FREE SHIPPING over $40
OVER 9000 FIVE-STAR REVIEWS
5% OFF ALL SUBSCRIPTIONS
100% HAPPINESS GUARANTEE
Analyze Diet
0
Home / Equine Research Bank / Sensors (Basel) / Identifying Novel Emotions and Wellbeing of Horses from Vide...
Sensors (Basel, Switzerland)2025; 25(3); doi: 10.3390/s25030859

Identifying Novel Emotions and Wellbeing of Horses from Videos Through Unsupervised Learning.

Abstract: This research applies unsupervised learning on a large original dataset of horses in the wild to identify previously unidentified horse emotions. We construct a novel, high-quality, diverse dataset of 3929 images consisting of five wild horse breeds worldwide at different geographical locations. We base our analysis on the seven Panksepp emotions of mammals "Exploring", "Sadness", "Playing", "Rage", "Fear", "Affectionate" and "Lust", along with one additional emotion "Pain" which has been shown to be highly relevant for horses. We apply the contrastive learning framework MoCo (Momentum Contrast for Unsupervised Visual Representation Learning) on our dataset to predict the seven Panksepp emotions and "Pain" using unsupervised learning. We significantly modify the MoCo framework, building a custom downstream classifier network that connects with a frozen CNN encoder that is pretrained using MoCo. Our method allows the encoder network to learn similarities and differences within image groups on its own without labels. The clusters thus formed are indicative of deeper nuances and complexities within a horse's mood, which can possibly hint towards the existence of novel and complex equine emotions.
Get Full Text
Publication Date: 2025-01-31 PubMed ID: 39943498PubMed Central: PMC11819734DOI: 10.3390/s25030859Google Scholar: Lookup
The Equine Research Bank provides access to a large database of publicly available scientific literature. Inclusion in the Research Bank does not imply endorsement of study methods or findings by Mad Barn.
LinkedInCopy
  • Journal Article

Summary

This research summary has been generated with artificial intelligence and may contain errors and omissions. Refer to the original study to confirm details provided. Submit correction.

This study utilizes machine learning to learn and identify new emotional states in horses from video footage. It employs a modified version of an established machine learning framework called Momentum Contrast (MoCo) to create a novel dataset and classifier for horse emotions.

Data Collection

The research team compiled a dataset of 3929 images depicting five breeds of wild horses from various global locations. The dataset’s size and variety are key to allowing the unsupervised learning process to capture a wide range of horse behaviours and emotions.

  • Unsupervised learning is a type of machine learning where a model learns to identify patterns without being explicitly told what to look for.
  • The dataset used in this study, created through fieldwork and video analysis, is large and diverse. It includes different horse breeds located in various geographical areas.

Emotional Framework

The analysis adopted the seven mammalian emotions suggested by neuroscientist Jaak Panksepp: Exploration, Sadness, Play, Rage, Fear, Affection, and Lust. An additional emotion was included – Pain – which is particularly relevant in analysing horse behaviour.

  • These emotions were not used as labels for the machine learning process but rather as a lens through which the outcomes could be understood.

Methodology

The Momentum Contrast (MoCo) machine learning framework was used to predict the identified emotions in an unsupervised manner. The MoCo framework was significantly modified to include a custom downstream classifier that connects with a pretrained CNN encoder.

  • The MoCo framework was trained using the collected dataset to understand and map out the visual representation of horse emotions.
  • The research introduces a new downstream classifier, which is a separate network designed to classify the output of the pretrained CNN encoder. The downstream classifier is not trained on any labelled data, enabling it to discover patterns independently.

Findings

The research methodology allowed the encoder network to autonomously learn the similarities and differences within image groups. The image clusters formed because of this learning process may indicate deeper complexity in a horse’s mood, potentially suggesting the existence of previously unidentified complex equine emotions.

  • The image clusters are not pre-defined categories, but groups discovered by the machine learning algorithm. The formation of these clusters indicates that horses could potentially express more nuanced and complex emotions than previously understood.

