## 5 Research Questions
They are often used to optimize complex models or to search a large solution space for the best parameters that minimize risks or improve safety conditions. As the research shows, genetic algorithms can be beneficial in scenarios where finding an optimal solution involves exploring a vast solution space [34; 103]. They are especially useful when searching for the best combination of parameters or features that contribute to risk levels [103; 104]. Machine learning techniques have gained significant popularity across various fields in recent years, including occupational risk assessment. Machine learning enables complex pattern recognition and can effectively handle large datasets. Neural networks, as a subset of machine learning, have been used for tasks such as risk prediction, risk recognition, and safety assessment in occupational environments [85; 105]. Machine learning, in general, includes various techniques, including the aforementioned neural networks, such as HRA models, K-nearest, BN, Stacked ML, and even fuzzy risks, among others [106; 107; 108; 109; 110; 111; 112; 113; 114; 115; 116; 117]. Researchers often use machine learning algorithms for classification, prediction, and pattern recognition in occupational risk data. Supervised learning methods, such as decision trees, Support Vector Machines, and ensemble methods, are commonly applied to predict and assess risks based on labeled training data. Unsupervised learning techniques, such as clustering, can help to identify hidden patterns in occupational risk datasets [114]. From the above, it becomes clear that, for techniques like neural networks, genetic algorithms, and machine learning to function, a significant, if not large, volume of data is required. Typically, such datasets and data volumes either do not exist or are not easily accessible to researchers due to concerns about publishing such data or the lengthy and time-consuming process of acquiring it [67; 114; 115]. Moreover, the nature of work in various sectors is never the same, so it is not easy to borrow data and patterns from one industry sector (e.g., the industrial sector) for another, such as transportation [116], or vice versa, for instance the construction sector to borrow data from the industrial sector. The same applies within a sector, such as mining, where a mine [117; 103; 118] may be above ground or underground, resulting in different characteristics that must be studied each time. This is also true for sectors with a large volume of articles, such as industry. For example, data from the food industry cannot be used to ensure worker safety in the metallurgy industry. Another example is the transportation sector, where, even if we choose the same vehicle, such as a truck or a ship [119], the literature shows that, depending on the cargo being transported, there are specific characteristics that affect risk assessment and the nature of the work. In contrast, fuzzy logic enables the setting of parameters and obtaining approximate results that can solve safety issues in workplaces without always relying on a data reservoir. This helps authors to academically address problems that would otherwise be challenging due to the lack of data [120]. As shown in the literature database, many researchers propose solutions for various issues related to safety and occupational risk without having prior data from the sector [121; 122]. Problem-solving is conducted either through a mathematical model or by fuzzifying traditional techniques for addressing safety issues, such as FTA, Bowtie, QRA, FMEA, AHP, etc. [123]. In summary, the main reason researchers are turning more and more to fuzzy logic compared to other techniques is primarily the result of insufficient data, the ease of clarifying specific parameters, and the fact that constructing a fuzzy model for risk management and problem-solving in occupational safety is easier than building and modeling a neural network, genetic algorithm, or machine learning system [124].