  The rapid advancement of Large Language Models (LLMs) has ushered in an era
where AI-generated text is increasingly indistinguishable from human-generated
content. Detecting AI-generated text has become imperative to combat
misinformation, ensure content authenticity, and safeguard against malicious
uses of AI. In this paper, we propose a novel hybrid approach that combines
traditional TF-IDF techniques with advanced machine learning models, including
Bayesian classifiers, Stochastic Gradient Descent (SGD), Categorical Gradient
Boosting (CatBoost), and 12 instances of Deberta-v3-large models. Our approach
aims to address the challenges associated with detecting AI-generated text by
leveraging the strengths of both traditional feature extraction methods and
state-of-the-art deep learning models. Through extensive experiments on a
comprehensive dataset, we demonstrate the effectiveness of our proposed method
in accurately distinguishing between human and AI-generated text. Our approach
achieves superior performance compared to existing methods. This research
contributes to the advancement of AI-generated text detection techniques and
lays the foundation for developing robust solutions to mitigate the challenges
posed by AI-generated content.
