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

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.

Download Full-text

Explicit-implicit dual stream network for image quality assessment

EURASIP Journal on Image and Video Processing ◽

10.1186/s13640-020-00538-y ◽

2020 ◽

Vol 2020 (1) ◽

Author(s):

Guangyi Yang ◽

Xingyu Ding ◽

Tian Huang ◽

Kun Cheng ◽

Weizheng Jin

Keyword(s):

Deep Learning ◽

Image Quality ◽

Quality Assessment ◽

Frequency Domain ◽

Image Quality Assessment ◽

Feature Fusion ◽

Learning Model ◽

Stream Network ◽

Perception System ◽

Deep Learning Model

Abstract Communications industry has remarkably changed with the development of fifth-generation cellular networks. Image, as an indispensable component of communication, has attracted wide attention. Thus, finding a suitable approach to assess image quality is important. Therefore, we propose a deep learning model for image quality assessment (IQA) based on explicit-implicit dual stream network. We use frequency domain features of kurtosis based on wavelet transform to represent explicit features and spatial features extracted by convolutional neural network (CNN) to represent implicit features. Thus, we constructed an explicit-implicit (EI) parallel deep learning model, namely, EI-IQA model. The EI-IQA model is based on the VGGNet that extracts the spatial domain features. On this basis, the number of network layers of VGGNet is reduced by adding the parallel wavelet kurtosis value frequency domain features. Thus, the training parameters and the sample requirements decline. We verified, by cross-validation of different databases, that the wavelet kurtosis feature fusion method based on deep learning has a more complete feature extraction effect and a better generalisation ability. Thus, the method can simulate the human visual perception system better, and subjective feelings become closer to the human eye. The source code about the proposed EI-IQA model is available on github https://github.com/jacob6/EI-IQA.

Download Full-text

Region-to-boundary deep learning model with multi-scale feature fusion for medical image segmentation

Biomedical Signal Processing and Control ◽

10.1016/j.bspc.2021.103165 ◽

2022 ◽

Vol 71 ◽

pp. 103165

Author(s):

Xiaowei Liu ◽

Lei Yang ◽

Jianguo Chen ◽

Siyang Yu ◽

Keqin Li

Keyword(s):

Image Segmentation ◽

Deep Learning ◽

Medical Image ◽

Feature Fusion ◽

Learning Model ◽

Medical Image Segmentation ◽

Scale Feature ◽

Multi Scale ◽

Deep Learning Model

Download Full-text

Insights into deep learning for earthquake magnitude and location estimation

10.5194/egusphere-egu21-4718 ◽

2021 ◽

Author(s):

Jannes Münchmeyer ◽

Dino Bindi ◽

Ulf Leser ◽

Frederik Tilmann

Keyword(s):

Deep Learning ◽

Real Time ◽

Early Warning ◽

Source Parameters ◽

Spectral Composition ◽

Learning Model ◽

Location Estimation ◽

Training Set ◽

Earthquake Source Parameters ◽

Deep Learning Model

The estimation of earthquake source parameters, in particular magnitude and location, in real time is one of the key tasks for earthquake early warning and rapid response. In recent years, several publications introduced deep learning approaches for these fast assessment tasks. Deep learning is well suited for these tasks, as it can work directly on waveforms and can learn features and their relation from data.A drawback of deep learning models is their lack of interpretability, i.e., it is usually unknown what reasoning the network uses. Due to this issue, it is also hard to estimate how the model will handle new data whose properties differ in some aspects from the training set, for example earthquakes in previously seismically quite regions. The discussions of previous studies usually focused on the average performance of models and did not consider this point in any detail.Here we analyze a deep learning model for real time magnitude and location estimation through targeted experiments and a qualitative error analysis. We conduct our analysis on three large scale regional data sets from regions with diverse seismotectonic settings and network properties: Italy and Japan with dense networks (station spacing down to 10 km) of strong motion sensors, and North Chile with a sparser network (station spacing around 40 km) of broadband stations. We obtained several key insights. First, the deep learning model does not seem to follow the classical approaches for magnitude and location estimation. For magnitude, one would classically expect the model to estimate attenuation, but the network rather seems to focus its attention on the spectral composition of the waveforms. For location, one would expect a triangulation approach, but our experiments instead show indications of a fingerprinting approach. Second, we can pinpoint the effect of training data size on model performance. For example, a four times larger training set reduces average errors for both magnitude and location prediction by more than half, and reduces the required time for real time assessment by a factor of four. Third, the model fails for events with few similar training examples. For magnitude, this means that the largest events are systematically underestimated. For location, events in regions with few events in the training set tend to get mislocated to regions with more training events. These characteristics can have severe consequences in downstream tasks like early warning and need to be taken into account for future model development and evaluation.

