AIMC Topic: Neural Networks, Computer

Clear Filters Showing 11771 to 11780 of 31376 articles

Convolutional neural network models for automatic diagnosis and graduation in skin frostbite.

International wound journal
The study aimed to develop and validate a convolutional neural network (CNN)-based deep learning method for automatic diagnosis and graduation of skin frostbite. A dataset of 71 annotated images was used for the training, the validation, and the test...

Efficient contour-based annotation by iterative deep learning for organ segmentation from volumetric medical images.

International journal of computer assisted radiology and surgery
PURPOSE: Training deep neural networks usually require a large number of human-annotated data. For organ segmentation from volumetric medical images, human annotation is tedious and inefficient. To save human labour and to accelerate the training pro...

Multi-input multi-output temporal convolutional network for predicting the long-term water quality of ocean ranches.

Environmental science and pollution research international
The prediction of water quality parameters is of great significance to the control of marine environments and provides a scientific decision-making basis for maintaining the stability of water environments and ensuring the normal survival and growth ...

BAT-Net: An enhanced RNA Secondary Structure prediction via bidirectional GRU-based network with attention mechanism.

Computational biology and chemistry
BACKGROUND: RNA Secondary Structure (RSS) has drawn growing concern, both for their pivotal roles in RNA tertiary structures prediction and critical effect in penetrating the mechanism of functional non-coding RNA. Computational techniques that can r...

Air quality index forecast in Beijing based on CNN-LSTM multi-model.

Chemosphere
Accurate predicting the air quality trend can provide a theoretical basis for environmental protection management and decision-making. This study proposed the convolutional neural networks-long short-term memory (CNN-LSTM) model, which was proposed t...

Frame-rate up-conversion detection based on convolutional neural network for learning spatiotemporal features.

Forensic science international
With the advance in user-friendly and powerful video editing tools, anyone can easily manipulate videos without leaving prominent visual traces. Frame-rate up-conversion (FRUC), a representative temporal-domain operation, increases the motion continu...

Human activity recognition using tools of convolutional neural networks: A state of the art review, data sets, challenges, and future prospects.

Computers in biology and medicine
Human Activity Recognition (HAR) plays a significant role in the everyday life of people because of its ability to learn extensive high-level information about human activity from wearable or stationary devices. A substantial amount of research has b...

GCHN-DTI: Predicting drug-target interactions by graph convolution on heterogeneous networks.

Methods (San Diego, Calif.)
Determining the interaction of drug and target plays a key role in the process of drug development and discovery. The calculation methods can predict new interactions and speed up the process of drug development. In recent studies, the network-based ...

An Energy Data-Driven Approach for Operating Status Recognition of Machine Tools Based on Deep Learning.

Sensors (Basel, Switzerland)
Machine tools, as an indispensable equipment in the manufacturing industry, are widely used in industrial production. The harsh and complex working environment can easily cause the failure of machine tools during operation, and there is an urgent req...

A Hardware-Friendly Low-Bit Power-of-Two Quantization Method for CNNs and Its FPGA Implementation.

Sensors (Basel, Switzerland)
To address the problems of convolutional neural networks (CNNs) consuming more hardware resources (such as DSPs and RAMs on FPGAs) and their accuracy, efficiency, and resources being difficult to balance, meaning they cannot meet the requirements of ...