  In the Soccer Simulation 2D environment, accurate observation is crucial for
effective decision making. However, challenges such as partial observation and
noisy data can hinder performance. To address these issues, we propose a
denoising algorithm that leverages predictive modeling and intersection
analysis to enhance the accuracy of observations. Our approach aims to mitigate
the impact of noise and partial data, leading to improved gameplay performance.
This paper presents the framework, implementation, and preliminary results of
our algorithm, demonstrating its potential in refining observations in Soccer
Simulation 2D. Cyrus 2D Team is using a combination of Helios, Gliders, and
Cyrus base codes.
