PFU: Profiling Forum users in online social networks, a knowledge driven data mining approach

Online Social Networks (OSNs) provide platform to raise opinions on various issues, create and spread news rapidly in Online Social Network Forums (OSNFs). This work proposes a novel method for Profiling Forum Users (PFU) by exploring their behavioral characteristics based on their involvement in various topics of discussion and number of posts in respective topics posted by them in OSNFs dynamically. Modeling the proposed method mathematically, the PFU algorithm is illustrated for its adequacy and accuracy

RDUP3: Relative Distance based User Profiling from Profile Picture in Multi-Social Networking

User Profiling in Online Social Network (OSN) requires the frontal photographs of the users as thier Profile Pictures in Multi-Social Networking. The existing algorithms are ineffective in detecting the facial features like eyes, mouth and nose on the face appropriately, making it inefficient. This work proposes a novel approach to efficiently detect the facial features and improve the effectiveness of face detection and recognition by bifurcating the detected face horizontally, vertically and cropping it. The algorithm is effectively run only on the portion of the detected face Bounded Box (BB) and area to generate bounded boxes of other facial objects and later the Euclidian Distance (ED) between those BBs with respect to that of the face is computed to get Logarithm of Determinant of Euclidian Distance Matrix (LDEDM) in Relative-Distance (RD) method and stored in the database. The LDEDM so computed is unique for every user under consideration and is further utilized for identity matching recognizing from the database. The results show that the Equal Error Rate (EER) is considerably low indicating accurate threshold fixation for better performance with the proposed Relative Distance based User Profiling from Profile Picture (RDUP3) algorithm

PIB: Profiling Influential Blogger in Online Social Networks, A Knowledge Driven Data Mining Approach

AbstractOnline Social Networks (OSNs) facilitate to create and spread information easily and rapidly, influencing others to participate and propagandize. This work proposes a novel method of profiling Influential Blogger (IB) based on the activities performed on one's blog documents who influences various other bloggers in Social Blog Network (SBN). After constructing a social blogging site, a SBN is analyzed with appropriate parameters to get the Influential Blog Power (IBP) of each blogger in the network and demonstrate that profiling IB is adequate and accurate. The proposed Profiling Influential Blogger (PIB) Algorithm survival rate of IB is high and stable

Ptib: profiling top influential blogger in online social networks

Online Social Networks (OSNs) facilitate to create and spread information easily and rapidly, influencing others to participate and propagandize. This work proposes a novel method of profiling Influential Blogger (IB) based on the activities performed on one’s blog documents who influences various other bloggers in Social Blog Network (SBN). After constructing a social blogging site, a SBN is analyzed with appropriate parameters to get the Influential Blog Power (IBP) of each blogger in the network and demonstrate that profiling IB is adequate and accurate. The proposed Profiling Influential Blogger (PIB) Algorithm survival rate of IB is high and stabl

An overview on user profiling in online social networks

Advances in Online Social Networks is creating huge data day in and out providing lot of opportunities to its users to express their interest and opinion. Due to the popularity and exposure of social networks, many intruders are using this platform for illegal purposes. Identifying such users is challenging and requires digging huge knowledge out of the data being flown in the social media. This work gives an insight to profile users in online social networks. User Profiles are established based on the behavioral patterns, correlations and activities of the user analyzed from the aggregated data using techniques like clustering, behavioral analysis, content analysis and face detection. Depending on application and purpose, the mechanism used in profiling users varies. Further study on other mechanisms used in profiling users is under the scope of future endeavors

A Comprehensive Survey on Tools for Effective Alzheimer’s Disease Detection

Neuroimaging is considered as a valuable technique to study the structure and function of the human brain. Rapid advancement in medical imaging technologies has contributed significantly towards the development of neuroimaging tools. These tools focus on extracting and enhancing the relevant information from brain images, which facilitates neuroimaging experts to make better and quick decision for diagnosing enormous number of patients without requiring manual interventions. This paper describes the general outline of such tools including image file formats, ability to handle data from multiple modalities, supported platforms, implemented language, advantages and disadvantages. This brief review of tools gives a clear outlook for researchers to utilize existing techniques to handle the image data obtained from different modalities and focus further for improving and developing advanced tools

