2026 May - 68 articles
The Internet of Vehicles (IoV) is rapidly emerging as a core component of intelligent transportation systems, enabling real-time communication among vehicles, infrastructure, and cloud platforms. However, the increasing interconnectivity of vehicular...
The timely detection of psycho-emotional risks has become increasingly important due to the rapid growth of social media platforms. This study examines user-generated text as a potential source of early indicators of psychological vulnerability. The...
Role-Based Access Control (RBAC) is the de-facto mechanism for preserving Kubernetes and other cloud-native container platforms, however real deployments occasionally drift away from the principle of least privilege as clusters, teams, and services i...
Crime prediction can support proactive public-safety planning, but practical deployment also requires outputs that are reliable and explainable. This study proposes XCrime-LLM for next-week crime occurrence prediction, in which engineered spatio-temp...
Edge server placement (ESP) is a critical determinant of service quality in edge–cloud computing systems, yet existing solutions often neglect the inherent collaboration between edge and cloud, leading to suboptimal performance under dynamic wo...
Single image super-resolution (SR) is an important part of image processing, which aims to improve the spatial resolution of images. This is a typical ill-posed inverse problem. The main difficulty is that a low-resolution image block usually corresp...
This paper presents a framework for integrating Artificial Intelligence (AI) into Knowledge Management (KM), using the Jennex–Olfman KM Success Model as a foundation. Through a literature review and a thematic analysis of 400 practitioner comme...
The integration of quadruped robotics with advanced sensing technologies offers a transformative approach to forest management, particularly for real-time measurement of tree Diameter at Breast Height (DBH). This paper introduces a novel methodology...
Automated short-answer grading (ASAG) in educational contexts faces a fundamental trade-off between predictive performance, interpretability, and methodological transparency, particularly under data-constrained educational settings. While recent appr...
Recent studies suggest that few-shot and zero-shot learning methods, drawing on meta-learning, self-supervised approaches and metric-learning ideas, can classify encrypted traffic (TLS 1.3 and QUIC) with competitive accuracy across different protocol...
Open-vocabulary semantic segmentation (OVSS) aims to achieve pixel-level object segmentation guided by arbitrary natural language descriptions. Although pre-trained vision–language models (VLMs) have significantly advanced the development of OV...
Large language models (LLMs) are increasingly used in professional examinations, but their relative performance on dental board-style questions remains unclear. This study compared two reasoning-optimized models, GPT-o3 and GPT-5T, with a general-pur...
Edge Internet of Things (IoT) blockchain deployments increasingly rely on continuous transaction ingestion from resource-constrained IoT devices to nearby edge gateways over heterogeneous wireless links. In this setting, transient channel noise and p...
Sustainable manufacturing requires schedules that balance environmental responsibility with delivery reliability. This paper studies the Carbon-Aware Flexible Job Shop Scheduling Problem with Tardiness Penalty (CAFJSP-T), where total carbon emissions...
We investigate the linguistic complexity and emotional valence of popular song lyrics across English (
High-resolution satellite imagery is pivotal for accurate analysis in remote sensing applications, including land-use monitoring, urban planning, and environmental assessment. However, obtaining such data is often costly and limited. Consequently, su...
Because textured contact lenses obscure the iris’s natural texture, they pose a serious threat to the accuracy of iris recognition systems and may make identity theft possible. Therefore, this work proposes a reliable method for textured contac...
In recent years, wildfires have occurred with increasing frequency. Pixel-level annotation of high-resolution remote sensing wildfire imagery is costly and labor-intensive. Therefore, there is an urgent need for a weakly supervised wildfire detection...
Problem: In the deep image steganography field, the main challenge is to achieve a balance between visual image quality, reliably recovering the message, robustness, and interpretability, especially regarding image distortion because of noise, attack...
Automated defect detection in industrial casting processes is important for improving product quality while reducing the cost of manual inspection. In this work, two deep convolutional neural network (CNN) architectures, InceptionV3 and InceptionResN...
The rapid growth of Internet of Things (IoT) applications over 5G networks demands secure, low-latency data transmission while operating under strict resource constraints. However, existing studies have relied on simulations or partial implementation...
The objective evaluation of Daqu cross-sectional quality is challenging due to its heterogeneous structure, small features, and low contrast. This study proposes a semantic-segmentation-based framework for the automated identification and quantitativ...
Patients with chronic kidney disease (CKD) frequently experience depressive symptoms, which substantially impair their quality of life. To facilitate the early identification of high-risk individuals, this study aimed to develop a predictive model fo...
Knowledge management (KM) is increasingly becoming a critical success factor in Germany’s manufacturing industry due to demographic change, the shortage of a skilled workforce, and the growing need for flexible and resilient production systems....
To provide reliable services in the cloud, fault tolerance is perhaps the most important consideration. The inherent sensitivity to failure hampers cloud services’ performance and reliability. As a result, fault tolerance becomes a required cha...
