scholarly journals A Deep Learning Ensemble to Classify Anxiety, Depression, and their Comorbidity from Texts of Social Networks

2021 ◽  
Vol 12 (3) ◽  
Author(s):  
Vanessa Souza ◽  
Jeferson Nobre ◽  
Karin Becker
Keyword(s):  
Social Networks ◽  
Deep Learning ◽  
Automatic Identification ◽  
Exact Match ◽  
Learning Techniques ◽  
Binary Classifiers ◽  
Using Data ◽  
Specific Disorders ◽  
And Control ◽  
Anxiety Depression

The use of social networks to expose personal difficulties has enabled works on the automatic identification of specific mental conditions, particularly depression. Depression is the most incapacitating disease worldwide, and it has an alarming comorbidity rate with anxiety. In this paper, we explore deep learning techniques to develop a stacking ensemble to automatically identify depression, anxiety, and comorbidity, using data extracted from Reddit. The stacking is composed of specialized single-label binary classifiers that distinguish between specific disorders and control users. A meta-learner explores these base classifiers as a context for reaching a multi-label, multi-class decision. We developed extensive experiments using alternative architectures (LSTM, CNN, and their combination), word embeddings, and ensemble topologies. All base classifiers and ensembles outperformed the baselines. The CNN-based binary classifiers achieved the best performance, with f-measures of 0.79 for depression, 0.78 for anxiety, and 0.78 for comorbidity. The ensemble topology with best performance (Hamming Loss of 0.29 and Exact Match Ratio of 0.47) combines base classifiers according to three architectures, and do not include comorbidity classifiers. Using SHAP, we confirmed the influential features are related to symptoms of these disorders.

scholarly journals Characterization of Anxiety, Depression, and their Comorbidity from Texts of Social Networks

2020 ◽  
Author(s):  
Vanessa Borba de Souza ◽  
Jéferson Campos Nobre ◽  
Karin Becker
Keyword(s):  
Social Networks ◽  
Public Health Issue ◽  
Automatic Identification ◽  
Learning Techniques ◽  
Binary Classifiers ◽  
Specific Disorders ◽  
Anxiety Depression ◽  
The Relationship ◽  
High Comorbidity

Depression has become a public health issue, and the high comorbidity rate with anxiety worsens the clinical picture. Early identification is crucial for decisions on the proper line of treatment. The use of social networks to expose personal difficulties has enabled works on the automatic identification of specific mental conditions, particularly depression. This paper explores deep learning techniques to develop an ensemble stacking classifier for the automatic identification of depression, anxiety, and their comorbidity, using a self-diagnosed dataset extracted from Reddit. At the lowest level, binary classifiers make predictions about specific disorders, outperforming all baseline models. A meta-learner explores these weak classifiers as a context for reaching a multi-label decision, achieving a Hamming Loss of 0.29 and Exact Match Ratio of 0.47. We performed a qualitative analysis using SHAP, which confirmed the relationship between the influential features and symptoms of these disorders.

scholarly journals DeepOSN: Bringing deep learning as malicious detection scheme in online social network

2020 ◽  
Vol 9 (1) ◽  
pp. 146
Author(s):  
Putra Wanda ◽  
Marselina Endah Hiswati ◽  
Huang J. Jie
Keyword(s):  
Social Networks ◽  
Computer Vision ◽  
Deep Learning ◽  
Online Social Networks ◽  
Link Prediction ◽  
Online Social Network ◽  
Detection Scheme ◽  
Malicious Activity ◽  
Manual Analysis ◽  
Learning Techniques

Manual analysis for malicious prediction in Online Social Networks (OSN) is time-consuming and costly. With growing users within the environment, it becomes one of the main obstacles. Deep learning is growing algorithm that gains a big success in computer vision problem. Currently, many research communities have proposed deep learning techniques to automate security tasks, including anomalous detection, malicious link prediction, and intrusion detection in OSN. Notably, this article describes how deep learning makes the OSN security technique more intelligent for detecting malicious activity by establishing a classifier model.

scholarly journals Dynamical Modeling, Analysis, and Control of Information Diffusion over Social Networks: A Deep Learning-Based Recommendation Algorithm in Social Network

2020 ◽  
Vol 2020 ◽  
pp. 1-8
Author(s):  
Kefei Cheng ◽  
Xiaoyong Guo ◽  
Xiaotong Cui ◽  
Fengchi Shan
Keyword(s):  
Social Networks ◽  
Deep Learning ◽  
Social Network ◽  
Information Diffusion ◽  
Recommendation Algorithm ◽  
Central Sequence ◽  
Modeling Analysis ◽  
News Recommendation ◽  
Browsing Behavior ◽  
And Control

