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Abstract The growing importance of machine learning in geological data computing, as well as in the broader geoscience and energy sectors, makes it essential to evaluate the impact of data augmentation on model performan...

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...

Reliable trajectory similarity estimation underpins applications such as map matching, mobility anomaly detection, and travel behavior analysis. However, traditional distance-based methods are computationally expensive a...

Abstract Air pollution, particularly Nitrogen Dioxide ( $$\text {NO}_2$$ ) and ground-level ozone ( $$\text {O}_3$$ ), poses major risks to human health and the environment, making accurate forecasting essential for effe...

Learners face numerous hurdles while acquiring a second language. The use of mobile applications and digital technologies in language learning enhances the language proficiency of the learners. The objective of the study...

Botnet communications often rely on Domain Generation Algorithms (DGAs) to establish channels among attack orchestrators and compromised hosts (bots). Our approach leverages on Artificial Intelligence (AI) to differentia...

This paper addresses the challenge of packet-based information routing in large-scale communication networks. The problem is framed as a constrained statistical learning task, where each network node operates using only ...

This study aimed to evaluate the performance of deep learning models for segmenting the augmented bone following transalveolar sinus floor elevation (TSFE). Cone-beam computed tomography (CBCT) data from 103 patients und...

Abstract The inherently low thermal conductivity of phase change materials (PCMs) stills a major limitation for their use in latent heat thermal energy storage (LHTES) units, as it significantly reduces the charging proc...

Medical image prediction plays a very significant role in clinical decision-making and early detection and diagnosis of different diseases. However, the quality of medical images has a huge impact on the predictive model...

We tackle the challenging problem of single-source domain generalization (DG) for medical image segmentation, where we train a network on one domain (e.g., CT) and directly apply it to a different domain (e.g., MR) witho...

In the face of socioeconomic challenges, this paper develops and empirically demonstrates the Gondauri Index (GI) as a reproducible, diagnostics-first composite framework for benchmarking macro-financial resilience acros...

The effect of imbalanced data on the optical character recognition of Braille text is investigated by applying two techniques to a set of convolutional neural network image classification models. A multilabel classificat...

Abstract Background Thalassemia is the most prevalent single-gene disorder in Sri Lanka, imposing a significant socioeconomic and healthcare burden. Early carrier detection is essential for genetic counselling and the pr...

Abstract A compartmental mathematical model was developed to examine the transmission dynamics of chikungunya, integrating media awareness and optimal control strategies. Both asymptomatic and symptomatic infectious cate...

Kaissar Tabynov,1,2,* Kairat Tabynov,2,3 Nikolai Petrovsky4,* 1International Center for Vaccinology, Kazakh National Agrarian Research University, Almaty, Kazakhstan; 2Preclinical Research Laboratory with...

Diabetic retinopathy (DR) is one of the leading causes of preventable blindness worldwide, making timely and accurate detection crucial for effective management. This study introduces DR-NetFusion, a novel hybrid deep le...

The paper deals with modeling three-dimensional illumination distribution from light sourсes using artificial neural network (ANN). This problem is especially relevant in greenhouse irradiation systems due to the design...

The article presents the developed algorithm and software for automated monitoring of strawberry plant growth using neural network technologies. The YOLO11x and YOLOx-seg models, pre-trained by transfer learning, are ada...

Abstract Introduction This study aimed to develop a deep learning-based system to automatically assess patient positioning compliance in chest radiographs, focusing on cervical spine visibility, scapular overlap, and cos...

The application of Artificial Intelligence (AI) in project management is transforming decision-making processes, enhancing task execution, and improving risk management. This study aimed to elucidate the challenges raise...

Abstract Background Maxillary sinus (MS) floor elevation using the lateral window approach is a predictable technique for bone augmentation in the posterior maxilla. However, injury to the posterior superior alveolar art...

Abstract Background Intramuscular fat (IMF) accumulation in lumbar‑spine muscles is associated with sarcopenia, low‑back pain and poorer surgical outcomes. Dixon MRI allows the measurement of IMF with voxel-wise fat ...

Abstract Reliable interpretation of clinical imaging requires integrating complementary evidence across modalities, yet most AI systems remain limited by single-modality analysis and poor generalization across institutio...