Cite This Article

APA
Bhave A, Kieson E, Hafner A, Gloor PA. (2025). Identifying Novel Emotions and Wellbeing of Horses from Videos Through Unsupervised Learning. Sensors (Basel), 25(3). https://doi.org/10.3390/s25030859

Publication

Sensors (Basel, Switzerland)
ISSN: 1424-8220
NlmUniqueID: 101204366
Country: Switzerland
Language: English
Volume: 25
Issue: 3

Researcher Affiliations

Bhave, Aarya
  • Massachusetts Institute of Technology, System Design & Management, Cambridge, MA 02142, USA.
Kieson, Emily
  • Equine International, Cambridge CB22 5LD, UK.
Hafner, Alina
  • TUM School of Computation, Information and Technology, Technical University of Munich, Arcisstraße 21, 80333 Munich, Germany.
Gloor, Peter A
  • Massachusetts Institute of Technology, System Design & Management, Cambridge, MA 02142, USA.

MeSH Terms

  • Animals
  • Horses / physiology
  • Emotions / physiology
  • Unsupervised Machine Learning
  • Video Recording

Conflict of Interest Statement

Author Emily Kieson serves on the board of Equine International. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

References

This article includes 86 references
  1. Li S, Deng W. Deep Facial Expression Recognition: A Survey. IEEE Trans. Affect. Comput. 2022;13:1195–1215.
    doi: 10.1109/TAFFC.2020.2981446google scholar: lookup
  2. Chen T, Kornblith S, Norouzi M, Hinton G.E. A Simple Framework for Contrastive Learning of Visual Representations. arXiv 20202002.05709.
  3. He K, Fan H, Wu Y, Xie S, Girshick R.B. Momentum Contrast for Unsupervised Visual Representation Learning. arXiv 20191911.05722.
  4. Grill J, Strub F, Altché F, Tallec C, Richemond P.H, Buchatskaya E, Doersch C, Pires B.Á, Guo Z.D, Azar M.G. Bootstrap Your Own Latent: A New Approach to Self-Supervised Learning. arXiv 20202006.07733.
  5. Chen X, He K. Exploring Simple Siamese Representation Learning. arXiv 20202011.10566.
  6. Khosla P, Teterwak P, Wang C, Sarna A, Tian Y, Isola P, Maschinot A, Liu C, Krishnan D. Supervised Contrastive Learning. arXiv 20202004.11362.
  7. Li M, Yang B, Levy J, Stolcke A, Rozgic V, Matsoukas S, Papayiannis C, Bone D, Wang C. Contrastive Unsupervised Learning for Speech Emotion Recognition. arXiv 20212102.06357.
  8. Sun L, Lian Z, Liu B, Tao J. HiCMAE: Hierarchical Contrastive Masked Autoencoder for self-supervised Audio-Visual Emotion Recognition. Inf. Fusion 2024;108:102382.
    doi: 10.1016/j.inffus.2024.102382google scholar: lookup
  9. Panksepp J. Affective Neuroscience: The Foundations of Human and Animal Emotions. Oxford University Press; Oxford, UK: 2004.
  10. Bhave A, Hafner A, Bhave A, Gloor P.A. Unsupervised Canine Emotion Recognition Using Momentum Contrast. Sensors 2024;24:7324.
    doi: 10.3390/s24227324pmc: PMC11597957pubmed: 39599101google scholar: lookup
  11. Chamove A.S, Crawley-Hartrick O.J, Stafford K.J. Horse reactions to human attitudes and behavior. Anthrozoös 2002;15:323–331.
    doi: 10.2752/089279302786992423google scholar: lookup
  12. Wathan J, McComb K. The eyes and ears are visual indicators of attention in domestic horses. Curr. Biol. 2014;24:R677–R679.
    doi: 10.1016/j.cub.2014.06.023pmc: PMC4123162pubmed: 25093554google scholar: lookup
  13. Proctor H.S, Carder G. Can ear postures reliably measure the positive emotional state of cows?. Appl. Anim. Behav. Sci. 2014;161:20–27.
    doi: 10.1016/j.applanim.2014.09.015google scholar: lookup
  14. Hall C, Randle H, Pearson G, Preshaw L, Waran N. Assessing equine emotional state. Appl. Anim. Behav. Sci. 2018;205:183–193.
