AIMC Topic: Neural Networks, Computer

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MWTP: A heterogeneous multiplex representation learning framework for link prediction of weak ties.

Neural networks : the official journal of the International Neural Network Society
Weak ties that bridge different communities are crucial for preserving global connectivity, enhancing resilience, and maintaining functionality and dynamics of complex networks, However, making accurate link predictions for weak ties remain challengi...

Spectral integrated neural networks with large time steps for 2D and 3D transient elastodynamic analysis.

Neural networks : the official journal of the International Neural Network Society
This paper provides a neural network architecture, called spectral integrated neural networks (SINNs), designed to tackle two- and three-dimensional elastodynamic problems. In the SINNs, the second-order time derivatives of displacements are approxim...

Medical image translation with deep learning: Advances, datasets and perspectives.

Medical image analysis
Traditional medical image generation often lacks patient-specific clinical information, limiting its clinical utility despite enhancing downstream task performance. In contrast, medical image translation precisely converts images from one modality to...

Sweet pepper yield modeling via deep learning and selection of superior genotypes using GBLUP and MGIDI.

Scientific reports
Intelligent knowledge about Capsicum annuum L. germplasm could lead to effective management of germplasm. Here, 29 accessions of sweet pepper were investigated in two separate randomized complete block design with three replications in the field cond...

The use of a convolutional neural network to automate radiologic scoring of computed tomography of paranasal sinuses.

Biomedical engineering online
BACKGROUND: Chronic rhinosinusitis (CRS) is diagnosed with symptoms and objective endoscopy or computed tomography (CT). The Lund-Mackay score (LMS) is often used to determine the radiologic severity of CRS and make clinical decisions. This proof-of-...

Generalization and differentiation of affective associative memory circuit based on memristive neural network with emotion transfer.

Neural networks : the official journal of the International Neural Network Society
Most existing research on affective associative memory neural network circuits has predominantly concentrated on reinforcement and extinction, with insufficient attention given to the integration of emotion transfer alongside the principles of genera...

SSSLN:Multivariate Time Series Forecasting via Collaborative Dynamic Graph Learning.

Neural networks : the official journal of the International Neural Network Society
Multivariate time series (MTS) forecasting has achieved notable progress through graph modeling. However, existing approaches often face two key challenges. First, traditional dynamic graph learning (DGL) methods typically maintain dynamic graphs dir...

Integrating attention networks into a hybrid model for HER2 status prediction in breast cancer.

Biochemical and biophysical research communications
Breast cancer is one of the most prevalent cancers amongst women, caused by uncontrolled cell growth in breast tissue. Human Epidermal growth factor Receptor 2 (HER2) proteins play a vital role in regulating normal breast cell development and divisio...

Hybrid Neural network and machine learning models with improved optimization method for gut microbiome effects on the sleep quality in patients with endometriosis.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVE: Endometriosis is a chronic gynecological condition known to affect the quality of life of millions of women globally, often manifesting with symptoms that impact sleep quality. Emerging evidence suggests a crucial role of th...

Phytophagous, blood-suckers or predators? Automated identification of Chagas disease vectors and similar bugs using convolutional neural network algorithms.

Acta tropica
Correct identification of blood-sucking bugs, such as triatomines, is important because they are vectors of Chagas' disease. Identifying these insects is often difficult for non-specialists. Deep learning is emerging as a solution for automated ident...