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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:

Evaluation of sampling frequency, window size and sensor position for classification of sheep behaviour
E. K. Walton, Christy Casey, Jürgen Mitsch, et al.
Royal Society Open Science (2018) Vol. 5, Iss. 2, pp. 171442-171442
Open Access | Times Cited: 95

Showing 1-25 of 95 citing articles:

Machine Learning in Agriculture: A Comprehensive Updated Review
Lefteris Benos, Aristotelis C. Tagarakis, Georgios Dolias, et al.
Sensors (2021) Vol. 21, Iss. 11, pp. 3758-3758
Open Access | Times Cited: 526
Predicting livestock behaviour using accelerometers: A systematic review of processing techniques for ruminant behaviour prediction from raw accelerometer data
Lucile Riaboff, L. Shalloo, Alan F. Smeaton, et al.
Computers and Electronics in Agriculture (2021) Vol. 192, pp. 106610-106610
Open Access | Times Cited: 131
Deep learning-based animal activity recognition with wearable sensors: Overview, challenges, and future directions
Axiu Mao, Endai Huang, Xiaoshuai Wang, et al.
Computers and Electronics in Agriculture (2023) Vol. 211, pp. 108043-108043
Closed Access | Times Cited: 46
Feature Selection and Comparison of Machine Learning Algorithms in Classification of Grazing and Rumination Behaviour in Sheep
Nicola Mansbridge, Jürgen Mitsch, Nicola Bollard, et al.
Sensors (2018) Vol. 18, Iss. 10, pp. 3532-3532
Open Access | Times Cited: 118
Behaviour classification of extensively grazed sheep using machine learning
Eloise S. Fogarty, David L. Swain, G. M. Cronin, et al.
Computers and Electronics in Agriculture (2019) Vol. 169, pp. 105175-105175
Closed Access | Times Cited: 97
A survey of machine learning approaches in animal behaviour
Natasa Kleanthous, Abir Hussain, Wasiq Khan, et al.
Neurocomputing (2022) Vol. 491, pp. 442-463
Open Access | Times Cited: 39
Development and Analysis of a CNN- and Transfer-Learning-Based Classification Model for Automated Dairy Cow Feeding Behavior Recognition from Accelerometer Data
Victor Bloch, Lilli Frondelius, Claudia Arcidiacono, et al.
Sensors (2023) Vol. 23, Iss. 5, pp. 2611-2611
Open Access | Times Cited: 25
Machine Learning Algorithms to Classify and Quantify Multiple Behaviours in Dairy Calves Using a Sensor: Moving beyond Classification in Precision Livestock
Charles Carslake, Jorge A. Vázquez-Diosdado, Jasmeet Kaler
Sensors (2020) Vol. 21, Iss. 1, pp. 88-88
Open Access | Times Cited: 52
Application of machine learning models in the behavioral study of forest fires in the Brazilian Federal District region
Jesús N.S. Rubí, Paulo H.P. de Carvalho, Paulo Roberto de Lira Gondim
Engineering Applications of Artificial Intelligence (2022) Vol. 118, pp. 105649-105649
Closed Access | Times Cited: 34
A Systematic Literature Review on the Use of Deep Learning in Precision Livestock Detection and Localization Using Unmanned Aerial Vehicles
D. B. Mamehgol Yousefi, Azmin Shakrine Mohd Rafie, S. A. R. Al-Haddad, et al.
IEEE Access (2022) Vol. 10, pp. 80071-80091
Open Access | Times Cited: 29
Behavior classification and spatiotemporal analysis of grazing sheep using deep learning
Zhongming Jin, Hang Shu, Tianci Hu, et al.
Computers and Electronics in Agriculture (2024) Vol. 220, pp. 108894-108894
Closed Access | Times Cited: 8
Improving Aboveground Biomass Estimation in Lowland Tropical Forests across Aspect and Age Stratification: A Case Study in Xishuangbanna
