Classification is an important data mining technique with a wide range of applications to classify the various types of data existing in almost all areas of our lives. The purpose of this discovery study can be used to estimate the potential of having breast cancer by taking advantage of anthropometric data and collected routine blood analysis parameters. The study was performed using data from patients who were admitted to the clinic with the suspicion of breast cancer. The values of Age (years), BMI (kg/m2), Glucose (mg/dL), Insulin (µU/mL), HOMA, Leptin (ng/mL), Adiponectin (µg/mL), Resistin (ng/mL), MCP-1(pg/dL) were used. In our study, classification algorithms were applied to the data and they were asked to estimate the disease diagnosis. The classification performance of Artificial neural networks and Naïve Bayes classifiers which were applied to data with 9 inputs and one output were calculated and theperformance results were compared. This article sheds light on the performance evaluation based on correct and incorrect data classification examples using ANN and Naïve Bayes classification algorithm. When we look at the performances obtained, it is predicted that using the anthropometric data and the collected routine blood analysis parameters, the potential for diagnosing breast cancer is high using these data.

The rapid advancement of today's technologies, it is tried to facilitate whichever system will be used by using voice features such as person recognition and speech recognition by making use of the voices of the users. Organizations serving in these systems need less manpower and facilitate the operation by helping users faster. The decision-making process using sound features is a very challenging process. With gender recognition, which is one of these steps, it is possible to address the user by gender. In this study, it is aimed to define the genders according to the voices in terms of both forensic informatics and the rapid and accurate progress of the processes. In this study, 3168 male and female voice samples were taken as a dataset. Sound samples were first analyzed by acoustic analysis in R using seewave and tuneR packages. Artificial neural networks were used in the classification stage. In order to increase the classification accuracy, the dataset was divided into 10 parts and each part was excluded from training for testing and used for retesting. Average classification success was found by taking the arithmetic mean of the results. In the classification made with artificial neural networks, male and female voices could be distinguished from each other with a success of 97.9%.

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The "cold chain" enables to remain safely the vaccines and medicines at the recommended temperature ranges during their transportation and storage period from the production site to the end user. The cold chain system is very important because of that vaccines and medicines lose their effectiveness when they exposed to a temperature above or below the limited ranges. There is no benefit of the vaccination also, if the vaccines and medicines used were ineffective; on the contrary, the vaccine can injure the applied organism. In this study, a kit based on "Arduino" was designed to ensure the continuity of the cold chain effectiveness. With this study intended was a new system designed, which enables a real time temperature and humidity control of the cold chain system in the transport ambient conditions so that it will alert when the cold chain system should have a malfunction. On the other hand, the experimental results obtained were compared which were sensitively measured with a laser thermometer capable for remote measuring. There was no difference between the results obtained and the values measured by the laser thermometer. Additionally, performance analyses of the system at the different temperatures have revealed that it is capable to stabilize the temperature value successfully.