scholarly journals Detecting Fake News Tweets from Twitter

2021 ◽  
Vol 23 (08) ◽  
pp. 532-537
Author(s):  
Cherlakola Abhinav Reddy ◽  
◽  
Sai Nitesh Gadiraju ◽  
Dr. Samala Nagaraj ◽  
◽  
...  
Keyword(s):  
Support Vector Machines ◽  
Random Forest ◽  
Decision Tree ◽  
Random Forest Classifier ◽  
Support Vector ◽  
Online Media ◽  
Fake News ◽  
Breaking News ◽  
Learning Techniques ◽  
Vector Machines

Online media has progressively obtained integral to the route billions of individuals experience news and occasions, frequently bypassing writers—the conventional guardians of breaking news. Occasions,in reality, make a relating spike of posts (tweets) on Twitter. This projects a great deal of significance on the validity of data found via online media stages like Twitter. We have utilized different managed learning techniques like Naïve Bayes, Decision Trees, and Support Vector Machines on the information to separate tweets among genuine and counterfeit news. For our AI models, we have utilized tweet and client highlights as our indicators. We accomplished a precision of 88% utilizing the Random Forest classifier and 88% utilizing the Decision tree. Notwithstanding, we accept that breaking down client records would build the accuracy of our models.

scholarly journals Evaluating machine learning techniques for archaeological lithic sourcing: a case study of flint in Britain

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Tom Elliot ◽  
Robert Morse ◽  
Duane Smythe ◽  
Ashley Norris
Keyword(s):  
Machine Learning ◽  
Support Vector Machines ◽  
Random Forest ◽  
Objective Evaluation ◽  
Classification Performance ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Learning Techniques ◽  
Vector Machines

AbstractIt is 50 years since Sieveking et al. published their pioneering research in Nature on the geochemical analysis of artefacts from Neolithic flint mines in southern Britain. In the decades since, geochemical techniques to source stone artefacts have flourished globally, with a renaissance in recent years from new instrumentation, data analysis, and machine learning techniques. Despite the interest over these latter approaches, there has been variation in the quality with which these methods have been applied. Using the case study of flint artefacts and geological samples from England, we present a robust and objective evaluation of three popular techniques, Random Forest, K-Nearest-Neighbour, and Support Vector Machines, and present a pipeline for their appropriate use. When evaluated correctly, the results establish high model classification performance, with Random Forest leading with an average accuracy of 85% (measured through F1 Scores), and with Support Vector Machines following closely. The methodology developed in this paper demonstrates the potential to significantly improve on previous approaches, particularly in removing bias, and providing greater means of evaluation than previously utilised.

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