scholarly journals Deep learning for constructing microblog behavior representation to identify social media user’s personality

2016 ◽  
Vol 2 ◽  
pp. e81 ◽  
Author(s):  
Xiaoqian Liu ◽  
Tingshao Zhu
Keyword(s):  
Deep Learning ◽  
Prediction Model ◽  
Semantic Information ◽  
Learning Algorithm ◽  
Rapid Development ◽  
Feature Learning ◽  
Extraction Methods ◽  
Deep Learning Algorithm ◽  
Personality Prediction ◽  
Heuristic Analysis

Due to the rapid development of information technology, the Internet has gradually become a part of everyday life. People would like to communicate with friends to share their opinions on social networks. The diverse behavior on socials networks is an ideal reflection of users’ personality traits. Existing behavior analysis methods for personality prediction mostly extract behavior attributes with heuristic analysis. Although they work fairly well, they are hard to extend and maintain. In this paper, we utilize a deep learning algorithm to build a feature learning model for personality prediction, which could perform an unsupervised extraction of the Linguistic Representation Feature Vector (LRFV) activity without supervision from text actively published on the Sina microblog. Compared with other feature extractsion methods, LRFV, as an abstract representation of microblog content, could describe a user’s semantic information more objectively and comprehensively. In the experiments, the personality prediction model is built using a linear regression algorithm, and different attributes obtained through different feature extraction methods are taken as input of the prediction model, respectively. The results show that LRFV performs better in microblog behavior descriptions, and improves the performance of the personality prediction model.

scholarly journals Deep learning for constructing microblog behavior representation to identify social media user's personality

2016 ◽  
Author(s):  
Xiaoqian Liu ◽  
Tingshao Zhu
Keyword(s):  
Feature Extraction ◽  
Deep Learning ◽  
Prediction Model ◽  
Learning Algorithm ◽  
Rapid Development ◽  
Feature Learning ◽  
Extraction Methods ◽  
Deep Learning Algorithm ◽  
Personality Prediction ◽  
Behavior Description

Due to the rapid development of information technology, Internet has become part of everyday life gradually. People would like to communicate with friends to share their opinions on social networks. The diverse social network behavior is an ideal users' personality traits reflection. Existing behavior analysis methods for personality prediction mostly extract behavior attributes with heuristic. Although they work fairly well, but it is hard to extend and maintain. In this paper, for personality prediction, we utilize deep learning algorithm to build feature learning model, which could unsupervised extract Linguistic Representation Feature Vector (LRFV) from text published on Sina Micro-blog actively. Compared with other feature extraction methods, LRFV, as an abstract representation of Micro-blog content, could describe use's semantic information more objectively and comprehensively. In the experiments, the personality prediction model is built using linear regression algorithm, and different attributes obtained through different feature extraction methods are taken as input of prediction model respectively. The results show that LRFV performs more excellently in micro-blog behavior description and improve the performance of personality prediction model.

scholarly journals Deep learning for constructing microblog behavior representation to identify social media user's personality

2016 ◽  
Author(s):  
Xiaoqian Liu ◽  
Tingshao Zhu
Keyword(s):  
Feature Extraction ◽  
Deep Learning ◽  
Prediction Model ◽  
Learning Algorithm ◽  
Rapid Development ◽  
Feature Learning ◽  
Extraction Methods ◽  
Deep Learning Algorithm ◽  
Personality Prediction ◽  
Behavior Description

Due to the rapid development of information technology, Internet has become part of everyday life gradually. People would like to communicate with friends to share their opinions on social networks. The diverse social network behavior is an ideal users' personality traits reflection. Existing behavior analysis methods for personality prediction mostly extract behavior attributes with heuristic. Although they work fairly well, but it is hard to extend and maintain. In this paper, for personality prediction, we utilize deep learning algorithm to build feature learning model, which could unsupervised extract Linguistic Representation Feature Vector (LRFV) from text published on Sina Micro-blog actively. Compared with other feature extraction methods, LRFV, as an abstract representation of Micro-blog content, could describe use's semantic information more objectively and comprehensively. In the experiments, the personality prediction model is built using linear regression algorithm, and different attributes obtained through different feature extraction methods are taken as input of prediction model respectively. The results show that LRFV performs more excellently in micro-blog behavior description and improve the performance of personality prediction model.

