###### Abstract
Additionally, it is worth mentioning that the DU implementation should not require knowledge of the CIR during the design or run time. This CNN solution approach to building a high-performance DU is the core of this study. Using CNN technology is not new in optical communications. CNN networks have been previously used in optical performance monitoring (OPM) to measure the parameters of optical systems such as chromatic dispersion (CD), modulation format identification (MFI), and signal-to-noise ratio (SNR) [8; 9; 10; 11; 12]. Considering these measures to develop an affordable OPM system with great diagnostic capabilities is crucial. Further investigation and analysis are necessary to address the obstacles and issues that this field faces, including the natural factors in underwater environments and the accompanying phenomena, whether they are inherent optical properties (IOPs) such as absorption, scattering, and scintillation or apparent optical properties (AOPs), such as reflectance. Section 2 summarises the relevant published studies on ANNs, specifically focusing on CNNs. The purpose is to facilitate navigation and focus on implementing our proposed new CNN approach and the architecture and design elements. This study first applies the CNN model directly on eye diagram images to predict SNR values through regression; subsequently, the BER is extracted from the SNR for UWOC receiver systems. The CNN model can provide accurate predictions at a reasonable cost, regardless of water type, pulse shape, and noise sources. It achieves a Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE) in the range of 0.29-0.52 and 0.39-0.73, respectively, rendering it a fast and accurate way to assess the received signal inside the receiver of the UWOC system. Consequently, it becomes a core component in the decision unit, as depicted in Figure 3. The training of the CNN model is based on handling the nonlinearity of water channels under various noise environments, which helps identify and manage the UWOC systems' reliability even though the impulse response of the water channel is not yet fully characterised. Although the eye diagram images in this study have been generated from simulations, the model has proved that the concept can be conveniently expanded to assess real-time generated eye diagrams. Our proposed tool is vital for researchers and communication engineers interested in UWOC because of the difficulty of measuring SNR in real-world scenarios. Thus, when a new pulse is received, an image of the corresponding eye diagram is generated at run-time. Then, the ML model can learn and deduce the SNR value with high accuracy. This reliable approach deals with the nonlinearity of channels in underwater environments, such as multiple scattering, turbulence, scintillation, propagation time jitter, and multipath effect, which causes intersymbol interference (ISI) and receiver thermal noise. Furthermore, the decision unit will be an ML base unit using the NN to pick the best-matched image to make a decision. Because training high-performance ML applications and big processing units take a long time, the Microsoft Azure Virtual Machine (VM) was used in this study. This study also used various other resources, including Python 3.9.13, TensorFlow and Keras 2.12.0, cloud computing, SQLite Database Management System (SQLite DBMS), and eye diagram images. The essential features for signal processing in a UWOC system are seawater type, channel model, pulse shape, pulse width, and the zero-position symbol. Based on these features, the proposed algorithm generates eye diagram images. This study employed a CNN approach to perform regression analysis on eye diagram images to determine SNR values and, subsequently, the corresponding BER. The CNN contained a flattened feature map as the input, a hidden layer with a ReLU activation function, and an output layer with a linear activation function. The CNN took eye diagram images as inputs and processed them through subsequent layers to obtain feature maps.