Camera-based visual sensing for classroom behavior detection remains challenging because fine-grained behaviors often differ only in subtle posture and hand–object cues, while dense seating, occlusion, scale variation, and background interferen...
Evaluations of teacher professional development programmes are often constrained by reliance on single-source self-report data and partial evaluation designs, limiting the validity of conclusions on training effectiveness. This study evaluates a Refl...
Background/Objectives: Estimating energy and macronutrients from food images is clinically relevant yet challenging, and rigorous evaluation requires transparent accuracy metrics with uncertainty and clear acknowledgement of reference data limitation...
Comprehensive evaluation of open-pit blasting is challenging because performance indicators originate from different sensing sources and are predicted with different levels of model performance. This study proposes a multi-source sensing and interpre...
Continuous eye tracking is difficult to deploy in production vehicles, while eye-movement-derived proxy labels are susceptible to same-source circularity and road-type confounding. We propose a driver-independent, confounder-aware cross-source framew...
Spam and phishing email remain a persistent cybersecurity threat and a primary delivery vector for credential theft, malware, and ransomware. Detection accuracies above 99% are routinely reported. Such figures are typically obtained on small, single-...
Point cloud fusion is crucial in geospatial analysis, combining data from multiple sources (e.g, LiDAR and photogrammetry) to provide a more complete and accurate environmental representation. However, integrating airborne hybrid sensors or cross-sou...
Public concrete datasets often contain duplicate records, coupled variables, and cross-source distribution shifts, which may lead to overly optimistic model evaluation. Based on a deduplicated UCI high-performance concrete dataset (1005 samples), thi...
Predictive models of lightweight aggregate (LWA) properties are renowned for their excellent prediction performance within a single source of data, but their generalization power across multiple literature sources is highly questionable. Examination...
Unmanned aerial vehicle (UAV) systems are heavily adopted nowadays to collect high-resolution imagery with the purpose of documenting and mapping environment and cultural heritage. Such data are currently processed by programs based on the Structure...
Bearing fault diagnosis under varying operating conditions faces speed and data-source shifts that can make models unreliable outside their development regime. Record-level diagnosis therefore requires both fault classification and an automatic accep...
Building reconstruction using high-resolution satellite-based synthetic SAR tomography (TomoSAR) is of great importance in urban planning and city modeling applications. However, since the imaging mode of SAR is side-by-side, the TomoSAR point cloud...
Brain-tumor MRI classification and explainable artificial intelligence (XAI) evaluation can be affected by hidden source overlap and architecture-dependent explanation rankings. This study presents a focused contamination-aware, cross-source, and cro...
Accurate delineation of cardiac structures in cardiac magnetic resonance (CMR) images is a prerequisite for quantifying cardiac function. This paper presents an end-to-end pipeline for automated cardiac segmentation and five-class pathology classific...
Detectors of text generated by large language models can achieve high scores by exploiting shared source documents, data sources, or generator-specific artifacts rather than genuine linguistic signals. This is a serious concern for textual deepfakes...
Multi-source urban indicator datasets are frequently used in clustering, machine learning, and regression analyses without prior statistical auditing, potentially distorting results through non-normality, multicollinearity, and cross-source measureme...
Ransomware attacks pose a serious threat to computer networks, causing widespread disruption to individual, corporate, governmental, and critical national infrastructures. To mitigate their impact, extensive research has been conducted to analyze ran...
Media texts convey emotions and stances that can shape the evolution of public opinion, which calls for comparable quantitative sentiment analysis. However, most existing approaches assign a single sentiment score to an entire article, making it diff...
This study deploys a scalable machine learning pipeline: combining a transformer-based classifier applied to 2.01 million English-language AI-related news headlines (July 2022–July 2024) with large-language-model and human-annotator validation...
The evolution of technology facilitates efficient construction-site monitoring, hence improving the assessment of project performance and resource utilization. You Only Look Once (YOLO) algorithms are employed for real-time object detection; however,...
The analysis of medical data presents an opportunity for healthcare systems to support decision-making and improve patient outcomes. In this context, the automated analysis of user-generated drug reviews offers a promising approach for monitoring med...
When large language models (LLMs) are implemented in the field of health information quality evaluation, ensuring that their outputs align with human judgment, logic, and strategic preferences has become a focal point of current research on AI alignm...
Reported accuracies for banana ripeness recognition are high but rarely comparable, because public releases mix source images with augmented exports and results are usually given as point estimates. This study proposes a reproducible, task-separated...
(1) Background: Phishing remains a pervasive and economically damaging cyberthreat. The dominant detection paradigm has moved toward deep neural and transformer-based classifiers, a literature that reports high accuracy and that does not, in general,...
Accurate land-use polygon mapping for forest-oriented natural resource monitoring requires both image-based class prediction and the reconciliation of heterogeneous geospatial semantics. In operational mapping, land survey, forestry survey, and natur...
