AIMC Topic: Neural Networks, Computer

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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...

Aerial Separation and Receiver Arrangements on Identifying Lung Syndromes Using the Artificial Neural Network.

Computational intelligence and neuroscience
Lung disease is one of the most harmful diseases in traditional days and is the same nowadays. Early detection is one of the most crucial ways to prevent a human from developing these types of diseases. Many researchers are involved in finding variou...

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...

Few-Shot Learning for Image-Based Nonintrusive Appliance Signal Recognition.

Computational intelligence and neuroscience
In this article, we present the recognition of nonintrusive disaggregated appliance signals through a reduced dataset computer vision deep learning approach. Deep learning data requirements are costly in terms of acquisition time, storage memory requ...

Text-Based Emotion Recognition Using Deep Learning Approach.

Computational intelligence and neuroscience
Sentiment analysis is a method to identify people's attitudes, sentiments, and emotions towards a given goal, such as people, activities, organizations, services, subjects, and products. Emotion detection is a subset of sentiment analysis as it predi...

Deep Convolutional Neural Network Mechanism Assessment of COVID-19 Severity.

BioMed research international
As an epidemic, COVID-19's core test instrument still has serious flaws. To improve the present condition, all capabilities and tools available in this field are being used to combat the pandemic. Because of the contagious characteristics of the uniq...

Hospital Intelligent Power Operation and Maintenance Information Evaluation with the Long and Short Memory Neural Network.

BioMed research international
The invention describes a deep learning-based technique for monitoring power grid information operation and maintenance. Based on the time series data information in the power grid information operation and maintenance monitoring system, this method ...

Accurate image-based identification of macroinvertebrate specimens using deep learning-How much training data is needed?

PeerJ
Image-based methods for species identification offer cost-efficient solutions for biomonitoring. This is particularly relevant for invertebrate studies, where bulk samples often represent insurmountable workloads for sorting, identifying, and countin...