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Abstract Segmenting tumor regions from the whole-slide images (WSIs) is crucial for quantitative characterizing of the tumor microenvironment and supporting clinical diagnosis. Compared with classification tasks, segment...

Abstract Physical activity and mobility are critical for healthy aging and predict diverse health outcomes. While wrist-worn accelerometers are widely used to monitor physical activity, estimating gait metrics from wrist...

Customer analytics in financial retail, payments, and bank marketing frequently relies on segmentation and propensity prediction, but transactional logs are sparse, high-dimensional, and only weakly labeled. This paper p...

Abstract Single-cell RNA sequencing (scRNA-seq) has revolutionized our understanding of cellular heterogeneity in complex biological systems. However, analyzing and integrating scRNA-seq data poses unique computational c...

Abstract Background Reliable identification of protein‑protein interactions (PPIs) is crucial for deciphering cellular functional networks. Current research models still face limitations in aligning heterogeneous featu...

Insider threats pose a significant and growing risk to organizational cybersecurity, with recent studies indicating a 47% increase in insider incidents from 2018 to 2022. This paper presents a comparative analysis of uns...

The low visual contrast between dermatological lesions and normal skin, coupled with the masking effect of surface artefacts, presents two major obstacles to precise automated skin lesion segmentation. In order to addres...

Carbon allowances constitute a foundational component of national carbon emission control frameworks, as they govern the equitable distribution of subsequent allocations and directly shape the overall effectiveness of gr...

Abstract Fractures are among the most common presentations to emergency and orthopedic services, yet radiograph interpretation remains inconsistent, particularly in resource-limited and off-hours settings. Weakly supervi...

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...

The departure time uncertainty exacerbates the inaccuracy of arrival time estimation and demand for arrival slots, particularly for movements to capacity constrained airports. The Estimated Take-Off Time (ETOT) or Estima...

Abstract Background Machine learning methods are widely used to detect behavioural data patterns. Although these new mathematical methods are useful tools, the interpretation of the results are often ambivalent unless ...

Hyperspectral single image super-resolution (HS-SISR) aims to enhance the spatial resolution of hyperspectral images to fully exploit their spectral information. While considerable progress has been made in this field, m...

ABSTRACT This research aimed to compare statistical methods (random forest, RIDGE, LASSO, and elastic net regression) for the prediction of body weight in purebred and crossbred pigs reared in Brazil. This prediction was...

With the rapid expansion of the digital music industry, streaming platforms are required to efficiently and accurately classify massive music collections to support personalized recommendation, content organization, and ...

Abstract Cell–cell communication regulates complex biological processes in multicellular systems. Existing scRNA-seq-based methods typically aggregate gene expression by clusters, overlooking within-cluster heterogenei...

The prediction and design of photonic features have traditionally been guided by theory-driven computational methods, spanning a wide range of direct solvers and optimization techniques. Motivated by enormous advances in...

Abstract Accurate prediction of cascading faults in power grids can ensure system stability and prevent large-scale outages. Existing methods typically require learning effective statistical information from datasets wit...

In modern industrial process monitoring of multivariate time series, the effectiveness of data-driven event detection is often constrained by scarce labels and the dynamic complexity of real-world telemetry. While unsupe...

Abstract Purpose This study aimed to identify the effectiveness of free water MRI (FW-MRI) features for predicting amyloid-beta (Aβ) statuses in Alzheimer’s disease (AD) by constructing diagnostic models using machine...

Abstract Background A solid understanding of anatomy is fundamental in dental education, and accurate interpretation of maxillofacial structures is essential across clinical contexts. Implantology is an example of a comp...

ObjectiveTo develop and internally validate an interpretable, non-invasive machine learning framework to predict detrusor underactivity (DU) in patients with benign prostatic hyperplasia (BPH).MethodsThis retrospective c...

Abstract Model instability remains a core challenge for data‐driven parameterizations, especially those developed with supervised algorithms, and rigorous methods to address it are lacking. Here, by integrating machine...