Dataset Information

The use of deep learning algorithm and digital media art in all-media intelligent electronic music system.

ABSTRACT: In the development of digital media art, to explore the preliminary application of deep learning method in intelligent electronic music system, and promote the integration of deep learning method and digital media technology, thus providing a direction for the development of all media intelligent system, based on deep deterministic policy gradient (DDPG), to solve the multi-task problem in intelligent system, a multi-task learning-based DDPG algorithm (M-DDPG) is proposed. Furthermore, a DDPG algorithm based on hierarchical learning (H-DDPG) is proposed for the hierarchical analysis of images in intelligent system. Aiming at the problem of image classification in intelligent system, through the setting of simulation environment, the application effect of several algorithms in intelligent electronic music system is evaluated. The results show that: M-DDPG algorithm can more accurately complete the operation of related tasks, the reward received by the intelligent system is more than 0.35, and the test results based on eight tasks are more accurate and effective. Even in the case of task error, the algorithm still shows good training results. H-DDPG algorithm has good effect for complex task processing. The accuracy rate of task test corresponding to intelligent system in different scenarios is above 95%, which is better than other conventional algorithms in task test; the self-reinforcement network algorithm can promote the improvement of image classification effect. Several algorithms proposed show excellent performance in image processing of intelligent system, and have great application potential.

SUBMITTER: Zheng Y

PROVIDER: S-EPMC7571708 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature

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The use of deep learning algorithm and digital media art in all-media intelligent electronic music system.

Zheng Yingming Y

PloS one 20201019 10

In the development of digital media art, to explore the preliminary application of deep learning method in intelligent electronic music system, and promote the integration of deep learning method and digital media technology, thus providing a direction for the development of all media intelligent system, based on deep deterministic policy gradient (DDPG), to solve the multi-task problem in intelligent system, a multi-task learning-based DDPG algorithm (M-DDPG) is proposed. Furthermore, a DDPG al ...[more]

PMID: 33075083

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Project description:BackgroundCOVID-19 is caused by the SARS-CoV-2 virus and has strikingly heterogeneous clinical manifestations, with most individuals contracting mild disease but a substantial minority experiencing fulminant cardiopulmonary symptoms or death. The clinical covariates and the laboratory tests performed on a patient provide robust statistics to guide clinical treatment. Deep learning approaches on a data set of this nature enable patient stratification and provide methods to guide clinical treatment.ObjectiveHere, we report on the development and prospective validation of a state-of-the-art machine learning model to provide mortality prediction shortly after confirmation of SARS-CoV-2 infection in the Mayo Clinic patient population.MethodsWe retrospectively constructed one of the largest reported and most geographically diverse laboratory information system and electronic health record of COVID-19 data sets in the published literature, which included 11,807 patients residing in 41 states of the United States of America and treated at medical sites across 5 states in 3 time zones. Traditional machine learning models were evaluated independently as well as in a stacked learner approach by using AutoGluon, and various recurrent neural network architectures were considered. The traditional machine learning models were implemented using the AutoGluon-Tabular framework, whereas the recurrent neural networks utilized the TensorFlow Keras framework. We trained these models to operate solely using routine laboratory measurements and clinical covariates available within 72 hours of a patient's first positive COVID-19 nucleic acid test result.ResultsThe GRU-D recurrent neural network achieved peak cross-validation performance with 0.938 (SE 0.004) as the area under the receiver operating characteristic (AUROC) curve. This model retained strong performance by reducing the follow-up time to 12 hours (0.916 [SE 0.005] AUROC), and the leave-one-out feature importance analysis indicated that the most independently valuable features were age, Charlson comorbidity index, minimum oxygen saturation, fibrinogen level, and serum iron level. In the prospective testing cohort, this model provided an AUROC of 0.901 and a statistically significant difference in survival (P<.001, hazard ratio for those predicted to survive, 95% CI 0.043-0.106).ConclusionsOur deep learning approach using GRU-D provides an alert system to flag mortality for COVID-19-positive patients by using clinical covariates and laboratory values within a 72-hour window after the first positive nucleic acid test result.

Dataset Information

The use of deep learning algorithm and digital media art in all-media intelligent electronic music system.

Publications

The use of deep learning algorithm and digital media art in all-media intelligent electronic music system.

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