Using Artificial Intelligence and Deep Learning for Predictive Modeling in Regional Development

Abstract

ABSTRACT

This study explores the application of artificial intelligence and deep learning, particularly deep neural networks (DNNs), in predictive modeling for regional development. The research uses a dataset of development conventions from the Tanger-Tétouan-Al Hoceïma region in Morocco to forecast the success or failure of these initiatives. Utilizing advanced data mining and deep learning techniques, the study achieves a perfect regression value (R=1), indicating high accuracy in predictions. The results underscore the potential of AI to enhance decision-making processes in regional development while emphasizing the need for meticulous model design and parameter optimization. The study also identifies challenges such as data collection limitations and the presence of outliers. Future research will focus on incorporating qualitative data, expanding the geographical scope, and exploring additional machine learning methods to further refine predictive accuracy and support sustainable regional growth.

KEYWORDS: Artificial Intelligence, Deep Learning, Deep Neural Networks, Regional Development, Predictive Modeling.