Open AccessRecognition of Position Group Relationship in Dynamic Social Networks
Author(s) -
D. Thabitha*,
Dr.B. Jhansi Vazram
Publication year - 2019
Publication title -
international journal of innovative technology and exploring engineering
Language(s) - English
Resource type - Journals
ISSN - 2278-3075
DOI - 10.35940/ijitee.l2510.1081219
Subject(s) - computer science , support vector machine , social media , convolutional neural network , point (geometry) , feeling , everyday life , anomaly detection , artificial intelligence , social network (sociolinguistics) , position (finance) , data mining , machine learning , world wide web , psychology , social psychology , political science , mathematics , geometry , finance , law , economics
Mental stress is turning into a threat to people's health currently days. With the last step of life, a lot of and a lot of folks are feeling stressed. A novel hybrid model combined with Convolution Neural Network (CNN) to control tweet content and social interaction information for stress detection effectively. Network anomaly detection is an important and dynamic research area. Many network intrusion detection methods and systems (NIDS) have been proposed in the literature. Fake news detection on social media presents unique characteristics and challenges that make existing detection algorithms from traditional news media ineffective or not applicable. Based on the information that is provided by the online social network, the conditions are limited. This method can opinion investigation of Facebook post after Formation of point utilizing Support Vector Method (SVM). After grouping client is in pressure or not k-closest neighbor calculation (KNN) is utilized for proposal emergency clinic on a guide just as Admin can send letters of precautionary measure list for the client for end up solid and upbeat throughout everyday life