AIMC Topic: Neural Networks, Computer

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Optimal Training Configurations of a CNN-LSTM-Based Tracker for a Fall Frame Detection System.

Sensors (Basel, Switzerland)
In recent years, there has been an immense amount of research into fall event detection. Generally, a fall event is defined as a situation in which a person unintentionally drops down onto a lower surface. It is crucial to detect the occurrence of fa...

Recovery of Ionospheric Signals Using Fully Convolutional DenseNet and Its Challenges.

Sensors (Basel, Switzerland)
The technique of active ionospheric sounding by ionosondes requires sophisticated methods for the recovery of experimental data on ionograms. In this work, we applied an advanced algorithm of deep learning for the identification and classification of...

Hyperspectral Image Classification Using Deep Genome Graph-Based Approach.

Sensors (Basel, Switzerland)
Recently developed hybrid models that stack 3D with 2D CNN in their structure have enjoyed high popularity due to their appealing performance in hyperspectral image classification tasks. On the other hand, biological genome graphs have demonstrated t...

Dynamic memory to alleviate catastrophic forgetting in continual learning with medical imaging.

Nature communications
Medical imaging is a central part of clinical diagnosis and treatment guidance. Machine learning has increasingly gained relevance because it captures features of disease and treatment response that are relevant for therapeutic decision-making. In cl...

A Novel Medical Image Denoising Method Based on Conditional Generative Adversarial Network.

Computational and mathematical methods in medicine
Medical image quality is highly relative to clinical diagnosis and treatment, leading to a popular research topic of medical image denoising. Image denoising based on deep learning methods has attracted considerable attention owing to its excellent a...

An Economic Forecasting Method Based on the LightGBM-Optimized LSTM and Time-Series Model.

Computational intelligence and neuroscience
Stock price prediction is very important in financial decision-making, and it is also the most difficult part of economic forecasting. The factors affecting stock prices are complex and changeable, and stock price fluctuations have a certain degree o...

Compensated Fuzzy Neural Network-Based Music Teaching Ability Assessment Model.

Computational intelligence and neuroscience
College is the main place to carry out music teaching, and it is important to assess the music teaching ability in college effectively. Based on this, this paper firstly analyzes the necessity of music teaching ability assessment and briefly summariz...

Video Abnormal Event Detection Based on One-Class Neural Network.

Computational intelligence and neuroscience
Video abnormal event detection is a challenging problem in pattern recognition field. Existing methods usually design the two steps of video feature extraction and anomaly detection model establishment independently, which leads to the failure to ach...

Highly parallelized memristive binary neural network.

Neural networks : the official journal of the International Neural Network Society
At present, in the new hardware design work of deep learning, memristor as a non-volatile memory with computing power has become a research hotspot. The weights in the deep neural network are the floating-point number. Writing a floating-point value ...

Paroxysmal atrial fibrillation prediction based on morphological variant P-wave analysis with wideband ECG and deep learning.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVE: Atrial fibrillation (AF) is one of the most frequent asymptomatic arrhythmias associated with significant morbidity and mortality. Identifying the susceptibility to AF based on routine or continuous ECG recording is of consi...