Social media channels, such as Facebook, Twitter, and Instagram, have altered our world forever. People are now increasingly connected than ever and reveal a sort of digital persona. Although social media certainly has several remarkable features, the demerits are undeniable as well. Recent studies have indicated a correlation between high usage of social media sites and increased depression. The present study aims to exploit machine learning techniques for detecting a probable depressed Twitter user based on both, his/her network behavior and tweets. For this purpose, we trained and tested classifiers to distinguish whether a user is depressed or not using features extracted from his/her activities in the network and tweets. The results showed that the more features are used, the higher are the accuracy and F-measure scores in detecting depressed users. This method is a data-driven, predictive approach for early detection of depression or other mental illnesses. This study's main contribution is the exploration part of the features and its impact on detecting the depression level.

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Online social networks are becoming increasingly popular in Saudi society, with their usage rising rapidly and with sites such as Twitter, Facebook, and LinkedIn in particular experiencing a dramatic uptake in new users over the last year. Indeed, Snapchat has indicated that Saudi Arabia is one of its ten strongest markets globally. In this study, we identify and measure various awareness aspects of privacy for online social networks in Saudi Arabia and contrast them with individuals protective actions. The results in this paper are based on a statistical analysis of a survey questionnaire. A reliability test was conducted to assure the internal consistency and the reliability of the measures used in the study. Analysis of the study showed high levels of privacy concerns among Saudi society. A correlation analysis was conducted and showed that although individuals seem to be concerned about privacy and the protection of their personal information, their behavior was not proportionate with their privacy concerns.This observation was further verified among the different genders and age groups with respect to their claimed privacy concerns, where the results revealed no significant difference between the different groups. A closer investigation of the awareness of privacy issues in Snapchat -the social platform chosen as the research subject for this study -revealed that users are highly aware of its privacy issues. The results of this study can be useful to assist developing new privacy techniques, whether technological or awareness-based, that can facilitate the safe use of social networks, with increased privacy protection capabilities.

<abstract> <p>Rehabilitation engineering is playing a more vital role in the field of healthcare for humanity. It is providing many assistive devices to diplegia patients (The patients whose conditions are weak in terms of muscle mobility on both sides of the body and their paralyzing effects are high either in the arms or in the legs). Therefore, in order to rehabilitate such types of patients, an intelligent healthcare system is proposed in this research. The electric sticks and chairs are also a type of this system which was used previously to facilitate the diplegia patients. It is worth noting that a voice recognition system along with wireless control feature has been integrated intelligently in the proposed healthcare system in order to replace the common and conventional assistive tools for diplegia patients. These features will make the proposed system more user friendly, convenient and comfortable. The voice recognition system has been used for movements of system in any desired direction along with the ultrasonic sensor and light detecting technology. These sensors detect the obstacles and low light environment intelligently during the movement of the wheelchair and then take the necessary actions accordingly.</p> </abstract>