msambare/fer2013: papers linking to it

Kaggle · www.kaggle.com/datasets/msambare/fer2013

← Code and data repositories cited in research papers

Research papers in the 480 tracked journals that link to msambare/fer2013 (https://www.kaggle.com/datasets/msambare/fer2013), 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#

6 papers in the tracked journals link to the Kaggle dataset msambare/fer2013 in their Crossref records, the first in 2022; most of them (3) in 2026.
By category: AI (5) and Statistics (1); the journals with the most are Neural Computing and Applications (4), Computational Statistics (1) and Expert Systems with Applications (1).

By year#

20222026

Papers linking to msambare/fer2013 by publication year
Year	Papers
2022	1
2024	1
2025	1
2026	3

Journals#

Journals whose papers link to msambare/fer2013 most
Journal	Papers
Neural Computing and Applications	4
Computational Statistics	1
Expert Systems with Applications	1

Papers linking to msambare/fer2013, most cited first
Published	Title	Journal	Authors	Citations	Link
2024-02-15	Early detection of depression through facial expression recognition and electroencephalogram-based artificial intelligence-assisted graphical user interface	Neural Computing and Applications	Gajendra Kumar, Tanaya Das, Kuldeep Singh	28	cites it
2022-09-21	Facial emotion recognition and encoding application for the visually impaired	Neural Computing and Applications	M. N. Pushpalatha, Harshubh Meherishi, Avani Vaishnav et al.	9	cites it
2026-02-06	CNN-DET: A hybrid deep learning architecture for emotion recognition	Expert Systems with Applications	BERROUACHEDI Abdelkader, Rakia Jaziri, Bernard Gilles	7	cites it
2026-02-01	Encoding using three-channel deep convolutional neural network and decoding in multi-layer perceptron for facial emotion recognition	Neural Computing and Applications	Shervan Fekri-Ershad	1	cites it
2025-08-13	Voting based optimized deep ensemble model: an effective visual and textual fusion for recognition of face emotions	Computational Statistics	Dipti Pandit, Sangeeta Jadhav	0	cites it
2026-10-01	An investigation of innovative computational approaches utilizing auditory, visual, and multimedia data to enhance quality of clinical practices in mental health: a systematic literature review utilizing the PRISMA framework	Neural Computing and Applications	Uygar Aydin, Reyhan Şahinbaş, İnci Zaim Gökbay	0	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). msambare/fer2013: papers linking to it. Journal and Conference Research Rankings. Retrieved November 3, 2026, from https://scholar4ai.com/code/kaggle-msambare-fer2013

@misc{scholar4ai-code-kaggle-msambare-fer2013,
  author = {{Scholar4AI}},
  title = {msambare/fer2013: papers linking to it},
  year = {2026},
  howpublished = {\url{https://scholar4ai.com/code/kaggle-msambare-fer2013}},
  urldate = {2026-11-03},
  note = {Journal and Conference Research Rankings; data from OpenAlex (CC0); licensed CC BY-NC 4.0}
}

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