Frontiers in Artificial Intelligence | New and Recent Articles

Credit card fraud has become a significant threat to individuals, financial institutions, businesses, and governments, causing substantial annual economic losses through increasingly sophisticated fraudulent activities. This research aims to enhance credit card fraud detection by leveraging machine learning algorithms and ensemble learning techniques to improve detection accuracy and model robust…

In recent years, there has been a notable increase in the deployment of machine learning (ML) models as services (MLaaS) across diverse production software applications. In parallel, explainable AI (XAI) continues to evolve, addressing the necessity for transparency in ML models. XAI techniques aim to enhance the transparency of ML models by providing insights, in terms of model's explanations, i…

IntroductionMulti-modal image registration leverages complementary information from diverse imaging sources to achieve precise spatial alignment. However, aligning visible (VIS), near-infrared (NIR), and thermal (TH) modalities remains challenging due to appearance differences and limited annotated datasets.MethodsThis study proposes a ResU-Net-inspired framework combining heatmap prediction and …

IntroductionThis article presents an in-depth single-case study of Kazakhstan and examines the latent negative effects that may accompany the expanding use of artificial intelligence (AI) in the public sector. The central premise is that such risks arise less from isolated technical performance indicators of particular AI systems than from the broader architecture of legal regulation, institution…

BackgroundKawasaki disease (KD) is an acute systemic vasculitis predominantly affecting children under 5 years of age. Its pathogenesis remains incompletely understood, and the lack of specific diagnostic biomarkers during the acute phase poses substantial challenges to clinical diagnosis. Such diagnostic uncertainty often results in missed or delayed cases, leading to lost therapeutic opportunit…

Diagnosing Alzheimer's disease (AD) is necessary to determine treatment options. AD categorization using machine learning (ML) relies on difficult, manually specified features. The most important stage in AD diagnosis is denoising to restore image stability and quality. An ensemble image denoising technique that combines Attention Guided Convolutional Neural Network (AGCNN), Adaptive Denoising Au…

BackgroundLarge language models show promise in medical tasks, but their systematic error patterns in high-stakes clinical settings remain poorly understood, limiting safe deployment.MethodsA three-phase simulation study was conducted. In phase 1, researchers selected 103 multiple-choice questions and 30 clinical scenario questions, derived from an LBP examination question bank and clinical guide…

IntroductionWith the deep integration of generation-transmission-load-storage systems, the power demand side has become highly dynamic and stochastic, challenging the traditional assumption that user behavior remains stationary over time. Static clustering models therefore suffer from sensitivity to daily noise and false user identity switching.MethodsThis study proposes Dynamic Evolutionary Clus…

IntroductionTo evaluate whether routinely measured MRI-based patellofemoral joint morphometric parameters can support diagnostic modeling of selected adolescent knee pathologies and to compare a conventional multivariable logistic regression baseline with machine-learning approaches.MethodsThis retrospective single-center pilot diganostic modeling study included 168 adolescents (97 girls, 71 boys…

Neuro-symbolic (NeSy) approaches promise to overcome the limitations of purely neural and purely symbolic NLP. In this paper we survey recent development in NeSy NLP and propose a system classification framework that combines Kautz's integration types with Lappin's injective-federative distinction. We then apply this taxonomy and show that even though federative architectures consistently outperf…

The Fourth Industrial Revolution (4IR) has accelerated the adoption of Artificial Intelligence (AI) across knowledge intensive sectors, yet its integration in Library and Information Services (LIS) within resource constrained educational environments remains limited and under examined. This empirical paper seeks to explore the impact of AI integration in LIS and the associated challenges. This re…

Assessing the structural integrity of AI system outputs is typically based on surface-level metrics such as fluency or local coherence. This paper proposes an alternative perspective: measuring structural coherence as a multi-scale property of system outputs. We introduce the Dynamic Coherence Window (DCW) framework, which models structural viability as bounded behavior within coherence threshold…

Open-pit mine production scheduling is a complex optimization problem that requires balancing economic performance with operational feasibility and environmental responsibility. While significant advances have been made in mathematical formulations, many existing approaches remain computationally demanding, lack transparency, or are implemented within proprietary systems that limit reproducibilit…

Software vulnerability analysis is critical for maintaining secure and reliable systems, yet traditional Deep Learning (DL) models often act as “black boxes,” lacking transparency and failing to leverage the explicit structural semantics of code. In this paper, we propose KG-HiAttention, a novel neuro-symbolic framework that synergizes sub-symbolic deep learning with symbolic AI-based Knowledge G…

BackgroundTacrolimus dosing after kidney transplantation is complex, highly individualized, and prone to variability, which can impact graft outcomes. While machine learning (ML) approaches have been used primarily to predict tacrolimus concentrations, large language models (LLMs) may enable protocol-constrained clinical decision support by generating dosing recommendations aligned with establish…

BackgroundHigh-precision micro-displacement measurement is crucial for applications ranging from semiconductor manufacturing to biomedical diagnostics. However, conventional Michelson interferometry is fundamentally limited by the light source’s coherence length and the inefficiency of manual fringe counting.MethodsThis study introduces a modified Michelson interferometer that operates on Malus’s…

Cardiovascular Disease (CVD) is still the main cause of death globally. Hence, to have timely clinical intervention, there is a need for the prediction of early risk models which are accurate and dependable. Here is a lightweight and strong artificial intelligence-based system for the early prediction of CVD using clinical data. We have created an end-to-end method comprising data preprocessing, …

IntroductionDemand forecasting in pharmaceutical supply chains is not a simple task. In regulated markets it becomes more difficult, because seasonality, epidemic waves, and also policy changes can make demand behavior unstable. This study proposes a hybrid residual learning approach for forecasting pharmaceutical demand in Türkiye.MethodsThe model uses Support Vector Regression (SVR) together wi…

Three-dimensional (3D) procedural plant architecture models have emerged as an important tool for simulation-based studies of plant structure and function, extracting plant architectural parameters from field measurements, and for generating realistic plants in computer graphics. However, measuring the architectural parameters for these models at the field and population scales remains prohibitiv…

Climate change is accelerating spatially complex transformations in land, water, coasts, cryosphere, and ecosystems, creating a critical need for reliable, scalable, and timely monitoring based on Earth observation imagery. Conventional approaches that rely on sparse in-situ measurements, manual image interpretation, and simple spectral indices or thresholding often fail to capture subtle, hetero…