AIMC Topic: Deep Learning

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A hybrid deep learning model for regional O and NO concentrations prediction based on spatiotemporal dependencies in air quality monitoring network.

Environmental pollution (Barking, Essex : 1987)
Short-term prediction of urban air quality is critical to pollution management and public health. However, existing studies have failed to make full use of the spatiotemporal correlations or topological relationships among air quality monitoring netw...

Applications of deep learning in understanding gene regulation.

Cell reports methods
Gene regulation is a central topic in cell biology. Advances in omics technologies and the accumulation of omics data have provided better opportunities for gene regulation studies than ever before. For this reason deep learning, as a data-driven pre...

Adversarial Patch Attacks on Deep-Learning-Based Face Recognition Systems Using Generative Adversarial Networks.

Sensors (Basel, Switzerland)
Deep learning technology has developed rapidly in recent years and has been successfully applied in many fields, including face recognition. Face recognition is used in many scenarios nowadays, including security control systems, access control manag...

Intelligent Sensors for POI Recommendation Model Using Deep Learning in Location-Based Social Network Big Data.

Sensors (Basel, Switzerland)
Aiming at the problem that the existing Point of Interest (POI) recommendation model in social network big data is difficult to extract deep feature information, a POI recommendation model based on deep learning in social networks and big data is pro...

Novel Deep Learning Network for Gait Recognition Using Multimodal Inertial Sensors.

Sensors (Basel, Switzerland)
Some recent studies use a convolutional neural network (CNN) or long short-term memory (LSTM) to extract gait features, but the methods based on the CNN and LSTM have a high loss rate of time-series and spatial information, respectively. Since gait h...

Deep Learning-based Automatic Diagnosis of Breast Cancer on MRI Using Mask R-CNN for Detection Followed by ResNet50 for Classification.

Academic radiology
RATIONALE AND OBJECTIVES: Diagnosis of breast cancer on MRI requires, first, the identification of suspicious lesions; second, the characterization to give a diagnostic impression. We implemented Mask Reginal-Convolutional Neural Network (R-CNN) to d...

Identification of Chinese red wine origins based on Raman spectroscopy and deep learning.

Spectrochimica acta. Part A, Molecular and biomolecular spectroscopy
In this study, we combined Raman spectroscopy with deep learning for the first time to establish an accurate, simple, and fast method to identify the origin of red wines. We collected Raman spectra from 200 red wine samples of the Cabernet Sauvignon ...

Ultrasound Signal Processing: From Models to Deep Learning.

Ultrasound in medicine & biology
Medical ultrasound imaging relies heavily on high-quality signal processing to provide reliable and interpretable image reconstructions. Conventionally, reconstruction algorithms have been derived from physical principles. These algorithms rely on as...

Cervical cell classification with deep-learning algorithms.

Medical & biological engineering & computing
Cervical cancer is a serious threat to the lives and health of women. The accurate analysis of cervical cell smear images is an important diagnostic basis for cancer identification. However, pathological data are often complex and difficult to analyz...

Deep learning-based semantic vessel graph extraction for intracranial aneurysm rupture risk management.

International journal of computer assisted radiology and surgery
PURPOSE: Intracranial aneurysms are vascular deformations in the brain which are complicated to treat. In clinical routines, the risk assessment of intracranial aneurysm rupture is simplified and might be unreliable, especially for patients with mult...