Misri, KazhanHadi, Nedaa2024-11-112024-09https://hdl.handle.net/20.500.14154/73548The ability to distinguish between AI-generated and human-generated texts is becom- ing increasingly critical as AI technologies advance. This dissertation explores the development and evaluation of various machine learning models to accurately classify text as either AI-generated or human-generated. The research aims to identify the most effective classification techniques and preprocessing methods to enhance model performance and generalization across different text datasets. A range of machine learning and deep learning models, including Support Vec- tor Machine (SVM), Random Forest, Logistic Regression, Decision Tree, BERT, and LSTM, were employed to evaluate their effectiveness in distinguishing between the two types of texts. The study utilized a balanced and representative dataset through data sampling and augmentation techniques. Key preprocessing steps were implemented to refine the input data, and hyperparameter tuning was conducted to optimize model performance. The generalization capabilities of the models were further tested on additional datasets with varying text characteristics. The findings revealed that SVM and Random Forest models achieved the highest accuracy and reliability in classifying texts, demonstrating strong performance across multiple evaluation metrics. In contrast, deep learning models like BERT and LSTM were less effective under the given conditions, suggesting a need for more extensive datasets and computational resources to leverage their full potential. These results highlight the strengths and limitations of different approaches to text classification, providing a foundation for future research to enhance AI detection in diverse applications.46en-USArtificial intelligenceAIData MiningText ClassificationHuman TextAI TextThesis