HCBiLSTM-WOA: hybrid convolutional bidirectional long short-term memory with water optimization algorithm for autism spectrum disorder

Computer Methods in Biomechanics & Biomedical Engineering, Journal Year: 2024, Volume and Issue: unknown, P. 1 - 23

Published: Sept. 18, 2024

Autism Spectrum Disorder (ASD) is a type of brain developmental disability that cannot be completely treated, but its impact can reduced through early interventions. Early identification neurological disorders will better assist in preserving the subjects' physical and mental health. Although numerous research works exist for detecting autism spectrum disorder, they are cumbersome insufficient dealing with real-time datasets. Therefore, to address these issues, this paper proposes an ASD detection mechanism using novel Hybrid Convolutional Bidirectional Long Short-Term Memory based Water Optimization Algorithm (HCBiLSTM-WOA). The prediction efficiency proposed HCBiLSTM-WOA method investigated datasets containing both non-ASD data from toddlers, children, adolescents, adults. inconsistent incomplete representations raw dataset modified preprocessing procedures such as handling missing values, predicting outliers, discretization, reduction. preprocessed obtained then fed into classification model effectively predict classes. initially randomly initialized hyperparameters HCBiLSTM adjusted tuned water optimization algorithm (WOA) increase accuracy ASD. After classes, further classifies cases respective stages on autistic traits observed Also, ethical considerations should taken account when campaign risk communication complex due privacy unpredictability surrounding factors. fusion sophisticated deep learning techniques presents promising framework diagnosis. This innovative approach shows potential managing intricate data, enhancing diagnostic precision, improving result interpretation. Consequently, it offers clinicians tool precise detection, allowing timely intervention cases. Moreover, performance evaluated various indicators accuracy, kappa statistics, sensitivity, specificity, log loss, Area Under Receiver Operating Characteristics (AUROC). simulation results reveal superiority compared other existing methods. achieves higher about 98.53% than methods being compared.

Language: Английский

Solving a Stochastic Multi-Objective Sequence Dependence Disassembly Sequence Planning Problem with an Innovative Bees Algorithm

Automation, Journal Year: 2024, Volume and Issue: 5(3), P. 432 - 449

Published: Aug. 23, 2024

As the number of end-of-life products multiplies, issue their efficient disassembly has become a critical problem that urgently needs addressing. The field sequence planning consequently attracted considerable attention. In actual process, complex structures can lead to significant delays due interference between different tasks. Overlooking this result in inefficiencies and waste resources. Therefore, it is particularly important study sequence-dependent problem. Additionally, activities are inherently fraught with uncertainties, neglecting these further impact effectiveness disassembly. This first analyze an uncertain environment. It utilizes stochastic programming approach address uncertainties. Furthermore, mixed-integer optimization model constructed minimize time energy consumption simultaneously. Recognizing complexity problem, introduces innovative bees algorithm, which proven its by showing superior performance compared other state-of-the-art algorithms various test cases. research offers solutions for holds implications advancing sustainable development recycling

Language: Английский