Simultaneous Tracking and Separation of Multiple Sources Using Factor Graph Model

2020 ◽  
Vol 28 ◽  
pp. 2848-2864
Author(s):  
Koby Weisberg ◽  
Bracha Laufer-Goldshtein ◽  
Sharon Gannot
Keyword(s):  
Graph Model ◽  
Factor Graph ◽  
Multiple Sources ◽  
Factor Graph Model

scholarly journals Factor Graph Model Based User Profile Matching Across Social Networks

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 152429-152442
Author(s):  
Lidong Wang ◽  
Keyong Hu ◽  
Yun Zhang ◽  
Shihua Cao
Keyword(s):  
Social Networks ◽  
Graph Model ◽  
User Profile ◽  
Factor Graph ◽  
Model Based ◽  
Profile Matching ◽  
Factor Graph Model

Individual Doctor Recommendation in Large Networks by Constrained Optimization

2015 ◽  
Vol 12 (4) ◽  
pp. 16-28 ◽  
Author(s):  
Jibing Gong ◽  
Hong Cheng ◽  
Lili Wang
Keyword(s):  
Graph Model ◽  
Weighted Average ◽  
Factor Graph ◽  
Patient Relationship ◽  
Doctor Patient Relationship ◽  
Data Set ◽  
Medical Social Network ◽  
Doctor Recommendation ◽  
Factor Graph Model ◽  
Individual Doctor

In this paper, the authors try to systematically investigate the problem of individual doctor recommendation and propose a novel method to enable patients to access such intelligent medical service. In their method, the authors first mine doctor-patient ties/relationships via Time-constraint Probability Factor Graph model (TPFG) from a medical social network. Next, they design a constraint-based optimization framework to efficiently improve the accuracy for doctor-patient relationship mining. Last, they propose a novel Individual Doctor Recommendation Model, namely IDR-Model, to compute doctor recommendation success rate based on weighted average method. The authors conduct experiments to verify the method on a real medical data set. Experimental results show that they obtain better accuracy of mining doctor-patient relationship from the network, and doctor recommendation results of IDR-Model are reasonable and satisfactory.

scholarly journals Multi-Pattern Data Mining and Recognition of Primary Electric Appliances from Single Non-Intrusive Load Monitoring Data

Energies ◽  
2019 ◽  
Vol 12 (6) ◽  
pp. 992 ◽  
Author(s):  
Shengli Du ◽  
Mingchao Li ◽  
Shuai Han ◽  
Jonathan Shi ◽  
Heng Li
Keyword(s):  
Clustering Algorithm ◽  
Belief Propagation ◽  
Graph Model ◽  
Electric Power Industry ◽  
Factor Graph ◽  
Monitoring And Control ◽  
Factor Graph Model ◽  
Load Monitoring ◽  
Actual Energy Consumption ◽  
And Control

The electric power industry is an essential part of the energy industry as it strengthens the monitoring and control management of household electricity for the construction of an economic power system. In this paper, a non-intrusive affinity propagation (AP) clustering algorithm is improved according to the factor graph model and the belief propagation theory. The energy data of non-intrusive monitoring consists of the actual energy consumption data of each electronic appliance. The experimental results show that this improved algorithm identifies the basic and combined class of home appliances. According to the possibility of conversion between different classes, the combination of classes is broken down into different basic classes. This method provides the basis for power management companies to allocate electricity scientifically and rationally.

Learning soft domain constraints in a factor graph model for template-based information extraction

2020 ◽  
Vol 125 ◽  
pp. 101764
Author(s):  
Hendrik ter Horst ◽  
Matthias Hartung ◽  
Philipp Cimiano ◽  
Nicole Brazda ◽  
Hans Werner Müller ◽  
...  
Keyword(s):  
Information Extraction ◽  
Graph Model ◽  
Factor Graph ◽  
Factor Graph Model ◽  
Domain Constraints

scholarly journals Detection of Stress Level in Social Media users using Cnn-Fg Model

2019 ◽  
Vol 8 (4) ◽  
pp. 9733-9736
Keyword(s):  
Social Media ◽  
Stress Level ◽  
Social Networking Sites ◽  
Hybrid Approach ◽  
Graph Model ◽  
Factor Graph ◽  
Factor Graph Model ◽  
The Social ◽  
Detection Of Stress ◽  
The Given

Psychological stress has become a common condition in today's world owing to the busy life style and competitive environment. This has led to increase of suicidal rates in the recent years. Lately, there has been a tremendous increase in interactions in the social networking sites. As people are spending long hours in the virtual world it is easier to detect and analyze the stress levels of the social media users. In this paper, we have proposed a hybrid approach which is a combination of Factor Graph (FG) model and Convolutional Neural Network (CNN) to analyze the textual contents in social media users’ tweets and posts to detect the level of stress of a user. The tweets of an individual user are gathered from Twitter platform which is preprocessed and passed to the cross autoencoder embedded CNN Model which outputs user level attributes. These are then input to the Factor Graph model that detects the stressed tweets. A mechanism has been proposed to inform the friends or relatives of the concerned stressed user if the detected stress level is above the given threshold

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