AIMC Topic: Neural Networks, Computer

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Two-Level Model for Detecting Substation Defects from Infrared Images.

Sensors (Basel, Switzerland)
Training a deep convolutional neural network (DCNN) to detect defects in substation equipment often requires many defect datasets. However, this dataset is not easily acquired, and the complex background of the infrared images makes defect detection ...

Single-Shot Object Detection via Feature Enhancement and Channel Attention.

Sensors (Basel, Switzerland)
Features play a critical role in computer vision tasks. Deep learning methods have resulted in significant breakthroughs in the field of object detection, but it is still an extremely challenging obstacle when an object is very small. In this work, w...

Cooperative Downloading for LEO Satellite Networks: A DRL-Based Approach.

Sensors (Basel, Switzerland)
In low earth orbit (LEO) satellite-based applications (e.g., remote sensing and surveillance), it is important to efficiently transmit collected data to ground stations (GS). However, LEO satellites' high mobility and resultant insufficient time for ...

Construction of a Basic Japanese Teaching Resource Base Based on a Deep Neural Network under a Big Data Environment.

Journal of environmental and public health
A challenge for education and teaching in universities is posed by "Internet plus," which has made numerous educational resources at universities richer and more accessible. The development of a professional Japanese teaching resource base should be ...

Medical Data Classification Assisted by Machine Learning Strategy.

Computational and mathematical methods in medicine
With the development of science and technology, data plays an increasingly important role in our daily life. Therefore, much attention has been paid to the field of data mining. Data classification is the premise of data mining, and how well the data...

Region Convolutional Neural Network for Brain Tumor Segmentation.

Computational intelligence and neuroscience
Gliomas are often difficult to find and distinguish using typical manual segmentation approaches because of their vast range of changes in size, shape, and appearance. Furthermore, the manual annotation of cancer tissue segmentation under the close s...

Network Architecture for Intelligent Identification of Faults in Rabbit Farm Environment Monitoring Based on a Biological Neural Network Model.

Computational intelligence and neuroscience
Currently, livestock and poultry farming is gradually developing towards modernization and scale, and closed livestock and poultry farms are widely used for poultry feeding management, but at the same time, the farming risks of large-scale farms are ...

Dementia in Convolutional Neural Networks: Using Deep Learning Models to Simulate Neurodegeneration of the Visual System.

Neuroinformatics
Although current research aims to improve deep learning networks by applying knowledge about the healthy human brain and vice versa, the potential of using such networks to model and study neurodegenerative diseases remains largely unexplored. In thi...

A Deep Sequence Learning Framework for Action Recognition in Small-Scale Depth Video Dataset.

Sensors (Basel, Switzerland)
Depth video sequence-based deep models for recognizing human actions are scarce compared to RGB and skeleton video sequences-based models. This scarcity limits the research advancements based on depth data, as training deep models with small-scale da...

Acne Detection by Ensemble Neural Networks.

Sensors (Basel, Switzerland)
Acne detection, utilizing prior knowledge to diagnose acne severity, number or position through facial images, plays a very important role in medical diagnoses and treatment for patients with skin problems. Recently, deep learning algorithms were int...