scholarly journals NHAD: Neuro-Fuzzy Based Horizontal Anomaly Detection In Online Social Networks

2018 ◽  
pp. 1-1 ◽  
Author(s):  
vishal sharma ◽  
RAVINDER KUMAR ◽  
Wen-Huang Cheng ◽  
Mohammed Atiquzzaman ◽  
Kathiravan Srinivasan ◽  
...  
Keyword(s):  
Social Networks ◽  
Anomaly Detection ◽  
Online Social Networks ◽  
Neuro Fuzzy

scholarly journals Anomaly detection over differential preserved privacy in online social networks

PLoS ONE ◽  
2019 ◽  
Vol 14 (4) ◽  
pp. e0215856 ◽  
Author(s):  
Randa Aljably ◽  
Yuan Tian ◽  
Mznah Al-Rodhaan ◽  
Abdullah Al-Dhelaan
Keyword(s):  
Social Networks ◽  
Anomaly Detection ◽  
Online Social Networks

scholarly journals Anomaly detection in online social networks

2014 ◽  
Vol 39 ◽  
pp. 62-70 ◽  
Author(s):  
David Savage ◽  
Xiuzhen Zhang ◽  
Xinghuo Yu ◽  
Pauline Chou ◽  
Qingmai Wang
Keyword(s):  
Social Networks ◽  
Anomaly Detection ◽  
Online Social Networks

A Neuro-Fuzzy Approach to Detect Rumors in Online Social Networks

2020 ◽  
Vol 17 (1) ◽  
pp. 64-82 ◽  
Author(s):  
Santhoshkumar Srinivasan ◽  
Dhinesh Babu L.D.
Keyword(s):  
Social Networks ◽  
Online Social Networks ◽  
Fuzzy Systems ◽  
Input Data ◽  
State Of The Art ◽  
Fuzzy Rules ◽  
Fuzzy Classification ◽  
Fuzzy Approach ◽  
Neuro Fuzzy ◽  
Common Problems

Along with true information, rumors spread in online social networks (OSN) on an unprecedented scale. In recent days, rumor identification gains more interest among the researchers. Finding rumors also poses other critical challenges like noisy and imprecise input data, data sparsity, and unclear interpretations of the output. To address these issues, we propose a neuro-fuzzy classification approach called the neuro-fuzzy rumor detector (NFRD) to automatically identify the rumors in OSNs. NFRD quickly transforms the input to fuzzy rules which classify the rumor. Neural networks handle larger input data. Fuzzy systems are better in handling uncertainty and imprecision in input data by producing fuzzy rules that effectively eliminate the unclear inputs. NFRD also considers the semantic aspects of information to ensure better classification. The neuro-fuzzy approach addresses the most common problems such as uncertainty elimination, noise reduction, and quicker generalization. Experimental results show the proposed approach performs well against state-of-the-art rumor detecting techniques.

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