“Prevention is better than cure”, true statement which all of us neglect. One of the most reasons which cause speedy recovery from any diseases is to discover it in advanced stages. From here come the importance of computer systems which preserve time and achieve accurate results in knowing the diseases and its first symptoms .One of these systems is retinal image analysis system which considered as a key role and the first step of Computer Aided Diagnosis Systems (CAD). In addition to monitor the patient health status under different treatment methods to ensure How it effects on the disease.. In this chapter the authors examine most of approaches that are used for vessel segmentation for retinal images, and a review of techniques is presented comparing between their quality and accessibility, analyzing and catgrizing them. This chapter gives a description and highlights the key points and the performance measures of each one.

The increasing adoption of social network applications has been an important source of information nowadays. The analysis of human behaviors in social networks has been brought to the forefront of several studies. Location-Based Social Networks (LBSN) are one of the possible means that allow the prediction of human behaviors through the efficient analysis of user's mobility patterns. Despite the remarkable progress in this research direction, however, LBSN is still hindered by the lack of literature defining the semantic aspects of the user's mobility. This research presents a contribution to the latest knowledge representation languages and Semantic Web technologies. We focus on studying human behavior mobility which is the core in location recommendation systems. Bringing to the ridesharing context, an ontology model with its underlying description logics to efficiently annotate human mobility is presented. Finally, experimental results, performed on two location-based social networks, namely, Brightkite (https://snap.stanford.edu/data), and BlaBlaCar (https://www.blablacar.co.uk/) are presented.