In this experiment, a haptic glove with vibratory motors on the fingertips was tested against the standard HTC Vive controller to see if the additional vibrations provided by the glove increased immersion in common gaming scenarios where haptic feedback is provided. Specifically, two scenarios were developed: an explosion scene containing a small and large explosion and a box interaction scene that allowed the participants to touch the box virtually with their hand. At the start of this project, it was hypothesized that the haptic glove would have a significant positive impact in at least one of these scenarios. Nine participants took place in the study and immersion was measured through a post-experiment questionnaire. Statistical analysis on the results showed that the haptic glove did have a significant impact on immersion in the box interaction scene, but not in the explosion scene. In the end, I conclude that since this haptic glove does not significantly increase immersion across all scenarios when compared to the standard Vive controller, it should not be used at a replacement in its current state.

This thesis is based on bringing together three different components: non-Euclidean geometric worlds, virtual reality, and environmental puzzles in video games. While all three exist in their own right in the world of video games, as well as combined in pairs, there are virtually no examples of all three together. Non-Euclidean environmental puzzle games have existed for around 10 years in various forms, short environmental puzzle games in virtual reality have come into existence in around the past five years, and non-Euclidean virtual reality exists mainly as non-video game short demos from the past few years. This project seeks to be able to bring these components together to create a proof of concept for how a game like this should function, particularly the integration of non-Euclidean virtual reality in the context of a video game. To do this, a Unity package which uses a custom system for creating worlds in a non-Euclidean way rather than Unity’s built-in components such as for transforms, collisions, and rendering was used. This was used in conjunction with the SteamVR implementation with Unity to create a cohesive and immersive player experience.

Breast cancer is one of the most common types of cancer worldwide. Early detection and diagnosis are crucial for improving the chances of successful treatment and survival. In this thesis, many different machine learning algorithms were evaluated and compared to predict breast cancer malignancy from diagnostic features extracted from digitized images of breast tissue samples, called fine-needle aspirates. Breast cancer diagnosis typically involves a combination of mammography, ultrasound, and biopsy. However, machine learning algorithms can assist in the detection and diagnosis of breast cancer by analyzing large amounts of data and identifying patterns that may not be discernible to the human eye. By using these algorithms, healthcare professionals can potentially detect breast cancer at an earlier stage, leading to more effective treatment and better patient outcomes. The results showed that the gradient boosting classifier performed the best, achieving an accuracy of 96% on the test set. This indicates that this algorithm can be a useful tool for healthcare professionals in the early detection and diagnosis of breast cancer, potentially leading to improved patient outcomes.