Frontiers in Artificial Intelligence | New and Recent Articles

BackgroundFalls represent a major clinical and financial challenge for healthcare systems. Accurately predicting first falls remains challenging, especially when using routinely collected data.ObjectiveDevelop and evaluate a predictive model to identify elderly people at risk of first fall—defined as a fall after a 90-day fall-free period—in the Basque Country using routinely collected health rec…

Vision Transformers (ViTs) perform well on clean aerial imagery but degrade sharply when deployed in post-earthquake UAV operations, where motion blur, dust haze, illumination variation, and sensor noise combine to produce what we term post-earthquake visual drift, a structured distributional shift that can render an otherwise capable model dangerously unreliable in the field. Retraining or conve…

BackgroundAccessing large-scale clinical and biomedical databases remains a significant barrier for clinicians and researchers, requiring substantial computational expertise. Agentic artificial intelligence frameworks, in which large language models (LLMs) orchestrate multi-step reasoning and query execution under interactive human supervision, offer the potential to democratize data access and a…

IntroductionKidney abnormalities, including cysts, tumors, and stones, are the most common renal disorders that can lead to severe complications such as chronic kidney disease or renal failure. Deep learning-based medical image analysis offers an effective approach for the accurate classification of kidney abnormalities, aiding the early diagnosis of renal disorders. However, its centralized trai…

IntroductionComputational classifiers can be benchmarked against public encoded splice-junction datasets, but the performance estimate will depend on the input representation, preprocessing pipeline, model set-up and evaluation design.MethodsThis study investigates the performance of 15 different Convolutional, Hybrid and Deep Learning architectures on a public encoded splice-junction dataset for…

As large language models become primary communication partners, the stylistic and communicative character of their output—specifically the degree to which it relies on intellectual, affective, or action-oriented language—shapes how users interpret, and act on what they read. Yet this property is rarely measured or controlled directly. We introduce the Intellect-Emotion-Action Profile (IEAP), a pu…

IntroductionEducational data mining has been applied to examine students' data collected from different educational organizations to forecast academic students' performance, which could assist them in accomplishing improved results in their upcoming courses.MethodsThis study presents the impact of artificial intelligence with multi-head self-attention on students' academic development using metah…

Early and accurate detection of plant diseases is critical in precision agriculture to improve crop management and yield. Mungbean (Vigna radiata L.) is highly susceptible to several foliar diseases, including yellow mosaic, powdery mildew, leaf crinkle, and cercospora leaf spot, which cause substantial productivity losses. Despite expanding applications of deep learning in plant disease diagnosi…

IntroductionDiabetic foot ulcer (DFU) image classification can support timely clinical triage; however, many existing systems provide limited severity granularity, rely on single-source image datasets, and predominantly use pixel-level explanation methods. This study developed a seven-class, severity-aware, multi-task artificial intelligence framework for interpretable DFU image analysis and smar…

IntroductionThe integration of 5G and Software-Defined Networking (SDN) has introduced new security challenges due to limited processing capabilities, inefficient resource allocation, frequent user mobility, and the rapid growth of Internet of Things (IoT) devices. These dynamic network conditions increase the likelihood of malicious activities, highlighting the need for robust and intelligent in…

Fault diagnosis in power transmission systems requires the simultaneous identification of fault type and location from high-dimensional, multichannel, and temporally evolving measurements. Conventional deep learning approaches may struggle to jointly capture localized transient patterns, global temporal dependencies, and spatially distributed fault signatures while providing interpretable diagnos…

BackgroundAI-enabled retinal imaging provides a non-invasive method for assessing cardiovascular risk by identifying microvascular changes linked to atherosclerotic cardiovascular disease. This study assessed the feasibility and clinical associations of the AI-derived retinal risk score (Reti-CVD) in a hybrid care model and compared its categorical alignment with the American Heart Association PR…

IntroductionDyslexia affects around 10% of the global population, yet early screening depends heavily on resource-intensive clinical assessments that are difficult to scale. Gamified cognitive platforms offer a practical alternative, but most existing machine learning approaches rely on raw task metrics without exploiting the hierarchical cognitive structure of multi-task assessment batteries.Met…

The forecasting of financial time series has gained more significance in decision making within a dynamic economic setting. Over the last few years, there has been a push in exploring both classical machine learning methods and novel quantum machine learning models with a view to enhance predictive accuracy. This paper suggests a quantum-classical regression framework developed that combines the …

Digital nudging, a behavioral approach that subtly guides decision-making, is gaining increasing attention in software development as a means to support developers in complex and cognitively demanding tasks. This article analyzes the evolution of digital nudging and persuasive technology in software engineering from 2010 to April 2026. Based on a structured literature review, a temporal heatmap a…

IntroductionHuman–AI interaction is commonly framed as a problem of uniformly minimizing uncertainty across the joint system. We challenge this assumption by proposing Asymmetric Uncertainty Regulation (AUR), a dynamical framework in which stable and adaptive collaboration requires directional rather than symmetric uncertainty regulation.MethodsHuman–AI systems are modeled as coupled entropy dyna…

As human relationships with artificial intelligence systems become increasingly frequent and sustained, existing language and theory fail to accurately capture the nature of these affiliations. Common descriptors such as mutual “understanding,” “connection,” or “friendship” risk anthropomorphizing systems that lack subjective experience, while dominant frameworks tend to reduce AI to either a too…

IntroductionAbstractive text summarization remains a fundamental challenge in Natural Language Processing (NLP), particularly for long documents that require models to preserve long-range dependencies and maintain semantic coherence. Although Transformer-based architectures have achieved strong summarization performance, their full self-attention mechanism scales quadratically with sequence lengt…

As AI technology rapidly matures, suppliers are actively establishing self-operated AI-hosted channels while continuing to rely on MCN resale channels. The cross-channel traffic spillover effect and consumers' AI preference have intensified channel conflicts, prompting some suppliers to consider exiting MCN partnerships. To examine the feasibility of such supplier exit behavior, this paper develo…