| _version_ | 1866902140724707328 |
|---|---|
| author | Nitesh Gupta Rakesh Kumar |
| author_facet | Nitesh Gupta Rakesh Kumar |
| contents | <p>The behavior of animals is a good way to see how well a living being has adapted to its surroundings and how well it is doing overall. Researchers and viewers can learn a great deal about social dynamics, health, ecological relationships, and neuroethological aspects of animals' lives by closely observing their behaviors and interactions. Even while cutting-edge deep learning models have shown impressive accuracy in categorizing different types of animal data, there is still a lack of use for them in animal behavior research. The goal of the developing area of computational animal behavior analysis is to use Deep Learning methods to assist with animal behavior analysis. It is commonly accepted in a number of scientific fields pertaining to animals that computational methods that enable the measurement of animal behavior are necessary. Modern and innovative Computer vision methods are used with machine learning (ML) techniques in computational animal behavior analysis methodologies. Deep learning techniques and architectures used in audiovisual, visual, and auditory approaches for behavioral recognition. Furthermore, the paper provides a thorough analysis of the fundamental issues facing this field of study by reviewing existing datasets on behavior of animals.</p> |
| format | Recurso digital |
| id | zenodo_https___doi_org_10_5281_zenodo_15653999 |
| institution | Zenodo |
| language | |
| publishDate | 2024 |
| publisher | Zenodo |
| record_format | zenodo |
| spellingShingle | Integration of Deep Learning and Knowledge Representation in the Automated Analysis of Animal Behavior Nitesh Gupta Rakesh Kumar <p>The behavior of animals is a good way to see how well a living being has adapted to its surroundings and how well it is doing overall. Researchers and viewers can learn a great deal about social dynamics, health, ecological relationships, and neuroethological aspects of animals' lives by closely observing their behaviors and interactions. Even while cutting-edge deep learning models have shown impressive accuracy in categorizing different types of animal data, there is still a lack of use for them in animal behavior research. The goal of the developing area of computational animal behavior analysis is to use Deep Learning methods to assist with animal behavior analysis. It is commonly accepted in a number of scientific fields pertaining to animals that computational methods that enable the measurement of animal behavior are necessary. Modern and innovative Computer vision methods are used with machine learning (ML) techniques in computational animal behavior analysis methodologies. Deep learning techniques and architectures used in audiovisual, visual, and auditory approaches for behavioral recognition. Furthermore, the paper provides a thorough analysis of the fundamental issues facing this field of study by reviewing existing datasets on behavior of animals.</p> |
| title | Integration of Deep Learning and Knowledge Representation in the Automated Analysis of Animal Behavior |
| url | https://doi.org/10.5281/zenodo.15653999 |