AIMC Topic: Neural Networks, Computer

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HyperPCM: Robust Task-Conditioned Modeling of Drug-Target Interactions.

Journal of chemical information and modeling
A central problem in drug discovery is to identify the interactions between drug-like compounds and protein targets. Over the past few decades, various quantitative structure-activity relationship (QSAR) and proteo-chemometric (PCM) approaches have b...

EMAT: Efficient feature fusion network for visual tracking via optimized multi-head attention.

Neural networks : the official journal of the International Neural Network Society
The tracking methods based on Transformer have shown great potential in visual tracking and achieved significant tracking performance. The traditional transformer based feature fusion network divides a whole feature map into multiple image patches as...

Contrastive learning of graphs under label noise.

Neural networks : the official journal of the International Neural Network Society
In the domain of graph-structured data learning, semi-supervised node classification serves as a critical task, relying mainly on the information from unlabeled nodes and a minor fraction of labeled nodes for training. However, real-world graph-struc...

Reinforcement learning-based consensus control for MASs with intermittent constraints.

Neural networks : the official journal of the International Neural Network Society
In this article, an adaptive optimal consensus control problem is studied for multiagent systems in the strict-feedback structure with intermittent constraints (the constraints appear intermittently). More specifically, by designing a novel switch-li...

A convolutional neural network-based method for the generation of super-resolution 3D models from clinical CT images.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVE: The accurate evaluation of bone mechanical properties is essential for predicting fracture risk based on clinical computed tomography (CT) images. However, blurring and noise in clinical CT images can compromise the accuracy...

Using AI/ML to predict blending performance and process sensitivity for Continuous Direct Compression (CDC).

International journal of pharmaceutics
Utilising three artificial intelligence (AI)/machine learning (ML) tools, this study explores the prediction of fill level in inclined linear blenders at steady state by mapping a wide range of bulk powder characteristics to processing parameters. Pr...

Automated artificial intelligence-based phase-recognition system for esophageal endoscopic submucosal dissection (with video).

Gastrointestinal endoscopy
BACKGROUND AND AIMS: Endoscopic submucosal dissection (ESD) for superficial esophageal cancer is a multistep treatment involving several endoscopic processes. Although analyzing each phase separately is worthwhile, it is not realistic in practice owi...

psoResNet: An improved PSO-based residual network search algorithm.

Neural networks : the official journal of the International Neural Network Society
Neural Architecture Search (NAS) methods are widely employed to address the time-consuming and costly challenges associated with manual operation and design of deep convolutional neural networks (DCNNs). Nonetheless, prevailing methods still encounte...

Learning from crowds for automated histopathological image segmentation.

Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society
Automated semantic segmentation of histopathological images is an essential task in Computational Pathology (CPATH). The main limitation of Deep Learning (DL) to address this task is the scarcity of expert annotations. Crowdsourcing (CR) has emerged ...