AIMC Journal:
Scientific reports
Showing 621 to 630 of 5345 articles
Two-tier nature inspired optimization-driven ensemble of deep learning models for effective autism spectrum disorder diagnosis in disabled persons.
Scientific reports
Mar 24, 2025
Autism spectrum disorder (ASD) includes a varied set of neuropsychiatric illnesses. This disorder is described by a definite grade of loss in social communication, academic functioning, personal contact, and limited and repetitive behaviours. Individ...
Employing artificial bee and ant colony optimization in machine learning techniques as a cognitive neuroscience tool.
Scientific reports
Mar 24, 2025
Higher education is essential because it exposes students to a variety of areas. The academic performance of IT students is crucial and might fail if it isn't documented to identify the features influencing them, as well as their strengths and shortc...
Early prediction of intraventricular hemorrhage in very low birth weight infants using deep neural networks with attention in low-resource settings.
Scientific reports
Mar 24, 2025
Early prediction of intraventricular hemorrhage (IVH) in very low-birthweight infants (VLBWIs) remains challenging because of multifactorial risk factors. IVH often occurs within a few hours after birth, yet its onset cannot be reliably predicted usi...
Bio-inspired neural networks with central pattern generators for learning multi-skill locomotion.
Scientific reports
Mar 24, 2025
Biological neural circuits, central pattern generators (CPGs), located at the spinal cord are the underlying mechanisms that play a crucial role in generating rhythmic locomotion patterns. In this paper, we propose a novel approach that leverages the...
The optimization and impact of public sports service quality based on the supervised learning model and artificial intelligence.
Scientific reports
Mar 22, 2025
Aiming at the optimization of public sports service quality, this study analyzes the public sports service data deeply by constructing a supervised learning model. Firstly, the theoretical framework of this study is established. Secondly, the technic...
A stacking ensemble machine learning model for predicting postoperative axial pain intensity in patients with degenerative cervical myelopathy.
Scientific reports
Mar 22, 2025
Machine learning (ML) has been extensively utilized to predict complications associated with various diseases. This study aimed to develop ML-based classifiers employing a stacking ensemble strategy to forecast the intensity of postoperative axial pa...
Machine learning analysis of cardiovascular risk factors and their associations with hearing loss.
Scientific reports
Mar 22, 2025
Hearing loss poses immense burden worldwide and early detection is crucial. The accurate models identify high-risk groups, enabling timely intervention to improve quality of life. The subtle changes in hearing often go unnoticed, presenting a challen...
Vowel segmentation impact on machine learning classification for chronic obstructive pulmonary disease.
Scientific reports
Mar 22, 2025
Vowel-based voice analysis is gaining attention as a potential non-invasive tool for COPD classification, offering insights into phonatory function. The growing need for voice data has necessitated the adoption of various techniques, including segmen...
A novel framework for segmentation of small targets in medical images.
Scientific reports
Mar 22, 2025
Medical image segmentation represents a pivotal and intricate procedure in the domain of medical image processing and analysis. With the progression of artificial intelligence in recent years, the utilization of deep learning techniques for medical i...
Machine learning-based prediction of vesicoureteral reflux outcomes in infants under antibiotic prophylaxis.
Scientific reports
Mar 22, 2025
We aimed to investigate the independent outcome predictors of continuous antibiotic prophylaxis (CAP) in vesicoureteral reflux, train a model to predict the outcome, and evaluate which infants should be referred for endoscopic vesicoureteral reflux c...
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