AutYOLO-ATT: an attention-based YOLOv8 algorithm for early autism diagnosis through facial expression recognition

Neural Computing and Applications, Год журнала: 2024, Номер 36(27), С. 17199 - 17219

Опубликована: Июнь 6, 2024

Abstract Autism Spectrum Disorder (ASD) is a developmental condition resulting from abnormalities in brain structure and function, which can manifest as communication social interaction difficulties. Conventional methods for diagnosing ASD may not be effective the early stages of disorder. Hence, diagnosis crucial to improving patient's overall health well-being. One alternative method autism facial expression recognition since autistic children typically exhibit distinct expressions that aid distinguishing them other children. This paper provides deep convolutional neural network (DCNN)-based real-time emotion system kids. The proposed designed identify six emotions, including surprise, delight, sadness, fear, joy, natural, assist medical professionals families recognizing intervention. In this study, an attention-based YOLOv8 (AutYOLO-ATT) algorithm proposed, enhances model's performance by integrating attention mechanism. outperforms all classifiers metrics, achieving precision 93.97%, recall 97.5%, F1-score 92.99%, accuracy 97.2%. These results highlight potential real-world applications, particularly fields where high essential.

Язык: Английский

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Enhanced handwriting recognition through hybrid UNet-based architecture with global classical features

Journal of Ambient Intelligence and Humanized Computing, Год журнала: 2025, Номер unknown

Опубликована: Янв. 18, 2025

Язык: Английский

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Optimized SVR with nature-inspired algorithms for environmental modelling of mycotoxins in food virtual-water samples

Scientific Reports, Год журнала: 2025, Номер 15(1)

Опубликована: Май 13, 2025

The accurate determination of mycotoxins in food samples is crucial to guarantee safety and minimize their toxic effects on human animal health. This study proposed the use a support vector regression (SVR) predictive model improved by two metaheuristic algorithms used for optimization namely, Harris Hawks Optimization (HHO) Particle Swarm (PSO) predict chromatographic retention time various mycotoxin groups. dataset was collected from secondary sources train validate SVR-HHO SVR-PSO models. performance models assessed via mean square error, correlation coefficient, Nash-Sutcliffe efficiency. outperformed existing methods 4-7% both learning (training testing) phases respectively. By using optimization, parameter adjustment became more effective, avoiding trapping local minima improving generalization. These results demonstrate how machine metaheuristics may be combined accurately forecast levels, providing useful tool regulatory compliance monitoring. framework perfect commercial quality assurance, testing, extensive programs because it provides exceptional accuracy resilience predicting times. In contrast conventional models, effectively manages intricate nonlinear interactions, guaranteeing identification while lowering hazards

Язык: Английский

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A Comprehensive Survey of Aquila Optimizer: Theory, Variants, Hybridization, and Applications

Archives of Computational Methods in Engineering, Год журнала: 2025, Номер unknown

Опубликована: Май 7, 2025

Язык: Английский

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Optimizing Wind Power Forecasting with RNN-LSTM Models through Grid Search Cross-validation

Sustainable Computing Informatics and Systems, Год журнала: 2024, Номер unknown, С. 101054 - 101054

Опубликована: Ноя. 1, 2024

Язык: Английский

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