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Exploring emotional intelligence in artificial intelligence systems: a comprehensive analysis of emotion recognition and response mechanisms.
Narimisaei, Jale; Naeim, Mahdi; Imannezhad, Shima; Samian, Pooya; Sobhani, Mohammadreza.
Afiliación
  • Narimisaei J; Department of Computer, Energy and Data Science Faculty, Behbahan Khatam Alanbia University of Technology, Behbahan.
  • Naeim M; Department of Research, Psychology and Counseling Organization, Tehran.
  • Imannezhad S; Department of Pediatrics, Faculty of Medicine, Mashhad University of Medical Sciences, Mashhad.
  • Samian P; Department of Educational Sciences, Faculty of Educational Sciences and Psychology, Shahid Madani University of Azerbaijan, Tabriz, Iran.
  • Sobhani M; Department of Research, Psychology and Counseling Organization, Tehran.
Ann Med Surg (Lond) ; 86(8): 4657-4663, 2024 Aug.
Article en En | MEDLINE | ID: mdl-39118764
ABSTRACT
This study aims to dissect the current state of emotion recognition and response mechanisms in artificial intelligence (AI) systems, exploring the progress made, challenges faced, and implicit operations of integrating emotional intelligence into AI. This study utilized a comprehensive review approach to investigate the integration of emotional intelligence (EI) into artificial intelligence (AI) systems, concentrating on emotion recognition and response mechanisms. The review process entailed formulating research questions, systematically searching academic databases such as PubMed, Scopus, and Web of Science, critically evaluating relevant literature, synthesizing the data, and presenting the findings in a comprehensive format. The study highlights the advancements in emotion recognition models, including the use of deep literacy ways and multimodal data emulsion. It discusses the challenges in emotion recognition, similar to variability in mortal expressions and the need for real-time processing. The integration of contextual information and individual traits is emphasized as enhancing the understanding of mortal feelings. The study also addresses ethical enterprises, similar as sequestration and impulses in training data. The integration of emotional intelligence into AI systems presents openings to revise mortal-computer relations. Emotion recognition and response mechanisms have made significant progress, but challenges remain. Unborn exploration directions include enhancing the robustness and interpretability of emotion recognition models, exploring cross-cultural and environment-apprehensive emotion understanding, and addressing long-term emotion shadowing and adaption. By further exploring emotional intelligence in AI systems, further compassionate and responsive machines can be developed, enabling deeper emotional connections with humans.
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Idioma: En Revista: Ann Med Surg (Lond) Año: 2024 Tipo del documento: Article Pais de publicación: Reino Unido
Texto completo
Añadir a Mi BVS
Imprimir
XML
PubMed Links
Buscar en Google

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Idioma: En Revista: Ann Med Surg (Lond) Año: 2024 Tipo del documento: Article Pais de publicación: Reino Unido