AIMC Topic: Neural Networks, Computer

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The Customs Clearance Efficiency of Guangdong-Hong Kong Land Transportation Based on BP Neural Network Algorithm.

Computational intelligence and neuroscience
With the continuous development of the economy, the trade volume of customs clearance by land transportation between Guangdong and Hong Kong is increasing, but there are many problems in customs clearance by land transportation between Guangdong and ...

Optimization of Choreography Teaching with Deep Learning and Neural Networks.

Computational intelligence and neuroscience
To improve the development level of intelligent dance education and choreography network technology, the research mainly focuses on the automatic formation system of continuous choreography by using the deep learning method. Firstly, it overcomes the...

Explanatory Optimization of the Prediction Model for Building Energy Consumption.

Computational intelligence and neuroscience
Traditional prediction models, which are based on artificial neural networks (ANNs), consider the various factors affecting building energy consumption comprehensively. However, their explanatory power is not ideal in actual application, resulting in...

A Dynamic Community Detection Method for Complex Networks Based on Deep Self-Coding Network.

Computational intelligence and neuroscience
Aiming at the problem of community detection in complex dynamic networks, a dynamic community detection method based on graph convolution neural network is proposed. An encoding-decoding mechanism is designed to reconstruct the feature information of...

Analysis of the Relevance Environment between Marxist Philosophy and System Theory Based on Deep Learning.

Journal of environmental and public health
In social science and natural science, MP (Marxist Philosophy) has played an active role in promoting its development, and MP also guides people's practice and understanding. There is an inevitable connection with system theory MP. In a sense, both s...

A New Approach to Quantify and Grade Radiation Dermatitis Using Deep-Learning Segmentation in Skin Photographs.

Clinical oncology (Royal College of Radiologists (Great Britain))
AIMS: Objective evaluation of radiation dermatitis is important for analysing the correlation between the severity of radiation dermatitis and dose distribution in clinical practice and for reliable reporting in clinical trials. We developed a novel ...

Deep multi-task learning and random forest for series classification by pulse sequence type and orientation.

Neuroradiology
PURPOSE: Increasingly complex MRI studies and variable series naming conventions reveal limitations of rule-based image routing, especially in health systems with multiple scanners and sites. Accurate methods to identify series based on image content...

Transformer-based unsupervised contrastive learning for histopathological image classification.

Medical image analysis
A large-scale and well-annotated dataset is a key factor for the success of deep learning in medical image analysis. However, assembling such large annotations is very challenging, especially for histopathological images with unique characteristics (...

Deep learning in ultrasound elastography imaging: A review.

Medical physics
It is known that changes in the mechanical properties of tissues are associated with the onset and progression of certain diseases. Ultrasound elastography is a technique to characterize tissue stiffness using ultrasound imaging either by measuring t...

Inferior and Coordinate Distillation for Object Detectors.

Sensors (Basel, Switzerland)
Current distillation methods only distill between corresponding layers, and do not consider the knowledge contained in preceding layers. To solve this problem, we analyzed the guiding effect of the inferior features of a teacher model on the coordina...