## 9 Future Studies  
In future studies, we plan to elevate the effectiveness of our ML model for the UWOC system through a two-pronged strategy: dataset expansion and CNN refinement. The first cornerstone of our approach is the extension of our dataset. We aim to generate more eye diagram images, diversifying and enriching the data available for the ML model. This broader dataset will fortify the model's learning capabilities and enhance its predictive precision. Simultaneously, we propose a strategic refinement of our CNN's hyperparameters. We contemplate introducing two or three hidden layers into the network's architecture, which could amplify the model's ability to detect intricate features and, in turn, boost its accuracy.  
Figure 21: SNR vs. BER for harbour water (**left**) and coastal water (**right**). The true (red) and predicted (blue) values are for different pulse widths using different channel models.  
We are also considering altering activation functions in both the convolutional and hidden layers (e.g., Tanh, Leaky ReLU, ELU, and SELU), aiming to introduce varying degrees of nonlinearity that could potentially enhance the model's learning and generalisation from the data.  
Additionally, we will explore the employment of non-overlapping filters in our convolutional operations. This adjustment could help retain original input data information, minimising information loss during the convolution process and potentially leading to more robust predictions. Aside from these strategies, we also plan to incorporate the capabilities of large language models, like GPT-4. We envision utilising these models for tasks such as automated hyperparameter tuning and predictive modelling based on text mining of recent research trends. Furthermore, they could aid in automatic feature extraction from eye diagram images and augment our dataset by generating synthetic images based on textual descriptions of various UWOC scenarios. These models can also help interpret the CNN model's results and enhance our understanding of the network's decisions. Lastly, we could facilitate knowledge transfer by identifying parallels between UWOC and other fields and refining our CNN techniques and approaches. Through this holistic strategy, integrating both traditional methods and advanced AI techniques, we aim to significantly elevate the accuracy and efficiency of our CNN model in UWOC.  
Conceptualisation, I.F.E.R. and Y.A.-H.; Methodology, N.M.B. and S.A.-Z.; Software, I.F.E.R., N.M.B. and S.A.-Z.; Validation, I.F.E.R., N.M.B. and A.Z.B.; Formal analysis, I.F.E.R. and Y.A.-H.; Investigation, I.F.E.R., S.A.-Z., Y.A.-H. and A.Z.B.; Writing--original draft, I.F.E.R.; Writing--review and editing, N.M.B., S.A.-Z., Y.A.-H., A.Z.B. and M.C.; visualisation, I.F.E.R. All authors have read and agreed to the published version of the manuscript.  
The Deanship of Scientific Research (DSR) at King Abdulaziz University (KAU), Jeddah, Saudi Arabia, under grant no. (GPIP-1512-130-2024).  
The digital image SQL database can be provided upon request through the corresponding author's email.  
This study was funded by the Deanship of Scientific Research (DSR) at King Abdulaziz University, Jeddah, Saudi Arabia, under grant no. (GPIP-1512-130-2024). The authors, therefore, acknowledge DSR's technical and financial support with thanks.  
The authors declare no conflicts of interest.