writing style based model

Today, with the significant growth of artificial intelligence and its products, many opportunities and threats have emerged. One of the most famous and popular products of artificial intelligence is text generation, also called machine text. In this research, a new method is introduced that combines features extracted from the text with its structural features, thus creating an automatic discriminator to distinguish between human-written text and artificial intelligence-generated text. The introduced method consists of two parts, the first part: the extended BERT (RoBERTa) model and the bidirectional long-term short-term memory (BiLSTM) model, which are improved with the fusion layer. The second part: the structural features of the text are extracted using a writing style-based method. Finally, the output of the model parts is combined together, and in this way, the model distinguishes human-written text from machine-generated text. The results of this research show that the proposed method is capable of recognizing machine texts with 90% accuracy and exhibits good performance.