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Recently, a three-terminal interface dipole modulation field-effect transistor (IDM FET) memory device has been proposed that leverages electric-field-induced dipole modulation at oxide/oxide interfaces. This device has ...

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...

The identification of exoplanets within habitable zones remains a central objective in modern astrophysics, particularly with the availability of large-scale photometric datasets from space-based missions such as the Tra...

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...

Abstract Unsupervised visible-infrared person re-identification (USL-VI-ReID) aims to match person images across visible and infrared modalities without any labeled data, which is severely hindered by the large cross-mod...

Inborn errors of immunity (IEI) are increasing in number, and prompt recognition and diagnosis remain a challenge for clinicians. Use of machine learning (ML) for clinical pattern recognition is promising but has been li...

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...

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 Intertidal macrobenthos are vital bioindicators of coastal ecosystem health due to their ecological roles, limited mobility, and sensitivity to environmental disturbances. However, traditional field‐based moni...

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 Air pollution is a major environmental and public health challenge in Greater Cairo due to rapid urbanization, intense traffic activity, and recurrent regional dust intrusions. This study presents a data driven ...

Botnet communications often rely on Domain Generation Algorithms (DGAs) to establish channels among attack orchestrators and compromised hosts (bots). Our approach leverages on Artificial Intelligence (AI) to differentia...

Abstract Background Deep learning faces a significant bottleneck in medical image analysis due to its reliance on large-scale, expert-annotated datasets. This challenge is acute in ophthalmology, particularly for detecti...

Abstract Credit risk assessment is a critical function in financial analytics, requiring models that can adapt to diverse borrower profiles while providing clear and interpretable insights. Although a range of data drive...

Abstract This study evaluated the viability of organic farming in the Upper (UGR), Middle (MGR), and Lower Ganga Regions (LGR) by analysing soil health, nutrient availability, and heavy metal contamination. Using standar...

Abstract BackgroundCardiovascular magnetic resonance (CMR) is widely used across various cardiac conditions and systematically assesses cardiac anatomical structures and functional dynamics. Machine learning ...

Unsupervised anomaly detection identifying both structural and logical anomalies is essential for modern industrial manufacturing. Although multi-score frameworks have been proposed, existing methods tend to reuse identi...

Abstract Genome-wide CRISPR-Cas9 knockout screens are often used to experimentally evaluate gene function. However, the efficacy of individual sgRNAs targeting unique genes varies and is difficult to integrate. In this s...

An accurate intraoperative assessment of tissue perfusion in digestive surgery is critical to prevent complications such as anastomotic leakage, an event in which a surgical connection between organs fails to heal proper...

Abstract This paper proposes a novel hybrid ensemble machine learning framework integrated with Explainable AI (XAI) to overcome the fundamental trade-off between non-detection zones (NDZ) and false positives in conventi...

Abstract Black rhinoceros are critically endangered due to poaching in the wild (in situ). Globally, fewer than 200 animals are maintained as an ex situ insurance population. Unfortunately, the ex situ population faces m...

HoNiSi3 is an intermetallic compound characterized by two successive antiferromagnetic transitions at TN1 = 6.3 K and TN2 = 10.4 K. Here, its zero-field microscopic magnetic structure is inferred from resonant x-ray magn...

Biotic and abiotic stresses reduce crop yields, yet existing image‑based diagnostic models are often crop‑specific, computationally intensive, and poorly interpretable. This study present: (i) a multi-crop diagnostic...

With the consolidation of deep learning in drug discovery, several novel algorithms for learning molecular representations have been proposed. Despite the interest of the community in developing new methods for learning ...