scholarly journals Deep Learning Method on Target Echo Signal Recognition for Obscurant Penetrating Lidar Detection in Degraded Visual Environments

Sensors ◽  
2020 ◽  
Vol 20 (12) ◽  
pp. 3424
Author(s):  
Xujia Liang ◽  
Zhonghua Huang ◽  
Liping Lu ◽  
Zhigang Tao ◽  
Bing Yang ◽  
...  
Keyword(s):  
Deep Learning ◽  
Autonomous Vehicles ◽  
Mobile Robotics ◽  
Learning Algorithm ◽  
Rapid Development ◽  
Detection Methods ◽  
Signal Recognition ◽  
Echo Signal ◽  
Deep Learning Algorithm ◽  
Recognition Criterion

With the rapid development of autonomous vehicles and mobile robotics, the desire to advance robust light detection and ranging (Lidar) detection methods for real world applications is increasing. However, this task still suffers in degraded visual environments (DVE), including smoke, dust, fog, and rain, as the aerosols lead to false alarm and dysfunction. Therefore, a novel Lidar target echo signal recognition method, based on a multi-distance measurement and deep learning algorithm is presented in this paper; neither the backscatter suppression nor the denoise functions are required. The 2-D spectrogram images are constructed by using the frequency-distance relation derived from the 1-D echo signals of the Lidar sensor individual cell in the course of approaching target. The characteristics of the target echo signal and noise in the spectrogram images are analyzed and determined; thus, the target recognition criterion is established accordingly. A customized deep learning algorithm is subsequently developed to perform the recognition. The simulation and experimental results demonstrate that the proposed method can significantly improve the Lidar detection performance in DVE.

scholarly journals Evaluation Method of the Influence of Sports Training on Physical Index Based on Deep Learning

2021 ◽  
Vol 2021 ◽  
pp. 1-8 ◽  
Author(s):  
Zhongxiao Wang
Keyword(s):  
Deep Learning ◽  
Physical Training ◽  
Evaluation System ◽  
Evaluation Method ◽  
Learning Algorithm ◽  
Rapid Development ◽  
Sports Training ◽  
Deep Learning Algorithm ◽  
The Impact ◽  
Physical Index

With the rapid development of deep learning, computer vision has also become a rapidly developing field in the field of artificial intelligence. Combining the physical training of deep learning will bring good practical value. Physical training has different effects on people’s body shape, physical function, and physical quality. It is mainly reflected in the changes of relevant physical indicators after physical training. Therefore, the purpose of this article is to study the method of evaluating the impact of sports training on physical indicators based on deep learning. This paper mainly uses the convolutional neural network in deep learning to design sports training, then constructs the evaluation system of physical index impact, and finally uses the deep learning algorithm to evaluate the impact of physical index. The experimental results show that the accuracy of the algorithm proposed in this paper is significantly higher than that of the other three algorithms. Firstly, in the angular motion, the accuracy of the mean algorithm is 0.4, the accuracy of the variance algorithm is 0.2, the accuracy of the RFE algorithm is 0.4, and the accuracy of the DLA algorithm is 0.6. Similarly, in foot racing and skill sports, the accuracy of the algorithm proposed in this paper is significantly higher than that of other algorithms. Therefore, the method proposed in this paper is more effective in the evaluation of the impact of physical training on physical indicators.

scholarly journals Research on Network Security Application Based on Deep Learning

CONVERTER ◽  
2021 ◽  
pp. 598-605
Author(s):  
Zhao Jianchao
Keyword(s):  
Deep Learning ◽  
Learning Algorithm ◽  
Rapid Development ◽  
Internet Security ◽  
Detection Accuracy ◽  
Data Sets ◽  
Learning Technology ◽  
Data Set ◽  
Deep Learning Algorithm ◽  
Internet Industry

Behind the rapid development of the Internet industry, Internet security has become a hidden danger. In recent years, the outstanding performance of deep learning in classification and behavior prediction based on massive data makes people begin to study how to use deep learning technology. Therefore, this paper attempts to apply deep learning to intrusion detection to learn and classify network attacks. Aiming at the nsl-kdd data set, this paper first uses the traditional classification methods and several different deep learning algorithms for learning classification. This paper deeply analyzes the correlation among data sets, algorithm characteristics and experimental classification results, and finds out the deep learning algorithm which is relatively good at. Then, a normalized coding algorithm is proposed. The experimental results show that the algorithm can improve the detection accuracy and reduce the false alarm rate.

