Objectives: The primary goal of this research is to evaluate countries' readiness for the Fourth Industrial Revolution. The study aims to develop a model using artificial intelligence algorithms, including clustering and classification techniques, to predict how countries will progress towards Industry 4.0.
Methodology: This research employs fast-density clustering (FDC) to categorize countries into distinct groups. Following this, the k-nearest Neighbors (kNN) and Support Vector Machine (SVM) algorithms are utilized for modeling and predicting countries' advancements. Additionally, swarm intelligence algorithms are applied to optimize the performance of these predictive models.
Findings: A significant achievement of this research is the creation of a composite index for classifying countries and predicting their progression toward Industry 4.0. Countries are divided into three clusters based on their readiness for the Fourth Industrial Revolution, with each cluster's characteristics and trends clearly defined. Furthermore, the optimization of the kNN and SVM algorithms is noteworthy, as they demonstrate enhanced performance compared to other methods.
Conclusion: The proposed composite index surpasses similar algorithms, highlighting the effectiveness of this approach in forecasting countries' progress toward Industry 4.0. The study also provides a detailed analysis of the Iran cluster, including its overall ranking and evaluations within specific sub-indices. These findings reveal insights into Iran's strategic movement patterns and offer recommendations for improving its technological capabilities. Overall, this research not only contributes to enhancing Iran's technological readiness but also seeks to strengthen the country's power and position, particularly in vital areas such as national defense and security