AIMC Topic: Deep Learning

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Optimization of Dechlorination Experiment Design Using Lightweight Deep Learning Model.

Computational intelligence and neuroscience
This exploration intends to remove chloride ions in production and life, enhance buildings' durability, and protect the natural environment from pollution. The current dechlorination technology is discussed based on the relevant theories, such as the...

Evolutionary Game Analysis of Farmers' Conservation Tillage Behavior in Black Soil Areas Guided by Deep Learning.

Computational intelligence and neuroscience
To better protect the rights and interests of farmers, the evolutionary game theory and deep learning (DL) technology are used to analyze the conservation tillage behavior of farmers in black soil areas. Firstly, the basic hypotheses are put forward ...

Evaluation of Traditional Culture Teaching Efficiency by Course Ideological and Political Integration Lightweight Deep Learning.

Computational intelligence and neuroscience
With the development of society, China pays more and more attention to cultural education. The teaching method of introducing ideological and political content into cultural teaching plays an important role in improving the overall teaching quality. ...

Forecasting Subway Passenger Flow for Station-Level Service Supply.

Big data
Demand forecasting is one of the managers' concerns in service supply chain management. With accurate passenger flow forecasting, the station-level service suppliers can make better service plans accordingly. However, the existing forecasting model c...

Lower-extremity fatigue fracture detection and grading based on deep learning models of radiographs.

European radiology
OBJECTIVES: To identify the feasibility of deep learning-based diagnostic models for detecting and assessing lower-extremity fatigue fracture severity on plain radiographs.

Can deep learning improve image quality of low-dose CT: a prospective study in interstitial lung disease.

European radiology
OBJECTIVES: To investigate whether deep learning reconstruction (DLR) could keep image quality and reduce radiation dose in interstitial lung disease (ILD) patients compared with HRCT reconstructed with hybrid iterative reconstruction (hybrid-IR).

Automated quantification of meibomian gland dropout in infrared meibography using deep learning.

The ocular surface
PURPOSE: Develop a deep learning-based automated method to segment meibomian glands (MG) and eyelids, quantitatively analyze the MG area and MG ratio, estimate the meiboscore, and remove specular reflections from infrared images.

Deep learning based low-activity PET reconstruction of [C]PiB and [F]FE-PE2I in neurodegenerative disorders.

NeuroImage
PURPOSE: Positron Emission Tomography (PET) can support a diagnosis of neurodegenerative disorder by identifying disease-specific pathologies. Our aim was to investigate the feasibility of using activity reduction in clinical [F]FE-PE2I and [C]PiB PE...

Speckle-Based Optical Cryptosystem and its Application for Human Face Recognition via Deep Learning.

Advanced science (Weinheim, Baden-Wurttemberg, Germany)
Face recognition has become ubiquitous for authentication or security purposes. Meanwhile, there are increasing concerns about the privacy of face images, which are sensitive biometric data and should be protected. Software-based cryptosystems are wi...

A new deep learning technique reveals the exclusive functional contributions of individual cancer mutations.

The Journal of biological chemistry
Cancers are caused by genomic alterations that may be inherited, induced by environmental carcinogens, or caused due to random replication errors. Postinduction of carcinogenicity, mutations further propagate and drastically alter the cancer genomes....