AIMC Topic: Databases, Factual

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GRAND: GAN-based software runtime anomaly detection method using trace information.

Neural networks : the official journal of the International Neural Network Society
Software runtime anomaly detection can detect manifestations (known as anomalies) caused by faults in complex systems before they lead to failure. Whereas most existing methods use external performance indicators, this study uses internal execution t...

Europe PMC annotated full-text corpus for gene/proteins, diseases and organisms.

Scientific data
Named entity recognition (NER) is a widely used text-mining and natural language processing (NLP) subtask. In recent years, deep learning methods have superseded traditional dictionary- and rule-based NER approaches. A high-quality dataset is essenti...

Unraveling the complexities of pathological voice through saliency analysis.

Computers in biology and medicine
The human voice is an essential communication tool, but various disorders and habits can disrupt it. Diagnosis of pathological and abnormal voices is very important. Conventional diagnosis of these voice pathologies can be invasive and costly. Voice ...

An interpretable ensemble learning model facilitates early risk stratification of ischemic stroke in intensive care unit: Development and external validation of ICU-ISPM.

Computers in biology and medicine
Ischemic stroke (IS) is a common and severe condition that requires intensive care unit (ICU) admission, with high mortality and variable prognosis. Accurate and reliable predictive tools that enable early risk stratification can facilitate intervent...

Recent Studies of Artificial Intelligence on Drug Absorption.

Journal of chemical information and modeling
Absorption is an important area of research in pharmacochemistry and drug development, because the drug has to be absorbed before any drug effects can occur. Furthermore, the ADMET (Absorption, Distribution, Metabolism, Excretion, and Toxicity) profi...

Zero time waste in pre-trained early exit neural networks.

Neural networks : the official journal of the International Neural Network Society
The problem of reducing processing time of large deep learning models is a fundamental challenge in many real-world applications. Early exit methods strive towards this goal by attaching additional Internal Classifiers (ICs) to intermediate layers of...

Insight Extraction From E-Health Bookings by Means of Hypergraph and Machine Learning.

IEEE journal of biomedical and health informatics
New technologies are transforming medicine, and this revolution starts with data. Usually, health services within public healthcare systems are accessed through a booking centre managed by local health authorities and controlled by the regional gover...

Enhancing the performance of premature ventricular contraction detection in unseen datasets through deep learning with denoise and contrast attention module.

Computers in biology and medicine
Premature ventricular contraction (PVC) is a common and harmless cardiac arrhythmia that can be asymptomatic or cause palpitations and chest pain in rare instances. However, frequent PVCs can lead to more serious arrhythmias, such as atrial fibrillat...

XA4C: eXplainable representation learning via Autoencoders revealing Critical genes.

PLoS computational biology
Machine Learning models have been frequently used in transcriptome analyses. Particularly, Representation Learning (RL), e.g., autoencoders, are effective in learning critical representations in noisy data. However, learned representations, e.g., the...

A multi-stage transfer learning strategy for diagnosing a class of rare laryngeal movement disorders.

Computers in biology and medicine
BACKGROUND: It remains hard to directly apply deep learning-based methods to assist diagnosing essential tremor of voice (ETV) and abductor and adductor spasmodic dysphonia (ABSD and ADSD). One of the main challenges is that, as a class of rare laryn...