[HTML][HTML] A graph convolutional autoencoder approach to model order reduction for parametrized PDEs
The present work proposes a framework for nonlinear model order reduction based on a
Graph Convolutional Autoencoder (GCA-ROM). In the reduced order modeling (ROM) …
Graph Convolutional Autoencoder (GCA-ROM). In the reduced order modeling (ROM) …
Advances in AI and machine learning for predictive medicine
The field of omics, driven by advances in high-throughput sequencing, faces a data
explosion. This abundance of data offers unprecedented opportunities for predictive …
explosion. This abundance of data offers unprecedented opportunities for predictive …
Learning spatial patterns and temporal dependencies for traffic accident severity prediction: A deep learning approach
Traffic accidents have a substantial impact on human life and property, resulting in millions
of injuries every year. To ensure road safety and enhance the research in this direction, it is …
of injuries every year. To ensure road safety and enhance the research in this direction, it is …
Visualizations for universal deep-feature representations: survey and taxonomy
In data science and content-based retrieval, we find many domain-specific techniques that
employ a data processing pipeline with two fundamental steps. First, data entities are …
employ a data processing pipeline with two fundamental steps. First, data entities are …
Predicting pedestrian-involved crash severity using inception-v3 deep learning model
This research leverages a novel deep learning model, Inception-v3, to predict pedestrian
crash severity using data collected over five years (2016–2021) from Louisiana. The final …
crash severity using data collected over five years (2016–2021) from Louisiana. The final …
ChurnNet: Deep Learning Enhanced Customer Churn Prediction in Telecommunication Industry
In the Telecommunication Industry (TCI) customer churn is a significant issue because the
revenue of the service provider is highly dependent on the retention of existing customers. In …
revenue of the service provider is highly dependent on the retention of existing customers. In …
Predicting high-resolution air quality using machine learning: Integration of large eddy simulation and urban morphology data
S Wang, J McGibbon, Y Zhang - Environmental Pollution, 2024 - Elsevier
Accurately predicting air pollutants, especially in urban areas with well-defined spatial
structures, is crucial. Over the past decade, machine learning techniques have been widely …
structures, is crucial. Over the past decade, machine learning techniques have been widely …
Evaluation of the impact of intensive PM2. 5 reduction policy in Seoul, South Korea using machine learning
The average PM2. 5 concentration in South Korea decreased steadily, but the monthly
average PM2. 5 concentration in January–March increased over time. The Seasonal Fine …
average PM2. 5 concentration in January–March increased over time. The Seasonal Fine …
Machine learning approaches for early detection of non-alcoholic steatohepatitis based on clinical and blood parameters
This study aims to develop a machine learning approach leveraging clinical data and blood
parameters to predict non-alcoholic steatohepatitis (NASH) based on the NAFLD Activity …
parameters to predict non-alcoholic steatohepatitis (NASH) based on the NAFLD Activity …
MetDIT: Transforming and Analyzing Clinical Metabolomics Data with Convolutional Neural Networks
Y Sha, W Meng, G Luo, X Zhai, HHY Tong… - Analytical …, 2024 - ACS Publications
Clinical metabolomics is growing as an essential tool for precision medicine. However,
classical machine learning algorithms struggle to comprehensively encode and analyze the …
classical machine learning algorithms struggle to comprehensively encode and analyze the …