Coordinated optimization of control parameters for improving the stability of wind-PV hybrid power systems under improved pelican optimization algorithm

Electrical Engineering, Год журнала: 2024, Номер unknown

Опубликована: Ноя. 7, 2024

Язык: Английский

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Multimodal and Multidomain Feature Fusion for Emotion Classification Based on Electrocardiogram and Galvanic Skin Response Signals

Sci, Год журнала: 2024, Номер 6(1), С. 10 - 10

Опубликована: Фев. 4, 2024

Emotion classification using physiological signals is a promising approach that likely to become the most prevalent method. Bio-signals such as those derived from Electrocardiograms (ECGs) and Galvanic Skin Response (GSR) are more reliable than facial voice recognition because they not influenced by participant’s subjective perception. However, precision of emotion with ECG GSR satisfactory, new methods need be developed improve it. In addition, fusion time frequency features should explored increase accuracy. Therefore, we propose novel technique for exploits early extracted data in AMIGOS database. To validate performance model, used various machine learning classifiers, Support Vector Machine (SVM), Decision Tree, Random Forest (RF), K-Nearest Neighbor (KNN) classifiers. The KNN classifier gives highest accuracy Valence Arousal, 69% 70% 96% 94% GSR, respectively. mutual information feature selection outperformed other Interestingly, was higher ECG, indicating preferred modality detection. Moreover, significantly enhances comparison ECG. Overall, our findings demonstrate proposed model based on multiple modalities suitable classifying emotions.

Язык: Английский

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FORMICARY SWARM OPTIMIZED DEEP CNN FOR FACIAL EMOTION RECOGNITION FROM HUMAN FACIAL EXPRESSIONS

Biomedical Engineering Applications Basis and Communications, Год журнала: 2024, Номер 36(05)

Опубликована: Июль 17, 2024

Facial emotion recognition (FER) is a dominant research area that captures the biological facial features and matches data with existing databases to analyze individual’s emotional state. Numerous techniques have been formulated for attaining effective FER. However, occlusions, different head positions, deformed faces, motion blur under unrestricted settings, complicated backgrounds make it complex images. In this paper, formicary swarm optimization-based deep convolutional neural network (FSO-opt DCNN) model utilized detection which JAFFE RAVDESS expression datasets are used. DCNNs proficient built-in feature extraction strategies from images map various expressions corresponding states adopted addition, intensity, directional, edge patterns as well correlation extracted utilizing hybrid textual pattern, RESNET 101 VGG 16-based modules assist DCNN attain informative high-resolution Further, optimization (FSO) incorporated effectively tunes capture relationships between learned excel FER capability. Evaluating metrics, face using dataset achieves notable efficiencies during training percentage (TP) of 90%, values 97.51%, 95.48%, 99.55%, 97.48%, 96.47%, minimum loss 2.49%. Simultaneously, demonstrates robust metric 96.75%, 98.49%, 95.01%, 96.72%, 97.59%, 3.25%. Finally, obtained results reveal efficacy FSO-opt DCNN, particularly in tasks, outperforms models across datasets, showcasing its versatility potential analysis applications.

Язык: Английский

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Thermal error prediction and optimal design of cooling structure for oscillating head housing

Case Studies in Thermal Engineering, Год журнала: 2024, Номер 61, С. 104963 - 104963

Опубликована: Авг. 13, 2024

Язык: Английский

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Automated Emotion Recognition from Facial Expressions using Convolutional Neural Network

Опубликована: Окт. 23, 2024

Язык: Английский

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