AIMC Topic: Neural Networks, Computer

Clear Filters Showing 14151 to 14160 of 31376 articles

A Small Network MicronNet-BF of Traffic Sign Classification.

Computational intelligence and neuroscience
One of a very significant computer vision task in many real-world applications is traffic sign recognition. With the development of deep neural networks, state-of-art performance traffic sign recognition has been provided in recent five years. Gettin...

Data-Driven Haptic Texture Modeling and Rendering Based on Deep Spatio-Temporal Networks.

IEEE transactions on haptics
Data-driven approaches are commonly used to model and render haptic textures for rigid stylus-based interaction. Current state-of-the-art data-driven methodologies synthesize acceleration signals through the interpolation of samples with different in...

Deep Learning Forecasts the Occurrence of Sleep Apnea from Single-Lead ECG.

Cardiovascular engineering and technology
OBJECTIVES: Sleep apnea is the most common sleep disorder that leads to serious health complications if not treated early. Forecasting apnea occurrence ahead in time provides the opportunity to take appropriate actions to control and manage it.

Improving generalization of deep neural networks by leveraging margin distribution.

Neural networks : the official journal of the International Neural Network Society
Recent research has used margin theory to analyze the generalization performance for deep neural networks (DNNs). The existed results are almost based on the spectrally-normalized minimum margin. However, optimizing the minimum margin ignores a mass ...

Testing the Ability of Convolutional Neural Networks to Learn Radiomic Features.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVE: Radiomics and deep learning have emerged as two distinct approaches to medical image analysis. However, their relative expressive power remains largely unknown. Theoretically, hand-crafted radiomic features represent a mere ...

Neural Network-Based Decoding Input Stimulus Data Based on Recurrent Neural Network Neural Activity Pattern.

Doklady biological sciences : proceedings of the Academy of Sciences of the USSR, Biological sciences sections
The paper reports the assessment of the possibility to recover information obtained using an artificial neural network via inspecting neural activity patterns. A simple recurrent neural network forms dynamic excitation patterns for storing data on in...

Predicting chemical hazard across taxa through machine learning.

Environment international
We applied machine learning methods to predict chemical hazards focusing on fish acute toxicity across taxa. We analyzed the relevance of taxonomy and experimental setup, showing that taking them into account can lead to considerable improvements in ...

Solving Inverse Electrocardiographic Mapping Using Machine Learning and Deep Learning Frameworks.

Sensors (Basel, Switzerland)
Electrocardiographic imaging (ECGi) reconstructs electrograms at the heart's surface using the potentials recorded at the body's surface. This is called the inverse problem of electrocardiography. This study aimed to improve on the current solution m...

All-fiber high-speed image detection enabled by deep learning.

Nature communications
Ultra-high-speed imaging serves as a foundation for modern science. While in biomedicine, optical-fiber-based endoscopy is often required for in vivo applications, the combination of high speed with the fiber endoscopy, which is vital for exploring t...

Real-Time Modulation of Physical Training Intensity Based on Wavelet Recursive Fuzzy Neural Networks.

Computational intelligence and neuroscience
In this study, a wavelet recurrent fuzzy neural network is used to conduct in-depth research and analysis on the real-time regulation of physical training intensity. Firstly, an inter-process control technique is proposed to solve the problem of inco...