## 2 Related Work  
The current study investigates machine learning (ML) classifiers for predicting fish mortality by analyzing relevant factors in caged fish management systems. This project involves developing, configuring, and applying ML techniques and installing Apache Spark and other tools for managing fish stock. The following literature reviews maps related works, focusing on ML classifier performance, fish mortality factors, or both, to provide a context for this study. A key concept related to this research is the \"Digital Twin\", which is a model designed to support a sophisticated AI Internet of Things (AIoT) system for monitoring caged fish populations in aquaculture. This system incorporates IoT devices and cloud technology \begin{table}
\begin{tabular}{c c} \hline \hline
**RO No** & **Research Objectives** \\ \hline \multirow{2}{*}{1} & To evaluate the classification performance of different machine learning models (DTs, Random Forest, and Linear Regression) using caged fish data. \\ \hline \multirow{2}{*}{2} & To analyze the classification accuracy using performance metrics like Mean Squared \\  & Error (MSE), R-squared (R\({}^{2}\)), Root Mean Squared Error (RMSE), and Concordance \\  & Index (C-index). \\ \hline \multirow{2}{*}{3} & To identify the key factors affecting the model’s predictions and provide insights into the model’s interpretability. \\ \hline \multirow{2}{*}{4} & To explore potential areas of improvement for model performance. \\ \hline \multirow{2}{*}{5} & To assess the broader implications of these insights for fish stock management, \\  & aquaculture policy-making, and operational strategies. \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Research objectives. to enable the AI-driven monitoring of fish stocks. Along with ML models, the system employs sensors and hardware extensions for \"smart\" fish feeding, metric estimation, environmental monitoring, and fish health assessment. These sensors are real-time data collectors and transmit data to cloud services via communication networks. The system facilitates advanced decision-making processes such as data analysis, prediction, and optimization to promote more sustainable and efficient fish farming practices [8]. Advanced monitoring technologies are crucial for sustainable aquaculture, particularly in early warning systems, disease outbreak detection, and managing mass mortality risks. A study addressing these challenges proposes a modified Gaussian distribution model tailored explicitly for caged fish stock monitoring. This model offers graphical representations of the production states using a scale of alert levels, ranging from normal to dangerous. The system uses 2D image recognition techniques to monitor the health and status of fish populations, providing critical data patterns that support decision-making in aquaculture [9]. In other research, fish poisoning from unidentified causes significantly threatens public health in regions like Fiji, where fish constitutes a primary food source. A study on this issue combines fishermen's insights with ML-based association rule mining (ARM) techniques to identify hidden patterns in fish poisoning risk factors. The research highlights the contribution of environmental factors, such as contaminated migration pathways and polluted waters, to increased fish mortality rates. These findings emphasize the importance of early warning diagnostic systems for fish poisoning and the development of risk management strategies to prevent human health risks [10]. Aquaculture, a key sector in the food industry, relies on analyzing environmental factors such as salinity, temperature, bromine, ammonia, nitrogen dioxide, and hydrogen to predict and manage fish mortality. A multivariate Gaussian probability model has been applied to these factors to detect anomalies in raw data from caged fish farms. By processing daily training data, the model generates highly accurate real-time predictions of fish mortality rates, helping to mitigate risks and improve decision-making in fish farming.