AIMC Topic: Neural Networks, Computer

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Extraction of polysaccharides from Camellia oleifera leaves by dual enzymes combined with deep eutectic solvents screened by ANN and COSMO-RS.

International journal of biological macromolecules
Camellia oleifera leaves were byproduct of the C. oleifera industry which was rich in polysaccharides. Deep eutectic solvent-dual enzyme system (DES-dEAE) was established to achieve the simultaneous hydrolysis reaction of dual enzymes and DES extract...

Prior knowledge-based multi-task learning network for pulmonary nodule classification.

Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society
The morphological characteristics of pulmonary nodule, also known as the attributes, are crucial for classification of benign and malignant nodules. In clinical, radiologists usually conduct a comprehensive analysis of correlations between different ...

Graph and Multi-Level Sequence Fusion Learning for Predicting the Molecular Activity of BACE-1 Inhibitors.

International journal of molecular sciences
The development of BACE-1 (β-site amyloid precursor protein cleaving enzyme 1) inhibitors is a crucial focus in exploring early treatments for Alzheimer's disease (AD). Recently, graph neural networks (GNNs) have demonstrated significant advantages i...

RNA-protein interaction prediction using network-guided deep learning.

Communications biology
Accurate computational determination of RNA-protein interactions remains challenging, particularly when encountering unknown RNAs and proteins. The limited number of RNAs and their flexibility constrained the effectiveness of the deep-learning models...

Z-SSMNet: Zonal-aware Self-supervised Mesh Network for prostate cancer detection and diagnosis with Bi-parametric MRI.

Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society
Bi-parametric magnetic resonance imaging (bpMRI) has become a pivotal modality in the detection and diagnosis of clinically significant prostate cancer (csPCa). Developing AI-based systems to identify csPCa using bpMRI can transform prostate cancer (...

Neural transition system abstraction for neural network dynamical system models and its application to Computational Tree Logic verification.

Neural networks : the official journal of the International Neural Network Society
This paper proposes an explainable abstraction-based verification method that prioritizes user interaction and enhances interpretability. By partitioning the system's state space using a data-driven process, we can abstract the dynamics into words co...

Learning temporal regularized spatial-aware deep correlation filter tracking via adaptive channel selection.

Neural networks : the official journal of the International Neural Network Society
In recent years, deep correlation filters have demonstrated outstanding performance in robust object tracking. Nevertheless, the correlation filters encounter challenges in managing huge occlusion, target deviation, and background clutter due to the ...

Distributed multi-timescale algorithm for nonconvex optimization problem: A control perspective.

Neural networks : the official journal of the International Neural Network Society
The distributed nonconvex constrained optimization problem with equality and inequality constraints is researched in this paper, where the objective function and the function for constraints are all nonconvex. To solve this problem from a control per...

Neighbor-aware calibration of segmentation networks with penalty-based constraints.

Medical image analysis
Ensuring reliable confidence scores from deep neural networks is of paramount significance in critical decision-making systems, particularly in real-world domains such as healthcare. Recent literature on calibrating deep segmentation networks has res...

Sensory-biased autoencoder enables prediction of texture perception from food rheology.

Food research international (Ottawa, Ont.)
Understanding how the physical properties of food affect sensory perception remains a critical challenge for food design. Here, we present an innovative machine learning strategy to decode the complex relationships between non-Newtonian rheological a...