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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:

Based investigate of beehive sound to detect air pollutants by machine learning
Yangguang Zhao, Guoqing Deng, Long Zhang, et al.
Ecological Informatics (2021) Vol. 61, pp. 101246-101246
Closed Access | Times Cited: 35

Showing 1-25 of 35 citing articles:

Automated Beehive Acoustics Monitoring: A Comprehensive Review of the Literature and Recommendations for Future Work
Mahsa Abdollahi, Pierre Giovenazzo, Tiago H. Falk
Applied Sciences (2022) Vol. 12, Iss. 8, pp. 3920-3920
Open Access | Times Cited: 41
Applicability of VGGish embedding in bee colony monitoring: comparison with MFCC in colony sound classification
Nayan Di, Muhammad Zahid Sharif, Zongwen Hu, et al.
PeerJ (2023) Vol. 11, pp. e14696-e14696
Open Access | Times Cited: 19
A framework for better sensor-based beehive health monitoring
Asaduz Zaman, Alan Dorin
Computers and Electronics in Agriculture (2023) Vol. 210, pp. 107906-107906
Open Access | Times Cited: 19
A deep learning-based approach for bee sound identification
Trương Thu Hương, Huu Du Nguyen, Thi Quynh Anh, et al.
Ecological Informatics (2023) Vol. 78, pp. 102274-102274
Closed Access | Times Cited: 17
From buzzes to bytes: A systematic review of automated bioacoustics models used to detect, classify and monitor insects
Anna B. Kohlberg, Christopher R. Myers, Laura L. Figueroa
Journal of Applied Ecology (2024) Vol. 61, Iss. 6, pp. 1199-1211
Open Access | Times Cited: 7
Evaluation of the content of macro and trace elements and the geographic origin of honey in North Brazil through statistical and machine learning techniques
Antônio dos Santos Silva, Marinalva Cardoso Maciel, Antônio Augusto Ferreira de Oliveira, et al.
Journal of Food Composition and Analysis (2024) Vol. 128, pp. 106050-106050
Closed Access | Times Cited: 6
Biomonitoring: Developing a Beehive Air Volatiles Profile as an Indicator of Environmental Contamination Using a Sustainable In-Field Technique
Daria Ilić, Boris Brkić, Maja Turk Sekulić
(2024)
Open Access | Times Cited: 4
Improving pollen-bearing honey bee detection from videos captured at hive entrance by combining deep learning and handling imbalance techniques
Dinh-Tu Nguyen, Thi-Nhung Le, Thi-Huong Phung, et al.
Ecological Informatics (2024) Vol. 82, pp. 102744-102744
Open Access | Times Cited: 4
Automatic synthesis of insects bioacoustics using machine learning: a systematic review
Henry Kyalo, Henri E. Z. Tonnang, James P. Egonyu, et al.
International Journal of Tropical Insect Science (2025)
Open Access
A citizen science platform to sample beehive sounds for monitoring ANSP
Baizhong Yu, Xinqiu Huang, Muhammad Zahid Sharif, et al.
Journal of Environmental Management (2025) Vol. 375, pp. 124247-124247
Closed Access
UrBAN: Urban Beehive Acoustics and PheNotyping Dataset
Mahsa Abdollahi, Yi Zhu, Heitor R. Guimarães, et al.
Scientific Data (2025) Vol. 12, Iss. 1
Open Access
Honeybee colony soundscapes: Decoding distance-based cues and environmental stressors
Nayan Di, Chao Zhu, Zongwen Hu, et al.
Ecotoxicology and Environmental Safety (2025) Vol. 297, pp. 118241-118241
Closed Access
Comparative Study of Machine Learning Models for Bee Colony Acoustic Pattern Classification on Low Computational Resources
Antonio Robles-Guerrero, Tonatiuh Saucedo-Anaya, Carlos Guerrero-Méndez, et al.
Sensors (2023) Vol. 23, Iss. 1, pp. 460-460
Open Access | Times Cited: 10
Biomonitoring: Developing a Beehive Air Volatiles Profile as an Indicator of Environmental Contamination Using a Sustainable In-Field Technique
Daria Ilić, Boris Brkić, Maja Turk Sekulić
Sustainability (2024) Vol. 16, Iss. 5, pp. 1713-1713
Open Access | Times Cited: 3
Deep learning-based classification models for beehive monitoring
Selcan Kaplan Berkaya, Efnan Şora Günal, Serkan Günal
Ecological Informatics (2021) Vol. 64, pp. 101353-101353
Closed Access | Times Cited: 23
Smart Embedded Framework using Arduino and IoT for Real-Time Noise and Air Pollution Monitoring and Alert system
D. A. Janeera, H Poovizhi., S.S Sheik Haseena., et al.
(2021), pp. 1416-1420
Closed Access | Times Cited: 18
The Importance of Context Awareness in Acoustics-Based Automated Beehive Monitoring
Mahsa Abdollahi, Evan Henry, Pierre Giovenazzo, et al.
Applied Sciences (2022) Vol. 13, Iss. 1, pp. 195-195
Open Access | Times Cited: 12
IoT Embedded Smart Monitoring System with Edge Machine Learning for Beehive Management
Mihai Doinea, Ioana Trandafir, Cristian-Valeriu Toma, et al.
International Journal of Computers Communications & Control (2024) Vol. 19, Iss. 4
Open Access | Times Cited: 2
A matter of the beehive sound: Can honey bees alert the pollution out of their hives?
Baizhong Yu, Xinqiu Huang, Muhammad Zahid Sharif, et al.
Environmental Science and Pollution Research (2022) Vol. 30, Iss. 6, pp. 16266-16276
Closed Access | Times Cited: 10
A machine learning-based multiclass classification model for bee colony anomaly identification using an IoT-based audio monitoring system with an edge computing framework
Sheng-Hao Chen, Jen-Cheng Wang, Hung‐Jen Lin, et al.
Expert Systems with Applications (2024) Vol. 255, pp. 124898-124898
Closed Access | Times Cited: 1
Optimization of the algorithms use ensemble and synthetic minority oversampling technique for air quality classification
Aziz Jihadian Barid, Hadiyanto Hadiyanto, Adi Wibowo
Indonesian Journal of Electrical Engineering and Computer Science (2024) Vol. 33, Iss. 3, pp. 1632-1632
Open Access | Times Cited: 1
Classification of Subspecies of Honey Bees using Convolutional Neural Network
Ashan Ratnayake, Hartini M. Yasin, Abdul Ghani Naim, et al.
(2023), pp. 1-6
Closed Access | Times Cited: 2
A Pest Intrusion Detection in Chinese Beehive Culture Using Deep Learning
Chao Liu, Shouying Lin
Scientific Programming (2022) Vol. 2022, pp. 1-10
Open Access | Times Cited: 4
Convolutional Neural Networks for Real Time Classification of Beehive Acoustic Patterns on Constrained Devices
Antonio Robles-Guerrero, Salvador Gómez-Jiménez, Tonatiuh Saucedo-Anaya, et al.
Sensors (2024) Vol. 24, Iss. 19, pp. 6384-6384
Open Access
Cepstral and Deep Features for Apis mellifera Hive Strength Classification
Jederson S. Luz, Myllena C. De Oliveira, F. de M. Pereira, et al.
Journal of Internet Services and Applications (2024) Vol. 15, Iss. 1, pp. 548-560
Open Access
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