Can Eyupoglu

Investigation of the bee-repellent properties of cotton fabrics treated with microencapsulated essential oils

In this study, to produce single-use bee-repellent fabrics, a variety of essential oils were encapsulated with gum arabic wall material at a 1:5 ratio of wall to the core substance. The following core substances were used: lavender oil, laurel oil, fennel oil, N, N-diethyl-3-methylbenzamide (DEET), lavender + laurel oil, lavender + fennel oil, laurel + fennel oil, lavender + fennel + laurel oil, lavender oil + DEET, fennel oil + DEET and laurel oil + DEET. Lavender, fennel and laurel oils were analyzed by high-performance liquid chromatography. In this context, 11 different microcapsules were produced. After the microencapsulation process, the microcapsules were analyzed with a light microscope and by Fourier transform infrared spectroscopy. Furthermore, an image processing application was developed and implemented to determine the particle size distribution of the microcapsules. After the analysis of the microcapsules, cotton fabric samples were treated with the microcapsules. In order to analyze the microcapsules on the fabric samples, scanning electron microscopy (SEM) was used. To analyze the bee-repellent abilities of the fabric samples, 12 different measurement cabinets made of pine tree and glass were produced. According to the results, lavender and fennel oils can be used as bee-repellent alternatives to DEET in beekeeping.

An Efficient Big Data Anonymization Algorithm Based on Chaos and Perturbation Techniques

The topic of big data has attracted increasing interest in recent years. The emergence of big data leads to new difficulties in terms of protection models used for data privacy, which is of necessity for sharing and processing data. Protecting individuals’ sensitive information while maintaining the usability of the data set published is the most important challenge in privacy preserving. In this regard, data anonymization methods are utilized in order to protect data against identity disclosure and linking attacks. In this study, a novel data anonymization algorithm based on chaos and perturbation has been proposed for privacy and utility preserving in big data. The performance of the proposed algorithm is evaluated in terms of Kullback–Leibler divergence, probabilistic anonymity, classification accuracy, F-measure and execution time. The experimental results have shown that the proposed algorithm is efficient and performs better in terms of Kullback–Leibler divergence, classification accuracy and F-measure compared to most of the existing algorithms using the same data set. Resulting from applying chaos to perturb data, such successful algorithm is promising to be used in privacy preserving data mining and data publishing.

An ensemble of neural networks for breast cancer diagnosis

Since breast cancer is a common disease in society all over the world, early diagnosis is of vital importance in order to treat patients before it reaches an irreversible phase. Expert systems are being developed to make it easier to diagnose the disease. In this study, an ensemble of neural networks named radial basis function network (RBFN), generalized regression neural network (GRNN) and feed forward neural network (FFNN) is implemented to separate breast cancer data samples into benign/malignant classes. The utilities of these common methods and the proposed hybrid model which is a combination of these methods are explored and their performances are comparatively presented. The experimental results on Wisconsin Diagnostic Breast Cancer (WDBC) dataset have proven that the proposed method presents a promise for diagnosis of breast cancer. The proposed model can be used as a tool to assist medical specialists in making their decision on the disease.