Application of Bayesian Network for Food Safety Risk in Cattle Slaugtering Industry The risk in food safety has become a significant research issue that has often been studied in food safety research. However, a few studies pay attention to investigate the relationships between risks event based on the processes using the bayesian network method. This study aims to apply the bayesian network method to measure and analyse the risk in food safety with a case study in the cattle slaughtering industry. Five (5) stages are used to apply the Bayesian network method including identification of risk event in case study, determination of probability of risk event, development of Bayesian network structure, calculation for condition probability table (CPT), and analysis of food safety in case study. Research data collection was conducted through observations, discussion and interviews with supervisors in case study. 50 cowu0027s observations are used to investigate food safety risk in cattle slaughtering industry. Eight (8) risk events for food safety were founded and the results of Bayesian network based on processes is the slaughter process is the highest food safety risk in cow slaughtering industry.

Analyzing Uncertainty in Complex SocioEcological Networks Socioecological systems are recognized as complex adaptive systems whose multiple interactions might change as a response to external or internal changes. Due to its complexity, the behavior of the system is often uncertain. Bayesian networks provide a sound approach for handling complex domains endowed with uncertainty. The aim of this paper is to analyze the impact of the Bayesian network structure on the uncertainty of the model, expressed as the Shannon entropy. In particular, three strategies for model structure have been followed: naive Bayes (NB), tree augmented network (TAN) and network with unrestricted structure (GSS). Using these network structures, two experiments are carried out: (1) the impact of the Bayesian network structure on the entropy of the model is assessed and (2) the entropy of the posterior distribution of the class variable obtained from the different structures is compared. The results show that GSS constantly outperforms both NB and TAN when it comes to evaluating the uncertainty of the entire model. On the other hand, NB and TAN yielded lower entropy values of the posterior distribution of the class variable, which makes them preferable when the goal is to carry out predictions.