DEPENDENCY ON NETWORK STRUCTURE AND INFORMATION DENSITY OF DEEP LEARNING BASED RIVER STAGE PREDICTION

Abstract To automatically detect dynamic EEG signals to reduce the time cost of epilepsy diagnosis. In the signal recognition of electroencephalogram (EEG) of epilepsy, traditional machine learning and statistical methods require manual feature labeling engineering in order to show excellent results on a single data set. And the artificially selected features may carry a bias, and cannot guarantee the validity and expansibility in real-world data. In practical applications, deep learning methods can release people from feature engineering to a certain extent. As long as the focus is on the expansion of data quality and quantity, the algorithm model can learn automatically to get better improvements. In addition, the deep learning method can also extract many features that are difficult for humans to perceive, thereby making the algorithm more robust. Based on the design idea of ResNeXt deep neural network, this paper designs a Time-ResNeXt network structure suitable for time series EEG epilepsy detection to identify EEG signals. The accuracy rate of Time-ResNeXt in the detection of EEG epilepsy can reach 91.50%. The Time-ResNeXt network structure produces extremely advanced performance on the benchmark dataset (Berne-Barcelona dataset) and has great potential for improving clinical practice.

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HPS-Net: Multi-Task Network for Medical Image Segmentation with Predictable Performance

Symmetry ◽

10.3390/sym13112107 ◽

2021 ◽

Vol 13 (11) ◽

pp. 2107

Author(s):

Xin Wei ◽

Huan Wan ◽

Fanghua Ye ◽

Weidong Min

Keyword(s):

Image Segmentation ◽

Deep Learning ◽

Loss Function ◽

Network Structure ◽

Medical Diagnosis ◽

Medical Image ◽

Medical Image Segmentation ◽

Training Mode ◽

Cooperative Training

In recent years, medical image segmentation (MIS) has made a huge breakthrough due to the success of deep learning. However, the existing MIS algorithms still suffer from two types of uncertainties: (1) the uncertainty of the plausible segmentation hypotheses and (2) the uncertainty of segmentation performance. These two types of uncertainties affect the effectiveness of the MIS algorithm and then affect the reliability of medical diagnosis. Many studies have been done on the former but ignore the latter. Therefore, we proposed the hierarchical predictable segmentation network (HPS-Net), which consists of a new network structure, a new loss function, and a cooperative training mode. According to our knowledge, HPS-Net is the first network in the MIS area that can generate both the diverse segmentation hypotheses to avoid the uncertainty of the plausible segmentation hypotheses and the measure predictions about these hypotheses to avoid the uncertainty of segmentation performance. Extensive experiments were conducted on the LIDC-IDRI dataset and the ISIC2018 dataset. The results show that HPS-Net has the highest Dice score compared with the benchmark methods, which means it has the best segmentation performance. The results also confirmed that the proposed HPS-Net can effectively predict TNR and TPR.

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DEVELOPMENT OF THE REAL-TIME RIVER STAGE PREDICTION METHOD USING DEEP LEARNING

Journal of Japan Society of Civil Engineers Ser B1 (Hydraulic Engineering) ◽

10.2208/jscejhe.72.i_187 ◽

2016 ◽

Vol 72 (4) ◽

pp. I_187-I_192 ◽

Cited By ~ 4

Author(s):

Masayuki HITOKOTO ◽

Masaaki SAKURABA ◽

Yuichi SEI

Keyword(s):

Deep Learning ◽

Real Time ◽

Prediction Method ◽

The Real ◽

River Stage

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Deep Learning Based Proactive Caching for Effective WSN-Enabled Vision Applications

Complexity ◽

10.1155/2019/5498606 ◽

2019 ◽

Vol 2019 ◽

pp. 1-12 ◽

Cited By ~ 5

Author(s):

Fangyuan Lei ◽

Jun Cai ◽

Qingyun Dai ◽

Huimin Zhao

Keyword(s):

Deep Learning ◽

Network Structure ◽

Pedestrian Detection ◽

Network Function Virtualization ◽

Model Parameters ◽

Caching Strategy ◽

User Request ◽

Cache Strategy ◽

Wide Range ◽

Sdn Controller

Wireless Sensor Networks (WSNs) have a wide range of applications scenarios in computer vision, from pedestrian detection to robotic visual navigation. In response to the growing visual data services in WSNs, we propose a proactive caching strategy based on Stacked Sparse Autoencoder (SSAE) to predict content popularity (PCDS2AW). Firstly, based on Software Defined Network (SDN) and Network Function Virtualization (NFV) technologies, a distributed deep learning network SSAE is constructed in the sink nodes and control nodes of the WSN network. Then, the SSAE network structure parameters and network model parameters are optimized through training. The proactive cache strategy implementation procedure is divided into four steps. (1) The SDN controller is responsible for dynamically collecting user request data package information in the WSNs network. (2) The SSAEs predicts the packet popularity based on the SDN controller obtaining user request data. (3) The SDN controller generates a corresponding proactive cache strategy according to the popularity prediction result. (4) Implement the proactive caching strategy at the WSNs cache node. In the simulation, we compare the influence of spatiotemporal data on the SSAE network structure. Compared with the classic caching strategy Hash + LRU, Betw + LRU, and classic prediction algorithms SVM and BPNN, the proposed PCDS2AW proactive caching strategy can significantly improve WSN performance.

