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OpenAlex Citation Counts

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OpenAlex is a bibliographic catalogue of scientific papers, authors and institutions accessible in open access mode, named after the Library of Alexandria. It's citation coverage is excellent and I hope you will find utility in this listing of citing articles!

If you click the article title, you'll navigate to the article, as listed in CrossRef. If you click the Open Access links, you'll navigate to the "best Open Access location". Clicking the citation count will open this listing for that article. Lastly at the bottom of the page, you'll find basic pagination options.

Requested Article:

A Survey on Automated Food Monitoring and Dietary Management Systems
Vieira Bruno, Silva Resende, Juan Cui
Journal of Health & Medical Informatics (2017) Vol. 08, Iss. 03
Open Access | Times Cited: 41

Showing 1-25 of 41 citing articles:

A Survey on Food Computing
Weiqing Min, Shuqiang Jiang, Linhu Liu, et al.
ACM Computing Surveys (2019) Vol. 52, Iss. 5, pp. 1-36
Closed Access | Times Cited: 246
Automatic food detection in egocentric images using artificial intelligence technology
Wenyan Jia, Yuecheng Li, Ruowei Qu, et al.
Public Health Nutrition (2018), pp. 1-12
Open Access | Times Cited: 89
A Review of Image-Based Food Recognition and Volume Estimation Artificial Intelligence Systems
Fotios S. Konstantakopoulos, Eleni I. Georga, Dimitrios I. Fotiadis
IEEE Reviews in Biomedical Engineering (2023) Vol. 17, pp. 136-152
Open Access | Times Cited: 30
The Application of Digital Technologies and Artificial Intelligence in Healthcare: An Overview on Nutrition Assessment
Alessia Salinari, Michele Machì, Yasmany Armas Díaz, et al.
Diseases (2023) Vol. 11, Iss. 3, pp. 97-97
Open Access | Times Cited: 21
Artificial intelligence in nutrition research: perspectives on current and future applications
Mélina Côté, Benoı̂t Lamarche
Applied Physiology Nutrition and Metabolism (2021) Vol. 47, Iss. 1, pp. 1-8
Open Access | Times Cited: 37
Recognition of food type and calorie estimation using neural network
Rakesh Kumar, E. Golden Julie, Harold Robinson, et al.
The Journal of Supercomputing (2021) Vol. 77, Iss. 8, pp. 8172-8193
Closed Access | Times Cited: 31
Automated food intake tracking requires depth-refined semantic segmentation to rectify visual-volume discordance in long-term care homes
Kaylen J. Pfisterer, Robert Amelard, Audrey G. Chung, et al.
Scientific Reports (2022) Vol. 12, Iss. 1
Open Access | Times Cited: 19
Health benefits and future research of phytochemicals: a literature review
Yan Yang, Wenhua Ling
Journal of Nutrition (2024)
Closed Access | Times Cited: 3
Use of artificial intelligence in precision nutrition and fitness
María Helena Baena de Moraes Lopes, Danton Diego Ferreira, Ana Cláudia Barbosa Honório Ferreira, et al.
Elsevier eBooks (2020), pp. 465-496
Closed Access | Times Cited: 27
Real-Time Food Intake Monitoring Using Wearable Egocnetric Camera
Delwar Hossain, Masudul H. Imtiaz, Tonmoy Ghosh, et al.
(2020), pp. 4191-4195
Closed Access | Times Cited: 24
Evaluating machine learning technologies for food computing from a data set perspective
Nauman Ullah Gilal, Khaled Al-Thelaya, Jumana Khalid Al-Saeed, et al.
Multimedia Tools and Applications (2023) Vol. 83, Iss. 11, pp. 32041-32068
Open Access | Times Cited: 8
Dietary assessment can be based on pattern recognition rather than recall
David L. Katz, Lauren Q. Rhee, Carmit Katz, et al.
Medical Hypotheses (2020) Vol. 140, pp. 109644-109644
Closed Access | Times Cited: 23
A Mobile-Based Diet Monitoring System for Obesity Management
Bruno Vieira Resende E Silva, Milad Ghiasi Rad, Juan Cui, et al.
Journal of Health & Medical Informatics (2018) Vol. 09, Iss. 02
Open Access | Times Cited: 21
AIFood: A Large Scale Food Images Dataset for Ingredient Recognition
Gwo Giun Lee, Chin‐Wei Huang, Jiahong Chen, et al.
TENCON 2021 - 2021 IEEE Region 10 Conference (TENCON) (2019), pp. 802-805
Closed Access | Times Cited: 16
Recognition of cooking activities through air quality sensor data for supporting food journaling
Federica Gerina, Silvia M. Massa, Francesca Moi, et al.
Human-centric Computing and Information Sciences (2020) Vol. 10, Iss. 1
Open Access | Times Cited: 15
Meat Quality Assessment based on Deep Learning
Oguzhan Ulucan, Diclehan Karakaya, Mehmet Türkan
2022 Innovations in Intelligent Systems and Applications Conference (ASYU) (2019)
Closed Access | Times Cited: 13
Uncertainty-aware selecting for an ensemble of deep food recognition models
Eduardo Aguilar, Bhalaji Nagarajan, Petia Radeva
Computers in Biology and Medicine (2022) Vol. 146, pp. 105645-105645
Closed Access | Times Cited: 7
ResSeg: Residual encoder-decoder convolutional neural network for food segmentation
Javier Orlando Pinzón-Arenas, Róbinson Jiménez Moreno, César G. Pachón
International Journal of Electrical and Computer Engineering (IJECE) (2020) Vol. 10, Iss. 1, pp. 1017-1017
Open Access | Times Cited: 9
Improving Heart Disease Risk Through Quality-Focused Diet Logging: Pre-Post Study of a Diet Quality Tracking App
Bum Chul Kwon, Courtland VanDam, Stephanie E. Chiuve, et al.
JMIR mhealth and uhealth (2020) Vol. 8, Iss. 12, pp. e21733-e21733
Open Access | Times Cited: 8
A Survey on Food Computing
Weiqing Min, Shuqiang Jiang, Linhu Liu, et al.
(2018)
Closed Access | Times Cited: 8
A Two-Level Food Classification System For People With Diabetes Mellitus Using Convolutional Neural Networks
K. Kogias, Ioannis Andreadis, Kalliopi Dalakleidi, et al.
(2018), pp. 2603-2606
Closed Access | Times Cited: 8
Malnutrition Risk Assessment in Frail Older Adults using m-Health and Machine Learning
Flavio Di Martino, Franca Delmastro, Cristina Dolciotti
ICC 2022 - IEEE International Conference on Communications (2021), pp. 1-6
Closed Access | Times Cited: 6
Transfer Learning using Very Deep Pre-Trained Models for Food Image Classification
Pranjal Kumar Singh, Seba Susan
2022 13th International Conference on Computing Communication and Networking Technologies (ICCCNT) (2023)
Closed Access | Times Cited: 2
Uncertainty-Aware Data Augmentation for Food Recognition
Eduardo Aguilar, Bhalaji Nagarajan, Rupali Khantun, et al.
2022 26th International Conference on Pattern Recognition (ICPR) (2021), pp. 4017-4024
Closed Access | Times Cited: 5
The Use of Social Media and mEMA Technology in Comparing Compliance Rate Among Users
Reimund Serafica, Nada Lukkahatai, Brendan Morris, et al.
Asian/Pacific Island Nursing Journal (2018) Vol. 3, Iss. 4
Open Access | Times Cited: 3
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