scholarly journals Dual Co-Attention-Based Multi-Feature Fusion Method for Rumor Detection

Information ◽  
2022 ◽  
Vol 13 (1) ◽  
pp. 25
Author(s):  
Changsong Bing ◽  
Yirong Wu ◽  
Fangmin Dong ◽  
Shouzhi Xu ◽  
Xiaodi Liu ◽  
...  
Keyword(s):  
Neural Network ◽  
Social Media ◽  
Feature Fusion ◽  
Instant Messaging ◽  
Attention Mechanism ◽  
Fusion Method ◽  
User Profiles ◽  
Hidden Layer ◽  
Rumor Detection ◽  
The Impact

Social media has become more popular these days due to widely used instant messaging. Nevertheless, rumor propagation on social media has become an increasingly important issue. The purpose of this study is to investigate the impact of various features in social media on rumor detection, propose a dual co-attention-based multi-feature fusion method for rumor detection, and explore the detection capability of the proposed method in early rumor detection tasks. The proposed BERT-based Dual Co-attention Neural Network (BDCoNN) method for rumor detection, which uses BERT for word embedding . It simultaneously integrates features from three sources: publishing user profiles, source tweets, and comments. In the BDCoNN method, user discrete features and identity descriptors in user profiles are extracted using a one-dimensional convolutional neural network (CNN) and TextCNN, respectively. The bidirectional gate recurrent unit network (BiGRU) with a hierarchical attention mechanism is used to learn the hidden layer representation of tweet sequence and comment sequence. A dual collaborative attention mechanism is used to explore the correlation among publishing user profiles, tweet content, and comments. Then the feature vector is fed into classifier to identify the implicit differences between rumor spreaders and non-rumor spreaders. In this study, we conducted several experiments on the Weibo and CED datasets collected from microblog. The results show that the proposed method achieves the state-of-the-art performance compared with baseline methods, which is 5.2% and 5% higher than the dEFEND. The F1 value is increased by 4.4% and 4%, respectively. In addition, this paper conducts research on early rumor detection tasks, which verifies the proposed method detects rumors more quickly and accurately than competitors.

scholarly journals A Geometric Perspective on Information Plane Analysis

Entropy ◽  
2021 ◽  
Vol 23 (6) ◽  
pp. 711
Author(s):  
Mina Basirat ◽  
Bernhard C. Geiger ◽  
Peter M. Roth
Keyword(s):  
Neural Network ◽  
Mutual Information ◽  
Geometric Interpretation ◽  
Neural Network Training ◽  
Neural Network Learning ◽  
Network Learning ◽  
Plane Analysis ◽  
Network Training ◽  
Hidden Layer ◽  
The Impact

Information plane analysis, describing the mutual information between the input and a hidden layer and between a hidden layer and the target over time, has recently been proposed to analyze the training of neural networks. Since the activations of a hidden layer are typically continuous-valued, this mutual information cannot be computed analytically and must thus be estimated, resulting in apparently inconsistent or even contradicting results in the literature. The goal of this paper is to demonstrate how information plane analysis can still be a valuable tool for analyzing neural network training. To this end, we complement the prevailing binning estimator for mutual information with a geometric interpretation. With this geometric interpretation in mind, we evaluate the impact of regularization and interpret phenomena such as underfitting and overfitting. In addition, we investigate neural network learning in the presence of noisy data and noisy labels.

scholarly journals Applying of machine learning in the construction of a voice-controlled interface on the example of a music player

2019 ◽  
Vol 13 ◽  
pp. 302-309
Author(s):  
Jakub Basiakowski
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Convolutional Neural Network ◽  
Feedforward Neural Network ◽  
Hidden Layer ◽  
Music Player ◽  
The Impact

The following paper presents the results of research on the impact of machine learning in the construction of a voice-controlled interface. Two different models were used for the analysys: a feedforward neural network containing one hidden layer and a more complicated convolutional neural network. What is more, a comparison of the applied models was presented. This comparison was performed in terms of quality and the course of training.

scholarly journals Pregnancy-Related Information Seeking and Sharing in the Social Media Era Among Expectant Mothers: Qualitative Study (Preprint)

2019 ◽  
Author(s):  
Chengyan Zhu ◽  
Runxi Zeng ◽  
Wei Zhang ◽  
Richard Evans ◽  
Rongrong He
Keyword(s):  
Social Media ◽  
Pregnant Women ◽  
Information Seeking ◽  
Chinese Culture ◽  
Instant Messaging ◽  
Media Usage ◽  
Related Information ◽  
Expectant Mothers ◽  
Media Channels ◽  
The Impact