    doi: 10.1016/j.applanim.2018.03.006google scholar: lookup
  15. Stewart M, Stratton R, Beausoleil N, Stafford K, Worth G, Waran N. Assessment of positive emotions in horses: Implications for welfare and performance. J. Vet. Behav. Clin. Appl. Res. 2011;5:296.
    doi: 10.1016/j.jveb.2011.05.014google scholar: lookup
  16. Stratton R.B. Assessment of Positive Emotion in Horses. Ph.D. Thesis. Massey University; Manawatū, New Zealand: 2022.
  17. Waran N, Randle H. What we can measure, we can manage: The importance of using robust welfare indicators in Equitation Science. Appl. Anim. Behav. Sci. 2017;190:74–81.
    doi: 10.1016/j.applanim.2017.02.016google scholar: lookup
  18. Feh C. Social behaviour and relationships of Prezewalski horses in Dutch semi-reserves. Appl. Anim. Behav. Sci. 1988;21:71–87.
    doi: 10.1016/0168-1591(88)90101-3google scholar: lookup
  19. Feh C. Relationships and communication in socially natural horse herds. The Domestic Horse. Cambridge University Press; Cambridge, UK: 2005; pp. 83–93.
  20. Maeda T, Sueur C, Hirata S, Yamamoto S. Behavioural synchronization in a multilevel society of feral horses. PLoS ONE 2021;16:e0258944.
    doi: 10.1371/journal.pone.0258944pmc: PMC8547633pubmed: 34699556google scholar: lookup
  21. Maeda T, Ochi S, Ringhofer M, Sosa S, Sueur C, Hirata S, Yamamoto S. Aerial drone observations identified a multilevel society in feral horses. Sci. Rep. 2021;11:71.
    doi: 10.1038/s41598-020-79790-1pmc: PMC7794487pubmed: 33420148google scholar: lookup
  22. Heitor F, do Mar Oom M, Vicente L. Social relationships in a herd of Sorraia horses: Part II. Factors affecting affiliative relationships and sexual behaviours. Behav. Process. 2006;73:231–239.
    doi: 10.1016/j.beproc.2006.05.005pubmed: 16828984google scholar: lookup
  23. Heitor F, Vicente L. Maternal care and foal social relationships in a herd of Sorraia horses: Influence of maternal rank and experience. Appl. Anim. Behav. Sci. 2008;113:189–205.
    doi: 10.1016/j.applanim.2007.11.005google scholar: lookup
  24. Heitor F, do Mar Oom M, Vicente L. Social relationships in a herd of Sorraia horses: Part I. Correlates of social dominance and contexts of aggression. Behav. Process. 2006;73:170–177.
    doi: 10.1016/j.beproc.2006.05.004pubmed: 16815645google scholar: lookup
  25. Shimada M, Suzuki N. The contribution of mutual grooming to affiliative relationships in a feral misaki horse herd. Animals 2020;10:1564.
    doi: 10.3390/ani10091564pmc: PMC7552250pubmed: 32899116google scholar: lookup
  26. Farmer K, Krüger K, Byrne R.W, Marr I. Sensory laterality in affiliative interactions in domestic horses and ponies (Equus caballus). Anim. Cogn. 2018;21:631–637.
    doi: 10.1007/s10071-018-1196-9pmc: PMC6097077pubmed: 29948296google scholar: lookup
  27. Kieson E, Sams J. A preliminary investigation of preferred affiliative interactions within and between select bonded pairs of horses: A first look at equine “Love Languages”. Int. J. Zool. Anim. Biol. 2021;4:000318.
  28. Zeitler-Feicht M.H, Hartmann E, Erhard M.H, Baumgartner M. Which affiliative behaviour can be used as a valid, reliable and feasible indicator of positive welfare in horse husbandry?. Appl. Anim. Behav. Sci. 2024;273:106236.
    doi: 10.1016/j.applanim.2024.106236google scholar: lookup
  29. Bartlett E, Cameron L.J, Freeman M.S. A preliminary comparison between proximity and interaction-based methods to construct equine (Equus caballus) social networks. J. Vet. Behav. 2022;50:36–45.