Fatigue seriously affects work efficiency and brings potential safety hazards, and electroencephalogram (EEG) serves as a valuable physiological indicator for fatigue monitoring, as it directly reflects underlying brain neural activity. A key charact...
Internet of Things (IoT) nodes continuously acquire, buffer, and transmit sensor data under strict constraints on area, latency, and energy consumption. However, conventional complementary metal–oxide–semiconductor (CMOS)-based memory-acc...
Vision transformers (ViTs) have demonstrated considerable promise for classifying electrocardiogram (ECG) rhythms. However, much of the existing research is conducted in highly controlled, data-sterile settings that fail to reflect the substantial va...
Hemodynamic analysis of blood flow is critical for diagnosing cardiovascular diseases and investigating cardiovascular parameters, such as aneurysms and wall shear stress. For subject-specific analyses, the anatomy and blood flow of the subject can b...
Digital learning tools for American Sign Language (ASL) often lack the interactive depth necessary to engage learners effectively. This paper introduces a novel, browser-based word search game designed to facilitate ASL vocabulary familiarization thr...
Large-scale symbolic melody datasets are essential for data-driven music information retrieval and generation, yet traditional-style Chinese melodies remain scattered across heterogeneous score formats and image sources. Existing extraction pipelines...
Integrating structured linguistic resources into deep learning architectures represents a key challenge in domain-oriented NLP. This study proposes a framework for incorporating knowledge from a military thesaurus of the Ground Forces, structured acc...
A clustering analysis was conducted to identify distinct patient subgroups with White Blood Cells (WBC) count alongside Age and Total Immunoglobulin E (IgE) biomarkers. All data were obtained from a coordinated primary care network operating in Apuli...
Machine learning and statistical learning have become central to modern scientific discovery and technological innovation [...]
The rapid growth of heterogeneous web sources has created significant challenges for the automated extraction and structuring of critical domain-specific information, particularly in real-time seismic monitoring scenarios. Despite the existence of of...
Squats are essential for assessing lower limb strength. However, performing them incorrectly without professional guidance often leads to sports injuries. Currently, most detection methods rely heavily on deep neural networks and massive datasets. Th...
End-user development (EUD) and model-driven engineering (MDE) are particularly valuable for building classical intelligent systems that rely on declarative knowledge bases. In these knowledge bases, the key dependencies of the domain can be described...
Fetal health assessment is a crucial aspect of prenatal care, aimed at the early detection of potential complications to ensure optimal outcomes for both mother and child. Traditional methods, such as the visual analysis of cardiotocography (CTG) dat...
The integration of post-quantum digital signature (PQDS) algorithms into coherent wavelength-division multiplexing (WDM) optical networks introduces a non-negligible cryptographic overhead that fundamentally alters physical-layer performance characte...
This study investigates the educational potential of Large Language Models (LLMs) for automating rule-checking tasks in Building Information Modeling (BIM) instruction. A quasi-experimental classroom implementation was conducted over two consecutive...
Federated learning, a decentralized machine learning framework, allows multiple participants to jointly train models while keeping their raw data local and unshared. Nevertheless, during the exchange of model updates, the communicated information can...
Speech alterations are early markers of neurodegenerative diseases. Transformer-based speech models such as Whisper have advanced automated speech assessment, but most systems rely on cloud-based computation, raising privacy concerns. On-device proce...
This paper investigates federated multi-centre liver tumour classification from contrast-enhanced CT under realistic data heterogeneity and domain shift. To address the practical constraint that medical data are often siloed across institutions, we d...
The increasing complexity of modern network infrastructures has intensified the need for reliable and efficient intrusion detection systems. While advanced deep learning approaches have demonstrated strong performance, their high computational cost a...
Although motion capture has become integral to contemporary animation pipelines, university teaching still asks students to learn motion largely through screen-based keyframing. To address this gap, this classroom-based comparative study evaluated on...
Accurate detection of small intestinal lesions in wireless capsule endoscopy (WCE) images remains challenging because lesions are often small, weakly contrasted, irregular in shape, and easily confused with complex mucosal backgrounds. To address the...
This study presents a systematic empirical evaluation of monolithic and distributed database architectures deployed in Kubernetes environments. As containerized and cloud-native infrastructures become increasingly prevalent, understanding the perform...
The Swin Transformer exhibits limitations in fine-grained land use and land cover (LULC) classification, particularly in capturing high-frequency texture details and representing low-contrast regions. To address these issues, we propose a novel netwo...
Efficiently and accurately converting floating-point numbers to decimal strings remains a fundamental challenge in numerical computation, data serialization, and human–computer interaction. While modern algorithms such as Ryū, Dragonbox, a...
Large Language Models (LLMs) used for clinical decision support must not only make accurate predictions but also generate rationales that are consistent with, and sufficient for, those predictions. Building on Reason2Decide, a two-stage rationale-dri...
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