AIMC Topic: Neural Networks, Computer

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In vivo magnetic resonance P-Spectral Analysis With Neural Networks: 31P-SPAWNN.

Magnetic resonance in medicine
PURPOSE: We have introduced an artificial intelligence framework, 31P-SPAWNN, in order to fully analyze phosphorus-31 ( P) magnetic resonance spectra. The flexibility and speed of the technique rival traditional least-square fitting methods, with th...

Improved segmentation of collagen second harmonic generation images with a deep learning convolutional neural network.

Journal of biophotonics
Collagen fibers play an important role in both the structure and function of various tissues in the human body. Visualization and quantitative measurements of collagen fibers are possible through imaging modalities such as second harmonic generation ...

Shift Pose: A Lightweight Transformer-like Neural Network for Human Pose Estimation.

Sensors (Basel, Switzerland)
High-performing, real-time pose detection and tracking in real-time will enable computers to develop a finer-grained and more natural understanding of human behavior. However, the implementation of real-time human pose estimation remains a challenge....

Detecting upper extremity native joint dislocations using deep learning: A multicenter study.

Clinical imaging
OBJECTIVE: Joint dislocations are orthopedic emergencies that require prompt intervention. Automatic identification of these injuries could help improve timely patient care because diagnostic delays increase the difficulty of reduction. In this study...

Application of Deep Convolutional Neural Network for Automatic Detection of Digital Optical Fiber Repeater.

Sensors (Basel, Switzerland)
The digital optical fiber repeater (DOFR) is an important infrastructure in the LTE networks, which solve the problem of poor regional signal quality. Various types of conventional measurement data from the LTE network cannot indicate whether a worki...

Optimal Mapping of Spiking Neural Network to Neuromorphic Hardware for Edge-AI.

Sensors (Basel, Switzerland)
Neuromorphic hardware, the new generation of non-von Neumann computing system, implements spiking neurons and synapses to spiking neural network (SNN)-based applications. The energy-efficient property makes the neuromorphic hardware suitable for powe...

Synthetic neuromorphic computing in living cells.

Nature communications
Computational properties of neuronal networks have been applied to computing systems using simplified models comprising repeated connected nodes, e.g., perceptrons, with decision-making capabilities and flexible weighted links. Analogously to their r...

Logistics Finance Collaborative Development Model Based on Machine Learning.

Computational intelligence and neuroscience
In the context of rapid social development, a logistics financial model that can meet the financing needs of small and medium-sized enterprises and has high returns is widely used in all aspects of the logistics financial industry. Logistics finance ...

Progressive Rain Removal Based on the Combination Network of CNN and Transformer.

Computational intelligence and neuroscience
The rain removal method based on CNN develops rapidly. However, convolution operation has the disadvantages of limited receptive field and inadaptability to the input content. Recently, another neural network structure Transformer has shown excellent...

Assessing spatial connectivity effects on daily streamflow forecasting using Bayesian-based graph neural network.

The Science of the total environment
Data-driven models have been widely developed and achieved impressive results in streamflow prediction. However, the existing data-driven models mostly focus on the selection of input features and the adjustment of model structure, and less on the im...