Detecting malicious users in the social networks using machine learning approach

2021 ◽  
Vol 2 (3) ◽  
pp. 229
Author(s):  
U. Tanuja ◽  
B. Ramesh ◽  
H.L. Gururaj ◽  
V. Janhavi
Keyword(s):  
Machine Learning ◽  
Social Networks ◽  
Learning Approach ◽  
The Social ◽  
Machine Learning Approach

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

2018 ◽  
Vol 2 (3) ◽  
pp. 119-127 ◽  
Author(s):  
Abhishek Kumar ◽  
TVM SAIRAM
Keyword(s):  
Machine Learning ◽  
Social Media ◽  
Online Social Networks ◽  
Social Media Analytics ◽  
Learning Approach ◽  
Efficient Manner ◽  
Implementation Phase ◽  
Large Numbers ◽  
The Social ◽  
Machine Learning Approach

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.

scholarly journals Twitter Sentiment Analysis Model

2021 ◽  
Vol 9 (VII) ◽  
pp. 296-302
Author(s):  
Pushkar Dubey
Keyword(s):  
Machine Learning ◽  
Social Networks ◽  
Social Media ◽  
Deep Learning ◽  
Sentiment Analysis ◽  
Language Processing ◽  
Learning Approach ◽  
Analysis Model ◽  
Market Place ◽  
The Social

Social networks are the main resources to gather information about people’s opinion towards different topics as they spend hours daily on social media and share their opinion. Twitter is one of the social media that is gaining popularity. Twitter offers organizations a fast and effective way to analyze customers’ perspectives toward the critical to success in the market place. Developing a program for sentiment analysis is an approach to be used to computationally measure customers’ perceptions. .We use natural language processing and machine learning concepts to create a model for analysis . In this paper we are discussing how we can create a model for analysis of twittes which is trained by various nlp , machine learning and Deep learning Approach.

Detecting spammer and fake user on social networks using machine learning approach

2022 ◽  
Author(s):  
T. SudalaiMuthu ◽  
C. Dheeraj Kumar Reddy ◽  
B. Sairam Reddy ◽  
M. Lakshmi Sahithya ◽  
S. Visalaxi
Keyword(s):  
Machine Learning ◽  
Social Networks ◽  
Learning Approach ◽  
Machine Learning Approach

Predicting customer quality in e-commerce social networks: a machine learning approach

2018 ◽  
Vol 13 (3) ◽  
pp. 589-603 ◽  
Author(s):  
María Teresa Ballestar ◽  
Pilar Grau-Carles ◽  
Jorge Sainz
Keyword(s):  
Machine Learning ◽  
Social Networks ◽  
Learning Approach ◽  
Machine Learning Approach

scholarly journals Associated factors of white matter hyperintensity volume: a machine-learning approach

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Sergio Grosu ◽  
Susanne Rospleszcz ◽  
Felix Hartmann ◽  
Mohamad Habes ◽  
Fabian Bamberg ◽  
...  
Keyword(s):  
Machine Learning ◽  
White Matter ◽  
Social Environment ◽  
White Matter Hyperintensity ◽  
Cerebral White Matter ◽  
Learning Approach ◽  
Male Age ◽  
Independent Population ◽  
The Social ◽  
Machine Learning Approach

AbstractTo identify the most important parameters associated with cerebral white matter hyperintensities (WMH), in consideration of potential collinearity, we used a data-driven machine-learning approach. We analysed two independent cohorts (KORA and SHIP). WMH volumes were derived from cMRI-images (FLAIR). 90 (KORA) and 34 (SHIP) potential determinants of WMH including measures of diabetes, blood-pressure, medication-intake, sociodemographics, life-style factors, somatic/depressive-symptoms and sleep were collected. Elastic net regression was used to identify relevant predictor covariates associated with WMH volume. The ten most frequently selected variables in KORA were subsequently examined for robustness in SHIP. The final KORA sample consisted of 370 participants (58% male; age 55.7 ± 9.1 years), the SHIP sample comprised 854 participants (38% male; age 53.9 ± 9.3 years). The most often selected and highly replicable parameters associated with WMH volume were in descending order age, hypertension, components of the social environment (i.e. widowed, living alone) and prediabetes. A systematic machine-learning based analysis of two independent, population-based cohorts showed, that besides age and hypertension, prediabetes and components of the social environment might play important roles in the development of WMH. Our results enable personal risk assessment for the development of WMH and inform prevention strategies tailored to the individual patient.

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