AIMC Topic: Reproducibility of Results

Clear Filters Showing 2011 to 2020 of 5908 articles

Using source data to aid and build variational state-space autoencoders with sparse target data for process monitoring.

Neural networks : the official journal of the International Neural Network Society
In industrial processes, different operating conditions and ratios of ingredients are used to produce multi-grade products in the same production line. Yet, the production grade changes so quickly as the demand from customers varies from time to time...

Explainable deep drug-target representations for binding affinity prediction.

BMC bioinformatics
BACKGROUND: Several computational advances have been achieved in the drug discovery field, promoting the identification of novel drug-target interactions and new leads. However, most of these methodologies have been overlooking the importance of prov...

Trust in Shared-Space Collaborative Robots: Shedding Light on the Human Brain.

Human factors
BACKGROUND: Industry 4.0 is currently underway allowing for improved manufacturing processes that leverage the collective advantages of human and robot agents. Consideration of trust can improve the quality and safety in such shared-space human-robot...

Cephalometric Analysis in Orthodontics Using Artificial Intelligence-A Comprehensive Review.

BioMed research international
Artificial intelligence (AI) is a branch of science concerned with developing programs and computers that can gather data, reason about it, and then translate it into intelligent actions. AI is a broad area that includes reasoning, typical linguistic...

Motion correction for native myocardial T mapping using self-supervised deep learning registration with contrast separation.

NMR in biomedicine
In myocardial T mapping, undesirable motion poses significant challenges because uncorrected motion can affect T estimation accuracy and cause incorrect diagnosis. In this study, we propose and evaluate a motion correction method for myocardial T map...

Improving the robustness and accuracy of biomedical language models through adversarial training.

Journal of biomedical informatics
Deep transformer neural network models have improved the predictive accuracy of intelligent text processing systems in the biomedical domain. They have obtained state-of-the-art performance scores on a wide variety of biomedical and clinical Natural ...

Fully Automatic Knee Joint Segmentation and Quantitative Analysis for Osteoarthritis from Magnetic Resonance (MR) Images Using a Deep Learning Model.

Medical science monitor : international medical journal of experimental and clinical research
BACKGROUND We aimed to develop and evaluate a deep learning-based method for fully automatic segmentation of knee joint MR imaging and quantitative computation of knee osteoarthritis (OA)-related imaging biomarkers. MATERIAL AND METHODS This retrospe...

A Novel Deep Learning-Based Cooperative Communication Channel Model for Wireless Underground Sensor Networks.

Sensors (Basel, Switzerland)
Wireless Underground Sensor Networks (WUSNs) have been showing prospective supervising application domains in the underground region of the earth through sensing, computation, and communication. This paper presents a novel Deep Learning (DL)-based Co...

Polishing copy number variant calls on exome sequencing data via deep learning.

Genome research
Accurate and efficient detection of copy number variants (CNVs) is of critical importance owing to their significant association with complex genetic diseases. Although algorithms that use whole-genome sequencing (WGS) data provide stable results wit...

The Use of Big Data Combined with Artificial Intelligence Neural Network Technology in Urban Spatial Evaluation System.

Computational intelligence and neuroscience
This exploration aims to promote the development of urbanization in China and improve the utilization rate of urban resources. First, intensive theory and spatial economics are studied. Next, an input-output urban spatial evaluation system is establi...