Machine Learning Approach for User Accounts Identification with Unwanted Information and data

Abstract

Machine Learning used for many real-time issues in many organizations and the purpose of social media analytics machine learning models are used most prominently and to identify the genuine accounts and the information in the social media we are here with a new pattern of identification. In this pattern of the model, we are proposing some words which are hidden to identify the accounts with fake data and the some of the steps we are proposing will help to identify the fake and unwanted accounts in Facebook in an efficient manner. Clustering in machine learning will be used, and before that, we are proposing a suitable architecture and the process flow which can identify the fake and suspicious accounts in the social media. This article will be on machine learning implementations and will be working on OSN (online social networks). Our work will be more on Facebook which is maintaining more amount of accounts and identifying which are overruling the rules on privacy and protection of the user content. Machine learning supervised models will be used for text classification, and CNN of unsupervised learning performs the image classification, and the explanation will be given in the implementation phase. There are large numbers of algorithms we can consider for machine learning implementations in the social networking and here we considered mainly on CNN because of having the feasibility of implementation in different rules and we can eliminate the features we don’t need. Feature extraction is quite simple using CNN.

The Design of a Hybrid Model-Based Journal Recommendation System

“…In this section, the results obtained from the study were compared with existing methods, which were already discussed in section two. After comparing the classification accuracies, the classification accuracy derived in this work (89.73%) outperformed four related works [13], [16], [17] and [18].…”

“…The accuracy of recommendation results in this work was low and this was due to the small volume of books used as the experimental dataset. The authors in [17] were able to predict fake information and fake Facebook accounts using a machine learning-based recommendation approach, which can also work on online social network. The limitation of the work was the low prediction accuracies recorded.…”

Collaborative Filtering and Artificial Neural Network Based Recommendation System for Advanced Applications

“…Our proposed model is based on execution of machine learning as well as it will be work in OSN (online social network). Also, it is used for security purpose while types of text as well as image have been performed through CNN of unendorsed wisdom [18].…”