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Unraveling the World of Artificial Emotional Intelligence

Unraveling the World of Artificial Emotional Intelligence

Mohammed Belghachi
Copyright: © 2024 |Pages: 35
ISBN13: 9798369319109|EISBN13: 9798369319116
DOI: 10.4018/979-8-3693-1910-9.ch002
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MLA

Belghachi, Mohammed. "Unraveling the World of Artificial Emotional Intelligence." Using Machine Learning to Detect Emotions and Predict Human Psychology, edited by Mritunjay Rai and Jay Kumar Pandey, IGI Global, 2024, pp. 17-51. https://doi.org/10.4018/979-8-3693-1910-9.ch002

APA

Belghachi, M. (2024). Unraveling the World of Artificial Emotional Intelligence. In M. Rai & J. Pandey (Eds.), Using Machine Learning to Detect Emotions and Predict Human Psychology (pp. 17-51). IGI Global. https://doi.org/10.4018/979-8-3693-1910-9.ch002

Chicago

Belghachi, Mohammed. "Unraveling the World of Artificial Emotional Intelligence." In Using Machine Learning to Detect Emotions and Predict Human Psychology, edited by Mritunjay Rai and Jay Kumar Pandey, 17-51. Hershey, PA: IGI Global, 2024. https://doi.org/10.4018/979-8-3693-1910-9.ch002

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Abstract

Artificial emotional intelligence (AEI) is a burgeoning field at the intersection of technology and human emotion, seeking to imbue machines with the capacity to perceive, understand, and respond to human emotions. This chapter encapsulates a comprehensive exploration of AEI, encompassing its foundations, technical intricacies, applications, user perspectives, ethical considerations, challenges, and real-world case studies. The foundations of AEI involve unraveling the complexities of human emotions, from facial expressions and voice tones to physiological signals. Understanding the technical aspects of AEI, including data acquisition, feature extraction, machine learning models, and multi-modal fusion, provides insights into the sophisticated mechanisms driving emotionally intelligent systems. AEI's applications span diverse domains, from virtual health assistants providing mental well-being support to emotionally aware educational platforms adapting to students' learning needs.

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