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BackgroundChildhood obesity represents a significant global public health challenge. Accurate and rapid prediction models for identifying obesity risk in children and adolescents are essential for facilitating early prev...

IntroductionThe proliferation of generative artificial intelligence (AI) has created unprecedented opportunities for informal second language (L2) learning, yet the psychological mechanisms through which AI-mediated lear...

IntroductionThe COVID-19 pandemic has reduced system resilience, caused economic welfare loss, and widened disparities in healthcare access. This study aims to identify the macro-level factors contributing to COVID-19 va...

Adaptive learning systems require effective mechanisms to model students’ evolving learning states and provide personalized learning path recommendations. Traditional rule-based approaches are limited in capturing...

The remarkable success of large language models (LLMs) in code generation has transformed software development, with systems like Codex, CodeGen, and StarCoder demonstrating strong capabilities in synthesizing functional...

The study aimed to analyse the effectiveness of traditional, innovative, and digital approaches to individualising the educational process and student adaptation within the educational systems of Bulgaria, Kyrgyzstan, an...

Abstract Object classification in decentralized environments is critically hindered by data heterogeneity and privacy constraints, which cause models to learn spurious, client-specific correlations rather than generaliza...

Abstract Background Accurate and timely estimation of nitrogen nutrition index (NNI) is critical for assessing crop nitrogen (N) status and implementing precision N management. While machine learning (ML) techniques comb...

Abstract Objective Machine learning-based approaches were applied to explore prognostic risk factors for patients with tuberculous meningitis (TBM) and develop a risk prediction model for their 1-year post-treatment poor...

Di-Higgs production at the LHC associated with missing transverse energy is explored in the context of simplified models that generically parameterize a large class of models with heavy scalars and dark matter candidates...

Abstract The Broad Learning System (BLS) provides an effective framework for nonlinear mapping, offering advantages over traditional deep neural networks through its expanded input node architecture. While BLS has demons...

This study aims to determine the differences in students' mathematical representations through the Problem Based Learning (PBL) model assisted by GeoGebra and Realistic Mathematics Education (RME) models, differences in ...

Background: The rapid development of digital technology and the challenges of learning Arabic, which is often perceived as abstract, require innovation through a contextual approach based on Islamic digital content. Pu...

Abstract The accurate prediction of the deterioration of Continuously Reinforced Concrete Pavement (CRCP) is essential for the effective management of pavements and the maintenance of infrastructure. In this study, a co...

Autism spectrum disorder (ASD) is a neurodevelopmental disorder, which seriously affects children’s normal life. Screening potential autistic children before professional diagnose is helpful to early detection and earl...

With the constant advancement of the smart grid, zero-day attack processes in Advanced Metering Infrastructure (AMI) and Supervisory Control and Data Acquisition (SCADA) networks are also continually evolving. This study...

This study aims to describe strengthening students' character through TPACK-based learning. It's implemented in Pancasila and Civics Education teaching and learning process at SMA Negeri 1 Palembang, one of favorite high...

Identifying candidates with high crystal density using Quantitative Structure-Performance Relationship (QSPR) models is a primary task in screening energetic molecules. However, due to the scarcity of high-density traini...

Sevgi Ünal,1 Remzi Gürfidan2 1Department of Radiology, Izmir Katip Celebi University Ataturk Training and Research Hospital, Izmir, Türkiye; 2Database, Network Design and Management, Isparta University of Applied Scie...

In this work, a structured hierarchy of hyperdatasets for Monkeypox image classification is presented, aiming to evaluate the robustness and generalization ability of deep classification models for Mpox detection. A syst...

Background: Postpartum preeclampsia remains a significant contributor to maternal morbidity and mortality in Indonesia. Midwives, as frontline maternal health providers, are expected to deliver holistic care; however, st...

Introduction Conducting surveys on domestic violence across diverse countries, particularly in lower-middle-income nations like Nepal, poses significant challenges in understanding and addressing the multifaceted dynamic...

While physics models for predicting system states can reveal fundamental insights owing to their parsimonious structure, they may not always yield the most accurate predictions, particularly for complex systems. As an al...

Abstract High-throughput analysis of EEG data has significantly contributed to understanding neural dynamics in Alzheimer’s disease diagnosis. However, the complexity and high dimensionality of EEG signals pose challen...