Background: Racial and socioeconomic differences in prostate cancer outcomes are often described as survival disparities, but observed differences may also reflect healthcare access, treatment delivery, and resource allocation. Methods: NCDB and SEER...
Object detection has evolved from classical feature-based approaches to deep learning-based frameworks, with the YOLO (You Only Look Once) family emerging as one of the most widely adopted paradigms for real-time object detection. Despite rapid archi...
Bitcoin bubbles exhibit locally explosive dynamics, but identifying past episodes does not establish their predictability. We combine the generalized supremum augmented Dickey–Fuller test with nine probabilistic learners and three probability-o...
Urban nighttime lightscapes shape the tourist experience, urban image, and commercial identity, yet existing evaluation methods trade off objective measurement, real-scene coverage, and the interpretation of subjective perception. This study examines...
Accurate exploitation of spatial structures and spectral characteristics is essential for fine-grained crop classification using remote sensing imagery. Although multi-source remote sensing data provide complementary information, most existing method...
Accurate recognition of successive Tieguanyin fermentation stages is essential for consistent processing control; however, adjacent stages often exhibit highly similar appearance characteristics, making fine-grained discrimination difficult. Although...
This paper examines how recognisable artistic authorship becomes durable cultural authority through the interaction of artistic production, public communication, discursive positioning, and institutional legitimation. Martin Scorsese offers an inform...
Knowledge graph construction typically involves data preprocessing, entity recognition, relation extraction, knowledge fusion, conflict resolution, and quality assessment. However, heterogeneous documents in substation operation and maintenance (O&am...
Urban flooding is shaped by urban morphology, but the inferred relationships can change with the spatial units used to represent flooding and urban form. Existing studies often report results for a selected spatial configuration, leaving unclear whet...
Accurate and scalable estimation of rock shear strength parameters is essential for remote-sensing-supported geological hazard assessment, slope stability evaluation, and engineering geological mapping. However, determining cohesion and internal fric...
The mapping and monitoring of urban green space (UGS) are of great significance for ecological assessment and sustainable development in urban settlements. However, for fine-grained classification tasks in high-resolution remote sensing imagery, exis...
Motor-current-based bearing diagnosis is attractive because current measurements are often already available, yet a binary faulty label can combine defects with different physical origins. This study benchmarks source-defined binary evaluation under...
The digital preservation of built cultural heritage requires precise documentation techniques capable of capturing complex architectural geometries often affected by occlusions and data voids. This study presents a robust multi-sensor fusion workflow...
Background: Cancer screening is central to cancer control and an increasingly important component of personalized medicine, enabling risk-adaptive prevention, earlier detection, and more tailored intervention strategies. However, screening participat...
Computer vision with artificial intelligence (AI) offers a promising tool for blueberry growers to accomplish orchard tasks such as harvest maturity assessment and yield estimation, which otherwise would be labor-intensive and prone to error. However...
The aim of this study is to extract impervious surfaces and show their spatial distribution, using different machine learning algorithms. For this purpose, geoprocessing and remote sensing techniques were used and three classification methods for dig...
Integrated care is increasingly shaped by digital infrastructures, data governance, and AI-enabled analytics, yet the relevant literature remains fragmented across health-services research, digital health, and machine learning. This article reports a...
Pathology foundation models have recently emerged as powerful pretrained representations for computational pathology, yet whether complex downstream modeling is still necessary once frozen representations are evaluated under a common downstream frame...
Aflatoxin contamination of maize grain threatens human food and animal feed safety. Breeding for reduced grain aflatoxin accumulation is one of the best strategies presently available to lower grain aflatoxin accumulation. Previously identified sourc...
The passive detection of low-altitude signal sources is studied using an improved cross-correlation method in the time–frequency domain. A matching template is designed for signal cross-correlation, and a cross-correlation threshold is used to...
In this paper a Cross Axis evaluation methodology is presented. The Cross Axis Sensitivity value depends on backend testing position tolerances as well as technological features and design properties of the MEMS gyroscope. A numerical model was devel...
The main aim is evaluation of illumination intensity for side emitting plastic optical fibres in dependence on the distance from light source. The special device for measurement of light intensity on surface and cross section at various distances fro...
Histopathology foundation models (FMs) have become widely used as patch-level feature extractors in computational pathology (CPath), where domain shift is a central challenge, yet their generalization ability across acquisition centers and scanning p...
Cross-pollination is essential for the reproduction of self-incompatible plants and it improves the yield and fruit quality of some self-compatible plants. Cross-pollination of clonal horticultural crops is achieved only when pollen from one cultivar...
Cross-scene hyperspectral band selection must reduce spectral redundancy while retaining channels that remain useful beyond the source scene. We propose Adaptive Band Gating (ABG), a source-only selector that combines frequency-domain decoupling enha...
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