Cancer is one of the leading causes of death in the world. It is the main reason why research in this field becomes challenging. Not only for the pathologist but also from the view of a computer scientist. Hematoxylin and Eosin (H&E) images are the most common modalities used by the pathologist for cancer detection. The status of cancer with histopathology images can be classified based on the shape, morphology, intensity, and texture of the image. The use of full high-resolution histopathology images will take a longer time for the extraction of all information due to the huge amount of data. This study proposed advance texture extraction by multi-patch images pixel method with sliding windows that minimize loss of information in each pixel patch. We use texture feature Gray Level Co-Occurrence Matrix (GLCM) with a meanshift filter as the data pre-processing of the images. The mean-shift filter is a low-pass filter technique that considers the surrounding pixels of the images. The proposed GLCM method is then trained using Deep Neural Networks (DNN) and compared to other classification techniques for benchmarking. For training, we use two hardware: NVIDIA GPU GTX-980 and TESLA K40c. According to the study, Deep Neural Network outperforms other classifiers with the highest accuracy and deviation standard 96.72±0.48 for four cross-validations. The additional information is that training using Theano framework is faster than Tensorflow for both in GTX-980 and Tesla K40c.

Vehicle insurance companies in many countries use the Bonus-Malus System to determine the policyholder’s net premium. The determination of net premiums on the Bonus-Malus System is based solely on the frequency of claims and ignores the severity of claims. This is unfair to policyholders who have small claims. To overcome this problem, the net premium determination method in Bonus-Malus System was developed taking into account both the frequency and severity of claims. Frequency and severity be assumed to be independent. In determining the net premium, a posterior distribution of parameters of the frequency and severity distribution is required. In the case of frequency and severity independent, the determination of the posterior distribution for frequency and severity is performed separately. This thesis discusses the determination of net premium based on frequency distribution and severity distribution for frequency and severity independent.