Abdul Rahman

Addressing reviewer comments is a crucial part of the manuscript revision process. The reviewer's comment emphasizes the importance of robustness, which demonstrates the reliability of the approach you have used in the paper. To assess reliability, it's important to run experiments with different initializations, hyperparameters, and data splits. Reporting the results will demonstrate the stability of the findings. It's essential to ensure that the model's performance is consistent across different datasets, noise levels, and variations in input data.

Additionally, I would suggest conducting a "Sensitivity Analysis" if you haven't done so already. This analysis will provide insights into how changes in hyperparameters affect the model's performance, thereby demonstrating the robustness of the approach.

The reviewer also suggests introducing some originality in your paper. You should highlight any unique aspects of your approach to show originality. Emphasize the original aspects and clearly state the contributions of your work. What novel insights or improvements does your proposed method/approach offer? Make sure to clearly explain how your work or approach is different from previous works in the field.

Good Luck..!

Running AI experiments to verify the reliability of previous results and introduce originality is crucial for ensuring robustness. This involves re-running experiments with the same parameters and datasets to confirm reproducibility, benchmarking against established baselines, and conducting robustness checks such as adversarial testing and out-of-distribution evaluations. Sensitivity analyses through hyperparameter tuning and ablation studies help understand model dependencies. Introducing novel variations in model architectures or data sources fosters innovation, while comprehensive evaluations using diverse metrics and real-world scenarios ensure practical applicability. Detailed documentation and open-source sharing further enhance transparency and facilitate the validation and extension of findings by the broader research community.

As a good example, some answers here lack originality, they are clearly AI generated. By now everyone knows how to use AI and there's nothing wrong with using it as long as we add some valuable insight to it, not just copy-paste it as a final product. Reproducibility is essential in any study, as well as is adding new insights to previous results.