## 2 Related Work
Such predictive models provide valuable tools for enhancing sustainability and profitability in the industry [11]. Emerging technologies, particularly AI and blockchain, are transforming the aquaculture sector. These technologies enable real-time data collection, storage, and analysis, significantly impacting supply chain transparency, market competition, and consumer trust. Blockchain ensures transparency across the farmed fish industry by providing secure access to critical data, while AI-driven solutions improve decision-making and operational efficiency. Together, these technologies address challenges in the fish farming industry by enhancing transparency and building trust among stakeholders, from producers to consumers [12]. The performance of ML classifiers is influenced by various factors, including the characteristics of the data and their specific application. Research comparing k-Nearest Neighbors (k-NNs) and Naive Bayes (NB) classifiers reveals that these models perform differently depending on the dataset. For instance, k-NN shows high accuracy with small datasets but incurs a high computational cost. At the same time, NB is effective with large datasets but may suffer from reduced accuracy when feature independence assumptions are violated. This research highlights the need for the careful selection of classifiers based on the characteristics of the data to ensure high performance and accuracy in predictive analytics [13]. Further studies compare DTs and NB classifiers enhanced by Genetic Algorithms (GA) for feature selection. One such study, using UCI Machine Learning Repository data, demonstrates how incorporating GA improves classification accuracy for both models, with DTs generally outperforming NB in accuracy. This highlights the role of GA in optimizing model performance through more efficient feature selection processes [14]. Additional research explores the comparative performance of classifiers such as Artificial Neural Networks (ANNs), Support Vector Machines (SVMs), k-NN, and NB, identifyingstrengths and limitations. ANN offers high classification accuracy and powerful learning capabilities but requires long training times and is prone to overfitting. SVM excels in handling non-linear relationships and binary classification tasks with small and medium datasets but struggles with multi-class problems. k-NN is effective for small datasets but incurs high computational costs for predictions and becomes inefficient with large datasets. NB is well-suited for large datasets, demonstrating efficiency in classifying examples, but its assumption of feature independence can lead to performance limitations in complex real-world scenarios. On the other hand, DTs perform well with high interpretability and ease of decision rule generation, but they are vulnerable to overfitting and struggle with complex datasets [15; 16; 17]. Evaluating ML classifier performance often relies on traditional metrics like Recall, Precision, and F-measure. However, recent studies have shown that these metrics may introduce bias, particularly in multi-class classification tasks, leading to misleading assessments of model performance. One study suggests the adoption of alternative performance metrics to enhance the reliability and trustworthiness of ML model evaluations. By incorporating these new metrics, the research aims to improve model interpretability and the effectiveness of decision-making processes, ensuring more accurate evaluations of predictive systems [18].