Publication

Abstract : Eye-tracking technology has become increasingly popular in various fields such as psychology, human-computer interaction, and market research. However, the process of converting raw eye-tracking data into a usable format for analysis can be complex and time-consuming. This abstract proposes a novel approach to streamline this process by leveraging cloud computing and the Faster R-CNN (FRCNN) algorithm. The proposed method involves three key steps: data preprocessing, cloud-based processing, and FRCNN-based analysis. Cloud computing provides the necessary computational power and storage capabilities to handle large datasets and perform resource-intensive tasks. Finally, the preprocessed eye-tracking data is fed into an FRCNN model for advanced analysis and interpretation. The proposed approach offers several advantages over traditional methods. Firstly, it reduces the burden of data preprocessing by automating the cleaning and formatting tasks. Secondly, the scalability and flexibility of cloud computing enable researchers to analyse large datasets efficiently. Lastly, the integration of FRCNN enhances the analysis capabilities by providing accurate and detailed information about eye movements.