An artificial intelligence approach to predicting personality types in dogs

Article


Amirhosseini, M. H., Yadav, V., Serpell, J. A., Pettigrew, P. and Kain, P. 2024. An artificial intelligence approach to predicting personality types in dogs. Scientific Reports. 14 (Art. 2404). https://doi.org/10.1038/s41598-024-52920-9
AuthorsAmirhosseini, M. H., Yadav, V., Serpell, J. A., Pettigrew, P. and Kain, P.
Abstract

Canine personality and behavioural characteristics have a significant influence on relationships between domestic dogs and humans as well as determining the suitability of dogs for specific working roles. As a result, many researchers have attempted to develop reliable personality assessment tools for dogs. Most previous work has analysed dogs’ behavioural patterns collected via questionnaires using traditional statistical analytic approaches. Artificial Intelligence has been widely and successfully used for predicting human personality types. However, similar approaches have not been applied to data on canine personality. In this research, machine learning techniques were applied to the classification of canine personality types using behavioural data derived from the C-BARQ project. As the dataset was not labelled, in the first step, an unsupervised learning approach was adopted and K-Means algorithm was used to perform clustering and labelling of the data. Five distinct categories of dogs emerged from the K-Means clustering analysis of behavioural data, corresponding to five different personality types. Feature importance analysis was then conducted to identify the relative importance of each behavioural variable’s contribution to each cluster and descriptive labels were generated for each of the personality traits based on these associations. The five personality types identified in this paper were labelled: “Excitable/Hyperattached”, “Anxious/Fearful”, “Aloof/Predatory”, “Reactive/Assertive”, and “Calm/Agreeable”. Four machine learning models including Support Vector Machine (SVM), K-Nearest Neighbour (KNN), Naïve Bayes, and Decision Tree were implemented to predict the personality traits of dogs based on the labelled data. The performance of the models was evaluated using fivefold cross validation method and the results demonstrated that the Decision Tree model provided the best performance with a substantial accuracy of 99%. The novel AI-based methodology in this research may be useful in the future to enhance the selection and training of dogs for specific working and non-working roles.

JournalScientific Reports
Journal citation14 (Art. 2404)
ISSN2045-2322
Year2024
PublisherNature Research
Publisher's version
License
File Access Level
Anyone
Digital Object Identifier (DOI)https://doi.org/10.1038/s41598-024-52920-9
Publication dates
Online29 Jan 2024
Publication process dates
Accepted25 May 2023
Deposited30 Jan 2024
Copyright holder© 2024, The Authors
Permalink -

https://repository.uel.ac.uk/item/8x35y

Download files


Publisher's version
Amirhosseini_Nature Scientific Reports.pdf
License: CC BY 4.0
File access level: Anyone

  • 31
    total views
  • 4
    total downloads
  • 30
    views this month
  • 4
    downloads this month

Export as

Related outputs

Predictive precision in battery recycling: unveiling lithium battery recycling potential through machine learning
Valizadeh, A., Amirhosseini, M. H. and Ghorbani, Y. 2024. Predictive precision in battery recycling: unveiling lithium battery recycling potential through machine learning. Computers and Chemical Engineering. 183 (Art. 108623). https://doi.org/10.1016/j.compchemeng.2024.108623
Forecasting Bitcoin Prices in the Context of the COVID-19 Pandemic Using Machine Learning Approaches
Sontakke, P., Jafari, F., Saeedi, M. and Amirhosseini, M. 2024. Forecasting Bitcoin Prices in the Context of the COVID-19 Pandemic Using Machine Learning Approaches. 4th International Conference on Data Analytics & Management (ICDAM-2023). London, UK 23 - 24 Jun 2023 Springer.
An AI powered system to enhance self-reflection practice in coaching
Jelodari, M., Amirhosseini, M. H. and Giraldez Hayes, A. 2023. An AI powered system to enhance self-reflection practice in coaching. Cognitive Computation and Systems. 5 (4), pp. 243-254. https://doi.org/10.1049/ccs2.12087
Sentiment-Driven Cryptocurrency Price Prediction: A Machine Learning Approach Utilizing Historical Data and Social Media Sentiment Analysis
Bhatt, S., Ghazanfar, M. and Amirhosseini, M. 2023. Sentiment-Driven Cryptocurrency Price Prediction: A Machine Learning Approach Utilizing Historical Data and Social Media Sentiment Analysis. Machine Learning and Applications: An International Journal (MLAIJ). 10 (2/3), pp. 1-15. https://doi.org/10.5121/mlaij.2023.10301
Machine Learning based Cryptocurrency Price Prediction using historical data and Social Media Sentiment
Bhatt, S., Ghazanfar, M. and Amirhosseini, M. 2023. Machine Learning based Cryptocurrency Price Prediction using historical data and Social Media Sentiment . 5th International Conference on Machine Learning & Applications (CMLA 2023). Sydney, Australia 17 - 18 Jun 2023 AIRCC Publishing Corporation.
A Machine Learning Approach to Identify the Preferred Representational System of a Person
Amirhosseini, M. and Wall, J. 2022. A Machine Learning Approach to Identify the Preferred Representational System of a Person. Multimodal Technologies and Interaction. 6 (12), p. 112. https://doi.org/10.3390/mti6120112
Application of Graph-Based Technique to Identity Resolution
Kazemian, H., Amirhosseini, M. H. and Phillips, M. 2022. Application of Graph-Based Technique to Identity Resolution. AIAI 2022: 18th International Conference on Artificial Intelligence Applications and Innovations. Crete, Greece 17 - 20 Jun 2022 Springer. https://doi.org/10.1007/978-3-031-08333-4_38
A Rule and Graph-Based Approach for Targeted Identity Resolution on Policing Data
Phillips, M., Amirhosseini, M. and Kazemian, H. 2020. A Rule and Graph-Based Approach for Targeted Identity Resolution on Policing Data. 2020 IEEE Symposium Series on Computational Intelligence. Online 01 - 04 Dec 2020 IEEE. https://doi.org/10.1109/SSCI47803.2020.9308182
Machine Learning Approach to Personality Type Prediction Based on the Myers–Briggs Type Indicator®
Amirhosseini, M.H. and Kazemian, H. 2020. Machine Learning Approach to Personality Type Prediction Based on the Myers–Briggs Type Indicator®. Multimodal Technologies and Interaction. 4 (Art. 9). https://doi.org/10.3390/mti4010009
Automating the process of identifying the preferred representational system in Neuro Linguistic Programming using Natural Language Processing
Amirhosseini, M.H. and Kazemian, H. 2019. Automating the process of identifying the preferred representational system in Neuro Linguistic Programming using Natural Language Processing. Cognitive Processing. 20 (2), p. 175–193. https://doi.org/10.1007/s10339-019-00912-3
Natural Language Processing approach to NLP Meta model automation
Amirhosseini, M.H., Kazemian, H., Ouazzane, K. and Chandler, C. 2018. Natural Language Processing approach to NLP Meta model automation. 2018 International Joint Conference on Neural Networks (IJCNN). Rio de Janeiro, Brazil 08 - 13 Jul 2018 IEEE. https://doi.org/10.1109/IJCNN.2018.8489609