Results 231 to 240 of about 7,606,948 (285)
Some of the next articles are maybe not open access.
Affective computing: challenges
International Journal of Human-Computer Studies, 2003A number of researchers around the world have built machines that recognize, express, model, communicate, and respond to emotional information, instances of "affective computing." This article raises and responds to several criticisms of affective computing, articulating state-of-the art research challenges, especially with respect to affect in human ...
openaire +1 more source
Proceedings of the Human Factors and Ergonomics Society Annual Meeting, 1999
Although central to human development and functioning, emotions have, until recently, had a somewhat marginal status in scientific disciplines in general, and have been largely ignored in the more applied settings such as human factors. Over the past 10 years, however, research in emotion in both psychology and neuroscience has established that emotion
Eva Hudlicka +8 more
openaire +1 more source
Although central to human development and functioning, emotions have, until recently, had a somewhat marginal status in scientific disciplines in general, and have been largely ignored in the more applied settings such as human factors. Over the past 10 years, however, research in emotion in both psychology and neuroscience has established that emotion
Eva Hudlicka +8 more
openaire +1 more source
Affective Computing in the Era of Large Language Models: A Survey from the NLP Perspective
arXiv.orgAffective Computing (AC) integrates computer science, psychology, and cognitive science to enable machines to recognize, interpret, and simulate human emotions across domains such as social media, finance, healthcare, and education.
Yiqun Zhang +10 more
semanticscholar +1 more source
IEEE Sensors Journal, 2022
Machine learning (ML)-based algorithms have shown promising results in electroencephalogram (EEG)-based emotion recognition. This study compares five ensemble learning-based ML (EML) algorithms with five conventional ML (CML) algorithms for recognizing ...
Kranti S. Kamble, J. Sengupta
semanticscholar +1 more source
Machine learning (ML)-based algorithms have shown promising results in electroencephalogram (EEG)-based emotion recognition. This study compares five ensemble learning-based ML (EML) algorithms with five conventional ML (CML) algorithms for recognizing ...
Kranti S. Kamble, J. Sengupta
semanticscholar +1 more source
2005
Affective computing is currently one of the most active research topics, furthermore, having increasingly intensive attention. This strong interest is driven by a wide spectrum of promising applications in many areas such as virtual reality, smart surveillance, perceptual interface, etc.
Jianhua Tao 0001, Tieniu Tan
openaire +1 more source
Affective computing is currently one of the most active research topics, furthermore, having increasingly intensive attention. This strong interest is driven by a wide spectrum of promising applications in many areas such as virtual reality, smart surveillance, perceptual interface, etc.
Jianhua Tao 0001, Tieniu Tan
openaire +1 more source
Applied Sciences
This systematic literature review delves into the extensive landscape of emotion recognition, sentiment analysis, and affective computing, analyzing 609 articles.
Rosa A. García-Hernández +8 more
semanticscholar +1 more source
This systematic literature review delves into the extensive landscape of emotion recognition, sentiment analysis, and affective computing, analyzing 609 articles.
Rosa A. García-Hernández +8 more
semanticscholar +1 more source
Ubiquitous Affective Computing: A Review
IEEE Sensors Journal, 2022This review investigated research works on affective computing by using electrocardiogram (ECG) and electrodermal activity (EDA). The 27 related research papers, including 23 from IEEE Journals and 4 from other Q1 Journals in the last five years, were ...
Rawin Assabumrungrat +6 more
semanticscholar +1 more source
Proceedings of the ACM on Interactive Mobile Wearable and Ubiquitous Technologies, 2022
Collecting large-scale mobile and wearable sensor datasets from daily contexts is essential in developing machine learning models for enabling everyday affective computing applications.
Hyunsoo Lee, Soowon Kang, Uichin Lee
semanticscholar +1 more source
Collecting large-scale mobile and wearable sensor datasets from daily contexts is essential in developing machine learning models for enabling everyday affective computing applications.
Hyunsoo Lee, Soowon Kang, Uichin Lee
semanticscholar +1 more source
The Affective Growth of Computer Vision
2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2021The success of deep learning has led to intense growth and interest in computer vision, along with concerns about its potential impact on society. Yet we know little about how these changes have affected the people that research and practice computer vision: we as a community spend so much effort trying to replicate the abilities of humans, but so ...
Norman Makoto Su, David J. Crandall
openaire +1 more source
Social-Emotional-Sensory Design Map for Affective Computing Informed by Neurodivergent Experiences
Proc. ACM Hum. Comput. Interact., 2021One of the grand challenges of artificial intelligence and affective computing is for technology to become emotionally-aware and thus, more human-like. Modeling human emotions is particularly complicated when we consider the lived experiences of people ...
Annuska Zolyomi, Jaime Snyder
semanticscholar +1 more source