Search Results - Emotion Recognition and Analysis in Multimodal Data

RETRACTED ARTICLE: Exploring multimodal data analysis for emotion recognition in teachers’ teaching behavior based on LSTM and MSCNN: Exploring multimodal data analysis for emotion recognition in teachers’ teaching by Lu, Yuanyuan, Chen, Zengzhao, Zheng, Qiuyu, Zhu, Yanhui, Wang, Mengke

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Automated detection of pain levels using deep feature extraction from shutter blinds-based dynamic-sized horizontal patches with facial images by Prabal Datta Barua, Nursena Bayğin, Şengül Doğan, Mehmet Bayğın, N. Arunkumar, Hamido Fujita, Türker Tuncer, Ru‐San Tan, Elizabeth E. Palmer, Muhammad Mokhzaini Azizan, Nahrizul Adib Kadri, U. Rajendra Acharya

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Multimodal data analysis in emotion recognition: a review by Berdyshev, Daniil A., Shishkin, Aleksei G.

“…The use of multimodal data in emotion recognition systems has great potential for applications in various fields…”
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RETRACTED ARTICLE: Exploring multimodal data analysis for emotion recognition in teachers’ teaching behavior based on LSTM and MSCNN by Lu, Yuanyuan, Chen, Zengzhao, Zheng, Qiuyu, Zhu, Yanhui, Wang, Mengke

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Improved EEG-based emotion recognition through information enhancement in connectivity feature map by M. A. H. Akhand, Mahfuza Akter Maria, Md Abdus Samad Kamal, Kazuyuki Murase

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Emotion Recognition From EEG Signal Focusing on Deep Learning and Shallow Learning Techniques by Md. Rabiul Islam, Mohammad Ali Moni, Md. Milon Islam, Md. Rashed-Al-Mahfuz, Md. Saiful Islam, Md. Kamrul Hasan, Md. Sabir Hossain, Mohiuddin Ahmad, Shahadat Uddin, Akm Azad, Salem A. Alyami, Md. Atiqur Rahman Ahad, Pietro Lio

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Linear And Nonlinear Analysis of Multimodal Physiological Data for Emotion Recognition by Amirhosein Dolatabadi, Mahsa Heydari, Bahar Hashempour, Farnaz Asadiof, Shaghayegh Radmehr

“… Emotion recognition using physiological data analysis through machine learning approaches involves the utilization of various physiological signals to infer an individual's emotional state…”
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MindLink-Eumpy: An Open-Source Python Toolbox for Multimodal Emotion Recognition by Li, Ruixin, Liang, Yan, Liu, Xiaojian, Wang, Bingbing, Huang, Wenxin, Cai, Zhaoxin, Ye, Yaoguang, Qiu, Lina, Pan, Jiahui

“…Emotion recognition plays an important role in intelligent human–computer interaction, but the related research still faces the problems of low accuracy and subject dependence…”
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A systematic review on affective computing: emotion models, databases, and recent advances by Wang, Yan, Song, Wei, Tao, Wei, Liotta, Antonio, Yang, Dawei, Li, Xinlei, Gao, Shuyong, Sun, Yixuan, Ge, Weifeng, Zhang, Wei, Zhang, Wenqiang

“… Affective computing conjoins the research topics of emotion recognition and sentiment analysis, and can be realized with unimodal or multimodal data, consisting primarily of physical information (e.g…”
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Multimodal Emotion Detection via Attention-Based Fusion of Extracted Facial and Speech Features by Mamieva, Dilnoza, Abdusalomov, Akmalbek Bobomirzaevich, Kutlimuratov, Alpamis, Muminov, Bahodir, Whangbo, Taeg Keun

“… The research suggests a new attention-based approach to multimodal emotion recognition. This technique integrates facial and speech features that have been…”
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Investigating the brain network characteristics of multimodal emotion recognition and its classification applications based on functional connectivity patterns by Gu, Jin, Luo, Xiaoqi, Gong, Xinhao, Su, Chenxu

“… Emotion information can be expressed in multiple modal stimuli, and the brain can recognize multi-modal emotions efficiently and accurately…”
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Canonical Correlation Analysis for Data Fusion in Multimodal Emotion Recognition by Nemati, Shahla

“…Multimodal emotion recognition systems aim at classifying emotion data, usually from different natures, into discrete affective categories…”
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MIST: Multimodal emotion recognition using DeBERTa for text, Semi-CNN for speech, ResNet-50 for facial, and 3D-CNN for motion analysis by Boitel, Enguerrand, Mohasseb, Alaa, Haig, Ella

“…–computer interaction. This paper introduces the MIST (Motion, Image, Speech, and Text) framework, a novel multimodal approach to emotion recognition that integrates diverse data modalities…”
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Modality-uncertainty-aware knowledge distillation framework for multimodal sentiment analysis by Wang, Nan, Wang, Qi

“…Multimodal sentiment analysis (MSA) has become increasingly important for understanding human emotions, with applications in areas such as human-computer interaction, social media analysis, and emotion recognition…”
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Model for Determining the Psycho-Emotional State of a Person Based on Multimodal Data Analysis by Shakhovska, Nataliya, Zherebetskyi, Oleh, Lupenko, Serhii

“… The paper proposes different neural network structures for emotion recognition based on unimodal flows and models for the margin of the multimodal data…”
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Multimodal rough set transformer for sentiment analysis and emotion recognition by Sun, Xinwei, He, Huijie, Tang, Haoyang, Zeng, Kai, Shen, Tao

“… Transformer models have shown exceptional performance in multimodal fusion. However, traditional dot product transformers do not tolerate uncertainty inside sentiment analysis and emotion recognition data…”
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Comparing Recognition Performance and Robustness of Multimodal Deep Learning Models for Multimodal Emotion Recognition by Liu, Wei, Qiu, Jie-Lin, Zheng, Wei-Long, Lu, Bao-Liang

“…Multimodal signals are powerful for emotion recognition since they can represent emotions comprehensively…”
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K-Means Clustering-Based Kernel Canonical Correlation Analysis for Multimodal Emotion Recognition in Human-Robot Interaction by Chen, Luefeng, Wang, Kuanlin, Li, Min, Wu, Min, Pedrycz, Witold, Hirota, Kaoru

“…In this article, K -meansclustering-based Kernel canonical correlation analysis algorithm is proposed for multimodal emotion recognition in human-robot interaction (HRI…”
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A Multimodal Framework for Speech Emotion Recognition in Low-Resource Languages by Mamyr Altaibek, Zulkhazhav, Altanbek, Yergesh, Banu, Gulmira Bekmanova, Tileukhan Aibol

“…), which combines text-based semantic analysis and audio emotion recognition to leverage complementary features of linguistic and paralinguistic data…”
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Emotion recognition from unimodal to multimodal analysis: A review by Ezzameli, K., Mahersia, H.

“… Due to the variety of data, the research area of multimodal machine learning poses special problems for computer scientists…”
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