BACKGROUND Social media has become the most popular communication tool used by Chinese citizens, including expectant mothers. An increasing number of women have adopted various forms of social media channels, such as interactive websites, instant messaging, and mobile apps, to solve problems and obtain answers to queries during pregnancy. Although the use of the internet by pregnant women has been studied extensively worldwide, limited research exists that explores the changing social media usage habits in China, where the 1 child policy ended in 2015. OBJECTIVE This study aimed to (1) present the status quo of pregnancy-related information seeking and sharing via social media among Chinese expectant mothers, (2) reveal the impact of social media usage, and (3) shed light on pregnancy-related health services delivered via social media channels. METHODS A qualitative approach was employed to examine social media usage and its consequences on pregnant women. A total of 20 women who had conceived and were at various stages of pregnancy were interviewed from July 20 to August 10, 2017. Thematic analysis was conducted on the collected data to identify patterns in usage. RESULTS Overall, 80% (16/20) of participants were aged in their 20s (mean 28.5 years [SD 4.3]). All had used social media for pregnancy-related purposes. For the seeking behavior, 18 codes were merged into 4 themes, namely, gravida, fetus, delivery, and the postpartum period; whereas for sharing behaviors, 10 codes were merged into 4 themes, namely, gravida, fetus, delivery, and caretaker. Lurking, small group sharing, bad news avoidance, and cross-checking were identified as the preferred patterns for using social media. Overall, 95% (19/20) of participants reported a positive mental impact from using social media during their pregnancy. CONCLUSIONS It is indisputable that social media has played an increasingly important role in supporting expectant mothers in China. The specific seeking and sharing patterns identified in this study indicate that the general quality of pregnancy-related information on social media, as well as Chinese culture toward pregnancy, is improving. The new themes that merge in pregnancy-related social media use represent a shift toward safe pregnancy and the promotion of a more enjoyable pregnancy. Future prenatal care should provide further information on services related to being comfortable during pregnancy and reducing the inequality of social media–based services caused by the digital divide.

scholarly journals A feature fusion deep-projection convolution neural network for vehicle detection in aerial images

PLoS ONE ◽  
2021 ◽  
Vol 16 (5) ◽  
pp. e0250782
Author(s):  
Bin Wang ◽  
Bin Xu
Keyword(s):  
Neural Network ◽  
Feature Fusion ◽  
Rapid Development ◽  
Vehicle Detection ◽  
Convolution Neural Network ◽  
Aerial Images ◽  
Semantic Features ◽  
General Object ◽  
High Level ◽  
The Impact

With the rapid development of Unmanned Aerial Vehicles, vehicle detection in aerial images plays an important role in different applications. Comparing with general object detection problems, vehicle detection in aerial images is still a challenging research topic since it is plagued by various unique factors, e.g. different camera angle, small vehicle size and complex background. In this paper, a Feature Fusion Deep-Projection Convolution Neural Network is proposed to enhance the ability to detect small vehicles in aerial images. The backbone of the proposed framework utilizes a novel residual block named stepwise res-block to explore high-level semantic features as well as conserve low-level detail features at the same time. A specially designed feature fusion module is adopted in the proposed framework to further balance the features obtained from different levels of the backbone. A deep-projection deconvolution module is used to minimize the impact of the information contamination introduced by down-sampling/up-sampling processes. The proposed framework has been evaluated by UCAS-AOD, VEDAI, and DOTA datasets. According to the evaluation results, the proposed framework outperforms other state-of-the-art vehicle detection algorithms for aerial images.

scholarly journals Emotion-Semantic-Enhanced Bidirectional LSTM with Multi-Head Attention Mechanism for Microblog Sentiment Analysis

Information ◽  
2020 ◽  
Vol 11 (5) ◽  
pp. 280
Author(s):  
Shaoxiu Wang ◽  
Yonghua Zhu ◽  
Wenjing Gao ◽  
Meng Cao ◽  
Mengyao Li
Keyword(s):  
Sentiment Analysis ◽  
Short Term Memory ◽  
Syntactic Structure ◽  
Contextual Information ◽  
Research Field ◽  
Attention Mechanism ◽  
Feature Representation ◽  
Mechanism Model ◽  
Hidden Layer ◽  
The Impact

