AIMC Topic: Deep Learning

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A Bayesian deep learning method for freeway incident detection with uncertainty quantification.

Accident; analysis and prevention
Incident detection is fundamental for freeway management to reduce non-recurrent congestions and secondary incidents. Recently, machine learning technologies have made considerable progress in the incident detection field, but many still face challen...

RIS-Assisted Multi-Antenna AmBC Signal Detection Using Deep Reinforcement Learning.

Sensors (Basel, Switzerland)
Signal detection is one of the most critical and challenging issues in ambient backscatter communication (AmBC) systems. In this paper, a multi-antenna AmBC signal detection method is proposed based on reconfigurable intelligent surface (RIS) and dee...

Fall-from-Height Detection Using Deep Learning Based on IMU Sensor Data for Accident Prevention at Construction Sites.

Sensors (Basel, Switzerland)
Workers at construction sites are prone to fall-from-height (FFH) accidents. The severity of injury can be represented by the acceleration peak value. In the study, a risk prediction against FFH was made using IMU sensor data for accident prevention ...

Facial Emotion Recognition in Verbal Communication Based on Deep Learning.

Sensors (Basel, Switzerland)
Facial emotion recognition from facial images is considered a challenging task due to the unpredictable nature of human facial expressions. The current literature on emotion classification has achieved high performance over deep learning (DL)-based m...

RAt-CapsNet: A Deep Learning Network Utilizing Attention and Regional Information for Abnormality Detection in Wireless Capsule Endoscopy.

IEEE journal of translational engineering in health and medicine
: The emergence of wireless capsule endoscopy (WCE) has presented a viable non-invasive mean of identifying gastrointestinal diseases in the field of clinical gastroenterology. However, to overcome its extended time of manual inspection, a computer a...

Application Research on Face Image Evaluation Algorithm of Deep Learning Mobile Terminal for Student Check-In Management.

Computational intelligence and neuroscience
With the increase in the number of data, the traditional shallow image features cannot meet the needs of image representation. As an important means of image research, deep learning network has been paid attention to. In the field of face image evalu...

GLD-Net: Deep Learning to Detect DDoS Attack via Topological and Traffic Feature Fusion.

Computational intelligence and neuroscience
Distributed denial of service (DDoS) attacks are the most common means of cyberattacks against infrastructure, and detection is the first step in combating them. The current DDoS detection mainly uses the improvement or fusion of machine learning and...

An Efficient Outlier Detection with Deep Learning-Based Financial Crisis Prediction Model in Big Data Environment.

Computational intelligence and neuroscience
As Big Data, Internet of Things (IoT), cloud computing (CC), and other ideas and technologies are combined for social interactions. Big data technologies improve the treatment of financial data for businesses. At present, an effective tool can be use...

Fusion of CT images and clinical variables based on deep learning for predicting invasiveness risk of stage I lung adenocarcinoma.

Medical physics
PURPOSE: To develop a novel multimodal data fusion model by incorporating computed tomography (CT) images and clinical variables based on deep learning for predicting the invasiveness risk of stage I lung adenocarcinoma that manifests as ground-glass...

Deep learning predicts DNA methylation regulatory variants in the human brain and elucidates the genetics of psychiatric disorders.

Proceedings of the National Academy of Sciences of the United States of America
There is growing evidence for the role of DNA methylation (DNAm) quantitative trait loci (mQTLs) in the genetics of complex traits, including psychiatric disorders. However, due to extensive linkage disequilibrium (LD) of the genome, it is challengin...