AIMC Topic: Deep Learning

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Deep Learning of Subject Context in Ideological and Political Class Based on Recursive Neural Network.

Computational intelligence and neuroscience
Ideological and political education is the most important way to cultivate students' humanistic qualities, which can directly determine the development of other qualities. However, at present, the direction of ideological and political innovation in ...

A Deep Neural Network-Based Model for Quantitative Evaluation of the Effects of Swimming Training.

Computational intelligence and neuroscience
This paper analyzes the quantitative assessment model of the swimming training effect based on the deep neural network by constructing a deep neural network model and designing a quantitative assessment model of the swimming training effect. This pap...

The Analysis of Environmental Cost Control of Manufacturing Enterprises Using Deep Learning Optimization Algorithm and Internet of Things.

Computational intelligence and neuroscience
Under the background of the Internet of things (IoT), the problems between the actual production and the environment are also prominent. The environmental cost control in the production process of manufacturing enterprises are discussed to reduce the...

Detection of intracranial aneurysms using deep learning-based CAD system: usefulness of the scores of CNN's final layer for distinguishing between aneurysm and infundibular dilatation.

Japanese journal of radiology
PURPOSE: We evaluated the diagnostic performance of a clinically available deep learning-based computer-assisted diagnosis software for detecting unruptured aneurysms (UANs) using magnetic resonance angiography and assessed the functionality of the c...

Artificial ecosystem optimization with Deep Learning Enabled Water Quality Prediction and Classification model.

Chemosphere
The majority of what is needed to maintain life is found in the approximately 70 percent of the earth's surface that is composed of water. Water quality has been deteriorating at an alarming rate as a direct result of rapid industrialization and urba...

Reducing uncertainty in cancer risk estimation for patients with indeterminate pulmonary nodules using an integrated deep learning model.

Computers in biology and medicine
OBJECTIVE: Patients with indeterminate pulmonary nodules (IPN) with an intermediate to a high probability of lung cancer generally undergo invasive diagnostic procedures. Chest computed tomography image and clinical data have been in estimating the p...

Improvement in Image Quality and Visibility of Coronary Arteries, Stents, and Valve Structures on CT Angiography by Deep Learning Reconstruction.

Korean journal of radiology
OBJECTIVE: This study aimed to investigate whether a deep learning reconstruction (DLR) method improves the image quality, stent evaluation, and visibility of the valve apparatus in coronary computed tomography angiography (CCTA) when compared with f...

Accelerated Synthetic MRI with Deep Learning-Based Reconstruction for Pediatric Neuroimaging.

AJNR. American journal of neuroradiology
BACKGROUND AND PURPOSE: Synthetic MR imaging is a time-efficient technique. However, its rather long scan time can be challenging for children. This study aimed to evaluate the clinical feasibility of accelerated synthetic MR imaging with deep learni...

Accurate detection of dairy cow mastitis with deep learning technology: a new and comprehensive detection method based on infrared thermal images.

Animal : an international journal of animal bioscience
Mastitis is one of the most common diseases in dairy cows and has a negative impact on their welfare and life, causing significant economic losses to the dairy industry. Many attempts have been made to develop a detection method for mastitis using th...

Deep Learning-Based Remaining Useful Life Estimation of Bearings with Time-Frequency Information.

Sensors (Basel, Switzerland)
In modern industrial production, the prediction ability of remaining useful life of bearings directly affects the safety and stability of the system. Traditional methods require rigorous physical modeling and perform poorly for complex systems. In th...