The sentiment analysis of microblog text has always been a challenging research field due to the limited and complex contextual information. However, most of the existing sentiment analysis methods for microblogs focus on classifying the polarity of emotional keywords while ignoring the transition or progressive impact of words in different positions in the Chinese syntactic structure on global sentiment, as well as the utilization of emojis. To this end, we propose the emotion-semantic-enhanced bidirectional long short-term memory (BiLSTM) network with the multi-head attention mechanism model (EBILSTM-MH) for sentiment analysis. This model uses BiLSTM to learn feature representation of input texts, given the word embedding. Subsequently, the attention mechanism is used to assign the attentive weights of each words to the sentiment analysis based on the impact of emojis. The attentive weights can be combined with the output of the hidden layer to obtain the feature representation of posts. Finally, the sentiment polarity of microblog can be obtained through the dense connection layer. The experimental results show the feasibility of our proposed model on microblog sentiment analysis when compared with other baseline models.

scholarly journals Pearson Correlation-Based Feature Selection for Document Classification Using Balanced Training

Sensors ◽  
2020 ◽  
Vol 20 (23) ◽  
pp. 6793
Author(s):  
Inzamam Mashood Nasir ◽  
Muhammad Attique Khan ◽  
Mussarat Yasmin ◽  
Jamal Hussain Shah ◽  
Marcin Gabryel ◽  
...  
Keyword(s):  
Neural Network ◽  
Feature Selection ◽  
Data Augmentation ◽  
Feature Fusion ◽  
Pearson Correlation ◽  
Document Classification ◽  
Document Image ◽  
Support Vector ◽  
Neural Network Models ◽  
The Impact

Documents are stored in a digital form across several organizations. Printing this amount of data and placing it into folders instead of storing digitally is against the practical, economical, and ecological perspective. An efficient way of retrieving data from digitally stored documents is also required. This article presents a real-time supervised learning technique for document classification based on deep convolutional neural network (DCNN), which aims to reduce the impact of adverse document image issues such as signatures, marks, logo, and handwritten notes. The proposed technique’s major steps include data augmentation, feature extraction using pre-trained neural network models, feature fusion, and feature selection. We propose a novel data augmentation technique, which normalizes the imbalanced dataset using the secondary dataset RVL-CDIP. The DCNN features are extracted using the VGG19 and AlexNet networks. The extracted features are fused, and the fused feature vector is optimized by applying a Pearson correlation coefficient-based technique to select the optimized features while removing the redundant features. The proposed technique is tested on the Tobacco3482 dataset, which gives a classification accuracy of 93.1% using a cubic support vector machine classifier, proving the validity of the proposed technique.

scholarly journals A New Time-Space Attention Mechanism Driven Multi-Feature Fusion Method for Tool Wear Monitoring

2021 ◽  
Author(s):  
Tingting Feng ◽  
Liang Guo ◽  
Hongli Gao ◽  
Tao Chen ◽  
Yaoxiang Yu ◽  
...  
Keyword(s):  
Tool Wear ◽  
Feature Fusion ◽  
Attention Mechanism ◽  
Fusion Method ◽  
Tool Wear Monitoring ◽  
Time And Space ◽  
Time Space ◽  
Sensor Signals ◽  
Wear Monitoring ◽  
New Time

Abstract In order to accurately monitor the tool wear process, it is usually necessary to collect a variety of sensor signals during the cutting process. Different sensor signals in the feature space can provide complementary information. In addition, the monitoring signal is time series data, which also contains a wealth of tool degradation information in the time dimension. However, how to fuse multi-sensor information in time and space dimensions is a key issue that needs to be solved. This paper proposes a new time-space attention mechanism driven multi-feature fusion method to realize the tool wear monitoring. Firstly, lots of features are established from different sensor signals and selected preliminarily. Then, a new feature fusion model with time-space attention mechanism is constructed to fuse features in time and space dimensions. Finally, the tool degradation model is established according to the predicted wear, and the tool remaining useful life is predicted by particle filter. The effectiveness of this method is verified by a tool life cycle wear experiment. Through comparing with other feature fusion models, it is demonstrated that the proposed method realizes the tool wear monitoring more accurately and has better stability.

scholarly journals A Hybrid Deep Learning Model to Predict the Impact of COVID-19 on Mental Health form Social Media Big Data

