDI). 1733–1758. 8 indexed citations

4.

Mathur, Umang, Andreas Pavlogiannis, Hünkar Can Tunç, & Mahesh Viswanathan. (2022). A tree clock data structure for causal orderings in concurrent executions. 710–725. 6 indexed citations

5.

Tunç, Hünkar Can, C. Okan Sakar, Hülya Apaydın, et al.. (2020). Estimation of Parkinson’s disease severity using speech features and extreme gradient boosting. Medical & Biological Engineering & Computing. 58(11). 2757–2773. 36 indexed citations

6.

Tunç, Hünkar Can, et al.. (2019). Detection of e-Commerce Anomalies using LSTM-recurrent Neural Networks. 217–224. 2 indexed citations

7.

Sakar, C. Okan, Görkem Serbes, Ayşegül Gündüz, et al.. (2018). A comparative analysis of speech signal processing algorithms for Parkinson’s disease classification and the use of the tunable Q-factor wavelet transform. Applied Soft Computing. 74. 255–263. 352 indexed citations breakdown →

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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