Developing a two stage optimized random vector functional link neural network based predictor model utilizing a swift crow search algorithm

Cluster Computing, Journal Year: 2025, Volume and Issue: 28(3)

Published: Jan. 21, 2025

Language: Английский

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Optimization of Video Stimuli Parameters in EMDR Therapy Using Artificial Neural Networks for Enhanced Treatment Efficacy

Applied Sciences, Journal Year: 2025, Volume and Issue: 15(2), P. 934 - 934

Published: Jan. 18, 2025

Eye Movement Desensitization and Reprocessing (EMDR) was recognized by the World Health Organization in 2013 as an evidence-based therapy for post-traumatic stress disorder (PTSD) found to be effective depression. Since then, EMDR has evolved into a personalized treatment focusing on stabilizing physiological psychological processes alleviate symptoms of depression stress. However, optimized parameters video stimuli, such speed (ssp), distance (d), size (ssz), are not yet well defined protocols. This study addresses this gap employing artificial neural network (ANN) methodology based Francine Shapiro’s Adaptive Information Processing (AIP) model. The ANN used determine ideal values stimuli parameters, developing integrated model enhance outcomes. Of 2860 ANN-modeled combinations, stimulus settings 1.8 Hz speed, 70-pixel size, 1440-pixel achieved highest Predicted Effectiveness Score (PES) 98.7%. An field test with electroencephalography (EEG) conducted assess stimuli’s efficacy. Further, 16 participants, selected from sample 56 meeting CES-D criteria, were evaluated, top 50 PES further analysis. EEG results indicated 12.31% increase effectiveness, showing reduction right frontal lobe beta waves. These findings highlight technical advancements therapeutic potential proposed ANN-optimized demonstrating statistically significant improvements over traditional methods.

Language: Английский

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Forecasting Stock Market Indices Using Integration of Encoder, Decoder, and Attention Mechanism

Entropy, Journal Year: 2025, Volume and Issue: 27(1), P. 82 - 82

Published: Jan. 17, 2025

Accurate forecasting of stock market indices is crucial for investors, financial analysts, and policymakers. The integration encoder decoder architectures, coupled with an attention mechanism, has emerged as a powerful approach to enhance prediction accuracy. This paper presents novel framework that leverages these components capture complex temporal dependencies patterns within price data. effectively transforms input sequence into dense representation, which the then uses reconstruct future values. mechanism provides additional layer sophistication, allowing model selectively focus on relevant parts making predictions. Furthermore, Bayesian optimization employed fine-tune hyperparameters, further improving forecast precision. Our results demonstrate significant improvement in precision over traditional recurrent neural networks. indicates potential our integrated handle

Language: Английский

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Optimisation De La Sélection De Bande Pour Les Caméras Multispectrales Par Des Algorithmes Génétiques

Published: Jan. 1, 2025

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A Hybrid Approach for Stock Market Price Forecasting Using Long Short-Term Memory and Seahorse Optimization Algorithm

Annals of Data Science, Journal Year: 2025, Volume and Issue: unknown

Published: May 5, 2025

Language: Английский

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