    doi: 10.1016/j.jveb.2022.01.005google scholar: lookup
  30. Benhajali H, Richard-Yris M.A, Leroux M, Ezzaouia M, Charfi F, Hausberger M. A note on the time budget and social behaviour of densely housed horses: A case study in Arab breeding mares. Appl. Anim. Behav. Sci. 2008;112:196–200.
    doi: 10.1016/j.applanim.2007.08.007google scholar: lookup
  31. Kieson E, Goma A.A, Radi M. Tend and Befriend in Horses: Partner Preferences, Lateralization, and Contextualization of Allogrooming in Two Socially Stable Herds of Quarter Horse Mares. Animals 2023;13:225.
    doi: 10.3390/ani13020225pmc: PMC9854972pubmed: 36670764google scholar: lookup
  32. Lansade L, Lemarchand J, Reigner F, Arnould C, Bertin A. Automatic brushes induce positive emotions and foster positive social interactions in group-housed horses. Appl. Anim. Behav. Sci. 2022;246:105538.
    doi: 10.1016/j.applanim.2021.105538google scholar: lookup
  33. Van Dierendonck M, Sigurjónsdóttir H, Colenbrander B, Thorhallsdóttir A.G. Differences in social behaviour between late pregnant, post-partum and barren mares in a herd of Icelandic horses. Appl. Anim. Behav. Sci. 2004;89:283–297.
    doi: 10.1016/j.applanim.2004.06.010google scholar: lookup
  34. Pugh H.K, Heatwole Shank K.S. Multispecies Occupations Involving Equines: An Action-Oriented Inquiry to Inform Occupational Therapy Practitioners. OTJR Occup. Ther. J. Res. 2024;44:196–204.
    doi: 10.1177/15394492231203724pubmed: 37853726google scholar: lookup
  35. Souris A.C, Kaczensky P, Julliard R, Walzer C. Time budget-, behavioral synchrony-and body score development of a newly released Przewalski’s horse group Equus ferus przewalskii, in the Great Gobi B strictly protected area in SW Mongolia. Appl. Anim. Behav. Sci. 2007;107:307–321.
    doi: 10.1016/j.applanim.2006.09.023pmc: PMC3207227pubmed: 22064904google scholar: lookup
  36. Mendonça R.S, Pinto P, Inoue S, Ringhofer M, Godinho R, Hirata S. Social determinants of affiliation and cohesion in a population of feral horses. Appl. Anim. Behav. Sci. 2021;245:105496.
    doi: 10.1016/j.applanim.2021.105496google scholar: lookup
  37. Wolter R, Stefanski V, Krueger K. Parameters for the analysis of social bonds in horses. Animals 2018;8:191.
    doi: 10.3390/ani8110191pmc: PMC6262610pubmed: 30373257google scholar: lookup
  38. Burke C.J, Whishaw I.Q. Sniff, look and loop excursions as the unit of “exploration” in the horse (Equus ferus caballis) when free or under saddle in an equestrian arena. Behav. Process. 2020;173:104065.
    doi: 10.1016/j.beproc.2020.104065pubmed: 32006619google scholar: lookup
  39. Pawluski J, Jego P, Henry S, Bruchet A, Palme R, Coste C, Hausberger M. Low plasma cortisol and fecal cortisol metabolite measures as indicators of compromised welfare in domestic horses (Equus caballus). PLoS ONE 2017;12:e0182257.
    doi: 10.1371/journal.pone.0182257pmc: PMC5590897pubmed: 28886020google scholar: lookup
  40. Nuñez C.M, Adelman J.S, Smith J, Gesquiere L.R, Rubenstein D.I. Linking social environment and stress physiology in feral mares (Equus caballus): Group transfers elevate fecal cortisol levels. Gen. Comp. Endocrinol. 2014;196:26–33.
    doi: 10.1016/j.ygcen.2013.11.012pubmed: 24275609google scholar: lookup
  41. Sauveroche M, Henriksson J, Theodorsson E, Holm A.C.S, Roth L.S. Hair cortisol in horses (Equus caballus) in relation to management regimes, personality, and breed. J. Vet. Behav. 2020;37:1–7.
