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Learner learning style represents a key principle and core value of the adaptive learning systems (ALS). Moreover, understanding individual learner learning styles is a very good condition for having the best services of resource adaptation. However, the majority of the ALS, which consider learning styles, use questionnaires in order to detect it, whereas this method has a various disadvantages, For example, it is unsuitable for some kinds of respondents, time-consuming to complete, it may be misunderstood by respondent, etc. In the present paper, we propose an approach for automatically detecting learning styles in ALS based on eye tracking technology, because it represents one of the most informative characteristics of gaze behavior. The experimental results showed a high relationship among the Felder-Silverman Learning Style and the eye movements recorded whilst learning.

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Nowadays, the world's population is increasingly waiting for permanent and constant access to information. Accessing the right information at any time and any place is becoming a necessity. A learning system is called ubiquitous if it is able to adapt itself to its context (user, platform, environment, device, etc.). In this sense, theories and methods of adaptations keep rolling in order to make learning processes more efficient and relevant. In this paper, we propose an approach for providing personalized course content in ubiquitous learning, considering learning styles and context-awareness. The proposed approach aims to support learners by presenting course materials generated by an adaptive engine based on adaptation rules.

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Brain imaging techniques play an important role in determining the causes of brain cell injury. Therefore, earlier diagnosis of these diseases can be led to give rise to bring huge benefits in improving treatment possibilities and avoiding any potential complications that may occur to the patient. Recently, brain tumor segmentation has become a common task in medical image analysis due to its efficacy in diagnosing the type, size, and location of the tumor in automatic methods. Several researches have developed new methods in order to obtain the best results in brain tumor segmentation, including using deep learning techniques such as the convolutional neural network (CNN). The goal of this survey is to present a brief overview of MRI modalities and discuss common methods of brain tumor segmentation from MRI images, including brain tumor segmentation using deep learning techniques, as well as the most important contributions in this field, which have shown significant improvements in recent years. Finally, we focused in summary on the building blocks of the convolutional neural network (CNN) algorithms used for image segmentation. the entire survey methodology, it has been observed that hybrid techniques and CNN-based segmentation are more effective for brain tumor segmentation from MRI images.

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