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Decoding the Digital Barnyard - Cognitive Computing in Farm Animal Emotions and Welfare
Version 1
: Received: 20 September 2023 / Approved: 21 September 2023 / Online: 26 September 2023 (13:43:02 CEST)
How to cite: Neethirajan, S. Decoding the Digital Barnyard - Cognitive Computing in Farm Animal Emotions and Welfare. Preprints 2023, 2023091714. https://doi.org/10.20944/preprints202309.1714.v1 Neethirajan, S. Decoding the Digital Barnyard - Cognitive Computing in Farm Animal Emotions and Welfare. Preprints 2023, 2023091714. https://doi.org/10.20944/preprints202309.1714.v1
Abstract
In this paper, we critically examine the burgeoning role of advanced computational methodologies in deciphering the complex tapestry of farm animal behaviors and emotions. Leveraging digital imaging and artificial intelligence, we unearth nuanced behavioral patterns and micro-expressions, offering predictive insights into animal emotional states. Sound vocalization analysis, often overlooked, emerges as a pivotal tool, decoding intricate communicative nuances and emotional undertones. Cognitive tests, including mirror and bias assessments, challenge long-standing perceptions, revealing surprising depths of animal self-awareness and cognitive sophistication. However, the paper also underscores the imperative of integrating these tools with a profound understanding of animal psyche, ensuring technology serves as an enhancer, not a replacement, of traditional observational methods. This research not only highlights the transformative potential of cognitive computing in animal welfare but also calls for a judicious application, ensuring technology augments, not undermines, the intrinsic value of human-animal interactions and understanding.
Keywords
Cognitive Computing; Digital Imaging; Sound Vocalization Analysis; Animal Self-awareness; Behavioral Predictions; Micro-expressions; Animal Welfare Enhancement; Human-Animal-Computer Interactions
Subject
Biology and Life Sciences, Animal Science, Veterinary Science and Zoology
Copyright: This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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