    doi: 10.1016/j.jveb.2019.12.002google scholar: lookup
  42. Heimbürge S, Kanitz E, Otten W. The use of hair cortisol for the assessment of stress in animals. Gen. Comp. Endocrinol. 2019;270:10–17.
    doi: 10.1016/j.ygcen.2018.09.016pubmed: 30287191google scholar: lookup
  43. Johnson R.A, Johnson P.J, Megarani D.V, Patel S.D, Yaglom H.D, Osterlind S, Grindler K, Vogelweid C.M, Parker T.M, Pascua C.K. Horses working in therapeutic riding programs: Cortisol, adrenocorticotropic hormone, glucose, and behavior stress indicators. J. Equine Vet. Sci. 2017;57:77–85.
    doi: 10.1016/j.jevs.2017.05.006google scholar: lookup
  44. Sgorbini M, Bonelli F, Rota A, Aurich C, Ille N, Camillo F, Panzani D. Determination of salivary cortisol in donkey stallions. bioRxiv 2018.
    doi: 10.1101/493965pubmed: 31133319google scholar: lookup
  45. Sikorska U, Maśko M, Ciesielska A, Zdrojkowski Ł, Domino M. Role of Cortisol in Horse’s Welfare and Health. Agriculture 2023;13:2219.
    doi: 10.3390/agriculture13122219google scholar: lookup
  46. Dai F, Cogi N.H, Heinzl E.U.L, Dalla Costa E, Canali E, Minero M. Validation of a fear test in sport horses using infrared thermography. J. Vet. Behav. 2015;10:128–136.
    doi: 10.1016/j.jveb.2014.12.001google scholar: lookup
  47. Forkman B, Boissy A, Meunier-Salaün M.C, Canali E, Jones R.B. A critical review of fear tests used on cattle, pigs, sheep, poultry and horses. Physiol. Behav. 2007;92:340–374.
    doi: 10.1016/j.physbeh.2007.03.016pubmed: 18046784google scholar: lookup
  48. McDonnell S.M. Reproductive behavior of stallions and mares: Comparison of free-running and domestic in-hand breeding. Anim. Reprod. Sci. 2000;60:211–219.
    doi: 10.1016/S0378-4320(00)00136-6pubmed: 10844196google scholar: lookup
  49. Crowell-Davis S.L. Sexual behavior of mares. Horm. Behav. 2007;52:12–17.
    doi: 10.1016/j.yhbeh.2007.03.020pubmed: 17488645google scholar: lookup
  50. Stout T. Modulating reproductive activity in stallions: A review. Anim. Reprod. Sci. 2005;89:93–103.
    doi: 10.1016/j.anireprosci.2005.06.015pubmed: 16061332google scholar: lookup
  51. Boyd L.E. Time budgets of adult Przewalski horses: Effects of sex, reproductive status and enclosure. Appl. Anim. Behav. Sci. 1988;21:19–39.
    doi: 10.1016/0168-1591(88)90099-8google scholar: lookup
  52. McDonnell S.M, Poulin A. Equid play ethogram. Appl. Anim. Behav. Sci. 2002;78:263–290.
    doi: 10.1016/S0168-1591(02)00112-0google scholar: lookup
  53. Torcivia C, McDonnell S. Equine discomfort ethogram. Animals 2021;11:580.
    doi: 10.3390/ani11020580pmc: PMC7931104pubmed: 33672338google scholar: lookup
  54. Ludewig A, Gauly M, von Borstel U.K. Effect of shortened reins on rein tension, stress and discomfort behavior in dressage horses. J. Vet. Behav. Clin. Appl. Res. 2013;2:e15–e16.
    doi: 10.1016/j.jveb.2012.12.035google scholar: lookup
  55. Arnold G, Grassia A. Ethogram of agonistic behaviour for thoroughbred horses. Appl. Anim. Ethol. 1982;8:5–25.
    doi: 10.1016/0304-3762(82)90129-8google scholar: lookup
  56. McDonnell S, Haviland J. Agonistic ethogram of the equid bachelor band. Appl. Anim. Behav. Sci. 1995;43:147–188.
    doi: 10.1016/0168-1591(94)00550-Xgoogle scholar: lookup
  57. Fureix C, Jego P, Henry S, Lansade L, Hausberger M. Towards an Ethological Animal Model of Depression? A Study on Horses. PLoS ONE 2012;7:e39280.
    doi: 10.1371/journal.pone.0039280pmc: PMC3386251pubmed: 22761752google scholar: lookup
