Classification of human-written and AI-generated sentences using a hybrid CNN-GRU model optimized by the spotted hyena algorithm

IF 6.2 2区 工程技术 Q1 ENGINEERING, MULTIDISCIPLINARY
Mahmoud Ragab , Ehab Bahaudien Ashary , Faris Kateb , Abeer Hakeem , Rayan Mosli , Nasser N. Albogami , Sameer Nooh
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Abstract

The rapid advancement of artificial intelligence (AI) in generating human-like text poses significant challenges in distinguishing between human-written and AI-generated content. Recent advancements in natural language generation have significantly enhanced the quality and variety of AI-generated text, making it almost indistinguishable from human-written content. ChatGPT, a popular AI model, belongs to the generative pre-trained transformer family. While human content is created with a clear intent to convey meaning, AI-generated text aims to replicate human-like language. Classifying human-written and AI-generated sentences is crucial for addressing issues like fake news, plagiarism, and spamming. AI text often follows repetitive patterns, while human writing is more creative and original, making detection significant for combating misinformation. Therefore, this study proposes to classify human-written and AI-generated sentences using a hybrid CNN-GRU model optimized by the Spotted Hyena Algorithm (CHWAIG-DLSHO) approach. The approach involves preprocessing text data through tokenization, lemmatization, and data splitting, followed by word embedding using Latent Dirichlet Allocation (LDA). A hybrid convolutional neural network (CNN) and gated recurrent unit (GRU) model is employed for sentence classification. The spotted hyena optimizer (SHO) model is utilized to fine-tune the hyperparameters of the CNN-GRU model, enhancing its performance. The analysis of the CHWAIG-DLSHO method takes place utilizing AI vs. human text dataset. The performance validation of the CHWAIG-DLSHO method portrayed a superior accuracy value of 99.17 % over existing techniques.
使用斑点鬣狗算法优化的CNN-GRU混合模型对人类写作和人工智能生成的句子进行分类
人工智能(AI)在生成类人文本方面的快速发展,给区分人类编写的内容和人工智能生成的内容带来了重大挑战。自然语言生成的最新进展大大提高了人工智能生成文本的质量和多样性,使其与人类编写的内容几乎无法区分。ChatGPT是一种流行的人工智能模型,属于生成式预训练变压器家族。虽然人类的内容是有明确的意图来传达意义的,但人工智能生成的文本旨在复制类似人类的语言。对人工写作和人工智能生成的句子进行分类对于解决假新闻、抄袭和垃圾邮件等问题至关重要。人工智能文本通常遵循重复的模式,而人类写作更具创造性和原创性,这使得检测对打击错误信息具有重要意义。因此,本研究提出使用由斑点鬣狗算法(CHWAIG-DLSHO)优化的CNN-GRU混合模型对人类写作和人工智能生成的句子进行分类。该方法包括通过标记化、词序化和数据分割对文本数据进行预处理,然后使用潜在狄利克雷分配(Latent Dirichlet Allocation, LDA)进行词嵌入。采用卷积神经网络(CNN)和门控循环单元(GRU)混合模型进行句子分类。利用斑点鬣狗优化器(spot hyena optimizer, SHO)模型对CNN-GRU模型的超参数进行微调,提高其性能。CHWAIG-DLSHO方法的分析是利用AI和人类文本数据集进行的。CHWAIG-DLSHO方法的性能验证表明,与现有技术相比,CHWAIG-DLSHO方法的准确率为99.17 %。
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来源期刊
alexandria engineering journal
alexandria engineering journal Engineering-General Engineering
CiteScore
11.20
自引率
4.40%
发文量
1015
审稿时长
43 days
期刊介绍: Alexandria Engineering Journal is an international journal devoted to publishing high quality papers in the field of engineering and applied science. Alexandria Engineering Journal is cited in the Engineering Information Services (EIS) and the Chemical Abstracts (CA). The papers published in Alexandria Engineering Journal are grouped into five sections, according to the following classification: • Mechanical, Production, Marine and Textile Engineering • Electrical Engineering, Computer Science and Nuclear Engineering • Civil and Architecture Engineering • Chemical Engineering and Applied Sciences • Environmental Engineering
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