Prediction of Emotional Empathy in Intelligent Agents to Facilitate Precise Social Interaction

Alanazi, Saad; Shabbir, Maryam; Alshammari, Nasser; Alruwaili, Madallah; Hussain, Iftikhar; Ahmad, Fahad

doi:10.3390/app13021163

Cited by 9 publications

(6 citation statements)

References 57 publications

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“…Emotion recognition has gained a lot of attention recently ( Liao et al, 2022 ) due to its various applications in robotics, education, and human–computer interaction ( Alanazi et al, 2023 ). Previous studies ( Zhao et al, 2021 ; Mamieva et al, 2023 ) have focused on different human modalities, such as voice, writing, facial expressions, and physiological indicators to recognize emotions.…”

Section: Introductionmentioning

confidence: 99%

Contextual emotion detection in images using deep learning

Limami,

Hdioud,

Oulad Haj Thami

2024

Front. Artif. Intell.

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IntroductionComputerized sentiment detection, based on artificial intelligence and computer vision, has become essential in recent years. Thanks to developments in deep neural networks, this technology can now account for environmental, social, and cultural factors, as well as facial expressions. We aim to create more empathetic systems for various purposes, from medicine to interpreting emotional interactions on social media.MethodsTo develop this technology, we combined authentic images from various databases, including EMOTIC (ADE20K, MSCOCO), EMODB_SMALL, and FRAMESDB, to train our models. We developed two sophisticated algorithms based on deep learning techniques, DCNN and VGG19. By optimizing the hyperparameters of our models, we analyze context and body language to improve our understanding of human emotions in images. We merge the 26 discrete emotional categories with the three continuous emotional dimensions to identify emotions in context. The proposed pipeline is completed by fusing our models.ResultsWe adjusted the parameters to outperform previous methods in capturing various emotions in different contexts. Our study showed that the Sentiment_recognition_model and VGG19_contexte increased mAP by 42.81% and 44.12%, respectively, surpassing the results of previous studies.DiscussionThis groundbreaking research could significantly improve contextual emotion recognition in images. The implications of these promising results are far-reaching, extending to diverse fields such as social robotics, affective computing, human-machine interaction, and human-robot communication.

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Section: Introductionmentioning

confidence: 99%

Contextual emotion detection in images using deep learning

Limami,

Hdioud,

Oulad Haj Thami

2024

Front. Artif. Intell.

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“…A study reported that participants step back and think about why I am responding to the situation in that way while residents did eat more, stay away from the workplace and show aggression is alarming in my study. 24 All felt depressed in an emergency. Sharing with family and colleagues was the most relieved and satisfactory method for residents.…”

Section: Discussionmentioning

confidence: 99%

Impact of emotional intelligence in resolving clinical conflicts among postgraduate residents of surgery

Gillani,

Khan,

Sethi

2023

Pak J Med Sci

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Background & Objective: Emotional intelligence (EI) can become a vital tool for resolving clinical conflicts (CC) in surgery. The postgraduate residents focus on the technical skills and undermine the soft skills required for their better training. Our aim was to determine the EI of postgraduate resident (PGR) years one & two in General and Orthopedic Surgery. The CC in their workplace and how they use their EI to resolve these conflicts. Methods: This mixed-method study was conducted from March 10, 2019 to May 28, 2020 at Departments of General and Orthopedic Surgery, Mayo Hospital, Lahore. The study was conducted in two phases 1 & 2. In Phase-1, one hundred PGR years one & two were administered the Mayor-Salovey-Caruso Emotional Intelligence test (MSCEIT) to measure EI. In phase-2, semi-structured in-depth interviews of 10 PGRs five with high and five with low EI were conducted to determine the CC and use of EI to resolve the CC at the workplace. A thematic analysis was done. Results: Out of 100 PGR, the mean EI score was 46.25±14.8 with a maximum score of 75.4, and a minimum score of 18.16 (p-value =0.775). Ninety-one (91%) have not improving EI, and 09 (09%) have considered developing EI. Five themes in four settings, including emergency, ward, elective operation theatre, and outpatient department (OPD) were determined. The emerged themes for the CC were nepotism, gender biases, burnout, lack of professionalism, and toxic culture. The following were CC management strategies: self-study, deceit, gender affinity, performing attention-attaining work, aggrieved reaction and being disgruntled when alone. Conclusion: None of the PGR was emotionally intelligent in overall grades, as well as a particular aspect of MSCEIT. doi: https://doi.org/10.12669/pjms.40.3.7363 How to cite this: Gillani SFHS, Khan RA, Sethi A. Impact of emotional intelligence in resolving clinical conflicts among postgraduate residents of surgery. Pak J Med Sci. 2024;40(3):399-404. doi: https://doi.org/10.12669/pjms.40.3.7363 This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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“…The individual's internal emotional state becomes more discernible through personal experiences, physiological conditions, and environmental stimuli. Hence, valuable insights into a person's emotional state can be gleaned through the utilization of emotion recognition techniques [5,6]. Particularly within the realm of humancomputer interaction, the detection of individuals' emotional states assumes tremendous significance [7].…”

Section: A Backgroundmentioning

confidence: 99%

FF-BTP Model for Novel Sound-Based Community Emotion Detection

Yildiz,

Tanabe,

Kobayashi

et al. 2023

IEEE Access

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Background and purpose: Emotion classification models have focused on subject-but not crowd-level signal inputs. We proposed sound-based community emotion recognition (SCED) as a new machine learning challenge and have developed a novel deep learning-inspired forward-forward binary ternary pattern (FF-BTP)-based feature engineering model for crowd sentiment classification. Materials and Methods: From 187 YouTube videos, we curated a new dataset comprising 919, 905 and 909 (total 2733) 3-second recordings (sampling frequency 44.1 KHz) of crowd sounds (overlapping speech, environmental noise, etc.) belonging to verified dominant negative, neutral, and positive emotion classes. Our model architecture combined BTP-based textural feature extractor, which generated three feature vectors using signum, lower and upper ternary functions, with a novel Hinton's FF algorithm-inspired handcrafted feature vector selection function that identified the most distinctive feature vector based on maximum calculated mean square error. Our FF-BTP-based model also incorporated upstream multilevel discrete wavelet transform, which enabled multilevel feature generation in the spatial and frequency domains, and downstream iterative neighborhood component analysis-based feature selection and support vector machine classifier. Results: The FF-BTP-based model attained excellent 97.22% overall three-class classification accuracy on the SCED dataset. Conclusions: Our handcrafted feature engineering model emulated the deep feature characterization of deep learning models, and attained excellent classification results at low computation cost. It can be implemented for emerging SCED-related applications. INDEX TERMS FF-BTP; sound community emotion classification; sound processing; textural feature extraction.

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Prediction of Emotional Empathy in Intelligent Agents to Facilitate Precise Social Interaction

Cited by 9 publications

References 57 publications

Contextual emotion detection in images using deep learning

Contextual emotion detection in images using deep learning

Impact of emotional intelligence in resolving clinical conflicts among postgraduate residents of surgery

FF-BTP Model for Novel Sound-Based Community Emotion Detection

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