  58. Blois-Heulin C, Rochais C, Camus S, Fureix C, Lemasson A, Lunel C, Bezard E, Hausberger M. Animal welfare: Could adult play be a false friend?. Anim. Behav. Cogn. 2015;2:156–185.
    doi: 10.12966/abc.05.04.2015google scholar: lookup
  59. Hausberger M, Fureix C, Bourjade M, Wessel-Robert S, Richard-Yris M.A. On the significance of adult play: What does social play tell us about adult horse welfare?. Naturwissenschaften 2012;99:291–302.
    doi: 10.1007/s00114-012-0902-8pubmed: 22402927google scholar: lookup
  60. Sigurjónsdóttir H, Haraldsson H. Significance of group composition for the welfare of pastured horses. Animals 2019;9:14.
    doi: 10.3390/ani9010014pmc: PMC6356279pubmed: 30621272google scholar: lookup
  61. VanDierendonck M.C, Spruijt B.M. Coping in groups of domestic horses–Review from a social and neurobiological perspective. Appl. Anim. Behav. Sci. 2012;138:194–202.
    doi: 10.1016/j.applanim.2012.02.007google scholar: lookup
  62. Cameron E.Z, Linklater W.L, Stafford K.J, Minot E.O. Social grouping and maternal behaviour in feral horses (Equus caballus): The influence of males on maternal protectiveness. Behav. Ecol. Sociobiol. 2003;53:92–101.
    doi: 10.1007/s00265-002-0556-1google scholar: lookup
  63. Broomé S, Feighelstein M, Zamansky A, Carreira Lencioni G, Haubro Andersen P, Pessanha F, Mahmoud M, Kjellström H, Salah A.A. Going deeper than tracking: A survey of computer-vision based recognition of animal pain and emotions. Int. J. Comput. Vis. 2023;131:572–590.
    doi: 10.1007/s11263-022-01716-3google scholar: lookup
  64. Gleerup K.B, Forkman B, Lindegaard C, Andersen P.H. An equine pain face. Vet. Anaesth. Analg. 2015;42:103–114.
    doi: 10.1111/vaa.12212pmc: PMC4312484pubmed: 25082060google scholar: lookup
  65. Dalla Costa E, Minero M, Lebelt D, Stucke D, Canali E, Leach M.C. Development of the Horse Grimace Scale (HGS) as a pain assessment tool in horses undergoing routine castration. PLoS ONE 2014;9:e92281.
    doi: 10.1371/journal.pone.0092281pmc: PMC3960217pubmed: 24647606google scholar: lookup
  66. Coneglian M.M, Borges T.D, Weber S.H, Bertagnon H.G, Michelotto P.V. Use of the horse grimace scale to identify and quantify pain due to dental disorders in horses. Appl. Anim. Behav. Sci. 2020;225:104970.
    doi: 10.1016/j.applanim.2020.104970google scholar: lookup
  67. Andersen P.H, Broomé S, Rashid M, Lundblad J, Ask K, Li Z, Hernlund E, Rhodin M, Kjellström H. Towards machine recognition of facial expressions of pain in horses. Animals 2021;11:1643.
    doi: 10.3390/ani11061643pmc: PMC8229776pubmed: 34206077google scholar: lookup
  68. Andersen P.H, Gleerup K, Wathan J, Coles B, Kjellström H, Broomé S, Lee Y, Rashid M, Sonder C, Resenberg E. Can a machine learn to see horse pain? An interdisciplinary approach towards automated decoding of facial expressions of pain in the horse. Proc. Meas. Behav. 2018:6–8.
  69. Feighelstein M, Riccie-Bonot C, Hasan H, Weinberg H, Rettig T, Segal M, Distelfeld T, Shimshoni I, Mills D.S, Zamansky A. Automated recognition of emotional states of horses from facial expressions. PLoS ONE 2024;19:e0302893.
    doi: 10.1371/journal.pone.0302893pmc: PMC11249218pubmed: 39008504google scholar: lookup
  70. Lencioni G.C, de Sousa R.V, de Souza Sardinha E.J, Corrêa R.R, Zanella A.J. Pain assessment in horses using automatic facial expression recognition through deep learning-based modeling. PLoS ONE 2021;16:e0258672.
    doi: 10.1371/journal.pone.0258672pmc: PMC8525760pubmed: 34665834google scholar: lookup
