AIMC Topic: Neural Networks, Computer

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Generalizable AI approach for detecting projection type and left-right reversal in chest X-rays.

Radiological physics and technology
The verification of chest X-ray images involves several checkpoints, including orientation and reversal. To address the challenges of manual verification, this study developed an artificial intelligence (AI)-based system using a deep convolutional ne...

Intelligent chlorophyll estimation by attention-integrated deep learning and dual-modal fusion in tencha drying using snapshot multispectral camera.

Journal of the science of food and agriculture
BACKGROUND: Chlorophyll content during the drying process of tencha, as the precursor of matcha before grinding, is bound up with sensory evaluation of the final product. This study employed a snapshot multispectral technology in conjunction with che...

Dual-structure community preserving network embedding.

Neural networks : the official journal of the International Neural Network Society
Network embedding, an effective method for learning low-dimensional representations of nodes, plays a crucial role in various network learning scenarios. However, existing network embedding learning methods fail to learn node embeddings from the pers...

Generative deep learning model assisted multi-objective optimization for wastewater nitrogen to protein conversion by photosynthetic bacteria.

Bioresource technology
For decades, the photosynthetic bacteria (PSB)-based nitrogen treatment and valorization from wastewater have been explored. However, balancing nitrogen removal performance and resource recovery potential in PSB has remained a key unresolved issue fo...

S2LIC: Learned image compression with the SwinV2 block, Adaptive Channel-wise and Global-inter attention Context.

Neural networks : the official journal of the International Neural Network Society
Recently, deep learning technology has been successfully applied in the field of image compression, leading to superior rate-distortion performance. It is crucial to design an effective and efficient entropy model to estimate the probability distribu...

Modeling structured data learning with Restricted Boltzmann machines in the teacher-student setting.

Neural networks : the official journal of the International Neural Network Society
Restricted Boltzmann machines (RBM) are generative models capable to learn data with a rich underlying structure. We study the teacher-student setting where a student RBM learns structured data generated by a teacher RBM. The amount of structure in t...

Decomposition method-based global Mittag-Leffler synchronization for fractional-order Clifford-valued neural networks with transmission delays and impulses.

Neural networks : the official journal of the International Neural Network Society
This study examines the global Mittag-Leffler synchronization (GMLS) problem for fractional-order Clifford-valued neural networks (FOCLVNNs) including transmission delays and impulses. Firstly, a novel kind of FOCLVNNs is developed that incorporates ...

Rethinking cell-based neural architecture search: A theoretical perspective.

Neural networks : the official journal of the International Neural Network Society
In this paper, we explore several fundamental theoretical issues in cell-based neural architecture search, including whether different architectures in search space are equally important in terms of the minimal training loss they can achieve, and whe...

Memory Transmission Based Referring Video Object Segmentation.

Neural networks : the official journal of the International Neural Network Society
Referring Video Object Segmentation (RVOS) addresses the task of segmenting target objects described by textual descriptions from videos. In order to ensure the consistency of objects segmented from video frames, inter-frame modeling is adopted to ca...

Dual-stream interactive networks with pearson-mask awareness for multivariate time series forecasting.

Neural networks : the official journal of the International Neural Network Society
Multivariate time series forecasting (MTSF) aims to predict time series data containing multiple variates, which requires the consideration of both intra-series temporal trends and inter-series interactions. Benefiting from the success of Transformer...