Emotional computing based on cross-modal fusion and edge network data incentive

Ma, Lei; Ju, Feng; Wan, Jing; Shen, Xipeng

doi:10.1007/s00779-019-01232-1

Cited by 4 publications

(2 citation statements)

References 15 publications

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“…en, based on the recognition result, it is determined whether it is an information cross function or an information independent function [14]. When the user's behavior status is set to Taks T ∈ T1, T2, .…”

Section: Research Methods Of Neuronal Apoptosismentioning

confidence: 99%

Neuronal Apoptosis in Patients with Liver Cirrhosis and Neuronal Epileptiform Discharge Model Based upon Multi-Modal Fusion Deep Learning

Chi

Wang

et al. 2022

Journal of Healthcare Engineering

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Neurons refer to nerve cells. Each neuron is connected with thousands of other neurons to form a corresponding functional area and carry out complex communication with other functional areas. Its importance to the human body is self-evident. There are also many scholars studying the mechanism of apoptosis. This paper proposes a study of neuronal apoptosis in patients with liver cirrhosis and neuronal epileptiform discharge models based on multi-modal fusion deep learning, aiming to study the influencing factors of abnormal neuronal discharge in the brain. The method in this paper is to study multi-modal information fusion methods, perform Bayesian inference, and analyze multi-modal medical data. The function of these research methods is to obtain the relationship between the independence of information and the intersection of information among modalities. In the neuronal epileptiform discharge model, the mRNA expression level of the necroptotic signaling pathway related protein was detected, and the mechanism of neuronal necrosis in patients with liver cirrhosis was explored. Experiments show that the neuron recognition rate has been increased from 67.2% to 84.5%, and the time has been reduced, proving the effectiveness of deep learning.

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Section: Research Methods Of Neuronal Apoptosismentioning

confidence: 99%

Neuronal Apoptosis in Patients with Liver Cirrhosis and Neuronal Epileptiform Discharge Model Based upon Multi-Modal Fusion Deep Learning

Chi

Wang

et al. 2022

Journal of Healthcare Engineering

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“…Generally speaking, emotion recognition includes the steps of data information acquisition, preprocessing, emotion feature extraction, and classification (Ma et al, 2019). In order to make full use of the existing modal data, while capturing the relevant information within and between the modalities, multi-modal fusion is gradually developed and applied in emotion recognition.…”

Section: Introductionmentioning

confidence: 99%

Multi-Modal Fusion Emotion Recognition Method of Speech Expression Based on Deep Learning

Liu

Wang

et al. 2021

Front. Neurorobot.

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The redundant information, noise data generated in the process of single-modal feature extraction, and traditional learning algorithms are difficult to obtain ideal recognition performance. A multi-modal fusion emotion recognition method for speech expressions based on deep learning is proposed. Firstly, the corresponding feature extraction methods are set up for different single modalities. Among them, the voice uses the convolutional neural network-long and short term memory (CNN-LSTM) network, and the facial expression in the video uses the Inception-Res Net-v2 network to extract the feature data. Then, long and short term memory (LSTM) is used to capture the correlation between different modalities and within the modalities. After the feature selection process of the chi-square test, the single modalities are spliced to obtain a unified fusion feature. Finally, the fusion data features output by LSTM are used as the input of the classifier LIBSVM to realize the final emotion recognition. The experimental results show that the recognition accuracy of the proposed method on the MOSI and MELD datasets are 87.56 and 90.06%, respectively, which are better than other comparison methods. It has laid a certain theoretical foundation for the application of multimodal fusion in emotion recognition.

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Personalized Auxiliary Information Presentation System for Mobile Network Based on Multimodal Information

Liu

Ijaz

2022

Mobile Netw Appl

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Emotional computing based on cross-modal fusion and edge network data incentive

Cited by 4 publications

References 15 publications

Neuronal Apoptosis in Patients with Liver Cirrhosis and Neuronal Epileptiform Discharge Model Based upon Multi-Modal Fusion Deep Learning

Neuronal Apoptosis in Patients with Liver Cirrhosis and Neuronal Epileptiform Discharge Model Based upon Multi-Modal Fusion Deep Learning

Multi-Modal Fusion Emotion Recognition Method of Speech Expression Based on Deep Learning

Personalized Auxiliary Information Presentation System for Mobile Network Based on Multimodal Information

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