A Review on Data Privacy Detection in Social Networks using Data Mining, Machine Learning and Blockchain technologies

2020 ◽  
Vol 14 (11) ◽  
Keyword(s):  
Machine Learning ◽  
Data Mining ◽  
Social Networks ◽  
Data Privacy ◽  
Mining Machine ◽  
Using Data

scholarly journals A Psychoanalysis of Data Privacy Maintenance Issues in Social Network using Data mining

2019 ◽  
Vol 9 (1) ◽  
pp. 7018-7022
Keyword(s):  
Data Mining ◽  
Social Networks ◽  
Social Network ◽  
Elliptic Curve Cryptography ◽  
Data Privacy ◽  
On Line ◽  
The Common ◽  
Using Data ◽  
Present Subject ◽  
Existing Data

The existing data sharing systems relates with the on-line social networks (OSNs) suggest encoding of information before sharing, the multiparty get to the executives of scrambled information has turned into a troublesome issue. A safe information sharing subject proposed in OSNs upheld figure content approach trait based and Elliptic Curve Cryptography algorithmic principle re-encryption and mystery sharing. The work relates the gatekeeper clients' delicate information grants clients to redo get to approaches of their information thus source scrambled information to the OSNs administration provider. The proposed technique displays a multiparty get to the executive’s model that enables the communicator to refresh the entrance strategy of figure content. The characteristics fulfill the common access strategy. The work needs a fractional mystery composing development inside which the calculation overhead of client is essentially diminished by strengthening the vast majority of the mystery composing activities to the OSNs administration provider. Moreover, the check capacity on the outcomes originated from the OSNs administration provider to guarantee the rightness of fractional decoded figure content. The present subject partner affordable properties disavowal philosophy that accomplishes each forward and in reverse mystery. The insurance and execution examination results demonstrate that the arranged subject is secure and efficient in OSNs.

scholarly journals Machine Learning and Statistical Analysis Techniques on Terrorism

2020 ◽  
Author(s):  
Rajesh P ◽  
Babitha D ◽  
Mansoor Alam ◽  
Mansour Tahernezhadi ◽  
Monika A
Keyword(s):  
Machine Learning ◽  
Data Mining ◽  
Negative Impact ◽  
Machine Learning Techniques ◽  
Mining Machine ◽  
Sampled Data ◽  
Random Forest Classification ◽  
Forest Classification ◽  
Terrorist Groups ◽  
Using Data

Terrorism is a major issue facing the world today. It has negative impact on the economy of the nation suffering terrorist attacks from which it takes years to recover. Many developing countries are facing threats from terrorist groups and organizations. This paper examines various terrorist factors using data mining from the historical data to predict the terrorist groups most likely to attack a nation. In this paper we focus on sampled data primarily from India for the past two decades and also consider International database. To create meaningful insights, data mining, machine learning techniques and algorithms such as Decision Tree, Naïve Bayes, Support Vector Machine, Ensemble methods, Random Forest Classification are implemented to analyze comparative based classification results. Patterns and predictions are represented in the form of visualizations with the help of Python and Jupyter Notebook. This analysis will help to take appropriate preventive measures to stop Terrorism attacks and to increase investments, to grow the economy and tourism.

A Review of Machine Learning and Data Mining Approaches for Business Applications in Social Networks

2013 ◽  
Vol 9 (1) ◽  
pp. 36-53
Author(s):  
Evis Trandafili ◽  
Marenglen Biba
Keyword(s):  
Machine Learning ◽  
Data Mining ◽  
Social Networks ◽  
Large Scale ◽  
Viral Marketing ◽  
Social Network Mining ◽  
Business Applications ◽  
Mining Community ◽  
Recent Developments ◽  
Analysis Of Social Networks

Social networks have an outstanding marketing value and developing data mining methods for viral marketing is a hot topic in the research community. However, most social networks remain impossible to be fully analyzed and understood due to prohibiting sizes and the incapability of traditional machine learning and data mining approaches to deal with the new dimension in the learning process related to the large-scale environment where the data are produced. On one hand, the birth and evolution of such networks has posed outstanding challenges for the learning and mining community, and on the other has opened the possibility for very powerful business applications. However, little understanding exists regarding these business applications and the potential of social network mining to boost marketing. This paper presents a review of the most important state-of-the-art approaches in the machine learning and data mining community regarding analysis of social networks and their business applications. The authors review the problems related to social networks and describe the recent developments in the area discussing important achievements in the analysis of social networks and outlining future work. The focus of the review in not only on the technical aspects of the learning and mining approaches applied to social networks but also on the business potentials of such methods.

Towards Behaviour Recognition with Unlabelled Sensor Data

2013 ◽  
pp. 86-110
Author(s):  
Sook-Ling Chua ◽  
Stephen Marsland ◽  
Hans W. Guesgen
Keyword(s):  
Machine Learning ◽  
Data Mining ◽  
Inverse Problem ◽  
Sensor Data ◽  
Training Set ◽  
Learning Methods ◽  
Machine Learning Methods ◽  
Using Data ◽  
Symbolic Approach ◽  
Behaviour Recognition

The problem of behaviour recognition based on data from sensors is essentially an inverse problem: given a set of sensor observations, identify the sequence of behaviours that gave rise to them. In a smart home, the behaviours are likely to be the standard human behaviours of living, and the observations will depend upon the sensors that the house is equipped with. There are two main approaches to identifying behaviours from the sensor stream. One is to use a symbolic approach, which explicitly models the recognition process. Another is to use a sub-symbolic approach to behaviour recognition, which is the focus in this chapter, using data mining and machine learning methods. While there have been many machine learning methods of identifying behaviours from the sensor stream, they have generally relied upon a labelled dataset, where a person has manually identified their behaviour at each time. This is particularly tedious to do, resulting in relatively small datasets, and is also prone to significant errors as people do not pinpoint the end of one behaviour and commencement of the next correctly. In this chapter, the authors consider methods to deal with unlabelled sensor data for behaviour recognition, and investigate their use. They then consider whether they are best used in isolation, or should be used as preprocessing to provide a training set for a supervised method.

A Survey on Building Recommendation Systems Using Data Mining Techniques

2022 ◽  
pp. 24-56
Author(s):  
Rajab Ssemwogerere ◽  
Wamwoyo Faruk ◽  
Nambobi Mutwalibi
Keyword(s):  
Machine Learning ◽  
Data Mining ◽  
Recommender Systems ◽  
Performance Measures ◽  
Data Mining Technique ◽  
Data Mining Techniques ◽  
Learning Hypothesis ◽  
Depth Study ◽  
And Performance ◽  
Using Data

Classification is a data mining technique or approach used to estimate the grouped membership of items on a basis of a common feature. This technique is virtuous for future planning and discovering new knowledge about a specific dataset. An in-depth study of previous pieces of literature implementing data mining techniques in the design of recommender systems was performed. This chapter provides a broad study of the way of designing recommender systems using various data mining classification techniques of machine learning and also exploiting their methodological decisions in four aspects, the recommendation approaches, data mining techniques, recommendation types, and performance measures. This study focused on some selected classification methods and can be so supportive for both the researchers and the students in the field of computer science and machine learning in strengthening their knowledge about the machine learning hypothesis and data mining.

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