Abstract

Certainly, the impact of emerging technologies is changing our world and how we live, shaping our future significantly. In the constantly evolving landscape of these technologies, which attracts substantial yearly investments, spotting these trends early on is both challenging and expensive. However, applying an emerging technology detection method in an effective and efficient way is considered a challenging task for many stakeholders. In this thesis, we address these problems through applying a method to predict potential emerging technologies in the case study field of Artificial Intelligence (AI). Using this method may help policymakers to identify potential emerging technologies early in a more systematic way with little manual intervention. In the proposed method, using burst detection, machine learning, and deep learning, we attempt to predict the future sustaining emerging technologies. We applied the methodology by four methods, namely Random Forest, Gradient Boosting, XGBoost, and Multi-Layer Perceptron (MLP). Results showed that the method was successful in its tasks. The method had the Area under the Curve (AUC) rate of more than 75% to accurately predict the sustainability of the potential emerging technologies. More specifically, applying the MLP method showed the ability to increase the AUC rate and recall metric as the most important metrics of our work. In summary, this approach carries both theoretical and practical significance. Theoretically, the exploration of novel combinations, such as integrating deep learning and burst detection methods or employing transformers, offers researchers fresh insights into the challenge of detecting emergence. On the practical front, the application of methods providing high accuracy rates in machine learning methods empowers stakeholders to implement these methods effectively in practical scenarios.