Marker Based Augmented Reality Technique for Visualization of Data Analytics

Abstract

Augmented Reality (AR) enables the user to create and view the virtual objects in newlinephysical 3D space. Previously, immersive augmented reality experiences were only newlineused in the entertainment and gaming industries, but today other organizations are becoming interested in AR s capabilities, such as knowledge sharing, educating, controlling dynamic information, and scheduling interactive long-distance gatherings to name a few. There are several types of augmented reality technologies, however even the most experienced eyes do not make it expedient configuration in terms of augmenting the information and displaying it through QR codes on mobile applications. In an attempt to address this, the present research work aims at creating a framework for augmenting the visual analytics and embedding it into a mobile application so as to provide interactive representations. This work has been designed and modelled in two phases such as data analytics and augmented reality development. In the first phase, the analytics obtained from the periodic table is embedded in the augmented reality device via newlineapplication Microsoft HoloLens and the interactive gestured visualizations are displayed to the user in a virtual 3D space. Further, for second phase Bigmart dataset newlinehas been considered and provided as an input to the python code for executing classification with respect to item type,outlet size, and health status using machine learning.The visualizations of classification have been obtained in the form of confusion matrix.The analytics are further forwarded to the Unity engine for embedding it with the AR application. The hyper parameters tuning has assisted to investigate the impact of those parameters for classification. The best classifier and optimization algorithms are recommended through comparative study. Nine different classifiers such as J48 decision tree, Hoeffding tree, Random forest, Random Tree, REPTree, Bayes net, Naïve,Bayes, OneR, and Decision table have been designed for achieving this purpose. The classificat