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Public Opinion Early Warning Agent Model: A Deep Learning Cascade Virality Prediction Model Based on Multi-Feature Fusion

Frontiers in Neurorobotics ◽

10.3389/fnbot.2021.674322 ◽

2021 ◽

Vol 15 ◽

Author(s):

Liqun Gao ◽

Yujia Liu ◽

Hongwu Zhuang ◽

Haiyang Wang ◽

Bin Zhou ◽

...

Keyword(s):

Deep Learning ◽

Public Opinion ◽

Prediction Model ◽

Network Structure ◽

Early Warning ◽

Feature Fusion ◽

Learning Model ◽

Feature Model ◽

Underlying Network ◽

Deep Learning Model

With the rapid popularity of agent technology, a public opinion early warning agent has attracted wide attention. Furthermore, a deep learning model can make the agent more automatic and efficient. Therefore, for the agency of a public opinion early warning task, the deep learning model is very suitable for completing tasks such as popularity prediction or emergency outbreak. In this context, improving the ability to automatically analyze and predict the virality of information cascades is one of the tasks that deep learning model approaches address. However, most of the existing studies sought to address this task by analyzing cascade underlying network structure. Recent studies proposed cascade virality prediction for agnostic-networks (without network structure), but did not consider the fusion of more effective features. In this paper, we propose an innovative cascade virus prediction model named CasWarn. It can be quickly deployed in intelligent agents to effectively predict the virality of public opinion information for different industries. Inspired by the agnostic-network model, this model extracts the key features (independent of the underlying network structure) of an information cascade, including dissemination scale, emotional polarity ratio, and semantic evolution. We use two improved neural network frameworks to embed these features, and then apply the classification task to predict the cascade virality. We conduct comprehensive experiments on two large social network datasets. Furthermore, the experimental results prove that CasWarn can make timely and effective cascade virality predictions and verify that each feature model of CasWarn is beneficial to improve performance.

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DEEP LEARNING NEURAL NETWORK STRUCTURE OPTIMIZATION

Informatics and Applications ◽

10.14357/19922264200408 ◽

2020 ◽

Keyword(s):

Neural Network ◽

Deep Learning ◽

Network Structure ◽

Structure Optimization ◽

Neural Network Structure ◽

Network Structure Optimization ◽

Deep Learning Neural Network

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DEVELOPMENT OF THE REAL-TIME RIVER STAGE PREDICTION METHOD USING DEEP LEARNING

Journal of JSCE ◽

10.2208/journalofjsce.5.1_422 ◽

2017 ◽

Vol 5 (1) ◽

pp. 422-429 ◽

Cited By ~ 2

Author(s):

Masayuki HITOKOTO ◽

Masaaki SAKURABA ◽

Yuichi SEI

Keyword(s):

Deep Learning ◽

Real Time ◽

Prediction Method ◽

The Real ◽

River Stage

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A Review of Automatic Music Generation Based on Performance RNN

10.36227/techrxiv.12088980.v1 ◽

2020 ◽

Author(s):

Jiyanbo Cao ◽

Jinan Fiaidhi ◽

Maolin Qi

Keyword(s):

Neural Network ◽

Deep Learning ◽

Recurrent Neural Network ◽

Network Structure ◽

Network Performance ◽

State Of The Art ◽

Learning Techniques ◽

History Of ◽

Music Generation ◽

Automatic Music Generation

This paper has reviewed the deep learning techniques which used in music generation. The research was based on <i>Sageev Oore's</i> proposed LSTM based recurrent neural network (Performance RNN). We have study the history of automatic music generation, and now we are using a state of the art techniques to achieve this mission. We have conclude the process of making a MIDI file to a structure as input of Performance RNN and the network structure of it.

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Applicability of the Deep Learning Flood Forecast Model Against the Inexperienced Magnitude of Flood

10.29007/fdp5 ◽

2018 ◽

Author(s):

Masayuki Hitokoto ◽

Masaaki Sakuraba

Keyword(s):

Deep Learning ◽

First Grade ◽

Training Data ◽

Observation Data ◽

Validation Data ◽

River Catchment ◽

River Stage ◽

Hourly Rainfall ◽

Feed Forward Network ◽

Ann Models

Although artificial neural networks (ANN) is widely used for real-time flood prediction model, it is pointed out that the weak point of the model is poor applicability for the inexperienced magnitude of flood. In this study, the ANN models were applied to first-grade rivers in Japan, Tokoro River catchment and Abashiri River catchment. The training data of the ANN models were all the rainfall-runoff event which exceeded the Flood Watch Water Level during the period of 1998-2015. Types of observation data were river-stage and rainfall at 1-hour pitch. The validation data was the largest flood since the river-stage observation had started. The main component of the model was the four-layer feed-forward network. As a network training method, the deep learning based on the denoising autoencoder was applied. The output of the neural network was change in river-stage in T hours at the prediction point. The input data was the upstream river-stage, hourly change in river-stage and hourly rainfall. The river- stage prediction up to 6 hours showed very good accuracy, and It was proved that it can be nicely predicted even for the past largest flood.

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