Broad-Deep Network-Based Fuzzy Emotional Inference Model with Personal Information for Intention Understanding

Min Li; Luefeng Chen; Min Wu; Min Ding; Kaoru Hirota; Witold Pedrycz

doi:10.1016/j.ifacol.2023.10.569

Broad-Deep Network-Based Fuzzy Emotional Inference Model with Personal Information for Intention Understanding

Min Li, Luefeng Chen^*, Min Wu, Min Ding, Kaoru Hirota, Witold Pedrycz

^*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

1 Citation (Scopus)

Abstract

A broad-deep fusion network-based fuzzy emotional inference model with personal information (BDFEI) is proposed for emotional intention understanding in human-robot interaction. It aims to understand students' intentions in the university teaching scene. Specifically, feature extraction is carried out by convolution and maximum pooling, and then the ridge regression algorithm is used for emotional behavior recognition, which reduce the influence of the complex structure and slow network updates in deep learning. Multivariate analysis of variance is used to select the key personal information affecting the intention and obtain the coefficient of influence degree. Finally, fuzzy inference is used to understand the intention. According to the recognition results, the accuracy on FABO database of our proposal is 1.89%, 12.21% and 0.78% higher than those of the Residual Network combined with geodesic flow kernel (ResNet-101+GFK), a fuzzy deep neural network with sparse autoencoder (FDNNSA), and an affect recognition on a video-skeleton of bimodal information with a hierarchical classification fusion strategy (HCFS), respectively, indicating that our proposal can effectively capture the emotional intention of students in the teaching scene.

Original language	English
Title of host publication	IFAC-PapersOnLine
Editors	Hideaki Ishii, Yoshio Ebihara, Jun-ichi Imura, Masaki Yamakita
Publisher	Elsevier B.V.
Pages	7071-7076
Number of pages	6
Edition	2
ISBN (Electronic)	9781713872344
DOIs	https://doi.org/10.1016/j.ifacol.2023.10.569
Publication status	Published - 1 Jul 2023
Externally published	Yes
Event	22nd IFAC World Congress - Yokohama, Japan Duration: 9 Jul 2023 → 14 Jul 2023

Publication series

Name	IFAC-PapersOnLine
Number	2
Volume	56
ISSN (Electronic)	2405-8963

Conference

Conference	22nd IFAC World Congress
Country/Territory	Japan
City	Yokohama
Period	9/07/23 → 14/07/23

Keywords

Broad Learning
Convolution Neural Networks
Emotional Intention
Human-Robot Interaction

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10.1016/j.ifacol.2023.10.569

Cite this

Li, M., Chen, L., Wu, M., Ding, M., Hirota, K., & Pedrycz, W. (2023). Broad-Deep Network-Based Fuzzy Emotional Inference Model with Personal Information for Intention Understanding. In H. Ishii, Y. Ebihara, J. Imura, & M. Yamakita (Eds.), IFAC-PapersOnLine (2 ed., pp. 7071-7076). (IFAC-PapersOnLine; Vol. 56, No. 2). Elsevier B.V.. https://doi.org/10.1016/j.ifacol.2023.10.569

@inproceedings{db6cd213c04440ecbef8a6c116790fb3,

title = "Broad-Deep Network-Based Fuzzy Emotional Inference Model with Personal Information for Intention Understanding",

abstract = "A broad-deep fusion network-based fuzzy emotional inference model with personal information (BDFEI) is proposed for emotional intention understanding in human-robot interaction. It aims to understand students' intentions in the university teaching scene. Specifically, feature extraction is carried out by convolution and maximum pooling, and then the ridge regression algorithm is used for emotional behavior recognition, which reduce the influence of the complex structure and slow network updates in deep learning. Multivariate analysis of variance is used to select the key personal information affecting the intention and obtain the coefficient of influence degree. Finally, fuzzy inference is used to understand the intention. According to the recognition results, the accuracy on FABO database of our proposal is 1.89%, 12.21% and 0.78% higher than those of the Residual Network combined with geodesic flow kernel (ResNet-101+GFK), a fuzzy deep neural network with sparse autoencoder (FDNNSA), and an affect recognition on a video-skeleton of bimodal information with a hierarchical classification fusion strategy (HCFS), respectively, indicating that our proposal can effectively capture the emotional intention of students in the teaching scene.",

keywords = "Broad Learning, Convolution Neural Networks, Emotional Intention, Human-Robot Interaction",

author = "Min Li and Luefeng Chen and Min Wu and Min Ding and Kaoru Hirota and Witold Pedrycz",

note = "Publisher Copyright: Copyright {\textcopyright} 2023 The Authors. This is an open access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0/); 22nd IFAC World Congress ; Conference date: 09-07-2023 Through 14-07-2023",

