AIMC Topic: Neural Networks, Computer

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Robust Detection Model of Vascular Landmarks for Retinal Image Registration: A Two-Stage Convolutional Neural Network.

BioMed research international
Registration is useful for image processing in computer vision. It can be applied to retinal images and provide support for ophthalmologists in tracking disease progression and monitoring therapeutic responses. This study proposed a robust detection ...

GraphSite: Ligand Binding Site Classification with Deep Graph Learning.

Biomolecules
The binding of small organic molecules to protein targets is fundamental to a wide array of cellular functions. It is also routinely exploited to develop new therapeutic strategies against a variety of diseases. On that account, the ability to effect...

Fitness Movement Types and Completeness Detection Using a Transfer-Learning-Based Deep Neural Network.

Sensors (Basel, Switzerland)
Fitness is important in people's lives. Good fitness habits can improve cardiopulmonary capacity, increase concentration, prevent obesity, and effectively reduce the risk of death. Home fitness does not require large equipment but uses dumbbells, yog...

A DLSTM-Network-Based Approach for Mechanical Remaining Useful Life Prediction.

Sensors (Basel, Switzerland)
Remaining useful life prediction is one of the essential processes for machine system prognostics and health management. Although there are many new approaches based on deep learning for remaining useful life prediction emerging in recent years, thes...

Deep learning-based defects detection of certain aero-engine blades and vanes with DDSC-YOLOv5s.

Scientific reports
When performed by a person, aero-engine borescope inspection is easily influenced by individual experience and human factors that can lead to incorrect maintenance decisions, potentially resulting in serious disasters, as well as low efficiency. To a...

A deep learning framework for epileptic seizure detection based on neonatal EEG signals.

Scientific reports
Electroencephalogram (EEG) is one of the main diagnostic tests for epilepsy. The detection of epileptic activity is usually performed by a human expert and is based on finding specific patterns in the multi-channel electroencephalogram. This is a dif...

The proposed hybrid deep learning intrusion prediction IoT (HDLIP-IoT) framework.

PloS one
Throughout the past few years, the Internet of Things (IoT) has grown in popularity because of its ease of use and flexibility. Cyber criminals are interested in IoT because it offers a variety of benefits for users, but it still poses many types of ...

Tracked 3D ultrasound and deep neural network-based thyroid segmentation reduce interobserver variability in thyroid volumetry.

PloS one
Thyroid volumetry is crucial in the diagnosis, treatment, and monitoring of thyroid diseases. However, conventional thyroid volumetry with 2D ultrasound is highly operator-dependent. This study compares 2D and tracked 3D ultrasound with an automatic ...

SleepFCN: A Fully Convolutional Deep Learning Framework for Sleep Stage Classification Using Single-Channel Electroencephalograms.

IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society
Sleep is a vital process of our daily life as we roughly spend one-third of our lives asleep. In order to evaluate sleep quality and potential sleep disorders, sleep stage classification is a gold standard method. In this paper, we introduce a novel ...

Using Deep Learning to Fill Data Gaps in Environmental Footprint Accounting.

Environmental science & technology
Environmental footprint accounting relies on economic input-output (IO) models. However, the compilation of IO models is costly and time-consuming, leading to the lack of timely detailed IO data. The RAS method is traditionally used to predict future...