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 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 Language of disability as a factor of discrimination of persons with disabilities

Temida ◽  
2015 ◽  
Vol 18 (1) ◽  
pp. 111-126
Author(s):  
Filip Miric
Keyword(s):  
Social Networks ◽  
Special Needs ◽  
Language Disability ◽  
People With Disabilities ◽  
Social Situation ◽  
Contemporary Society ◽  
Persons With Disabilities ◽  
Full Participation ◽  
The Subject ◽  
The Impact

The incorrect labeling of people with disabilities as people with special needs constitutes not only a violation of equality but also a special criminological and criminal justice phenomenon. There are no special needs, but just different ways of satisfying them. The subject of this paper is an analyses of the impact of labeling people with disabilities and language disability on a discriminatory process and considers whether the victimization of persons with disabilities engenders inequality. The labeling of people with disabilities throughout history will also be considered. A questionnaire was distributed via Facebook in order to explore the opinions of users of social networks on language disability and its impact on discrimination. The aim of the paper is to highlight the effect labeling has on the overall social situation of people with disabilities. It is argued that the accurate usage of appropriate linguistic terminology would help prevent the victimization of persons with disabilities and accentuate the realization of their full participation in contemporary society.

scholarly journals Dynamical Modeling, Analysis, and Control of Information Diffusion over Social Networks: A Deep Learning-Based Recommendation Algorithm in Social Network

2020 ◽  
Vol 2020 ◽  
pp. 1-8
Author(s):  
Kefei Cheng ◽  
Xiaoyong Guo ◽  
Xiaotong Cui ◽  
Fengchi Shan
Keyword(s):  
Social Networks ◽  
Deep Learning ◽  
Social Network ◽  
Information Diffusion ◽  
Recommendation Algorithm ◽  
Central Sequence ◽  
Modeling Analysis ◽  
News Recommendation ◽  
Browsing Behavior ◽  
And Control

The recommendation algorithm can break the restriction of the topological structure of social networks, enhance the communication power of information (positive or negative) on social networks, and guide the information transmission way of the news in social networks to a certain extent. In order to solve the problem of data sparsity in news recommendation for social networks, this paper proposes a deep learning-based recommendation algorithm in social network (DLRASN). First, the algorithm is used to process behavioral data in a serializable way when users in the same social network browse information. Then, global variables are introduced to optimize the encoding way of the central sequence of Skip-gram, in which way online users’ browsing behavior habits can be learned. Finally, the information that the target users’ have interests in can be calculated by the similarity formula and the information is recommended in social networks. Experimental results show that the proposed algorithm can improve the recommendation accuracy.

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.

Two Phase Recommendation Algorithm Based on Clustering User Interest and Trust

2014 ◽  
Vol 543-547 ◽  
pp. 1856-1859
Author(s):  
Xiang Cui ◽  
Gui Sheng Yin
Keyword(s):  
Social Networks ◽  
Electronic Commerce ◽  
Recommender Systems ◽  
Information Overload ◽  
User Interest ◽  
Recommendation Algorithm ◽  
Two Phase ◽  
Rating Data ◽  
User Interests ◽  
The Future

Recommender systems have been proven to be valuable means for Web online users to cope with the information overload and have become one of the most powerful and popular tools in electronic commerce. We need a method to solve such as what items to buy, what music to listen, or what news to read. The diversification of user interests and untruthfulness of rating data are the important problems of recommendation. In this article, we propose to use two phase recommendation based on user interest and trust ratings that have been given by actors to items. In the paper, we deal with the uncertain user interests by clustering firstly. In the algorithm, we compute the between-class entropy of any two clusters and get the stable classes. Secondly, we construct trust based social networks, and work out the trust scoring, in the class. At last, we provide some evaluation of the algorithms and propose the more improve ideas in the future.

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.

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