Integrating artificial intelligence into cancers of unknown primary diagnosis and treatment.

A review on chemometrics for detecting emerging pollutants in the environment and assessing their toxicological impact on humans and the environment.

Reproducible bicarbonate thresholds predict critically ill patient mortality in an international personalized survival study.

NeuraMFs: A deep learning model for airborne microfiber identification in plant biomonitors.

Comparative analysis of traditional and deep learning time series architectures for influenza A infectious disease forecasting.

A deep learning framework integrating SMOTE algorithm and GC e-nose for tracing the geographical origins of food: taking Astragali Radix as an example.

Toward protocol simplification: Deep learning-based image synthesis in three-phase CT urography.

Advances in multispectral and hyperspectral inversion for soil heavy metal contamination: Mechanisms, machine learning algorithms, and future perspectives.

Hybrid fractional groupers and moray eels driven deep learning for pneumonia detection using multi-modal data in federated learning.

Rapid multi-parametric quantitative MRI via deep learning-based synthetic-to-real reconstruction and 3D SSFP-MOLED imaging.

Electrochemical sensing device based on Cu/PPy heterostructure: Detection of nitrite at low reduction potentials and machine learning-driven data analysis visualisation.

CNNs vs. transformers: A benchmark for multi-class marine debris identification.

Model-guided design of lymphatic drug delivery systems using osmotic pressure, viscosity and lymph node size.

Automated microplastic quantification is currently compromised by morphological mimicry: air bubbles, organic biofilms, and sediment create high false-positive rates in standard Convolutional Neural Networks (CNNs).This study introduces AquaEye, a containerized computer vision framework that mitigates artifact misclassification by fusing Bayesian Deep Learning with ISO-standard morphometrics.Unlike deterministic U-Net implementations, we deploy a Monte Carlo Dropout inference pipeline to estimate epistemic uncertainty, enabling the suppression of predictions where model variance exceeds a safety threshold.To enforce physical validity, a post-processing geometric gate rejects candidates based on Circularity (4A/P 2 ) and Solidity, filtering non-polymer structures that bypass the neural filter.The system, deployed via a Dockerized microservices architecture, ensures reproducibil-ity often absent in -lab-bench scripts.Experimental validation confirms that AquaEye statistically decouples true microplastic instances from background noise, offering a robust alternative to manual microscopy for highthroughput environmental monitoring.

Deep chemometrics with convolutional neural networks for the detection of honey adulteration using Fourier transform infrared spectroscopy.

ABSTRACT Passive Acoustic Monitoring (PAM) offers a powerful approach for detecting and assessing the presence of invasive species, thereby supporting the conservation of native ecosystems. In this study, we developed a species-specific classification model using convolutional neural networks (CNNs) to analyse the nocturnal calling activity patterns of Pelophylax nigromaculatus and Dryophytes leopardus in rice paddies, a microhabitat where interactions between translocated and native species are of ecological concern. Despite environmental noise from bird calls and wind, the mel-spectrogram-based model classified anuran vocalisations with high accuracy (88.45%). Misclassifications at dawn were mitigated by limiting the ecological analysis to specific nocturnal periods. The results revealed clearly distinct peak times of nocturnal calling activity between the two species at the study site. These findings demonstrate the usefulness of deep learning for describing fine-scale activity patterns in field-recorded amphibian soundscapes. This study provides methodological insights into the acoustic monitoring of domestically translocated and native species and highlights the potential of bioacoustics approaches for ecological assessment in paddy environments. Future research should focus on refining species classification models and integrating sound-source separation for more accurate species-specific assessments of calling activity patterns.

Explainable 3D VGG-style convolutional neural network for pediatric hydrocephalus detection on computed tomography: A segmentation-free and fully volumetric deep learning framework.

Digital literacy and scientific research ability among nursing postgraduates: The chain-mediating role of deep learning and research self-efficacy.

Light-molecular vibration coupling on metasurfaces induces complex optical phenomena, such as Fano resonance and Rabi splitting, which complicate molecular identification via conventional spectroscopic analysis. In this study, we developed an analytical system that uses deep feature learning to directly extract molecule-specific absorption information from these complex spectra. For a molecular vibration model with a single peak, we evaluated a DenseNet-169 convolutional neural network (CNN) model using 1,496 spectra generated via finite-difference time-domain (FDTD) simulations; however, the results were suboptimal. In contrast, for a two-peak molecular vibration model, we trained multiple CNN models, including DenseNet-169, on a dataset of 80,267 spectra. Consequently, we successfully reconstructed absorption coefficients with an extremely high accuracy, achieving a mean coefficient of determination ([Formula: see text]) of 0.9209, even in complex systems with overlapping vibrational peaks. This approach demonstrates significant potential as a foundational technology for next-generation molecular sensing.

Cross-site scripting (XSS) poses a significant threat to web application security. While Deep Learning (DL) has shown remarkable success in detecting XSS attacks, it remains vulnerable to adversarial attacks due to the discontinuous nature of the mapping between the input (i.e., the attack) and the output (i.e., the prediction of the model whether an input is classified as XSS or benign). These adversarial attacks employ mutation-based strategies for different components of XSS attack vectors, allowing adversarial agents to iteratively select mutations to evade detection. Our work replicates a state-of-the-art XSS adversarial attack, highlighting threats to validity in the reference work and extending it towards a more effective evaluation strategy. Moreover, we introduce an XSS Oracle to mitigate these threats. The experimental results show that our approach achieves an escape rate above 96% when the threats to validity of the replicated technique are addressed.