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In this paper, we propose a new identification technique based on an AR model with a complexity of size O(n) times in web form, with the aim of creating a unique serial number for texts and to detect authentic or similar texts. For the implementation of this purpose, we used an Autoregressive Model (AR) 15 order, and for the identification procedure, we employed the cross-correlation algorithm. Empirical investigation showed that the proposed method may be used as an accurate method for identifying same, similar, or different conceptual texts. This unique identification method for texts in combination with SCI and DOI may be the solution to many problems that the information society faces, such as plagiarism and clone detections, copyright related issues, and tracking, and also in many facets of the education process, such as lesson planning and student evaluation. The advantages of the exported serial number are obvious, and we aim to highlight them while discussing its combination with DOI. Finally, this method may be used by the information services sector and the publishing industry for standard serial-number definition identification, as a copyright management system, or both.

WEB AUTHENTIC AND SIMILAR TEXTS DETECTION USING AR DIGITAL SIGNATURE