Eeg based smart emotion recognition using meta heuristic optimization and hybrid deep learning techniques DOI Creative Commons

M. Karthiga,

E. Suganya,

S. Sountharrajan

et al.

Scientific Reports, Journal Year: 2024, Volume and Issue: 14(1)

Published: Dec. 4, 2024

Language: Английский

Develop an emotion recognition system using jointly connectivity between electroencephalogram and electrocardiogram signals DOI Creative Commons

Javid Farhadi Sedehi,

Nader Jafarnia Dabanloo, Keivan Maghooli

et al.

Heliyon, Journal Year: 2025, Volume and Issue: 11(2), P. e41767 - e41767

Published: Jan. 1, 2025

Language: Английский

Citations

3

EEGSNet: A novel EEG cognitive recognition model using spiking neural network DOI
Jiankai Shi, Yue Zhao, Chu Wang

et al.

Biomedical Signal Processing and Control, Journal Year: 2025, Volume and Issue: 105, P. 107610 - 107610

Published: Feb. 4, 2025

Language: Английский

Citations

1

Federated learning in Emotion Recognition Systems based on physiological signals for privacy preservation: a review DOI
Neha Gahlan, Divyashikha Sethia

Multimedia Tools and Applications, Journal Year: 2024, Volume and Issue: unknown

Published: June 3, 2024

Language: Английский

Citations

4

SimpleRNN Based Human Emotion Recognition Using EEG Signals DOI

Harsh Sonwani,

Earu Banoth,

Puneet Jain

et al.

Communications in computer and information science, Journal Year: 2025, Volume and Issue: unknown, P. 48 - 57

Published: Jan. 1, 2025

Language: Английский

Citations

0

DDNet: a hybrid network based on deep adaptive multi-head attention and dynamic graph convolution for EEG emotion recognition DOI

Bingyue Xu,

Xin Zhang, Xiu Zhang

et al.

Signal Image and Video Processing, Journal Year: 2025, Volume and Issue: 19(4)

Published: Feb. 14, 2025

Language: Английский

Citations

0

The Application of Artificial Intelligence Technology in Intellectual Property Protection and Its Impact on the Cultural Industry DOI Open Access
Yifei Dong,

Jianhua Yin

Applied Mathematics and Nonlinear Sciences, Journal Year: 2025, Volume and Issue: 10(1)

Published: Jan. 1, 2025

Abstract Patent data possesses characteristics such as a large amount of relevant data, volume and an application management process that is difficult to control. The protection patent intellectual property rights will be impacted by these features. In order solve the above problems, this paper, in context artificial intelligence technology, takes clothing appearance research object, adopts normalization method mine extract features, uses View-GCN graph convolution module learn features view, after builds DP-MVGCN model based on neural network for classification property. Finally, applied build effective discuss impact cultural industry. results show feature extraction rate >95% when number training times reaches 70. At k=3.0, has highest score result test set 4 evaluation indexes, good accuracy. Starting from 2020, measured values three indices judicial protection, administrative social have increased sharply over time. By 2023 indexes pathways 1.27 times, 1.29 1.26 respectively compared 2020. It clear reasonable patents can significantly improve patented knowledge, also enhance degree industry agglomeration promote self-production subcultural clusters.

Language: Английский

Citations

0

Electroencephalogram Based Emotion Recognition Using Hybrid Intelligent Method and Discrete Wavelet Transform DOI Creative Commons
Duy Nguyen, M.T. Nguyen, Kou Yamada

et al.

Applied Sciences, Journal Year: 2025, Volume and Issue: 15(5), P. 2328 - 2328

Published: Feb. 21, 2025

Electroencephalography-based emotion recognition is essential for brain-computer interface combined with artificial intelligence. This paper proposes a novel algorithm human detection using hybrid paradigm of convolutional neural networks and boosting model. The proposed employs two subsets 18 14 features extracted from four sub-bands discrete wavelet transform. These are identified as the optimal most relevant, among 42 original input 8 6 productive channels dual genetic wise-subject 5-fold cross validation procedure in which first second algorithms address efficient feature subsets. estimated by differently intelligent models on set. produces an accuracy 70.43%/76.05%, precision 69.88%/74.57%, recall 98.70%/99.17%, F1 score 81.83%/85.13% valence/arousal classifications, suggest that frontal left regions cortex associate especially to emotions.

Language: Английский

Citations

0

EEG-based emotion recognition model using fuzzy adjacency matrix combined with convolutional multi-head graph attention mechanism DOI
Mingwei Cao, Yindong Dong, Deli Chen

et al.

Cluster Computing, Journal Year: 2025, Volume and Issue: 28(4)

Published: Feb. 25, 2025

Language: Английский

Citations

0

MelNet: An End-to-End Adaptive Network with Adjustable Frequency for Preprocessing-Free Broadband Acoustic Emission Signals DOI
Jing Huang, Rui Qin, Zhifen Zhang

et al.

Information Fusion, Journal Year: 2025, Volume and Issue: unknown, P. 103229 - 103229

Published: April 1, 2025

Language: Английский

Citations

0

Study on real-time warning system of blind path for the visually impaired based on improved deep residual shrinkage network DOI Creative Commons

Zhezhou Yu,

Fuwang Wang

Scientific Reports, Journal Year: 2025, Volume and Issue: 15(1)

Published: April 29, 2025

Visually impaired individuals often face various obstacles when navigating blind roads, such as road disconnections, obstructions, and more complex emergencies, which can leave them in difficult situations. Traditional early warning methods suffer from low accuracy lack real-time capabilities. Therefore, this study proposes a novel system for traffic jams on roads. By analyzing the emotional state (normal, mild anxiety, extreme anxiety) electroencephalogram (EEG) signals of visually they are trapped, determine whether distress require assistance. Additionally, considering complexity environment fact that EEG prone to external interference during acquisition, introduces an improved deep residual shrinkage network based dense blocks (DB-DRSN). DB-DRSN replaces convolutional hidden layer original module with integrates connections optimize use both shallow features. The results show achieves 96.72% recognizing difficulties faced by impaired, significantly outperforming traditional models. Compared other methods, proposed offers quicker assistance individuals. demonstrated strong performance detecting about jams, greatly enhancing safety enabling timely detection intervention.

Language: Английский

Citations

0