Yong Wu, Guanglong Ou, Teng-Fei Lu, et al.
Remote Sensing (2024) Vol. 16, Iss. 7, pp. 1276-1276
Open Access | Times Cited: 7
Technology adoption on farms: Using Normalisation Process Theory to understand sheep farmers’ attitudes and behaviours in relation to using precision technology in flock management
Jasmeet Kaler, Annmarie Ruston
Preventive Veterinary Medicine (2019) Vol. 170, pp. 104715-104715
Open Access | Times Cited: 52
Behavior classification of goats using 9-axis multi sensors: The effect of imbalanced datasets on classification performance
Koki Sakai, Kazato Oishi, Masafumi MIWA, et al.
Computers and Electronics in Agriculture (2019) Vol. 166, pp. 105027-105027
Closed Access | Times Cited: 51
A Combined Offline and Online Algorithm for Real-Time and Long-Term Classification of Sheep Behaviour: Novel Approach for Precision Livestock Farming
Jorge A. Vázquez-Diosdado, Veronica Paul, Keith A. Ellis, et al.
Sensors (2019) Vol. 19, Iss. 14, pp. 3201-3201
Open Access | Times Cited: 50
Intent based recognition of walking and ramp activities for amputee using sEMG based lower limb prostheses
Tahir Hussain, Nadeem Iqbal, Hafiz Farhan Maqbool, et al.
Journal of Applied Biomedicine (2020) Vol. 40, Iss. 3, pp. 1110-1123
Closed Access | Times Cited: 45
Automated detection of lameness in sheep using machine learning approaches: novel insights into behavioural differences among lame and non-lame sheep
Jasmeet Kaler, Jürgen Mitsch, Jorge A. Vázquez-Diosdado, et al.
Royal Society Open Science (2020) Vol. 7, Iss. 1, pp. 190824-190824
Open Access | Times Cited: 41
Developing a Simulated Online Model That Integrates GNSS, Accelerometer and Weather Data to Detect Parturition Events in Grazing Sheep: A Machine Learning Approach
Eloise S. Fogarty, David L. Swain, G. M. Cronin, et al.
Animals (2021) Vol. 11, Iss. 2, pp. 303-303
Open Access | Times Cited: 35
Deep learning based classification of sheep behaviour from accelerometer data with imbalance
Kirk E. Turner, A. N. Thompson, Ian Harris, et al.
Information Processing in Agriculture (2022) Vol. 10, Iss. 3, pp. 377-390
Open Access | Times Cited: 23
Assessing the effects of sampling frequency on behavioural classification of accelerometer data
Jenna L. Hounslow, Lauran R. Brewster, Karissa O. Lear, et al.
Journal of Experimental Marine Biology and Ecology (2019) Vol. 512, pp. 22-30
Open Access | Times Cited: 41
Parasitic mites alter chicken behaviour and negatively impact animal welfare
Amy C. Murillo, Alireza Abdoli, Richard A. Blatchford, et al.
Scientific Reports (2020) Vol. 10, Iss. 1
Open Access | Times Cited: 38
Training and Validating a Machine Learning Model for the Sensor-Based Monitoring of Lying Behavior in Dairy Cows on Pasture and in the Barn
Lara Schmeling, Golnaz Elmamooz, Phan Thai Hoang, et al.
Animals (2021) Vol. 11, Iss. 9, pp. 2660-2660
Open Access | Times Cited: 30
Classifying the posture and activity of ewes and lambs using accelerometers and machine learning on a commercial flock
Emily Price, Joss Langford, Tim W. Fawcett, et al.
Applied Animal Behaviour Science (2022) Vol. 251, pp. 105630-105630
Closed Access | Times Cited: 22
Behavioral Fingerprinting: Acceleration Sensors for Identifying Changes in Livestock Health
Bowen Fan, Racheal H. Bryant, Andrew W. Greer
J — Multidisciplinary Scientific Journal (2022) Vol. 5, Iss. 4, pp. 435-454
Open Access | Times Cited: 20
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