The recommendation algorithm can break the restriction of the topological structure of social networks, enhance the communication power of information (positive or negative) on social networks, and guide the information transmission way of the news in social networks to a certain extent. In order to solve the problem of data sparsity in news recommendation for social networks, this paper proposes a deep learning-based recommendation algorithm in social network (DLRASN). First, the algorithm is used to process behavioral data in a serializable way when users in the same social network browse information. Then, global variables are introduced to optimize the encoding way of the central sequence of Skip-gram, in which way online users’ browsing behavior habits can be learned. Finally, the information that the target users’ have interests in can be calculated by the similarity formula and the information is recommended in social networks. Experimental results show that the proposed algorithm can improve the recommendation accuracy.

scholarly journals Deep Learning Techniques for Community Detection in Social Networks

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 96016-96026 ◽  
Author(s):  
Ling Wu ◽  
Qishan Zhang ◽  
Chi-Hua Chen ◽  
Kun Guo ◽  
Deqin Wang
Keyword(s):  
Social Networks ◽  
Deep Learning ◽  
Community Detection ◽  
Learning Techniques

scholarly journals Link Prediction in Facebook using Web Scrapping and Deep Learning Techniques

2021 ◽  
Vol 10 (1) ◽  
pp. 194-197
Keyword(s):  
Social Networks ◽  
Deep Learning ◽  
Link Prediction ◽  
Machine Learning Techniques ◽  
Feature Engineering ◽  
Internet Technologies ◽  
Learning Techniques ◽  
The Social ◽  
Feature Based ◽  
Day By Day

As Internet technologies develop continuously social networks are getting more popular day by day. People are connected with each other via virtual applications. Using the Link Prediction in social networks more people get connected, may be they are friends, may be work together at the same workplace and may be their education are. Machine learning techniques are used to analyze the link between the nodes of the network and also create a better link prediction model through deep learning. The objective of this research is to measure the performance using the different techniques to predict link between the social networks. Using deep learning, feature engineering can be reduced for link prediction. In this research, the feature based learning is used to predict the link for better performance. Dataset is obtained by scraping the profile of Facebook users and they are used along with the random forest and graph convolution neural network to measure the performance of link prediction in social networks.

scholarly journals Deep Learning Based Classification of Radar Spectral Maps

2021 ◽  
pp. 99-104
Author(s):  
Tong Lin ◽  
◽  
Xin Chen ◽  
Xiao Tang ◽  
Ling He ◽  
...  
Keyword(s):  
Deep Learning ◽  
Classification Accuracy ◽  
Region Of Interest ◽  
Radar Target ◽  
Automatic Annotation ◽  
Deep Convolutional Neural Networks ◽  
Learning Techniques ◽  
Using Data ◽  
Map Data

This paper discusses the use of deep convolutional neural networks for radar target classification. In this paper, three parts of the work are carried out: firstly, effective data enhancement methods are used to augment the dataset and address unbalanced datasets. Second, using deep learning techniques, we explore an effective framework for classifying and identifying targets based on radar spectral map data. By using data enhancement and the framework, we achieved an overall classification accuracy of 0.946. In the end, we researched the automatic annotation of image ROI (region of interest). By adjusting the model, we obtained a 93% accuracy in automatic labeling and classification of targets for both car and cyclist categories.

scholarly journals Data assimilation as a deep learning tool to infer ODE representations of dynamical models

2019 ◽  
Author(s):  
Marc Bocquet ◽  
Julien Brajard ◽  
Alberto Carrassi ◽  
Laurent Bertino
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Data Assimilation ◽  
Physical Model ◽  
High Dimensional ◽  
Learning Techniques ◽  
Dimensional Models ◽  
Using Data ◽  
Learning Software ◽  
Bayesian Data Assimilation

Abstract. Recent progress in machine learning has shown how to forecast and, to some extent, learn the dynamics of a model from its output, resorting in particular to neural networks and deep learning techniques. We will show how the same goal can be directly achieved using data assimilation techniques without leveraging on machine learning software libraries, with a view to high-dimensional models. The dynamics of a model are learned from its observation and an ordinary differential equation (ODE) representation of this model is inferred using a recursive nonlinear regression. Because the method is embedded in a Bayesian data assimilation framework, it can learn from partial and noisy observations of a state trajectory of the physical model. Moreover, a space-wise local representation of the ODE system is introduced and is key to cope with high-dimensional models. It has recently been suggested that neural network architectures could be interpreted as dynamical systems. Reciprocally, we show that our ODE representations are reminiscent of deep learning architectures. Furthermore, numerical analysis considerations on stability shed light on the assets and limitations of the method. The method is illustrated on several chaotic discrete and continuous models of various dimensions, with or without noisy observations, with the goal to identify or improve the model dynamics, build a surrogate or reduced model, or produce forecasts from mere observations of the physical model.

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