AIMC Topic: Deep Learning

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Deep learning-based image deconstruction method with maintained saliency.

Neural networks : the official journal of the International Neural Network Society
Visual properties that primarily attract bottom-up attention are collectively referred to as saliency. In this study, to understand the neural activity involved in top-down and bottom-up visual attention, we aim to prepare pairs of natural and unnatu...

Applying a CT texture analysis model trained with deep-learning reconstruction images to iterative reconstruction images in pulmonary nodule diagnosis.

Journal of applied clinical medical physics
OBJECTIVE: To investigate the feasibility and accuracy of applying a computed tomography (CT) texture analysis model trained with deep-learning reconstruction images to iterative reconstruction images for classifying pulmonary nodules.

Ensemble of deep capsule neural networks: an application to pediatric pneumonia prediction.

Physical and engineering sciences in medicine
Pneumonia disease accounts for 15% of all deaths in children under the age of five and early detection of the disease significantly improves survival chances. In this work, we introduce a novel deep neural network model for evaluating pediatric pneum...

Antenna Excitation Optimization with Deep Learning for Microwave Breast Cancer Hyperthermia.

Sensors (Basel, Switzerland)
Microwave hyperthermia (MH) requires the effective calibration of antenna excitations for the selective focusing of the microwave energy on the target region, with a nominal effect on the surrounding tissue. To this end, many different antenna calibr...

Deep Learning-Based Subtask Segmentation of Timed Up-and-Go Test Using RGB-D Cameras.

Sensors (Basel, Switzerland)
The timed up-and-go (TUG) test is an efficient way to evaluate an individual's basic functional mobility, such as standing up, walking, turning around, and sitting back. The total completion time of the TUG test is a metric indicating an individual's...

Ensemble latent assimilation with deep learning surrogate model: application to drop interaction in a microfluidics device.

Lab on a chip
A major challenge in the field of microfluidics is to predict and control drop interactions. This work develops an image-based data-driven model to forecast drop dynamics based on experiments performed on a microfluidics device. Reduced-order modelli...

Research on Mental Health Monitoring Scheme of Migrant Children Based on Convolutional Neural Network Based on Deep Learning.

Occupational therapy international
In recent years, with the acceleration of urbanization and the implementation of compulsory education, the pressure on students' study and life has increased, and the phenomenon of psychological and behavioral problems has become increasingly promine...

Lightweight Deep Learning Model for Marketing Strategy Optimization and Characteristic Analysis.

Computational intelligence and neuroscience
The business model of traditional market is declining day by day, and people's consumption cognition has risen to a new level with the leap in science and technology. Enterprises need to adjust and optimize their marketing strategies in time accordin...

The Acceptability of Traditional Culture under the Background of Deep Learning.

Computational intelligence and neuroscience
The cultural values of a country impact its national psychology and identity. Citizens' values and public opinions are conveyed to state leaders over the media and other information channels, both directly and indirectly influencing decisions on fore...

A Comparison on LSTM Deep Learning Method and Random Walk Model Used on Financial and Medical Applications: An Example in COVID-19 Development Prediction.

Computational intelligence and neuroscience
This study aims to establish the model of the cryptocurrency price trend based on a financial theory using the Long Short-Term Memory (LSTM) networks model with multiple combinations between the window length and the predicting horizons. The Random W...