Challenges of E-Learning Management Within the Croatian Higher Education System

For the past few years, e-learning has become synonymous with different learning and teaching techniques based on information and communication technologies. Generally speaking, elearning has been increasingly present in the Croatian higher education system, gradually changing its traditional character. However, this modern learning and teaching concept has not been equally accepted throughout student population. There are numerous reasons for this state of affairs, one of the most important ones being disproportion, i.e. unequal pace of its introduction at different university and vocational studies in Croatia. These discrepancies cannot be eliminated without active support by all the actors participating in the education process. The greatest responsibility, nevertheless, lies with the people directly in charge of the e-learning process. To fulfil its task more efficiently, e-learning management requires relevant information on different aspects of its usage, as well as its acceptance among students. With this aim in mind, we conducted a survey of student attitudes at Josip Juraj Strossmayer University of Osijek. This paper presents the results of this research, which are based on application of various statistical methods, primarily cluster analysis.e-learning management, attitudes of students, relevant information, cluster analysis

Computational methods to predict and enhance decision-making with biomedical data.

The proposed research applies machine learning techniques to healthcare applications. The core ideas were using intelligent techniques to find automatic methods to analyze healthcare applications. Different classification and feature extraction techniques on various clinical datasets are applied. The datasets include: brain MR images, breathing curves from vessels around tumor cells during in time, breathing curves extracted from patients with successful or rejected lung transplants, and lung cancer patients diagnosed in US from in 2004-2009 extracted from SEER database. The novel idea on brain MR images segmentation is to develop a multi-scale technique to segment blood vessel tissues from similar tissues in the brain. By analyzing the vascularization of the cancer tissue during time and the behavior of vessels (arteries and veins provided in time), a new feature extraction technique developed and classification techniques was used to rank the vascularization of each tumor type. Lung transplantation is a critical surgery for which predicting the acceptance or rejection of the transplant would be very important. A review of classification techniques on the SEER database was developed to analyze the survival rates of lung cancer patients, and the best feature vector that can be used to predict the most similar patients are analyzed