Download Full-text

Markerless Dog Pose Recognition in the Wild Using ResNet Deep Learning Model

Computers ◽

10.3390/computers11010002 ◽

2021 ◽

Vol 11 (1) ◽

pp. 2

Author(s):

Srinivasan Raman ◽

Rytis Maskeliūnas ◽

Robertas Damaševičius

Keyword(s):

Deep Learning ◽

Temporal Analysis ◽

Learning Model ◽

Mobile App ◽

Feature Model ◽

Pose Recognition ◽

In The Wild ◽

Spatio Temporal ◽

And Behavior ◽

Deep Learning Model

The analysis and perception of behavior has usually been a crucial task for researchers. The goal of this paper is to address the problem of recognition of animal poses, which has numerous applications in zoology, ecology, biology, and entertainment. We propose a methodology to recognize dog poses. The methodology includes the extraction of frames for labeling from videos and deep convolutional neural network (CNN) training for pose recognition. We employ a semi-supervised deep learning model of reinforcement. During training, we used a combination of restricted labeled data and a large amount of unlabeled data. Sequential CNN is also used for feature localization and to find the canine’s motions and posture for spatio-temporal analysis. To detect the canine’s features, we employ image frames to locate the annotations and estimate the dog posture. As a result of this process, we avoid starting from scratch with the feature model and reduce the need for a large dataset. We present the results of experiments on a dataset of more than 5000 images of dogs in different poses. We demonstrated the effectiveness of the proposed methodology for images of canine animals in various poses and behavior. The methodology implemented as a mobile app that can be used for animal tracking.

Download Full-text

Apply VGGNet-Based Deep Learning Model of Vibration Data for Prediction Model of Gravity Acceleration Equipment

10.20944/preprints202012.0646.v1 ◽

2020 ◽

Author(s):

SeonWoo Lee ◽

HyeonTak Yu ◽

HoJun Yang ◽

InSeo Song ◽

JaeHeung Yang ◽

...

Keyword(s):

Deep Learning ◽

Prediction Model ◽

Learning Model ◽

Learning Models ◽

Vibration Signals ◽

Vibration Data ◽

Amplitude Data ◽

Feature Based ◽

Gravity Acceleration ◽

Deep Learning Model

Hypergravity accelerators are a type of large machinery used for gravity training or medical research. A failure of such large equipment can be a serious problem in terms of safety or costs. This paper proposes a prediction model that can proactively prevent failures that may occur in a hy-pergravity accelerator. The method proposed in this paper was to convert vibration signals to spectograms and perform classification training using a deep learning model. An experiment was conducted to evaluate the performance of the method proposed in this paper. A 4-channel accel-erometer was attached to the bearing housing, which is a rotor, and time-amplitude data were obtained from the measured values by sampling. The data were converted to a two-dimensional spectrogram, and classification training was performed using a deep learning model for four conditions of the equipment: Unbalance, Misalignment, Shaft Rubbing, and Normal. The ex-perimental results showed that the proposed method had a 99.5% F1-Score, which was up to 23% higher than the 76.25% for existing feature-based learning models.

Download Full-text

Research on Community Public Service Information Collaborative Governance Based on Deep Learning Model

Journal of Mathematics ◽

10.1155/2021/4727617 ◽

2021 ◽

Vol 2021 ◽

pp. 1-9

Author(s):

Yajing Liu

Keyword(s):

Deep Learning ◽

Prediction Model ◽

Public Service ◽

Public Information ◽

Collaborative Governance ◽

Learning Model ◽

Social Risks ◽

Service Information ◽

Analysis Theory ◽

Deep Learning Model

The communities have significantly increased in number and the environment has become complex. There are problems such as poor information collection in community public service information governance, lack of relevant analysis standards and models, and unreliable prediction results. In order to forecast and manage the risk information of the community, this research analyzes the public information of the community through the collaborative deep learning model. First of all, the information characteristic factors are selected that affect social risks based on the correlation analysis theory. Secondly, the convolutional neural network is used in deep learning for simulation of the community risk prediction model. Finally, through the comparative analysis of the model prediction results, it can be concluded that the accuracy rate of the proposed prediction model reaches 92.5%. An effective collaborative deep learning model is used to govern community public service information.

Download Full-text