Halil Durmuş

621 total citations · 1 hit paper
7 papers, 422 citations indexed

About

Halil Durmuş is a scholar working on Computer Networks and Communications, Control and Systems Engineering and Computer Vision and Pattern Recognition. According to data from OpenAlex, Halil Durmuş has authored 7 papers receiving a total of 422 indexed citations (citations by other indexed papers that have themselves been cited), including 2 papers in Computer Networks and Communications, 2 papers in Control and Systems Engineering and 2 papers in Computer Vision and Pattern Recognition. Recurrent topics in Halil Durmuş's work include Greenhouse Technology and Climate Control (2 papers), Smart Agriculture and AI (2 papers) and IoT and Edge/Fog Computing (1 paper). Halil Durmuş is often cited by papers focused on Greenhouse Technology and Climate Control (2 papers), Smart Agriculture and AI (2 papers) and IoT and Edge/Fog Computing (1 paper). Halil Durmuş collaborates with scholars based in Türkiye. Halil Durmuş's co-authors include Ece Olcay Güneş, Mürvet Kırcı and Nazım Kemal Üre and has published in prestigious journals such as Istanbul Technical University Academic Open Archive (Istanbul Technical University), Pressacademia and 2022 IEEE 25th International Conference on Intelligent Transportation Systems (ITSC).

In The Last Decade

Halil Durmuş

6 papers receiving 392 citations

Hit Papers

Disease detection on the leaves of the tomato plants by u... 2017 2026 2020 2023 2017 100 200 300
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.

  1. Disease detection on the leaves of the tomato plants by using deep learning
    2017 347 citations Halil Durmuş, Ece Olcay Güneş et al. Istanbul Technical University Academic Open Archive (Istanbul Technical University) profile →

Peers

Halil Durmuş
Di Cai China
Sundar Anand India
Yanlei Xu China
Longzhe Quan China
Ajit Patil India
Qian Yan China
Nalini Kanta Barpanda India
Shilpi Harnal India
Sunil S. Harakannanavar India
Amit Sinha India
Di Cai China
Halil Durmuş
Citations per year, relative to Halil Durmuş Halil Durmuş (= 1×) peers Di Cai

Countries citing papers authored by Halil Durmuş

Since Specialization
Citations

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

Fields of papers citing papers by Halil Durmuş

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Halil Durmuş

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

All Works

7 of 7 papers shown
1.
Durmuş, Halil, et al.. (2022). DeFIX: Detecting and Fixing Failure Scenarios with Reinforcement Learning in Imitation Learning Based Autonomous Driving. 2022 IEEE 25th International Conference on Intelligent Transportation Systems (ITSC). 4215–4220. 1 indexed citations
2.
Durmuş, Halil & Ece Olcay Güneş. (2019). Integration of the Mobile Robot and Internet of Things to Collect Data from the Agricultural Fields. Istanbul Technical University Academic Open Archive (Istanbul Technical University). 1–5. 19 indexed citations
3.
Durmuş, Halil, et al.. (2019). Petri nets based procedure of hardware/software codesign of an urban agriculture monitoring system. Istanbul Technical University Academic Open Archive (Istanbul Technical University). 27. 1–4. 3 indexed citations
4.
Durmuş, Halil, Ece Olcay Güneş, & Mürvet Kırcı. (2017). Disease detection on the leaves of the tomato plants by using deep learning. Istanbul Technical University Academic Open Archive (Istanbul Technical University). 1–5. 347 indexed citations breakdown →
5.
Durmuş, Halil, et al.. (2016). Determining the relationship between emotional labour and occupational. Pressacademia. 2(1). 127–127.
6.
Durmuş, Halil, Ece Olcay Güneş, & Mürvet Kırcı. (2016). Data acquisition from greenhouses by using autonomous mobile robot. Istanbul Technical University Academic Open Archive (Istanbul Technical University). 1–5. 16 indexed citations
7.
Durmuş, Halil, et al.. (2015). The design of general purpose autonomous agricultural mobile-robot: “AGROBOT”. Istanbul Technical University Academic Open Archive (Istanbul Technical University). 49–53. 36 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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