## 5 Research Questions
### Industry  
The assessment of occupational risks is a critical aspect of ensuring workplace safety, and various industries utilize techniques like fuzzy logic, neural networks, genetic algorithms, and machine learning to address this issue [213]. Industries are increasingly recognizing the importance of leveraging advanced technologies to enhance occupational risk assessments and ensure the safety of their workforce. Integrating these technologies and techniques can lead to more proactive and effective risk management strategies. This approach improves risk assessment in addition to contributing to the overall safety culture within organizations [214; 215]. The classification of industrial occupational risks is usually divided into low-risk and high-risk categories. Using neural networks, valuable insights can be gained for diagnosing and preventing accidents. In this way, lower healthcare costs, fewer absences from work, and reduced disability rates can be achieved. Simultaneously, worker safety is improved [216; 217; 218]. Additionally, in many industries, fuzzy rules are used due to the ability to combine multiple parameters for risk assessment. However, researchers in this field point out that this also requires the evaluation of descriptive linguistic terms involved in risk, which is considered to be a disadvantage [20; 218]. [PERSON] and [PERSON] place particular emphasis on fuzzy set theories and combine them with event trees and probabilistic risk assessment, aiming to create a safety protocol for nuclear power plants. This philosophy is also adopted by other authors dealing with nuclear power plants. Specifically, [PERSON] and his colleagues [219] (highlight the complexity of a nuclear plant and present a study based on Artificial Neural Networks (ANNs) aimed at assisting in quick decision-making and corrective actions on safety issues [204]. Additionally, for many years, probability safety assessment (PSA) techniques and data have been used in nuclear industries. Their combination with neural networks aims to improve the reliability of safety in these environments [151; 214]. Many traditional techniques are transformed into estimation techniques with the help of intelligent systems, which can fortify the industry in safety matters. An example of this is the fuzzy-proportional risk assessment technique (PRAT), which, combined with fuzzy-PRAT, overcomes the disadvantages of the classic method. Probability, frequency, and severity are fuzzified, effectively enhancing the method. This new method provides highly detailed risk analysis results, enabling immediate interpretation of the findings [138; 220; 221; 222]. Moreover, a major problem that the nuclear industry [223] had in the past was the lack of quantitative historical data, and access to such data was often limited. Most assessments were conducted by experts, leading to uncertain outcomes. This gap is being filled through fuzzy models that generate basic probabilities of event failures and enhance the experience of scientists in workplace accidents [224]. The study by [PERSON] and colleagues [51], being in the same vein, aims to develop a support system based on Artificial Neural Networks that help the operator to detect Initiating Events (IEs) in their early stages, before an accident occurs [28; 225]. Similarly, [PERSON] et. al [158], used a traditional method, the Risk Assessment Matrix (RAM), and machine learning techniques to better capture risk. The authors claim that the research results are comparable to other methods such as KNN and Naive Bayes. The need for using neural networks and fuzzy logic in modeling accident prevention has existed for a long time [226; 227; 228]. Moreover, the first attempt to connect fuzzy logic with decision trees is undertaken, demonstrating the combination of methods using imprecise and uncertain input data [130; 229; 230; 231]. The same concerns are highlighted in processing plants, chemical factories [232; 233], and storage tanks for energy products such as oil and natural gas [130; 234; 235].