UP3: User profiling from Profile Picture in Multi-Social Networking

Abstract: Profiling Online Social Network (OSN) Users by matching their Profile Pictures in Multi-Social Networking requires their own frontal face images in consideration. Present State-of-the-Art algorithms are ineffective in detecting mouth and nose on the face, making it inefficient to be used in matching different faces by localizing their facial features. This work proposes a novel approach to improve the effectiveness and efficiency of face detection by bifurcating the detected face horizontally and vertically. The algorithm runs only on the portion of the detected face Bounded Box (BB) to generate bounded boxes of other facial objects, and later the Euclidian distance between the BBs with respect to that of the face is computed to get Logarithm of Determinant of Euclidian Distance Matrix (LDEDM) in Relative-Distance method and stored in the database. The LDEDM so computed is unique for the user image under consideration and is used for the purpose of matching the identity of the user images from the database. The Equal Error Rate (EER) is considerably low with the proposed User Profiling from Profile Picture (UP3) algorithm indicating better performance

P Deepa Shenoy and Venugopal KR,“PTMIB: Profiling Top Most Influential Blogger using Content Based Data Mining Approach,”

Users of Online Social Network (OSN) communicate with each other, exchange information and spread rapidly influencing others in the network for taking various decisions. Blog sites allow their users to create and publish thoughts on various topics of their interest in the form of blogs/blog documents, catching the attention and letting readers to perform various activities on them. Based on the content of the blog documents posted by the user, they become popular. In this work, a novel method to profile Top Most Influential Blogger (TMIB) is proposed based on content analysis. Content of blog documents of bloggers under consideration in the blog network are compared and analyzed. Term Frequency and Inverse Document Frequency (TF-IDF) of blog documents under consideration are obtained and their Cosine Similarity score is computed. Synonyms are substituted against those unmatched keywords if the Cosine Similarity score so computed is below the threshold and an improved Cosine Similarity score of those documents under consideration is obtained. Computing the Influence Score after Synonym substitution (ISaS) of those bloggers under conflict, the top most influential blogger is profiled. The simulation results demonstrate that the proposed Profiling Top Most Influential Blogger using Synonym Substitution (PTMIBSS) algorithm is adequately accurate in determining the top most influential blogger at any instant of time considered

PTMIB: Profiling top most influential blogger using content based data mining approach

Online Social Network (OSN) provides fastest way to communicate and spread information, influencing users in the network. Blog sites allow the users to reflect and share opinions on various topics of discussion in the form of blogs/online journals and letting readers to comment on their blogs/posts. In this work, a novel method to profile Top Most Influential Blogger (TMIB) is proposed based on content analysis. Contents of blog documents of bloggers under consideration in the blog network are compared and analyzed. Term Frequency and Inverse Document Frequency (TF-IDF) of two blog documents are obtained at a given point of time to get the Cosine Similarity score between those documents. The Influence Scores (IS) of bloggers under conflict are computed. The simulation results demonstrates that the proposed Profiling Top Most Influential Blogger (PTMIB) algorithm is adequately accurate in determining the top most influential blogger at any instant of time considered. © 2016 IEEE

UPLBSN: User Profiling in Location-Based Social Networking

Online social networks serve various purposes and help mankind in many ways. The amount of information in social networks is increasing everyday, making it huge data source for its users. All the data available in social networks may not be trustworthy. In this work, we present an intelligent, crowd-powered information collection system that identifies the set of trusted experts topic-wise in Twitter social network. The proposed UPLBSN algorithm presented in this work identifies trusted experts by finding the relationship between content of tweets and the tweet location. The topic(s) of user posts are clustered by extracting the keywords and are stored in the database. Profiled profound users are presented to the business users based on the topic searched by them. The proposed UPLBSN algorithm is evaluated by conducting experiments on Twitter data set to demonstrate its adequacy