Image Content Protection Using Hybrid Approach of Blocking and Embedding Algorithm in the Context of Social Network

2018 ◽  
pp. 248-258
Author(s):  
M. Naveen ◽  
G. N. K. Suresh Babu
Keyword(s):  
Social Network ◽  
Hybrid Approach ◽  
Image Content ◽  
Content Protection

A hybrid approach for article recommendation in research social networks

2017 ◽  
Vol 44 (5) ◽  
pp. 696-711 ◽  
Author(s):  
Jianshan Sun ◽  
Yuanchun Jiang ◽  
Xusen Cheng ◽  
Wei Du ◽  
Yezheng Liu ◽  
...  
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Social Networking ◽  
Collaborative Filtering ◽  
Digital Libraries ◽  
User Study ◽  
Hybrid Approach ◽  
Quality Information ◽  
Complex Task ◽  
Data Sparsity

With the prevalence of research social networks, determining effective methods for recommending scientific articles to online scholars has become a challenging and complex task. Current studies on article recommendation works are focused on digital libraries and reference sharing websites while studies on research social networking websites have seldom been conducted. Existing content-based approaches or collaborative filtering approaches suffer from the problem of data sparsity. The quality information of articles has been largely ignored in previous studies, thus raising the need for a unified recommendation framework. We propose a hybrid approach to combine relevance, connectivity and quality to recommend scientific articles. The effectiveness of the proposed framework and methods is verified using a user study on a real research social network website. The results demonstrate that our proposed methods outperform baseline methods.

scholarly journals Understanding Social Characteristic from Spatial Proximity in Mobile Social Network

2015 ◽  
Vol 10 (4) ◽  
pp. 539 ◽  
Author(s):  
Duan Hu ◽  
Benxiong Huang ◽  
Lai Tu ◽  
Shu Chen
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Mobile Devices ◽  
Hybrid Approach ◽  
Spatial Proximity ◽  
Mobile Social Networks ◽  
Mobile Social Network ◽  
Data Repositories ◽  
Sensing Applications ◽  
Proximity Matrix

Over the past decades, cities as gathering places of millions of people rapidly evolved in all aspects of population, society, and environments. As one recent trend, location-based social networking applications on mobile devices are becoming increasingly popular. Such mobile devices also become data repositories of massive human activities. Compared with sensing applications in traditional sensor network, Social sensing application in mobile social network, as in which all individuals are regarded as numerous sensors, would result in the fusion of mobile, social and sensor data. In particular, it has been observed that the fusion of these data can be a very powerful tool for series mining purposes. A clear knowledge about the interaction between individual mobility and social networks is essential for improving the existing individual activity model in this paper. We first propose a new measurement called geographic community for clustering spatial proximity in mobile social networks. A novel approach for detecting these geographic communities in mobile social networks has been proposed. Through developing a spatial proximity matrix, an improved symmetric nonnegative matrix factorization method (SNMF) is used to detect geographic communities in mobile social networks. By a real dataset containing thousands of mobile phone users in a provincial capital of China, the correlation between geographic community and common social properties of users have been tested. While exploring shared individual movement patterns, we propose a hybrid approach that utilizes spatial proximity and social proximity of individuals for mining network structure in mobile social networks. Several experimental results have been shown to verify the feasibility of this proposed hybrid approach based on the MIT dataset.

A Novel hybrid approach for Secured Multimedia Big Data Content Protection System (SMBDCPS)

2019 ◽  
Vol 12 ◽  
Author(s):  
S. Velliangiri
Keyword(s):  
Big Data ◽  
Research Method ◽  
Research Work ◽  
Hybrid Approach ◽  
Protection System ◽  
Security Level ◽  
Multimedia Big Data ◽  
Content Protection ◽  
Medical Health Care ◽  
Data Content

Most of existing work does not ensure the optimal and secured storage maintenance of big data. This is resolved in the proposed research method by introducing the method namely Secured Multimedia Big Data Content Protection System (SMBDCPS). In this work, initially keys are encryption and decryption is generated by introducing the Hybrid AES and SHA-256 algorithm. This method would integrate the key values of AES and SHA-256 together to generate the new key value which would increase the security level of the proposed system. The keys generated using this hybrid algorithm only requires small storage which is computationally efficient. The overall evaluation of the research work is carried out on matlab simulation environment over the medical health care datasets which proves that the proposed research method ensure the optimal and secured handling of multimedia big data.

scholarly journals A Hybrid Approach to Social Network User Feed Generation

2018 ◽  
Vol 179 (48) ◽  
pp. 7-9
Author(s):  
Akhil Sudhakaran ◽  
Devipriya Sarkar ◽  
Praveen Kumar ◽  
Ravikiran R. ◽  
Sushila Shidnal
Keyword(s):  
Social Network ◽  
Hybrid Approach
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