As a nature-inspired algorithm, artificial bee colony (ABC) is an optimization algorithm that is inspired by the search behaviour of honey bees. The main aim of this study is to examine the effects of the ABC-based feature selection algorithm on classification performance for cyberbullying, which has become a significant worldwide social issue in recent years. With this purpose, the classification performance of the proposed ABC-based feature selection method is compared with three different traditional methods such as information gain, ReliefF and chi square. Experimental results present that ABC-based feature selection method outperforms than three traditional methods for the detection of cyberbullying. The Macro averaged F_measure of the data set is increased from 0.659 to 0.8 using proposed ABC-based feature selection method.

In this study, a new approach to Fine-Kinney risk assessment method is developed in order to overcome the limitations of the conventional method with clustering algorithms. New risk level of classes are attempted to determine with K-Means and Hierarchical clustering algorithms with using two different distance functions which are Euclidean and Manhattan distances. According to the results, K-Means algorithms have provided accurate and sensitive cluster of classes. Classes from conventional and K-Means algorithms are applied and compared to the identified risks of a workshop of a medium sized textile company. Results of the study indicate that clustering techniques are new, original and applicable way to define new classes in order to prioritize risks by overcoming the drawbacks of conventional Fine-Kinney method.

Analytic hierarchy process Failure mode and effect analysis Risk priority number Stainless tankThis study aims to evaluate the hazards of the stainless tank production process in a company by using the Integrated Analytic Hierarchy Process (AHP) and Failure Mode and Effect Analysis (FMEA) methods. First, the hazards in the stainless tank production process were identified. Identified hazards were assessed with the FMEA method to calculate Risk Priority Number (RPN) for each hazard. The same hazards were then weighted by using the AHP method. Finally, AHP and FMEA are integrated to achieve a more objective result by using two subjective methods. According to the integrated method results, risks have been prioritized and an objective ranking has been established for action plans.

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