2021 ◽  
Author(s):  
Tapotosh Ghosh ◽  
Md. Hasan Al Banna ◽  
Md. Jaber Al Nahian ◽  
Kazi Abu Taher ◽  
M Shamim Kaiser ◽  
...  
Keyword(s):  
Neural Network ◽  
Mental Health ◽  
Social Media ◽  
Big Data ◽  
Language Processing ◽  
Short Term Memory ◽  
Daily Basis ◽  
Significant Rise ◽  
Special Focus ◽  
The Impact

The novel coronavirus disease (COVID-19) pandemic is provoking a prevalent consequence on mental health because of less interaction among people, economic collapse, negativity, fear of losing jobs, and death of the near and dear ones. To express their mental state, people often are using social media as one of the preferred means. Due to reduced outdoor activities, people are spending more time on social media than usual and expressing their emotion of anxiety, fear, and depression. On a daily basis, about 2.5 quintillion bytes of data are generated on social media, analyzing this big data can become an excellent means to evaluate the effect of COVID-19 on mental health. In this work, we have analyzed data from Twitter microblog (tweets) to find out the effect of COVID-19 on peoples mental health with a special focus on depression. We propose a novel pipeline, based on recurrent neural network (in the form of long-short term memory or LSTM) and convolutional neural network, capable of identifying depressive tweets with an accuracy of 99.42%. Preprocessed using various natural language processing techniques, the aim was to find out depressive emotion from these tweets. Analyzing over 571 thousand tweets posted between October 2019 and May 2020 by 482 users, a significant rise in depressing tweets was observed between February and May of 2020, which indicates as an impact of the long ongoing COVID-19 pandemic situation.

scholarly journals Evaluation of Parameter Settings for Training Neural Networks Using Backpropagation Algorithms

2020 ◽  
Vol 11 (4) ◽  
pp. 62-85
Author(s):  
Leema N. ◽  
Khanna H. Nehemiah ◽  
Elgin Christo V. R. ◽  
Kannan A.
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Activation Function ◽  
Neural Network Training ◽  
Network Parameter ◽  
Network Parameters ◽  
Network Training ◽  
Hidden Layer ◽  
Function Number ◽  
The Impact

Artificial neural networks (ANN) are widely used for classification, and the training algorithm commonly used is the backpropagation (BP) algorithm. The major bottleneck faced in the backpropagation neural network training is in fixing the appropriate values for network parameters. The network parameters are initial weights, biases, activation function, number of hidden layers and the number of neurons per hidden layer, number of training epochs, learning rate, minimum error, and momentum term for the classification task. The objective of this work is to investigate the performance of 12 different BP algorithms with the impact of variations in network parameter values for the neural network training. The algorithms were evaluated with different training and testing samples taken from the three benchmark clinical datasets, namely, Pima Indian Diabetes (PID), Hepatitis, and Wisconsin Breast Cancer (WBC) dataset obtained from the University of California Irvine (UCI) machine learning repository.

scholarly journals Impact of WhatsApp Interaction on Improving L2 Speaking Skills

2020 ◽  
Vol 15 (03) ◽  
pp. 250
Author(s):  
Sherine Akkara ◽  
V Surya Seshagiri Anumula ◽  
Mallikarjuna Sastry Mallampalli
Keyword(s):  
Social Media ◽  
Second Language ◽  
Second Language Acquisition ◽  
Collaborative Learning ◽  
Informal Learning ◽  
Instant Messaging ◽  
Target Language ◽  
L2 Speaking ◽  
The Impact ◽  
Speaking Skills

Mobile Assisted Language Learning (MALL) not only provides access to authentic learning resources and facilitates second language (L2) learning anytime and anywhere but also offers scope for informal learning beyond the classroom. Social media with instant messaging and multi-modal communication and information sharing provide platforms for interaction with peers and collaborative learning to hone their L2 skills. There is little research on informal learning through WhatsApp in enhancing L2 speaking skills. This paper studies the impact of interaction and informal learning in a WhatsApp group on improving a) fluency and coherence, b) lexical resource, c) grammatical range and accuracy and d) pronunciation which form the criteria for assessing speaking skills in IELTS. It also studies how participants perceive the changes in their speaking skills based on the band descriptors of IELTS. Mixed methods approach was adopted to obtain data from the group consisting of mixed ability participants (n=110) with pre and post speaking assessments and pre and post surveys. The participants were given collaborative learning activities and problem solving tasks at regular intervals for over two semesters. The results indicated statistically significant difference in their speaking skills and considerable change in their perceptions. The study has implications for both teachers and researchers of second language acquisition (SLA) for incorporating social media for interaction in the target language beyond the classroom.

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