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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:

Comprehensive analysis of machine learning models for prediction of sub-clinical mastitis: Deep Learning and Gradient-Boosted Trees outperform other models
Mansour Ebrahimi, Manijeh Mohammadi‐Dehcheshmeh, Esmaeil Ebrahimie, et al.
Computers in Biology and Medicine (2019) Vol. 114, pp. 103456-103456
Closed Access | Times Cited: 129

Showing 1-25 of 129 citing articles:

Machine Learning Applications for Precision Agriculture: A Comprehensive Review
Abhinav Sharma, Arpit Jain, Prateek Gupta, et al.
IEEE Access (2020) Vol. 9, pp. 4843-4873
Open Access | Times Cited: 649
The role of sensors, big data and machine learning in modern animal farming
Suresh Neethirajan
Sensing and Bio-Sensing Research (2020) Vol. 29, pp. 100367-100367
Open Access | Times Cited: 276
Digital Livestock Farming
Suresh Neethirajan, B. Kemp
Sensing and Bio-Sensing Research (2021) Vol. 32, pp. 100408-100408
Open Access | Times Cited: 189
The future of service: The power of emotion in human-robot interaction
Stephanie Hui-Wen Chuah, Chung-En Yu
Journal of Retailing and Consumer Services (2021) Vol. 61, pp. 102551-102551
Open Access | Times Cited: 188
Accelerating materials discovery using machine learning
Yongfei Juan, Yongbing Dai, Yang Yang, et al.
Journal of Material Science and Technology (2020) Vol. 79, pp. 178-190
Closed Access | Times Cited: 148
Review: Application and Prospective Discussion of Machine Learning for the Management of Dairy Farms
Marianne Cockburn
Animals (2020) Vol. 10, Iss. 9, pp. 1690-1690
Open Access | Times Cited: 91
Invited Review: Examples and opportunities for artificial intelligence (AI) in dairy farms*
Albert De Vries, Nikolay Bliznyuk, Pablo Pinedo
Applied Animal Science (2023) Vol. 39, Iss. 1, pp. 14-22
Open Access | Times Cited: 30
The Prediction of Clinical Mastitis in Dairy Cows Based on Milk Yield, Rumination Time, and Milk Electrical Conductivity Using Machine Learning Algorithms
Hong Tian, Xiaojing Zhou, Hao Wang, et al.
Animals (2024) Vol. 14, Iss. 3, pp. 427-427
Open Access | Times Cited: 12
Performance comparison of machine learning models used for predicting subclinical mastitis in dairy cows: Bagging, boosting, stacking, and super-learner ensembles versus single machine learning models
Alicja Satoła, Krzysztof Satoła
Journal of Dairy Science (2024) Vol. 107, Iss. 6, pp. 3959-3972
Open Access | Times Cited: 11
Promoting Wind Energy by Robust Wind Speed Forecasting Using Machine Learning Algorithms Optimization
Aminuddin Aminuddin, Nurry Widya Hesty, Nina Konitat Supriatna, et al.
Evergreen (2024) Vol. 11, Iss. 1, pp. 354-370
Open Access | Times Cited: 9
Automated prediction of mastitis infection patterns in dairy herds using machine learning
Robert Hyde, Peter Down, Andrew Bradley, et al.
Scientific Reports (2020) Vol. 10, Iss. 1
Open Access | Times Cited: 67
Transforming the Adaptation Physiology of Farm Animals through Sensors
Suresh Neethirajan
Animals (2020) Vol. 10, Iss. 9, pp. 1512-1512
Open Access | Times Cited: 63
A unified intelligent model for estimating the (gas + n-alkane) interfacial tension based on the eXtreme gradient boosting (XGBoost) trees
Jiyuan Zhang, Yanchun Sun, Lin Shang, et al.
Fuel (2020) Vol. 282, pp. 118783-118783
Closed Access | Times Cited: 53
Application of machine learning to improve dairy farm management: A systematic literature review
Naftali Slob, Cagatay Catal, Ayalew Kassahun
Preventive Veterinary Medicine (2020) Vol. 187, pp. 105237-105237
Open Access | Times Cited: 52
LiveCare: An IoT-Based Healthcare Framework for Livestock in Smart Agriculture
Pinaki Sankar Chatterjee, Niranjan Kumar Ray, Saraju P. Mohanty
IEEE Transactions on Consumer Electronics (2021) Vol. 67, Iss. 4, pp. 257-265
Closed Access | Times Cited: 46
Comparison of machine learning methods to predict udder health status based on somatic cell counts in dairy cows
Tania Bobbo, Stefano Biffani, Cristian Taccioli, et al.
Scientific Reports (2021) Vol. 11, Iss. 1
Open Access | Times Cited: 43
Antibiotic resistant bacteria: A bibliometric review of literature
Guojun Sun, Qian Zhang, Zuojun Dong, et al.
Frontiers in Public Health (2022) Vol. 10
Open Access | Times Cited: 28
Invited review: integration of technologies and systems for precision animal agriculture—a case study on precision dairy farming
Upinder Kaur, V.M.R. Malacco, Huiwen Bai, et al.
Journal of Animal Science (2023) Vol. 101
Closed Access | Times Cited: 20
Early detection of subclinical mastitis in lactating dairy cows using cow-level features
Arjun Pakrashi, Chris Ryan, Christophe Guéret, et al.
Journal of Dairy Science (2023) Vol. 106, Iss. 7, pp. 4978-4990
Open Access | Times Cited: 17
Monitoring of Urban Black-Odor Water Based on Nemerow Index and Gradient Boosting Decision Tree Regression Using UAV-Borne Hyperspectral Imagery
Lifei Wei, Can Huang, Zhengxiang Wang, et al.
Remote Sensing (2019) Vol. 11, Iss. 20, pp. 2402-2402
Open Access | Times Cited: 47
A New Hybrid Predictive Model to Predict the Early Mortality Risk in Intensive Care Units on a Highly Imbalanced Dataset
Ramin Ghorbani, Rouzbeh Ghousi, Ahmad Makui, et al.
IEEE Access (2020) Vol. 8, pp. 141066-141079
Open Access | Times Cited: 44
Exploring machine learning algorithms for early prediction of clinical mastitis
Liliana Fadul-Pacheco, Hector Delgado, Víctor E. Cabrera
International Dairy Journal (2021) Vol. 119, pp. 105051-105051
Open Access | Times Cited: 39
Predicting Subclinical Ketosis in Dairy Cows Using Machine Learning Techniques
Alicja Satoła, Edyta A. Bauer
Animals (2021) Vol. 11, Iss. 7, pp. 2131-2131
Open Access | Times Cited: 31
Corn variable-rate seeding decision based on gradient boosting decision tree model
Zhaohui Du, Yang Li, Dongxing Zhang, et al.
Computers and Electronics in Agriculture (2022) Vol. 198, pp. 107025-107025
Closed Access | Times Cited: 22
Using Machine Learning and Behavioral Patterns Observed by Automated Feeders and Accelerometers for the Early Indication of Clinical Bovine Respiratory Disease Status in Preweaned Dairy Calves
M.C. Cantor, Enrico Casella, Simone Silvestri, et al.
Frontiers in Animal Science (2022) Vol. 3
Open Access | Times Cited: 22
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