Extreme events management using multimedia social networks

2019 ◽  
Vol 94 ◽  
pp. 444-452 ◽  
Author(s):  
Flora Amato ◽  
Vincenzo Moscato ◽  
Antonio Picariello ◽  
Giancarlo Sperli’ì
Keyword(s):  
Social Networks ◽  
Extreme Events ◽  
Multimedia Social Networks

scholarly journals Preserving Privacy in Multimedia Social Networks Using Machine Learning Anomaly Detection

2020 ◽  
Vol 2020 ◽  
pp. 1-14 ◽  
Author(s):  
Randa Aljably ◽  
Yuan Tian ◽  
Mznah Al-Rodhaan
Keyword(s):  
Machine Learning ◽  
Social Networks ◽  
Access Control ◽  
Anomaly Detection ◽  
Privacy Preservation ◽  
Support Vector ◽  
Detection Techniques ◽  
Access Control Models ◽  
Control Models ◽  
Multimedia Social Networks

Nowadays, user’s privacy is a critical matter in multimedia social networks. However, traditional machine learning anomaly detection techniques that rely on user’s log files and behavioral patterns are not sufficient to preserve it. Hence, the social network security should have multiple security measures to take into account additional information to protect user’s data. More precisely, access control models could complement machine learning algorithms in the process of privacy preservation. The models could use further information derived from the user’s profiles to detect anomalous users. In this paper, we implement a privacy preservation algorithm that incorporates supervised and unsupervised machine learning anomaly detection techniques with access control models. Due to the rich and fine-grained policies, our control model continuously updates the list of attributes used to classify users. It has been successfully tested on real datasets, with over 95% accuracy using Bayesian classifier, and 95.53% on receiver operating characteristic curve using deep neural networks and long short-term memory recurrent neural network classifiers. Experimental results show that this approach outperforms other detection techniques such as support vector machine, isolation forest, principal component analysis, and Kolmogorov–Smirnov test.

scholarly journals A Novel Social Situation Analytics-Based Recommendation Algorithm for Multimedia Social Networks

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 117749-117760 ◽  
Author(s):  
Zhiyong Zhang ◽  
Ranran Sun ◽  
Kim-Kwang Raymond Choo ◽  
Kefeng Fan ◽  
Wei Wu ◽  
...  
Keyword(s):  
Social Networks ◽  
Social Situation ◽  
Recommendation Algorithm ◽  
Multimedia Social Networks

scholarly journals A Game-Theoretical Approach to Multimedia Social Networks Security

2014 ◽  
Vol 2014 ◽  
pp. 1-9 ◽  
Author(s):  
Enqiang Liu ◽  
Zengliang Liu ◽  
Fei Shao ◽  
Zhiyong Zhang
Keyword(s):  
Social Networks ◽  
Nash Equilibrium ◽  
Access Control ◽  
Control Model ◽  
Security Policies ◽  
Access Control Models ◽  
Multimedia Social Networks ◽  
Game Theoretical Approach ◽  
Game Theoretic ◽  
Stable Combination

The contents access and sharing in multimedia social networks (MSNs) mainly rely on access control models and mechanisms. Simple adoptions of security policies in the traditional access control model cannot effectively establish a trust relationship among parties. This paper proposed a novel two-party trust architecture (TPTA) to apply in a generic MSN scenario. According to the architecture, security policies are adopted through game-theoretic analyses and decisions. Based on formalized utilities of security policies and security rules, the choice of security policies in content access is described as a game between the content provider and the content requester. By the game method for the combination of security policies utility and its influences on each party’s benefits, the Nash equilibrium is achieved, that is, an optimal and stable combination of security policies, to establish and enhance trust among stakeholders.

scholarly journals The Transformation Of Marketing By Ai

2021 ◽  
Vol 23 (08) ◽  
pp. 391-410
Author(s):  
K V Bhanu Kiran ◽  
Keyword(s):  
Artificial Intelligence ◽  
Social Networks ◽  
Multimedia Social Networks ◽  
Crucial Part ◽  
Vast Area

From the ages, marketing has been a tough nut to crack as to how thoughtful to deal with the customer’s needs and to reach the specified products to them. Marketing is a vast area to deal with which is a crucial part of any business. In this decade we have a significant innovation to manage such issues effectively, which is Artificial Intelligence. Artificial Intelligence is quite possibly the most brilliant region of science today and can undoubtedly be utilized in the acts of marketing. Platforms for multimedia (social networks, news, images, video, Newsletters, infographics, podcasts, blogs, e-books.) are no longer accessible today are not just for the contact between users or users and companies, but also for companies to guide all aspects of business, collect and identify data of paramount importance. The artificial intelligence marketing technique has become climacteric for companies to find consumer conduct and needs. In this paper, I will walk you through the artificial intelligence marketing technique which transformed marketing into a whole new level. By the end of this paper, you will have a brief idea of how marketing has changed by knowing consumer conduct and needs using artificial intelligence.

Application of clustering algorithm in social network user scenario prediction

2020 ◽  
Vol 39 (4) ◽  
pp. 4971-4979
Author(s):  
Xiaoxian Wen ◽  
Yunhui Ma ◽  
Jiaxin Fu ◽  
Jing Li
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Behavior Analysis ◽  
Clustering Algorithm ◽  
User Behavior ◽  
Characteristic Analysis ◽  
User Behavior Analysis ◽  
Multimedia Social Networks ◽  
Scenario Prediction ◽  
User Scenario

In order to improve the ability of social network user behavior analysis and scenario pattern prediction, optimize social network construction, combine data mining and behavior analysis methods to perform social network user characteristic analysis and user scenario pattern optimization mining, and discover social network user behavior characteristics. Design multimedia content recommendation algorithms in multimedia social networks based on user behavior patterns. The current existing recommendation systems do not know how much the user likes the currently viewed content before the user scores the content or performs other operations, and the user’s preference may change at any time according to the user’s environment and the user’s identity, Usually in multimedia social networks, users have their own grading habits, or users’ ratings may be casual. Cluster-based algorithm, as an application of cluster analysis, based on clustering, the algorithm can predict the next position of the user. Because the algorithm has a “cold start”, it is suitable for new users without trajectories. You can also make predictions. In addition, the algorithm also considers the user’s feedback information, and constructs a scoring system, which can optimize the results of location prediction through iteration. The simulation results show that the accuracy of social network user scenario prediction using this method is higher, the accuracy of feature registration of social network user scenario mode is improved, and the real-time performance of algorithm processing is better.

A Trust Routing for Multimedia Social Networks

2014 ◽  
Vol 58 (4) ◽  
pp. 688-699 ◽  
Author(s):  
G. Wu ◽  
Z. Liu ◽  
L. Yao ◽  
J. Deng ◽  
J. Wang
Keyword(s):  
Social Networks ◽  
Multimedia Social Networks
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