Hande Çelikkanat

539 total citations
14 papers, 332 citations indexed

About

Hande Çelikkanat is a scholar working on Artificial Intelligence, Computer Networks and Communications and Mechanical Engineering. According to data from OpenAlex, Hande Çelikkanat has authored 14 papers receiving a total of 332 indexed citations (citations by other indexed papers that have themselves been cited), including 7 papers in Artificial Intelligence, 4 papers in Computer Networks and Communications and 4 papers in Mechanical Engineering. Recurrent topics in Hande Çelikkanat's work include Topic Modeling (5 papers), Natural Language Processing Techniques (5 papers) and Modular Robots and Swarm Intelligence (4 papers). Hande Çelikkanat is often cited by papers focused on Topic Modeling (5 papers), Natural Language Processing Techniques (5 papers) and Modular Robots and Swarm Intelligence (4 papers). Hande Çelikkanat collaborates with scholars based in Türkiye, Finland and United Kingdom. Hande Çelikkanat's co-authors include Erol Şahi̇n, Fatih Gökçe, Ali Emre Turgut, Erhan Öztop, Emre Uğur, Yukie Nagai, Sinan Kalkan, Frank Guérin, Nicolas Pugeault and Jörg Tiedemann and has published in prestigious journals such as Neural Computing and Applications, Robotica and IEEE Transactions on Cognitive and Developmental Systems.

In The Last Decade

Hande Çelikkanat

14 papers receiving 318 citations

Peers

Hande Çelikkanat
Hande Çelikkanat
Citations per year, relative to Hande Çelikkanat Hande Çelikkanat (= 1×) peers Levent Bayındır

Countries citing papers authored by Hande Çelikkanat

Since Specialization
Citations

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

Fields of papers citing papers by Hande Çelikkanat

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Hande Çelikkanat

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

All Works

14 of 14 papers shown
1.
Çelikkanat, Hande, et al.. (2021). On the differences between BERT and MT encoder spaces and how to address them in translation tasks. Työväentutkimus Vuosikirja. 337–347. 2 indexed citations
2.
Çelikkanat, Hande, Sámi Virpioja, Jörg Tiedemann, & Marianna Apidianaki. (2020). Controlling the Imprint of Passivization and Negation in Contextualized Representations. Työväentutkimus Vuosikirja. 136–148. 2 indexed citations
3.
Mareček, David, et al.. (2020). Are Multilingual Neural Machine Translation Models Better at Capturing Linguistic Features?. ˜The œPrague Bulletin of Mathematical Linguistics. 115(1). 143–162. 3 indexed citations
4.
Çelikkanat, Hande, Hiroki Moriya, Takeshi OGAWA, et al.. (2017). Decoding emotional valence from electroencephalographic rhythmic activity. PubMed. 2017. 4143–4146. 4 indexed citations
5.
Çelikkanat, Hande, Erol Şahi̇n, & Sinan Kalkan. (2015). Integrating spatial concepts into a probabilistic concept web. OpenMETU (Middle East Technical University). 259–264. 1 indexed citations
6.
Çelikkanat, Hande, et al.. (2015). A Probabilistic Concept Web on a Humanoid Robot. OpenMETU (Middle East Technical University). 7(2). 92–106. 12 indexed citations
7.
Çelikkanat, Hande, et al.. (2015). Learning Context on a Humanoid Robot using Incremental Latent Dirichlet Allocation. IEEE Transactions on Cognitive and Developmental Systems. 8(1). 42–59. 12 indexed citations
8.
Çelikkanat, Hande, et al.. (2014). Learning and using context on a humanoid robot using latent dirichlet allocation. Open Research Exeter (University of Exeter). 171. 201–207. 6 indexed citations
9.
Uğur, Emre, Yukie Nagai, Hande Çelikkanat, & Erhan Öztop. (2014). Parental scaffolding as a bootstrapping mechanism for learning grasp affordances and imitation skills. Robotica. 33(5). 1163–1180. 22 indexed citations
10.
Uğur, Emre, Hande Çelikkanat, Erol Şahi̇n, Yukie Nagai, & Erhan Öztop. (2011). Learning to grasp with parental scaffolding. ECS Journal of Solid State Science and Technology (The Electrochemical Society). 480–486. 12 indexed citations
11.
Çelikkanat, Hande & Erol Şahi̇n. (2010). Steering self-organized robot flocks through externally guided individuals. Neural Computing and Applications. 19(6). 849–865. 55 indexed citations
12.
Turgut, Ali Emre, Hande Çelikkanat, Fatih Gökçe, & Erol Şahi̇n. (2008). Self-organized flocking with a mobile robot swarm. Adaptive Agents and Multi-Agents Systems. 39–46. 22 indexed citations
13.
Turgut, Ali Emre, Hande Çelikkanat, Fatih Gökçe, & Erol Şahi̇n. (2008). Self-organized flocking in mobile robot swarms. Swarm Intelligence. 2(2-4). 97–120. 175 indexed citations
14.
Çelikkanat, Hande. (2008). Optimization of felf-organized flocking of a robot swarm via evolutionary strategies. 1–4. 4 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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