Deep learning-based multimodal emotion recognition from audio, visual, and text modalities: A systematic review of recent advancements and future prospects

Expert Systems with Applications, Journal Year: 2023, Volume and Issue: 237, P. 121692 - 121692

Published: Sept. 18, 2023

Language: Английский

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Ensemble Machine Learning of Gradient Boosting (XGBoost, LightGBM, CatBoost) and Attention-Based CNN-LSTM for Harmful Algal Blooms Forecasting

Toxins, Journal Year: 2023, Volume and Issue: 15(10), P. 608 - 608

Published: Oct. 10, 2023

Harmful algal blooms (HABs) are a serious threat to ecosystems and human health. The accurate prediction of HABs is crucial for their proactive preparation management. While mechanism-based numerical modeling, such as the Environmental Fluid Dynamics Code (EFDC), has been widely used in past, recent development machine learning technology with data-based processing capabilities opened up new possibilities prediction. In this study, we developed evaluated two types learning-based models prediction: Gradient Boosting (XGBoost, LightGBM, CatBoost) attention-based CNN-LSTM models. We Bayesian optimization techniques hyperparameter tuning, applied bagging stacking ensemble obtain final results. result was derived by applying optimal techniques, applicability evaluated. When predicting an technique, it judged that overall performance can be improved complementing advantages each model averaging errors overfitting individual Our study highlights potential emphasizes need incorporate latest into important field.

Language: Английский

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Development of an Integrated System of sEMG Signal Acquisition, Processing, and Analysis with AI Techniques

Signals, Journal Year: 2024, Volume and Issue: 5(3), P. 476 - 493

Published: July 26, 2024

The development of robust circuit structures remains a pivotal milestone in electronic device research. This article proposes an integrated hardware–software system designed for the acquisition, processing, and analysis surface electromyographic (sEMG) signals. analyzes sEMG signals to understand muscle function neuromuscular control, employing convolutional neural networks (CNNs) pattern recognition. electrical analyzed on healthy unhealthy subjects are acquired using meticulously developed featuring biopotential acquisition electrodes. captured database extracted, classified, interpreted by application CNNs with aim identifying patterns indicative problems. By leveraging advanced learning techniques, proposed method addresses non-stationary nature recordings mitigates cross-talk effects commonly observed interference sensors. integration AI algorithm signal enhances qualitative outcomes eliminating redundant information. reveals their effectiveness accurately deciphering complex data from signals, problems high precision. paper contributes landscape biomedical research, advocating computational techniques unravel physiological phenomena enhance utility analysis.

Language: Английский

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A new LSTM-based model to determine the atmospheric weighted mean temperature in GNSS PWV retrieval

GPS Solutions, Journal Year: 2024, Volume and Issue: 28(2)

Published: Feb. 19, 2024

Language: Английский

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A systematic review of trimodal affective computing approaches: Text, audio, and visual integration in emotion recognition and sentiment analysis

Expert Systems with Applications, Journal Year: 2024, Volume and Issue: 255, P. 124852 - 124852

Published: July 23, 2024

Language: Английский

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Hybrid deep learning-based cyberthreat detection and IoMT data authentication model in smart healthcare

Future Generation Computer Systems, Journal Year: 2025, Volume and Issue: unknown, P. 107711 - 107711

Published: Jan. 1, 2025

Language: Английский

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DoubleU-NetPlus: a novel attention and context-guided dual U-Net with multi-scale residual feature fusion network for semantic segmentation of medical images

Neural Computing and Applications, Journal Year: 2023, Volume and Issue: 35(19), P. 14379 - 14401

Published: March 26, 2023

Language: Английский

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Design of smart home system speech emotion recognition model based on ensemble deep learning and feature fusion

Applied Acoustics, Journal Year: 2024, Volume and Issue: 218, P. 109886 - 109886

Published: Jan. 31, 2024

Language: Английский

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Squeeze-and-excitation 3D convolutional attention recurrent network for end-to-end speech emotion recognition

Applied Soft Computing, Journal Year: 2024, Volume and Issue: 161, P. 111735 - 111735

Published: May 13, 2024

Language: Английский

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Smart audio signal classification for tracking of construction tasks

Automation in Construction, Journal Year: 2024, Volume and Issue: 165, P. 105485 - 105485

Published: May 31, 2024

This paper presents a model for sound classification in construction that leverages unique combination of Mel spectrograms and Mel-Frequency Cepstral Coefficient (MFCC) values. combines deep neural networks like Convolution Neural Networks (CNN) Long short-term memory (LSTM) to create CNN-LSTM MFCCs-LSTM architectures, enabling the extraction spectral temporal features from audio data. The data, generated activities real-time closed environment is used evaluate proposed resulted an overall Precision, Recall, F1-score 91%, 89%, respectively. performance surpasses other established models, including Deep (DNN), CNN, Recurrent (RNN), as well these models CNN-DNN, CNN-RNN, CNN-LSTM. These results underscore potential combining MFCC values provide more informative representation thereby enhancing noisy environments.

Language: Английский

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