A deep learning based dynamic COD prediction model for urban sewage

2019 ◽  
Vol 5 (12) ◽  
pp. 2210-2218
Author(s):  
Zifei Wang ◽  
Yi Man ◽  
Yusha Hu ◽  
Jigeng Li ◽  
Mengna Hong ◽  
...  
Keyword(s):  
Deep Learning ◽  
Prediction Model ◽  
Learning Algorithm ◽  
Urban Sewage ◽  
Model Based ◽  
Deep Learning Algorithm ◽  
Aeration Control

An influent COD prediction model based on the CNN-LSTM deep learning algorithm is proposed as the basis of aeration control in WWTPs.

scholarly journals Construction of cardiac arrhythmia prediction model using deep learning and gradient boosting

2021 ◽  
Vol 13 (3) ◽  
pp. 114-119
Author(s):  
Dhanar Bintang Pratama ◽  
Favian Dewanta ◽  
Syamsul Rizal
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Prediction Model ◽  
Medical Staff ◽  
Heart Attack ◽  
Learning Algorithm ◽  
Gradient Boosting ◽  
Ecg Signal ◽  
Deep Learning Algorithm ◽  
Boosting Algorithm

Arrhythmia is a condition in which the rhythm of heartbeat becomes irregular. This condition in extreme cases can lead to fatal heart attack accidents. In order to reduce heart attack risk, appropriate early treatments should be conducted right after getting results of Arrhythmia condition, which is generated by electrocardiography ECG tools. However, reading ECG results should be done by qualified medical staff in order to diagnose the existence of arrhythmia accurately. This paper proposes a deep learning algorithm method to classify and detect the existence of arrhythmia from ECG reading. Our proposed method relies on Convolutional Neural Network (CNN) to extract feature from a single lead ECG signal and also Gradient Boosting algorithm to predict the final outcome of single lead ECG reading. This method achieved the accuracy of 96.18% and minimized the number of parameters used in CNN Layer.

scholarly journals Plant Disease Identification Based on Deep Learning Algorithm in Smart Farming

2020 ◽  
Vol 2020 ◽  
pp. 1-11
Author(s):  
Yan Guo ◽  
Jin Zhang ◽  
Chengxin Yin ◽  
Xiaonan Hu ◽  
Yu Zou ◽  
...  
Keyword(s):  
Deep Learning ◽  
Agricultural Production ◽  
Plant Disease ◽  
Learning Algorithm ◽  
Rapid Development ◽  
Deep Learning Algorithm ◽  
Rust Diseases ◽  
Ecological Protection ◽  
Smart Farming ◽  
Bacterial Plaque

The identification of plant disease is the premise of the prevention of plant disease efficiently and precisely in the complex environment. With the rapid development of the smart farming, the identification of plant disease becomes digitalized and data-driven, enabling advanced decision support, smart analyses, and planning. This paper proposes a mathematical model of plant disease detection and recognition based on deep learning, which improves accuracy, generality, and training efficiency. Firstly, the region proposal network (RPN) is utilized to recognize and localize the leaves in complex surroundings. Then, images segmented based on the results of RPN algorithm contain the feature of symptoms through Chan–Vese (CV) algorithm. Finally, the segmented leaves are input into the transfer learning model and trained by the dataset of diseased leaves under simple background. Furthermore, the model is examined with black rot, bacterial plaque, and rust diseases. The results show that the accuracy of the method is 83.57%, which is better than the traditional method, thus reducing the influence of disease on agricultural production and being favorable to sustainable development of agriculture. Therefore, the deep learning algorithm proposed in the paper is of great significance in intelligent agriculture, ecological protection, and agricultural production.

scholarly journals Hybrid Deep Learning Algorithm with Open Innovation Perspective: A Prediction Model of Asthmatic Occurrence