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month = jul,

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doi = "10.1016/j.ifacol.2023.10.569",

language = "English",

series = "IFAC-PapersOnLine",

publisher = "Elsevier B.V.",

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pages = "7071--7076",

editor = "Hideaki Ishii and Yoshio Ebihara and Jun-ichi Imura and Masaki Yamakita",

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Li, M, Chen, L, Wu, M, Ding, M, Hirota, K & Pedrycz, W 2023, Broad-Deep Network-Based Fuzzy Emotional Inference Model with Personal Information for Intention Understanding. in H Ishii, Y Ebihara, J Imura & M Yamakita (eds), IFAC-PapersOnLine. 2 edn, IFAC-PapersOnLine, no. 2, vol. 56, Elsevier B.V., pp. 7071-7076, 22nd IFAC World Congress, Yokohama, Japan, 9/07/23. https://doi.org/10.1016/j.ifacol.2023.10.569

Broad-Deep Network-Based Fuzzy Emotional Inference Model with Personal Information for Intention Understanding. / Li, Min; Chen, Luefeng; Wu, Min et al.
IFAC-PapersOnLine. ed. / Hideaki Ishii; Yoshio Ebihara; Jun-ichi Imura; Masaki Yamakita. 2. ed. Elsevier B.V., 2023. p. 7071-7076 (IFAC-PapersOnLine; Vol. 56, No. 2).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Broad-Deep Network-Based Fuzzy Emotional Inference Model with Personal Information for Intention Understanding

AU - Li, Min

AU - Chen, Luefeng

AU - Wu, Min

AU - Ding, Min

AU - Hirota, Kaoru

AU - Pedrycz, Witold

PY - 2023/7/1

Y1 - 2023/7/1

N2 - A broad-deep fusion network-based fuzzy emotional inference model with personal information (BDFEI) is proposed for emotional intention understanding in human-robot interaction. It aims to understand students' intentions in the university teaching scene. Specifically, feature extraction is carried out by convolution and maximum pooling, and then the ridge regression algorithm is used for emotional behavior recognition, which reduce the influence of the complex structure and slow network updates in deep learning. Multivariate analysis of variance is used to select the key personal information affecting the intention and obtain the coefficient of influence degree. Finally, fuzzy inference is used to understand the intention. According to the recognition results, the accuracy on FABO database of our proposal is 1.89%, 12.21% and 0.78% higher than those of the Residual Network combined with geodesic flow kernel (ResNet-101+GFK), a fuzzy deep neural network with sparse autoencoder (FDNNSA), and an affect recognition on a video-skeleton of bimodal information with a hierarchical classification fusion strategy (HCFS), respectively, indicating that our proposal can effectively capture the emotional intention of students in the teaching scene.

AB - A broad-deep fusion network-based fuzzy emotional inference model with personal information (BDFEI) is proposed for emotional intention understanding in human-robot interaction. It aims to understand students' intentions in the university teaching scene. Specifically, feature extraction is carried out by convolution and maximum pooling, and then the ridge regression algorithm is used for emotional behavior recognition, which reduce the influence of the complex structure and slow network updates in deep learning. Multivariate analysis of variance is used to select the key personal information affecting the intention and obtain the coefficient of influence degree. Finally, fuzzy inference is used to understand the intention. According to the recognition results, the accuracy on FABO database of our proposal is 1.89%, 12.21% and 0.78% higher than those of the Residual Network combined with geodesic flow kernel (ResNet-101+GFK), a fuzzy deep neural network with sparse autoencoder (FDNNSA), and an affect recognition on a video-skeleton of bimodal information with a hierarchical classification fusion strategy (HCFS), respectively, indicating that our proposal can effectively capture the emotional intention of students in the teaching scene.

KW - Broad Learning

KW - Convolution Neural Networks

KW - Emotional Intention

KW - Human-Robot Interaction

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DO - 10.1016/j.ifacol.2023.10.569

M3 - Conference contribution

AN - SCOPUS:85184958028

T3 - IFAC-PapersOnLine

SP - 7071

EP - 7076

BT - IFAC-PapersOnLine

A2 - Ishii, Hideaki

A2 - Ebihara, Yoshio

A2 - Imura, Jun-ichi

A2 - Yamakita, Masaki

PB - Elsevier B.V.

T2 - 22nd IFAC World Congress

Y2 - 9 July 2023 through 14 July 2023

ER -

Broad-Deep Network-Based Fuzzy Emotional Inference Model with Personal Information for Intention Understanding

Abstract

Publication series

Conference

Keywords

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