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Abstract Metabolic dysfunction-Associated Steatotic Liver Disease (MASLD) affects 30–50% of obese children, yet accurate non-invasive quantification remains challenging. While magnetic resonance imaging-proton density ...

IntroductionWe address moral uncertainty in reinforcement learning (RL) by proposing a framework that integrates multiple ethical theories into decision-making. Existing approaches rely on single moral frameworks or hand...

The agricultural sector contributes significantly to the economic development of nations worldwide. Owing to the population explosion, agriculture must address the complex challenges to meet the growing food demand while...

Background and objectives: The multiple chest x-ray datasets released in the last years have ground-truth labels intended for different computer vision tasks, suggesting that performance in automated chest x-ray interpre...

Aims/objectives: This study aimed to evaluate the performance of leading large language models (LLMs) in orthopedic surgery, with a focus on diagnostic accuracy, radiographic interpretation, subspecialty-specific perform...

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...

Digital twins are increasingly used in wireless networks to provide geometry- and channel-aware replicas that support prediction and control, while large language models (LLMs) offer strong instruction-following capabili...

ABSTRACT Analysing learners' facial expressions during learning and exploring their learning processes and emotional changes are of great significance for assisting teachers' teaching and promoting smart education. In co...

Background: Conventional electrocardiography (ECG) analysis faces a persistent dichotomy: Expert-defined features provide interpretability but are limited in capturing latent high-dimensional patterns, whereas deep learn...

The February 2026 issue presents 17 research papers within its regular section, covering a wide range of areas including Artificial Intelligence (AI), Machine Learning (ML), Web and Cloud Technologies, Recommender System...

Thermoelectric generators offer a promising solution for sustainable, low-maintenance energy harvesting in smart agriculture, especially for powering off-grid sensor nodes and microgrid systems. However, their inherently...

Abstract Machine-generated music (MGM) has become a groundbreaking innovation with wide-ranging applications, such as music therapy, personalised editing, and creative inspiration within the music industry. However, the ...

IntroductionFlood risk assessment has become increasingly important in regions vulnerable to climate-induced disasters. This study addresses the need for a robust decision-support framework by proposing a hybrid multi-cr...

Carbon allowances constitute a foundational component of national carbon emission control frameworks, as they govern the equitable distribution of subsequent allocations and directly shape the overall effectiveness of gr...

Introduction: The identification of chemical compounds that interfere with SARS-CoV-2 replication continues to be a priority in several academic and pharmaceutical laboratories. Computational tools and approaches have th...

This article examines how generative AI (GenAI) is reshaping strategic crisis decision-making through the emergence of ‘synthetic foresight’ – the algorithmic simulation of adversary intentions, escalation pathways...

Maintaining a balanced diet is essential for overall well-being, yet many individuals face challenges in meal planning due to time constraints, limited nutritional knowledge, and difficulty aligning meals with personal d...

ABSTRACT Graph contrastive learning (GCL) has emerged as a dominant paradigm for self‐supervised representation learning for attributed graph data. However, existing GCL methods heavily rely on empirical graph data aug...

Abstract Accurate prediction of compressive strength is important for the efficient design of sustainable concrete incorporating palm oil fuel ash (POFA). This study developed a hybrid artificial neural network model opt...

IntroductionThis study examines the impact of Customer Big Data Analytics (CBDA) on customer satisfaction and firm performance in business-to-business (B2B) firms operating in emerging markets, specifically Pakistan. Des...

Abstract BackgroundDepression affects more than 300 million people worldwide and is a leading contributor to the global disease burden. Traditional diagnostic methods, such as structured clinical interviews, ...

ABSTRACT This paper presents HealthNet, a novel framework for the dynamic optimisation of healthcare transportation networks using multi‐agent reinforcement learning. HealthNet leverages a spatiotemporal dependency mod...

ABSTRACT Integrating healthcare systems with intelligent transportation networks represents a critical frontier in modern urban infrastructure, where efficient resource allocation and timely service delivery can signific...

Abstract Rapid identification of bacterial species from patient samples is crucial for clinical decision‐making. In severe infections, such as bloodstream infections, the early start of an effective treatment is direct...