## 6 Discussion
However, their computational intensity and resource demands often make them less accessible compared to other techniques. Advances in machine learning have further overshadowed genetic algorithms as newer methods offer comparable benefits with reduced complexity and greater efficiency. However, in recent years, authors have provided a multitude of models, typically specialized in a particular work sector or even a specific business. This has resulted in no significant need for the model optimization presented in the other three techniques. Furthermore, the research indicates that machine learning and neural networks are no longer two completely separate techniques, as they were in the past. Machine learning and neural networks do not exclude each other. In reality, neural networks are a subset of machine learning. Machine learning is a broader concept, which includes various techniques, one of which involves neural networks. This is also reflected in the research, where it is observed that, in the past few years, those techniques using purely neural networks have been declining and the use of machine learning is increasing, although neural networks are still being incorporated into these models. The scalability and ability of machine learning to handle diverse data sources position it as a critical tool for future advancements in occupational risk management. However, the limited availability of high-quality real-time data remains a significant challenge, impeding its full potential. Machine learning techniques, including neural networks, can handle complex and non-linear relationships in data. This is characteristic of accident-related data as accidents do not occur with a specific frequency and thus create complex patterns. In such cases, neural networks, as opposed to general machine learning, are recommended by researchers as the most suitable approach for addressing hazards. Computational intelligence techniques have primarily been applied in the fields of industry, transportation, construction, and mining. The industrial sector leads in adopting these methods due to the need for enhanced safety protocols in high-risk environments. The transportation and construction sectors also demonstrate significant usage, particularly in areas such as risk detection and compliance monitoring. A notable trend is the increasing integration of hybrid approaches, combining multiple techniques to address complex safety challenges [317]. For instance, neural networks have been combined with fuzzy logic to improve risk prediction accuracy in construction projects. Similarly, genetic algorithms have been integrated with machine learning to optimize training programs and safety protocols in the mining sector. These hybrid methodologies underscore the evolving landscape of occupational risk assessment, highlighting the need for interdisciplinary approaches to tackle emerging challenges [318]. The findings reveal a gradual shift from the traditional methods toward more advanced machine learning models. This trend reflects the developments in computational capabilities and the growing availability of big data in workplace safety. The ability of machine learning to adapt and scale with technological progress positions it as a cornerstone for future research [108]. However, the continued reliance on fuzzy logic emphasizes the importance of accessible user-friendly tools that address data limitations. Finally, taking all the above aspects into consideration, a conceptual framework could be developed that integrates the dominant techniques to provide a unified approach for the assessment of occupational risk. The integration of fuzzy logic, neural networks, and machine learning offers a powerful hybrid approach that leverages the strengths of each method. More specifically, fuzzy logic would manage and address ambiguities in human inputs. It would also process incomplete and qualitative characteristics that have descriptive factors (e.g., high noise levels, poor lighting, high fatigue, slippery floors, etc.).