Firesense Database Of Videos For Flame And Smoke Detection: papers linking to it

Zenodo · Dataset · 2017 · doi.org/10.5281/zenodo.836749

← Code and data repositories cited in research papers

Research papers in the 480 tracked journals that link to Firesense Database Of Videos For Flame And Smoke Detection (https://doi.org/10.5281/zenodo.836749), read from their Crossref records: by year, category and journal, the family tree of papers building on one another, and the linked repositories derived from it or that it derives from.

Key findings#

3 papers in the tracked journals link to the Zenodo dataset Firesense Database Of Videos For Flame And Smoke Detection in their Crossref records, the first in 2019; most of them (1) in 2025.
By category: AI (3); the journals with the most are Neural Computing and Applications (3).

By year#

20192025

Papers linking to Firesense Database Of Videos For Flame And Smoke Detection by publication year
Year	Papers
2019	1
2023	1
2025	1

Journals#

Journals whose papers link to Firesense Database Of Videos For Flame And Smoke Detection most
Journal	Papers
Neural Computing and Applications	3

Papers linking to Firesense Database Of Videos For Flame And Smoke Detection, most cited first
Published	Title	Journal	Authors	Citations	Link
2023-06-17	FireClassNet: a deep convolutional neural network approach for PJF fire images classification	Neural Computing and Applications	Zeineb Daoud, Amal Ben Hamida, Chokri Ben Amar	14	cites it
2019-08-16	Sniffer-Net: quantitative evaluation of smoke in the wild based on spatial–temporal motion spectrum	Neural Computing and Applications	Zeyang Mi, Weiwei Zhang, Xuncheng Wu et al.	4	cites it
2025-07-02	Fire and smoke detection using two-stream spatiotemporal network	Neural Computing and Applications	Rafaqat Alam Khan, Usama Ijaz Bajwa, Rana Hammad Raza et al.	3	cites it

How this is measured#

Links are read from each paper's Crossref record as its publisher deposited it: the URLs and repository DOIs in its reference list (the software and data it cites) and its supplementary links (“is-supplemented-by”: its own code or data). A paper that gives its code only in the text, a footnote or a data availability statement is not counted, and some publishers deposit reference lists without their URLs, so every count here is a lower bound. Repository details come from the GitHub REST API (stars, forks, and the repository a fork was made from), the Hugging Face Hub API (likes, downloads, and the base model a model was derived from) and DataCite (Zenodo and other data DOIs, and the GitHub release a Zenodo record archives), read again every month.

Cite this page

Scholar4AI. (2026). Firesense Database Of Videos For Flame And Smoke Detection: papers linking to it. Journal and Conference Research Rankings. Retrieved November 3, 2026, from https://scholar4ai.com/code/zenodo-836749

@misc{scholar4ai-code-zenodo-836749,
  author = {{Scholar4AI}},
  title = {Firesense Database Of Videos For Flame And Smoke Detection: papers linking to it},
  year = {2026},
  howpublished = {\url{https://scholar4ai.com/code/zenodo-836749}},
  urldate = {2026-11-03},
  note = {Journal and Conference Research Rankings; data from OpenAlex (CC0); licensed CC BY-NC 4.0}
}

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