Fault Diagnostic Model Based on Fusing Improved Evidence Theory and Multiple Neural Networks

Convolutional neural networks (CNNs) with log-mel audio representation and CNN-based end-to-end learning have both been used for environmental event sound recognition (ESC). However, log-mel features can be complemented by features learned from the raw audio waveform with an effective fusion method. In this paper, we first propose a novel stacked CNN model with multiple convolutional layers of decreasing filter sizes to improve the performance of CNN models with either log-mel feature input or raw waveform input. These two models are then combined using the Dempster–Shafer (DS) evidence theory to build the ensemble DS-CNN model for ESC. Our experiments over three public datasets showed that our method could achieve much higher performance in environmental sound recognition than other CNN models with the same types of input features. This is achieved by exploiting the complementarity of the model based on log-mel feature input and the model based on learning features directly from raw waveforms.

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Session-Based Recommendation Model Based on Multiple Neural Networks Hybrid Extraction Feature

2020 IEEE International Conference on Big Data (Big Data) ◽

10.1109/bigdata50022.2020.9378381 ◽

2020 ◽

Author(s):

Huaxiong Yao ◽

Jiabei Hu ◽

Wenqi Xie ◽

Yang Huang ◽

Wei Xie

Keyword(s):

Neural Networks ◽

Model Based ◽

Multiple Neural Networks

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Notice of Retraction: Research of integrated fault diagnosis for condenser based on multiple neural networks and D-S evidence theory

2010 2nd International Conference on Mechanical and Electronics Engineering ◽

10.1109/icmee.2010.5558480 ◽

2010 ◽

Author(s):

Peng Daogang ◽

Zhang Kai ◽

Zhang Hao ◽

Huang Conghua

Keyword(s):

Neural Networks ◽

Fault Diagnosis ◽

Evidence Theory ◽

Multiple Neural Networks

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Construction and Evaluation of Intelligent Medical Diagnosis Model Based on Integrated Deep Neural Network

Computational Intelligence and Neuroscience ◽

10.1155/2021/7171816 ◽

2021 ◽

Vol 2021 ◽

pp. 1-10

Author(s):

Lina Ma ◽

Tao Yang

Keyword(s):

Neural Networks ◽

Deep Neural Networks ◽

Imaging System ◽

Health Management ◽

Human Life ◽

Diagnostic Model ◽

Quality Healthcare ◽

Model Based ◽

Medical Diagnostic ◽

Demand For Health

In recent years, as human life expectancy increases, birth rate decreases and health management concerns; the traditional Healthcare imaging system, with its uneven Healthcare imaging resources, high Healthcare imaging costs, and diagnoses often relying on doctors’ clinical experience and equipment level limitations, has affected people’s demand for health, so there is a need for a more accurate, convenient, and affordable Healthcare imaging system that allows all people to enjoy fair and quality Healthcare imaging services. This paper discusses the construction and evaluation of an intelligent medical diagnostic model based on integrated deep neural networks, which not only provides a systematic diagnostic analysis of the various symptoms input by the inquirer but also has higher accuracy and efficiency compared with traditional medical diagnostic models. The construction of this model provides a theoretical basis for integrating deep neural networks applied to medical neighborhoods with big data algorithms.

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