Background/Objectives: Brainwave based accident avoidance system for drowsy drivers. Methods/Statistical Analysis: Accident avoidance system is an effective way to prevent accident caused due to drowsy driving. Every year, approximately 100,000 crashes are caused by drowsy driving. These kind of crashes are most common in highways during long journeys, youth, adults with children and shift workers. Till now, there is no test to determine sleepiness. Findings: The proposed idea brain wave accident avoidance system is to avoid this kind of accident using Electroencephalography (EEG) of human brain and Cruise control in automobiles. Whenever the driver tends to drowse, obviously his brain signals will change which will trigger a set of routine on cruise control to control the automobile to avoid accidents effectively. Application/Improvements: Brain computer interface.

In this paper, adapted learning methodology is proposed using Data mining techniques to satisfy the individual needs of the student. First part of the proposed methodology is to create student profile, so that need of the student is analyzed. K-means clustering is used to group the contents of a particular student. C4.5 Algorithm is implemented in the dashboard section to display the content to the user. Second part is to develop intelligent learning application by using Page ranking algorithm. For every user, the content displayed on the dashboard is different. Content are selected and displayed from the database to the user after analyzing their answers submitted in the questionnaire section. As every user has different area of interest and according to their choice the contents are displayed. The page ranking algorithm helps keeping the most preferred and referred material at the top.

ASYNCHRONOUS TIME ENCODER BASED QUASI-STATIC MODELLING FOR HUMAN AREA NETWORK IN VANET