Research highlights: Formica rufa is used widely for biocontrol in Turkish forests. Although ecological characteristics of red wood ant habitats are well known, the statistical significance of these characteristics and their effects on nest transplantation success are largely unknown. Having such knowledge on a local scale, however, can help to predict the success of a scheduled transplantation effort, and can prevent loss of time and money. Background and objectives: In the present study, we used nest transplantation data from southern Turkey to determine habitat parameters that have a significant impact on nest survival, and to investigate possibility of predicting transplantation success from habitat parameter data. Materials and methods: Algorithms of data mining are widely used in agricultural and forestry applications for a wide range of tasks. In the present study, we used descriptive statistics to summarize the transplantation profile according to six habitat parameters (altitude, aspect, canopy closure, landform, nest substrate, and slope). We also used classification, a data mining approach, with two of its methods (decision tree and naïve Bayes) to determine the most important habitat parameters for nest survival and predict nest transplantation success in southern Turkey. Results: We found that altitude, aspect, and canopy closure were the most important factors affecting transplantation success. We also show that classification methods can be used in not only classifying, but also predicting the success rate of future transplantations. Thus, we show that the possibility of success for a given area can be predicted when certain parameters are known. Conclusions: This method can assist biological control practitioners in planning biocontrol programs and selecting favorable spots for red wood ant nest transplantation.

Life insurance is a type of insurance that protects individuals or their relatives who are left behind after death or permanent injury. Although it is quite different from the diversity in the world, life insurance in Turkey, which also allows accumulation with providing death benefit, contribute to the tendency of people to save money. One of the reasons for slower development in life insurances in Turkey compared to the countries in the world stems from the education in the field of insurance. Indicators such as insufficient number of institutes and experts in the field and low ratio of insurance literacy are the outcomes of this inadequate education system. Hence, lack of education is directly connected with the level of students’ knowledge. The aim of this study, in which a cross-tabulated survey is used, is to measure the undergraduates’ knowledge level of basics of life insurance at the department of Insurance and Risk Management in Dumlupinar University. Results show that students who are taking education at the Dumlupinar University at the department of Insurance and Risk Management, have very low knowledge about basic life insurance. This study is important in determining the knowledge level of the students who will be in the insurance sector in the future and who will have an individual relationship with the customers.

Generalized age–period–cohort (GAPC) models are mortality models that incorporate stochasticity, which can be represented in a generalized linear or non-linear context. By fitting the data to either mortality model, one can make forecasts for the future under the extrapolation framework. Previous research indicates that tree-based machine learning (ML) methods are suitable for improving the forecasting ability of such mortality models using different training/testing time periods. However, there is no consensus about generalizing this phenomenon to the improvement of fitted/forecasted mortality rates without depending on a particular mortality model or the model’s training/testing period. Furthermore, GAPC models assume symmetry of the interaction between the features and the mortality rates. Tree-based ML methods can capture asymmetric relationships within demographic data and complement the rigid assumption of symmetry of stochastic mortality models. The objective in our study is to re-estimate the mortality rates obtained from each mortality model by applying tree-based machine learning (ML) methods within a procedure that creates a suitable environment to improve the forecasting accuracy of each GAPC model. By combining mortality models with tree-based methods, both the interpretability of the parameters of mortality models and the features used within machine learning methods can be ensured. In the application carried out in this study for Denmark and Sweden, the results show that all tree-based ML-integrated models reduced the error (root mean squared error) compared to each pure mortality model. This study shows that if the proper procedure is applied, the forecasting ability of each mortality model can be improved.

Generalized Age-Period-Cohort (GAPC) models are mortality models incorporates stochasticity which can be represented in generalized linear or non-linear context. By fitting the data to either mortality model, one can make forecasts for the future under the extrapolation framework. Previous research indicates that the machine learning techniques can be applied to improve forecasting ability of such mortality models using different training/testing time periods. However, there is no consensus about generalizing this phenomenon on the improvement of fitted/forecasted mortality rates without depending on particular mortality model or models’ training/testing period. The idea in this study is to re-estimate the mortality rates obtained from each mortality models by applying tree-based machine learning methods within a procedure that creates suitable environment for integrating machine learning models for each GAPC models. This study shows that if the proper procedure is applied, the forecasting ability of each mortality model can be improved.