Review of Various Approaches for Authorship Identification in Digital Forensics
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Publisher
Springer Nature Singapore
Link
https://link.springer.com/content/pdf/10.1007/978-981-97-0892-5_19
Reference44 articles.
1. Zheng, R., Li, J., Chen, H., Huang, Z.: A framework for authorship identification of online messages: writing-style features and classification techniques. J. Amer. Soc. Info. Sci. Technol. 57(3), 378–393 (2006). https://doi.org/10.1002/asi.20316.
2. Srinivasan, L., Nalini, C.: An improved framework for authorship identification in online messages. Clust. Comput. 22(s5), 12101–12110 (2019). https://doi.org/10.1007/s10586-017-1563-3
3. Benzebouchi, N. E., Azizi, N., Hammami, N. E., Schwab, D., Khelaifia, M.C.E., Aldwairi, M.: Authors’ writing styles based authorship identification system using the text representation vector. In: 2019 16th International Multi-Conference on Systems, Signals & Devices (SSD), pp. 371–376. IEEE (2019)
4. Gupta, S., Patra, T.K., Chaudhuri, C.: Role of machine learning in authorship attribution with select stylometric features. In: Intelligent Systems Design and Applications: 21st International Conference on Intelligent Systems Design and Applications (ISDA 2021) Held During December 13–15, 2021, pp. 920–932. Springer International Publishing, Cham (2022)
5. Tamboli, M.S., Prasad, R.: A robust authorship attribution on big period. Int. J. Electr. & Comput. Eng. 9(4), 2088–8708 (2019)
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