Abstract:In corporations, accurate planning should be applied to manage the in-service training task within an optimum time period and without hindering the working tempo of the employees. For this reason, it is better to consider the curriculum planning task as a timetabling problem. However, when the timetables are prepared manually, it may turn out to be a complicated and time-consuming problem. In this study, it is aimed to evaluate the results of software introduced previously, which seeks to find a solution to the curriculum planning problem of in-service training programs in corporations using a rule-based genetic algorithm (GA). The input data of the GA is the prerequisite rule set of the modules of the training program, where these rules are used for the fitness function of the system. The results are compared with the suggestion of an expert trainer using a nonparametric correlation test, and the best parameter combination of the GA giving the most similar result to that of the expert's is determined. According to the tests, the results gathered are considered to be 97% reliable when compared with the suggested module range.

Traditional authentication and key establishment protocols utilize nonce parameters as a means for message freshness, recent aliveness, and key derivation. Improving identity verification, increasing key space, or making secret updates more complex through nonces are not goals. Generating random numbers as nonces and not making the most out of them can be considered as a loss in resource stricken radio frequency identification (RFID) tags. By increasing the shared secrets slightly, a new functionality for the nonces is introduced, which makes the authentication and key establishment protocols of RFID systems more secure, in general. The proposed method contributes to the security of communication channels by increasing the key space. Attaining better security, with just a slight increase in the shared secrets and the already generated nonces, is beneficial compared to the existing costly, resource-demanding security primitives.