AIMC Topic: Neural Networks, Computer

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Unsupervised Hyperspectral Microscopic Image Segmentation Using Deep Embedded Clustering Algorithm.

Scanning
Hyperspectral microscopy in biology and minerals, unsupervised deep learning neural network denoising SRS photos: hyperspectral resolution enhancement and denoising one hyperspectral picture is enough to teach unsupervised method. An intuitive chemic...

Analysis of Traditional Cultural Acceptance Based on Deep Learning.

Computational intelligence and neuroscience
Technological development has resulted in the utilisation of advanced technology search using deep learning technology in all industries. Artificial intelligence is set to be filled with machines that can perform tasks that need human intelligence. T...

The Application of the Unsupervised Migration Method Based on Deep Learning Model in the Marketing Oriented Allocation of High Level Accounting Talents.

Computational intelligence and neuroscience
Deep learning is a branch of machine learning that uses neural networks to mimic the behaviour of the human brain. Various types of models are used in deep learning technology. This article will look at two important models and especially concentrate...

Research on Image Segmentation Algorithm Based on Multimodal Hierarchical Attention Mechanism and Genetic Neural Network.

Computational intelligence and neuroscience
Multimodal tasks based on attention mechanism and language face numerous problems. Based on multimodal hierarchical attention mechanism and genetic neural network, this paper studies the application of image segmentation algorithm in data completion ...

Design of experiments meets immersive environment: Optimising eating atmosphere using artificial neural network.

Appetite
Design of experiments (DOE) is a family of statistical tools commonly used in food science to optimise recipes and facilitate new food development. In a novel cross-disciplinary twist, we propose to adapt DOE approach to the optimisation of restauran...

A Learning-Rate Modulable and Reliable TiO Memristor Array for Robust, Fast, and Accurate Neuromorphic Computing.

Advanced science (Weinheim, Baden-Wurttemberg, Germany)
Realization of memristor-based neuromorphic hardware system is important to achieve energy efficient bigdata processing and artificial intelligence in integrated device system-level. In this sense, uniform and reliable titanium oxide (TiO ) memristor...

Graph Transformer Networks: Learning meta-path graphs to improve GNNs.

Neural networks : the official journal of the International Neural Network Society
Graph Neural Networks (GNNs) have been widely applied to various fields due to their powerful representations of graph-structured data. Despite the success of GNNs, most existing GNNs are designed to learn node representations on the fixed and homoge...

Machine learning-based protein crystal detection for monitoring of crystallization processes enabled with large-scale synthetic data sets of photorealistic images.

Analytical and bioanalytical chemistry
Since preparative chromatography is a sustainability challenge due to large amounts of consumables used in downstream processing of biomolecules, protein crystallization offers a promising alternative as a purification method. While the limited cryst...

Intracerebral hemorrhage detection on computed tomography images using a residual neural network.

Physica medica : PM : an international journal devoted to the applications of physics to medicine and biology : official journal of the Italian Association of Biomedical Physics (AIFB)
Intracerebral hemorrhage (ICH) is a high mortality rate, critical medical injury, produced by the rupture of a blood vessel of the vascular system inside the skull. ICH can lead to paralysis and even death. Therefore, it is considered a clinically da...

Automatic head computed tomography image noise quantification with deep learning.

Physica medica : PM : an international journal devoted to the applications of physics to medicine and biology : official journal of the Italian Association of Biomedical Physics (AIFB)
PURPOSE: Computed tomography (CT) image noise is usually determined by standard deviation (SD) of pixel values from uniform image regions. This study investigates how deep learning (DL) could be applied in head CT image noise estimation.