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Breast cancer is one of the most common types of cancer most often affecting women. It is a leading cause of cancer death in less developed countries. Thus, it is important to characterize the severity of the disease as soon as possible. In this paper, we applied deep learning methods to determine the severity degree of patients with breast cancer, using real data. The aim of this research is to characterize the severity of the disorder in a shorter time compared to the traditional methods. Deep learning methods are used because of their ability to detect target class more accurately than other machine learning methods, especially in the healthcare domain. In our research, several experiments were conducted using three different deep learning methods, which are: Deep Neural Network (DNN), Recurrent Neural Network (RNN) and Deep Boltzmann Machine (DBM). Then, we compared the performance of these methods with that of the traditional neural network method. We found that the f-measure of using the neural network was 74.52% compared to DNN which was 88.46 %, RNN which was 96.79% and DBM which was 97.28%.

There are increasing interests in designing and developing effective and usable websites to deliver high degree of quality. A university website is important to its users since it delivers to them information and services such as courses and programs, delivering online learning facilities and online registrations. Despite the fact that many academic websites do not satisfy their users' needs, the institutions' dependence on using these websites for a wide variety of tasks is increasing. In this paper, we propose an approach to automate usability evaluations of university websites by using data mining method. We conducted two experiments to evaluate University website. We first used a questionnaire directed towards students using the website to examine use of color, display space, scroll left and right, ...etc. And then we used automatic tools to measure task scenario of the website attributes which cannot be perceived by students such as task time, number of clicks and number of pages, ...etc. We carried out our research on Alazhar University-Gaza. Our approach is implemented using data mining tool and exploits association rules to evaluate usability on university website. The results show that the proposed approach generated strong rules of automatic evaluation; finally we give some useful recommendations for the university.

Breast cancer is one of the most common types of cancer most often affecting women. It is a leading cause of cancer death in less developed countries. Thus, it is important to characterize the severity of the disease as soon as possible. In this paper, we applied deep learning methods to determine the severity degree of patients with breast cancer, using real data. The aim of this research is to characterize the severity of the disorder in a shorter time compared to the traditional methods. Deep learning methods are used because of their ability to detect target class more accurately than other machine learning methods, especially in the healthcare domain. In our research, several experiments were conducted using three different deep learning methods, which are: Deep Neural Network (DNN), Recurrent Neural Network (RNN) and Deep Boltzmann Machine (DBM). Then, we compared the performance of these methods with that of the traditional neural network method. We found that the f-measure of using the neural network was 74.52% compared to DNN which was 88.46 %, RNN which was 96.79% and DBM which was 97.28%.