Knowledge is prodigious, and learning has no boundary. The curiosity of the one to learn, discover and invent decides the future of the world. Educational Institutions’ main objective is to provide qualitative knowledge to the students and supporting tools used for learning play a major role. The conventional education system is arduous to be used in the current situation of the global pandemic of COVID-19. New methods and tools are required to make learning and imparting knowledge more effective. Applications like Google Meet, Zoom, Cisco WebEx are being used in schools and colleges. When it comes to simulations in technical education, for instance, to develop any electrical circuits, robots, buildings, etc., software like MATLAB, 3Ds MAX, GNU Octave is being used. These methods neither are interactive nor provide an immersive experience to the user. To subdue this problem, Mixed Reality (MR) technology can be utilized as a boon by developing an application where students can have interactive classes, submerging themselves, and gaining the required knowledge. Also, the technical students can simulate their experiments onto the real world, providing an idea of how the world may look when new things are adopted and can undergo a walk-through experience in the MR world.

Traffic accidents are among the most censorious issues confronting the world as they cause numerous deaths, wounds and fatalities just as monetary misfortunes consistently. According to the world health organization (WHO) reports, 5,18,3626 accidents took place in India in the year 2019. Factors that contribute to these road crashes/ traffic accidents and resulting injuries include inattentive drivers, unenforced traffic laws, poor road infrastructure, driving in bad weather conditions and others. This investigation effort establishes models to select a set of influential factors and to build up a model for classifying the severity of injuries. Machine learning models can be applied to model and predict the severity of injury that occurs during road accidents. One such way is to apply unsupervised learning models such as Apriori, Apriori TID (transaction id), SFIT (set operation for frequent itemset using transaction database) and ECLAT (equivalence class clustering and bottom-up lattice traversal) which analyze the unlabeled traffic accidents dataset and determine the relationship between traffic accidents and injury. This research work is helpful for traffic departments to decrease the number of accidents and to distinguish the injury's seriousness extensive simulations were carried out to demonstrate the unsupervised learning algorithms for predicting the injury severity of traffic accidents. Apriori algorithm predicts the patterns in 962 milliseconds, Apriori TID (transaction id) algorithm predicts the pattern in 557 milliseconds, SFIT algorithm predicts the pattern in 516 milliseconds and ECLAT algorithm predicts the pattern in 124 milliseconds. ECLAT algorithm took less time compared to all the other algorithms.