2020 ◽  
Vol 12 (15) ◽  
pp. 6143
Author(s):  
Min-Seung Kim ◽  
Jeong-Hee Lee ◽  
Yong-Ju Jang ◽  
Chan-Ho Lee ◽  
Ji-Hye Choi ◽  
...  
Keyword(s):  
Air Pollution ◽  
Time Series ◽  
Deep Learning ◽  
Prediction Model ◽  
National Health ◽  
Respiratory Diseases ◽  
Learning Algorithm ◽  
Atmospheric Pollutants ◽  
Atmospheric Environment ◽  
Deep Learning Algorithm

Due to recent advancements in industrialization, climate change and overpopulation, air pollution has become an issue of global concern and air quality is being highlighted as a social issue. Public interest and concern over respiratory health are increasing in terms of a high reliability of a healthy life or the social sustainability of human beings. Air pollution can have various adverse or deleterious effects on human health. Respiratory diseases such as asthma, the subject of this study, are especially regarded as ‘directly affected’ by air pollution. Since such pollution is derived from the combined effects of atmospheric pollutants and meteorological environmental factors, and it is not easy to estimate its influence on feasible respiratory diseases in various atmospheric environments. Previous studies have used clinical and cohort data based on relatively a small number of samples to determine how atmospheric pollutants affect diseases such as asthma. This has significant limitations in that each sample of the collections is likely to produce inconsistent results and it is difficult to attempt the experiments and studies other than by those in the medical profession. This study mainly focuses on predicting the actual asthmatic occurrence while utilizing and analyzing the data on both the atmospheric and meteorological environment officially released by the government. We used one of the advanced analytic models, often referred to as the vector autoregressive model (VAR), which traditionally has an advantage in multivariate time-series analysis to verify that each variable has a significant causal effect on the asthmatic occurrence. Next, the VAR model was applied to a deep learning algorithm to find a prediction model optimized for the prediction of asthmatic occurrence. The average error rate of the hybrid deep neural network (DNN) model was numerically verified to be about 8.17%, indicating better performance than other time-series algorithms. The proposed model can help streamline the national health and medical insurance system and health budget management in South Korea much more effectively. It can also provide efficiency in the deployment and management of the supply and demand of medical personnel in hospitals. In addition, it can contribute to the promotion of national health, enabling advance alerts of the risk of outbreaks by the atmospheric environment for chronic asthma patients. Furthermore, the theoretical methodologies, experimental results and implications of this study will be able to contribute to our current issues of global change and development in that the meteorological and environmental data-driven, deep-learning prediction model proposed hereby would put forward a macroscopic directionality which leads to sustainable public health and sustainability science.

scholarly journals MOOCVERSITY - Deep Learning Based Dropout Prediction in MOOCs over Weeks

2020 ◽  
Vol 2 (3) ◽  
pp. 140-152 ◽  
Author(s):  
Vignesh Muthukumar ◽  
Dr. Bhalaji N.
Keyword(s):  
Deep Learning ◽  
Data Analysis ◽  
Prediction Model ◽  
Learning Algorithm ◽  
User Interaction ◽  
Initial Step ◽  
Individual Student ◽  
Student Dropout ◽  
Temporal Prediction ◽  
Deep Learning Algorithm

Massive Open Online Courses (MOOCs) has seen a dramatic increase of participants in the last few years with an exponential growth of internet users all around the world. MOOC allows users to attend lectures of top professors from world class universities. Despite flexible accessibility, the common trend observed in each course is that the number of active participants appears to decrease exponentially as the week’s progress. The structure and nature of the courses affects the number of active participants directly. A comprehensive review of the available literature shows that very little intensive work was done using the pattern of user interaction with courses in the field of MOOC data analysis. In this paper, we take an initial step to use the deep learning algorithm to construct the dropout prediction model and produce the predicted individual student dropout probability. Additional improvements are made to optimize the performance of the dropout prediction model and provide the course providers with appropriate interventions based on a temporal prediction mechanism. Our Exploratory Data Analysis demonstrates that there is a strong correlation between click stream actions and successful learner outcomes. Among other features, the deep learning algorithm takes the weekly history of student data into account and thus is able to notice changes in student behaviour over time.

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