Prediction of food fraud type using data from Rapid Alert System for Food and Feed (RASFF) and Bayesian network modelling
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[1] J. Spink,et al. Development and application of a database of food ingredient fraud and economically motivated adulteration from 1980 to 2010. , 2012, Journal of food science.
[2] Finn V. Jensen,et al. Bayesian Networks and Decision Graphs , 2001, Statistics for Engineering and Information Science.
[3] D. Jukes,et al. The national food safety control system of China – A systematic review , 2013 .
[4] Boaz Lerner,et al. Classification of fluorescence in situ hybridization images using belief networks , 2004, Pattern Recognit. Lett..
[5] J. Spink,et al. Defining the public health threat of food fraud. , 2011, Journal of food science.
[6] H. Korkeala,et al. Patterns of food frauds and adulterations reported in the EU rapid alert system for food and feed and in Finland , 2015 .
[7] H J P Marvin,et al. Identification of potentially emerging food safety issues by analysis of reports published by the European Community's Rapid Alert System for Food and Feed (RASFF) during a four-year period. , 2009, Food and chemical toxicology : an international journal published for the British Industrial Biological Research Association.
[8] C. E. Bonafede,et al. Bayesian networks for enterprise risk assessment , 2006, physics/0607226.
[9] Thierry Denœux. Maximum Likelihood from Evidential Data: An Extension of the EM Algorithm , 2010 .
[10] Kun Jai Lee,et al. Application of Bayesian network to the probabilistic risk assessment of nuclear waste disposal , 2006, Reliab. Eng. Syst. Saf..
[11] Kenneth H. Reckhow,et al. An evaluation of automated structure learning with Bayesian networks: An application to estuarine chlorophyll dynamics , 2011, Environ. Model. Softw..
[12] T. Nepusz,et al. Gate keepers of EU food safety: four states lead on notification patterns and effectiveness. , 2010, Food and chemical toxicology : an international journal published for the British Industrial Biological Research Association.
[13] K. Everstine,et al. Economically motivated adulteration (EMA) of food: common characteristics of EMA incidents. , 2013, Journal of food protection.
[14] Thierry Denœux. Maximum likelihood estimation from fuzzy data using the EM algorithm , 2011 .
[15] Yannis Manolopoulos,et al. Data Mining techniques for the detection of fraudulent financial statements , 2007, Expert Syst. Appl..
[16] Solomon Tesfamariam,et al. Consequence-based framework for electric power providers using Bayesian belief network , 2015 .
[17] Hilbert J. Kappen,et al. Approximate inference for medical diagnosis , 1999, Pattern Recognit. Lett..
[18] T. Nepusz,et al. The Procrustean bed of EU food safety notifications via the Rapid Alert System for Food and Feed: does one size fit all? , 2013, Food and chemical toxicology : an international journal published for the British Industrial Biological Research Association.
[19] Dan-hua Zhao,et al. Melamine-contaminated powdered formula and urolithiasis in young children. , 2009, The New England journal of medicine.
[20] Yong Hu,et al. The application of data mining techniques in financial fraud detection: A classification framework and an academic review of literature , 2011, Decis. Support Syst..