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Background: The recent progress and achievements in the advanced, accurate, and rigorously evaluated algorithms has revolutionized different aspects of the predictive microbiology including bacterial growth., Objectives: In this study, attempts were made to develop a more accurate hybrid algorithm for predicting the bacterial growth curve which can also be applicable in predictive microbiology studies., Materials and Methods: Sigmoid functions, including Logistic and Gompertz, as well as least square support vector machine (LSSVM) based algorithms were employed to model the bacterial growth of the two important strains comprising Listeria monocytogenes and Escherichia coli . Even though cross-validation is generally used for tuning the parameters in LSSVM, in this study, parameters tuning ( i.e. ,' c ' and ' σ ') of the LSSVM were optimized using non-dominated sorting genetic algorithm-II (NSGA-II), named as NSGA-II-LSSVM. Then, the results of each approach were compared with the mean absolute error (MAE) as well as the mean absolute percentage error (MAPE)., Results: Applying LSSVM, it was resulted in a precise bacterial growth modeling compared to the sigmoid functions. Moreover, our results have indicated that NSGA-II-LSSVM was more accurate in terms of prediction than LSSVM method., Conclusion: Application of the NSGA-II-LSSVM hybrid algorithm to predict precise values of ' c ' and ' σ ' parameters in the bacterial growth modeling resulted in a better growth prediction. In fact, the power of NSGA-II for estimating optimal coefficients led to a better disclosure of the predictive potential of the LSSVM.

Background: Esophagus cancer, the third most common gastrointestinal cancer overall, demonstrates high incidence in parts of Iran. The counties of Iran vary in size, shape and population size. The aim of this study was to account for spatial support with Area-to-Area (ATA) Poisson Kriging to increase precision of parameter estimates and yield correct variance and create maps of disease rates., Materials and Methods: This study involved application/ecology methodology, illustrated using esophagus cancer data recorded by the Ministry of Health and Medical Education (in the Non-infectious Diseases Management Center) of Iran. The analysis focused on the 336 counties over the years 2003-2007. ATA was used for estimating the parameters of the map with SpaceStat and ArcGIS9.3 software for analysing the data and drawing maps., Results: Northern counties of Iran have high risk estimation. The ATA Poisson Kriging approach yielded variance increase in large sparsely populated counties. So, central counties had the most prediction variance., Conclusions: The ATAPoisson kriging approach is recommended for estimating parameters of disease mapping since this method accounts for spatial support and patterns in irregular spatial areas. The results demonstrate that the counties in provinces Ardebil, Mazandaran and Kordestan have higher risk than other counties.