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Since the gaming industry is becoming saturated, players tend to raise their requirements to the quality of gaming experience. Fusion of physical and virtual gaming experience may provide a fresh feeling to the players. In this project, a single-player Augmented Reality (AR) mobile game called Hexplore Fort is developed, where the players can control a spider-like robot – hexapod to adventure with the storyline in the game. Players can explore the fort that is augmented by AR by controlling the movement of hexapod, collecting items, fighting with enemies and buying assistance provided by the spy, in order to capture the princess.

The aim of this project was to detect phishing emails or URLs with a high accuracy. For the same, our deliverable was in the form of an improved email client that scrapes the last unread email from a user’s inbox and checks if it is safe or malicious. A series of Machine Learning algorithms were tried and experiments and research was conducted. From the results obtained, it was determined that a Balanced Random Forest algorithm after optimization in the form of algorithm tuning, training-testing split, feature analysis and other methods, was the most suitable choice for the project as it gave an accuracy of around 98% and after some experiments on optimization, it even reached a 100% accuracy.

Mental health issues, especially depression and loneliness, are becoming more common amongst young people. Fortunately, they are usually treatable without any medical help, and games might be the answer. Since people who suffer with these issues turn to video games, it can be considered as perfect medium to provide them with the remedy. Onslaught Arena is a web-based, online game that was modified to introduce multiplayer support and communication services to combat depression and loneliness. The game is backed by a NodeJS server that analyses user’s preference to provide them with the best possible gaming partner.

Our main objective for this project is to design a deep learning model to perform an efficient and accurate object classification and localization on assembly line so that further human-robot collaboration can be facilitated. We further formulate our task as a weakly supervised object localization problem. More specifically, the model will be training with image-level labeled data and output a predicted category and a bounding box for the object that worker is interacting with given one input image.