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 self-developed electronic nose system combines data enhancement and multi-branch kernels channel attention to identify the gas information of industrial polypropylene
Yanwei Wang, Yang Yu, Haojie Zhao, et al.
Sensors and Actuators A Physical (2024) Vol. 366, pp. 115005-115005
Closed Access | Times Cited: 6

Showing 6 citing articles:

Olfactory Diagnosis Model for Lung Health Evaluation Based on Pyramid Pooling and SHAP-Based Dual Encoders
Jingyi Peng, Haixia Mei, Ruiming Yang, et al.
ACS Sensors (2024) Vol. 9, Iss. 9, pp. 4934-4946
Closed Access | Times Cited: 5

Electronic nose combines an effective deep learning method to identify the rice quality under different storage conditions and storage periods
Xiaoyan Tang, Na Wang
Sensors and Actuators A Physical (2024), pp. 115930-115930
Closed Access | Times Cited: 4

A gas detection system combined with a global extension extreme learning machine for early warning of electrical fires
Yanwei Wang, Qinghua Li, Jinyue Zhang, et al.
Sensors and Actuators B Chemical (2024), pp. 136801-136801
Closed Access | Times Cited: 3

A feature extractor for temporal data of electronic nose based on parallel long short-term memory network in flavor discrimination of Chinese vinegars
Yufei Chen, Jun Fu, Xin Weng, et al.
Journal of Food Engineering (2024) Vol. 379, pp. 112132-112132
Closed Access | Times Cited: 2

E-Nose: Time–Frequency Attention Convolutional Neural Network for Gas Classification and Concentration Prediction
Minglv Jiang, Na Li, Mingyong Li, et al.
Sensors (2024) Vol. 24, Iss. 13, pp. 4126-4126
Open Access | Times Cited: 1

Rapid and high-accuracy concentration prediction of gas mixtures based on PMH-TCN
Junwei Zhuo, Xingyu Chen, Jianguo Zhang, et al.
Measurement (2024) Vol. 242, pp. 116003-116003
Closed Access | Times Cited: 1

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