Neural networks : the official journal of the International Neural Network Society
Aug 31, 2024
Most existing log-driven anomaly detection methods assume that logs are static and unchanged, which is often impractical. To address this, we propose a log anomaly detection model called DualAttlog. This model includes word-level and sequence-level s...
Neural networks : the official journal of the International Neural Network Society
Aug 31, 2024
Humans have the ability to constantly learn new knowledge. However, for artificial intelligence, trying to continuously learn new knowledge usually results in catastrophic forgetting, the existing regularization-based and dynamic structure-based appr...
Neural networks : the official journal of the International Neural Network Society
Aug 31, 2024
Compared to pixel-level content loss, domain-level style loss in CycleGAN-based dehazing algorithms just imposes relatively soft constraints on the intermediate translated images, resulting in struggling to accurately model haze-free features from re...
Neural networks : the official journal of the International Neural Network Society
Aug 31, 2024
Selecting a set of initial users from a social network in order to maximize the envisaged number of influenced users is known as influence maximization (IM). Researchers have achieved significant advancements in the theoretical design and performance...
BACKGROUND: We aimed to determine the best-performing machine learning (ML)-based algorithm for predicting gestational diabetes mellitus (GDM) with sociodemographic and obstetrics features in the pre-conceptional period.
The assessment of mutagenicity is essential in drug discovery, as it may lead to cancer and germ cells damage. Although in silico methods have been proposed for mutagenicity prediction, their performance is hindered by the scarcity of labeled molecul...
Machine learning provides efficient ways to map compound-kinase interactions. However, diverse bioactivity data types, including single-dose and multi-dose-response assay results, present challenges. Traditional models utilize only multi-dose data, o...
OBJECTIVE: To compare compressed sensing (CS) and the Cascades of Independently Recurrent Inference Machines (CIRIM) with respect to image quality and reconstruction times when 12-fold accelerated scans of patients with neurological deficits are reco...
The limited data poses a crucial challenge for deep learning-based volumetric medical image segmentation, and many methods have tried to represent the volume by its subvolumes (i.e., multi-view slices) for alleviating this issue. However, such method...
Exosomes, as next-generation biomarkers, has great potential in tracking cancer progression. They face many detection limitations in cancer diagnosis. Plasmonic biosensors have attracted considerable attention at the forefront of exosome detection, d...
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