AIMC Topic: Neural Networks, Computer

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Analysis of Winning Experience and Technical Training Effect of Badminton Match Based on BP Neural Network.

Journal of healthcare engineering
The success or failure of badminton competition often depends on the training level of technical, physical, tactical, and psychological quality, as well as the competitive ability to comprehensively use these factors in the competition. Aiming at the...

Operational Scheduling of Behind-the-Meter Storage Systems Based on Multiple Nonstationary Decomposition and Deep Convolutional Neural Network for Price Forecasting.

Computational intelligence and neuroscience
In the competitive electricity market, electricity price reflects the relationship between power supply and demand and plays an important role in the strategic behavior of market players. With the development of energy storage systems after watt-hour...

A Simple and Efficient Deep Learning-Based Framework for Automatic Fruit Recognition.

Computational intelligence and neuroscience
Accurate detection and recognition of various kinds of fruits and vegetables by using the artificial intelligence (AI) approach always remain a challenging task due to similarity between various types of fruits and challenging environments such as li...

Evolving Long Short-Term Memory Network-Based Text Classification.

Computational intelligence and neuroscience
Recently, long short-term memory (LSTM) networks are extensively utilized for text classification. Compared to feed-forward neural networks, it has feedback connections, and thus, it has the ability to learn long-term dependencies. However, the LSTM ...

Integrating Multimodal Electronic Health Records for Diagnosis Prediction.

AMIA ... Annual Symposium proceedings. AMIA Symposium
Diagnosis prediction aims to predict the patient's future diagnosis based on their Electronic Health Records (EHRs). Most existing works adopt recurrent neural networks (RNNs) to model the sequential EHR data. However, they mainly utilize medical cod...

On the explainability of hospitalization prediction on a large COVID-19 patient dataset.

AMIA ... Annual Symposium proceedings. AMIA Symposium
We develop various AI models to predict hospitalization on a large (over 110k) cohort of COVID-19 positive-tested US patients, sourced from March 2020 to February 2021. Models range from Random Forest to Neural Network (NN) and Time Convolutional NN,...

Characterizing Brain Signals for Epileptic Pre-ictal Signal Classification.

AMIA ... Annual Symposium proceedings. AMIA Symposium
Epilepsy is a kind of neurological disorder characterized by recurrent epileptic seizures. While it is crucial to characterize pre-ictal brain electrical activities, the problem to this day still remains computationally challenging. Using brain signa...

Space Target Classification Improvement by Generating Micro-Doppler Signatures Considering Incident Angle.

Sensors (Basel, Switzerland)
Classifying space targets from debris is critical for radar resource management as well as rapid response during the mid-course phase of space target flight. Due to advances in deep learning techniques, various approaches have been studied to classif...

Progress and Challenges for Memtransistors in Neuromorphic Circuits and Systems.

Advanced materials (Deerfield Beach, Fla.)
Due to the increasing importance of artificial intelligence (AI), significant recent effort has been devoted to the development of neuromorphic circuits that seek to emulate the energy-efficient information processing of the brain. While non-volatile...

Accelerating susceptibility-weighted imaging with deep learning by complex-valued convolutional neural network (ComplexNet): validation in clinical brain imaging.

European radiology
OBJECTIVES: Susceptibility-weighted imaging (SWI) is crucial for the characterization of intracranial hemorrhage and mineralization, but has the drawback of long acquisition times. We aimed to propose a deep learning model to accelerate SWI, and eval...