Nobel Approach for Fake Profile Detection using different Machine Learning Algorithms
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Abstract
Social activity of everybody in today’s’ generation has gotten associated with online social networks. These sites have made extreme changes in the way we follow our social lives. With the help of these sites making friends and stay connected has became easier. . As with the fast progress, there are also diverse effects have been taking place .People face the problems of fake profiles, false statics and so on. In this paper, we provide a framework for direct recognition of fake profiles .This framework uses various classification techniques in Machine Learning like SVM, Random Forest, K-nearest Neighbor, Decision tree Classifier to group the profiles into fake or genuine classes. It can be easily implemented online on social networks.
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References
. Elyusufi, Z., Elyusufi, Y., Ait Kabir, M.: Customer profiling using CEP architecture in a Big Data context. In: SCA 2018 Proceedings of the 3rd International Conference on Smart City Applications Article No. 64, Tetouan, Morocco, 10–11 October 2018. ISBN: 978-1-4503- 6562-8
. Patel, M., & Sheikh, R. (2019). Handwritten digit recognition using different dimensionality reduction techniques. International Journal of Recent Technology and Engineering, 8(2), 999-1002.
.Ameena , A., Reeba, R.: Survey on different classification techniques for detection of fake profiles in social networks. Int. J. Sci. Technol. Manage. 04(01), (2015)
H. Gupta and M. Patel, "Study of Extractive Text Summarizer Using The Elmo Embedding," 2020 Fourth International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC), 2020, pp. 829-834, doi: 10.1109/I-SMAC49090.2020.9243610.
H. Gupta and M. Patel, "Method Of Text Summarization Using Lsa And Sentence Based Topic Modelling With Bert," 2021 International Conference on Artificial Intelligence and Smart Systems (ICAIS), 2021, pp. 511-517, doi: 10.1109/ICAIS50930.2021.9395976
Rao, K., Gutha, S., Raju, B. Detecting Fake Account On Social Media Using Machine Learning Algorithms. International Journal of Control and Automation. 13. 95-100 (2020).
Sen, S., Patel, M., Sharma, A.K. (2021). Software Development Life Cycle Performance Analysis. In: Mathur, R., Gupta, C.P., Katewa, V., Jat, D.S., Yadav, N. (eds) Emerging Trends in Data Driven Computing and Communications. Studies in Autonomic, Data-driven and Industrial Computing. Springer, Singapore. https://doi.org/10.1007/978-981-16-3915-9_27.
Bouckaert, R., Eibe, F., Hall, M., &Holnies, G., Pfahringer, B., Reutemann, P., Witten, I. (2010). WEKA— experiences with a Java Open-Source Project. Journal of Machine Leaming Research.
Ameta, U., Patel, M., Sharma, A.K. (2021). Scrum Framework Based on Agile Methodology in Software Development and Management. In: Mathur, R., Gupta, C.P., Katewa, V., Jat, D.S., Yadav, N. (eds) Emerging Trends in Data Driven Computing and Communications. Studies in Autonomic, Data-driven and Industrial Computing. Springer, Singapore. https://doi.org/10.1007/978-981-16-3915-9_28
Ramos-Pollà n, R., Guevara-López, M.A., Suérez-Ortega,C. et al. (2012) Discovering Mammography-based Machine LeamingClassifiers for Breast Cancer Diagnosis.
Alsaieh, M., Alwif, A., Al-Salman, A., AlFayez, M., & Almuhaysin, A. (2014). TSD: Hetecting Sybil Accounts in Twitter. 2014 13th International Conference on Machine Learning and Naïve bayes,
Kotsiantis, S. (2007). Supervised Machine Learning: A Review of Classification Techniques. Informatica (Ljubljana).
Algorithm for Data Mining. International
Journal of Advanced Research in ComputerScience and Software Engineering Volume 3, Issue 6, (2013 June).