Two-tier nature inspired optimization-driven ensemble of deep learning models for effective autism spectrum disorder diagnosis in disabled persons DOI Creative Commons
Saud S. Alotaibi, Turki Ali Alghamdi,

Reem Alharthi

et al.

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

Published: March 24, 2025

Autism spectrum disorder (ASD) includes a varied set of neuropsychiatric illnesses. This is described by definite grade loss in social communication, academic functioning, personal contact, and limited repetitive behaviours. Individuals with ASD might perform, convey, study different way than others. ASDs naturally are apparent before age 3 years, related impairments affecting manifold regions person's lifespan. Deep learning (DL) machine (ML) techniques used medical research to diagnose detect promptly. presents Two-Tier Metaheuristic-Driven Ensemble Learning for Effective Spectrum Disorder Diagnosis Disabled Persons (T2MEDL-EASDDP) model. The main aim the presented T2MEDL-EASDDP model analyze stages disabled individuals. To accomplish this, utilizes min-max normalization data pre-processing ensure that input scaled uniform range. Furthermore, improved butterfly optimization algorithm (IBOA)-based feature selection (FS) utilized identify most relevant features reduce dimensionality efficiently. Additionally, an ensemble DL holds three approaches, namely autoencoder (AE), long short-term memory (LSTM), deep belief network (DBN) approach employed analyzing detecting ASD. Finally, employs brownian motion (BM) directional mutation scheme-based coati optimizer (BDCOA) fine-tune hyperparameters involved methods. A wide range simulation analyses technique accomplished under ASD-Toddler ASD-Adult datasets. performance validation method portrayed superior accuracy value 97.79% over existing techniques.

Language: Английский

Short-term electrical load forecasting based on pattern label vector generation DOI
Haozhe Zhu,

Qingcheng Lin,

Xuefeng Li

et al.

Energy and Buildings, Journal Year: 2025, Volume and Issue: unknown, P. 115383 - 115383

Published: Jan. 1, 2025

Language: Английский

Citations

0
A novel prediction of the PV system output current based on integration of optimized hyperparameters of multi-layer neural networks and polynomial regression models DOI
Hussein Mohammed Ridha, Hashim Hizam, Seyedali Mirjalili

et al.

Next Energy, Journal Year: 2025, Volume and Issue: 8, P. 100256 - 100256

Published: Feb. 26, 2025

Language: Английский

Citations

0
A novel ensemble network based on CNN‐AM‐BiLSTM learner for temperature prediction of distillation columns DOI Open Access
Jianji Ren,

Linpeng Fu,

Yanan Li

et al.

The Canadian Journal of Chemical Engineering, Journal Year: 2025, Volume and Issue: unknown

Published: March 5, 2025

Abstract In recent years, complexity has significantly increased in chemical processes where a distillation column serves as crucial unit. It is worthwhile to develop an accurate and reliable predictive model maintain the steady operation condition of column. Although data‐driven models that do not rely on any prior knowledge present promising approach, they encounter challenges associated with nonlinearity dynamic behaviour within process data. To tackle these challenges, deep learning‐based combined distilled spatiotemporal attention ensemble network (CDSAEN) proposed. The CDSAEN constructed by sequentially integrating multiple base learners, which are iteratively generated decreasing span lengths through boosting method implemented specially designed extraction evaluation function. learner, convolutional neural (CNN), mechanism (AM), bidirectional long short‐term memory (BiLSTM) utilized adaptively capture intricate features establish robust mapping relationship from inputs output. Real‐world data system plant reconstructed time series dataset subsequently fed into for training forecast temperature apparatus advance. results exhibited effectiveness reliability. Additionally, comparison six other approaches, proposed attained superior performance mean absolute error (MAE) = 0.084, root squared (RMSE) 0.108, R 2 0.974. This study can provide support maintaining stable columns processes.

Language: Английский

Citations

0
Two-tier nature inspired optimization-driven ensemble of deep learning models for effective autism spectrum disorder diagnosis in disabled persons DOI Creative Commons
Saud S. Alotaibi, Turki Ali Alghamdi,

Reem Alharthi

et al.

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

Published: March 24, 2025

Autism spectrum disorder (ASD) includes a varied set of neuropsychiatric illnesses. This is described by definite grade loss in social communication, academic functioning, personal contact, and limited repetitive behaviours. Individuals with ASD might perform, convey, study different way than others. ASDs naturally are apparent before age 3 years, related impairments affecting manifold regions person's lifespan. Deep learning (DL) machine (ML) techniques used medical research to diagnose detect promptly. presents Two-Tier Metaheuristic-Driven Ensemble Learning for Effective Spectrum Disorder Diagnosis Disabled Persons (T2MEDL-EASDDP) model. The main aim the presented T2MEDL-EASDDP model analyze stages disabled individuals. To accomplish this, utilizes min-max normalization data pre-processing ensure that input scaled uniform range. Furthermore, improved butterfly optimization algorithm (IBOA)-based feature selection (FS) utilized identify most relevant features reduce dimensionality efficiently. Additionally, an ensemble DL holds three approaches, namely autoencoder (AE), long short-term memory (LSTM), deep belief network (DBN) approach employed analyzing detecting ASD. Finally, employs brownian motion (BM) directional mutation scheme-based coati optimizer (BDCOA) fine-tune hyperparameters involved methods. A wide range simulation analyses technique accomplished under ASD-Toddler ASD-Adult datasets. performance validation method portrayed superior accuracy value 97.79% over existing techniques.

Language: Английский

Citations

0