This study constructed a new aqueous solubility dataset and a solubility regression model which was ensembled by GCN and machine learning models. Aqueous solubility is a key physiochemical property of small molecules in drug discovery. In the past fe...
OBJECTIVE: The objective of this work is to present a novel technique using convolutional neural network (CNN) architectures for automatic segmentation of sella turcica (ST) on cephalometric radiographic image dataset. The proposed work suggests poss...
Medical decision making : an international journal of the Society for Medical Decision Making
Jun 23, 2022
PURPOSE: Metamodels are simplified approximations of more complex models that can be used as surrogates for the original models. Challenges in using metamodels for policy analysis arise when there are multiple correlated outputs of interest. We devel...
Neural networks : the official journal of the International Neural Network Society
Jun 23, 2022
This paper addresses portfolio selection based on neurodynamic optimization. The portfolio selection problem is formulated as a biconvex optimization problem with a variable weight in the Markowitz risk-return framework. In addition, the cardinality-...
In recent years, deep learning as a state-of-the-art machine learning technique has made great success in histopathological image classification. However, most of deep learning approaches rely heavily on the substantial task-specific annotations, whi...
Journal of chemical theory and computation
Jun 23, 2022
Virtual high-throughput screening (VHTS) and machine learning (ML) have greatly accelerated the design of single-site transition-metal catalysts. VHTS of catalysts, however, is often accompanied with a high calculation failure rate and wasted computa...
Journal of chemical information and modeling
Jun 23, 2022
Assessing whether compounds penetrate the brain can become critical in drug discovery, either to prevent adverse events or to reach the biological target. Generally, pre-clinical in vivo studies measuring the ratio of brain and blood concentrations (...
Effectively integrating the local features and their spatial distribution information for more effective point cloud analysis is a subject that has been explored for a long time. Inspired by convolutional neural networks (CNNs), this paper studies th...
This study proposes a simple convolutional neural network (CNN)-based model for vehicle classification in low resolution surveillance images collected by a standard security camera installed distant from a traffic scene. In order to evaluate its effe...
Accurate segmentation of drivable areas and road obstacles is critical for autonomous mobile robots to navigate safely in indoor and outdoor environments. With the fast advancement of deep learning, mobile robots may now perform autonomous navigation...
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