  71. Chiavaccini L, Gupta A, Chiavaccini G. From facial expressions to algorithms: A narrative review of animal pain recognition technologies. Front. Vet. Sci. 2024;11:1436795.
    doi: 10.3389/fvets.2024.1436795pmc: PMC11288915pubmed: 39086767google scholar: lookup
  72. Lund S.M, Nielsen J, Gammelgård F, Nielsen M.G, Jensen T.H, Pertoldi C. Behavioral Coding of Captive African Elephants (Loxodonta africana): Utilizing DeepLabCut and Create ML for Nocturnal Activity Tracking. Animals 2024;14:2820.
    doi: 10.3390/ani14192820pmc: PMC11482618pubmed: 39409769google scholar: lookup
  73. Blumrosen G, Hawellek D, Pesaran B. Towards automated recognition of facial expressions in animal models. Proceedings of the IEEE International Conference on Computer Vision Workshops Venice, Italy. 22–29 October 2017; pp. 2810–2819.
  74. Corujo L.A, Kieson E, Schloesser T, Gloor P.A. Emotion recognition in horses with convolutional neural networks. Future Internet 2021;13:250.
    doi: 10.3390/fi13100250google scholar: lookup
  75. Ashley F, Waterman-Pearson A, Whay H. Behavioural assessment of pain in horses and donkeys: Application to clinical practice and future studies. Equine Vet. J. 2005;37:565–575.
    doi: 10.2746/042516405775314826pubmed: 16295937google scholar: lookup
  76. Leiner L, Fendt M. Behavioural fear and heart rate responses of horses after exposure to novel objects: Effects of habituation. Appl. Anim. Behav. Sci. 2011;131:104–109.
    doi: 10.1016/j.applanim.2011.02.004google scholar: lookup
  77. Yaseen M. What is YOLOv8: An In-Depth Exploration of the Internal Features of the Next-Generation Object Detector. arXiv 20242408.15857.
  78. Hadsell R, Chopra S, LeCun Y. Dimensionality Reduction by Learning an Invariant Mapping. Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’06) New York, NY, USA. 17–22 June 2006; pp. 1735–1742.
    doi: 10.1109/CVPR.2006.100google scholar: lookup
  79. van den Oord A, Li Y, Vinyals O. Representation Learning with Contrastive Predictive Coding. arXiv 20181807.03748.
  80. Deng J, Dong W, Socher R, Li L.J, Li K, Fei-Fei L. Imagenet: A large-scale hierarchical image database. Proceedings of the 2009 IEEE Conference on Computer Vision and Pattern Recognition Miami, FL, USA. 20–25 June 2009; pp. 248–255.
  81. Assiri Y. Stochastic Optimization of Plain Convolutional Neural Networks with Simple methods. arXiv 20202001.08856.
  82. Ali J. MNIST-SOPCNN. 2024. [(accessed on 1 December 2024)].
  83. . Image Classification on MNIST. 2024. [(accessed on 1 December 2024)].
  84. Hoemann K, Lee Y, Kuppens P, Gendron M, Boyd R.L. Emotional granularity is associated with daily experiential diversity. Affect. Sci. 2023;4:291–306.
    doi: 10.1007/s42761-023-00185-2pmc: PMC10247944pubmed: 37304562google scholar: lookup
  85. Hoxhallari K, Purcell W, Neubauer T. The potential of Explainable Artificial Intelligence in Precision Livestock Farming. In: Berckmans D., Oczak M., Iwersen M., Wagener K., editors. Precision Livestock Farming 2022: Papers Presented at the 10th European Conference on Precision Livestock Farming. University of Veterinary Medicine Vienna; Vienna, Austria: 2022. pp. 710–717.
    doi: 10.34726/4701google scholar: lookup
  86. Caron M, Misra I, Mairal J, Goyal P, Bojanowski P, Joulin A. Unsupervised learning of visual features by contrasting cluster assignments. Adv. Neural Inf. Process. Syst. 2020;33:9912–9924.
Subscribe to our newsletter
Join 99,357 horse owners like you who receive our email updates on horse health and nutrition.