Computer Science - Student Works
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Item Evaluating the impact of virtual reality training simulations in nursing education(2025) Aizon, Jehdi; Qorbani, Sam; Neumeier, Melanie; Qorbani, SamAs part of their education, nursing students are required to learn how to operate a variety of complex medical equipment which is often only accessible in classroom or laboratory settings. Students are required to master operational skills, but are not able to access the equipment they need to practice. To address this issue we created a virtual reality IV pump training simulator so that students are able to practice anywhere, for as long as they need in order to master the skill. The purpose of this research is to evaluate the potential effectiveness and impact of the VR simulation by determining student satisfaction with this method of practice and their perceptions of the effectiveness of VR for their learning. Research questions: Do students view the VR training simulation as an effective alternative to a physical IV pump for training purposes? Are there any barriers that might limit students’ ability to access or use the VR simulation? How do students perceive the VR simulation in terms of effectiveness for learning? Our VR prototype was tested with current nursing students. This presentation will discuss the development, study results, and room for improvement.Item An analysis of electroencephalogram (EEG) with machine learning(2024) Jime, Isra; Emery, Jesse; Phan, Nhi; Cobzas, DanaOur capstone project was done in collaboration with Dr. Cameron Hassall from the Psychology department at MacEwan University. Our data was based on one of Dr. Hassall’s papers on “Task-level value affects trial-level reward processing” (Hassal, C, 2022), where he wanted to determine if the Anterior Cingulate Cortex was responsible or involved in decision making. To determine this, a task sequence was carried out 427 times using 12 participants over a 52 minute period. While the participants completed these tasks, brain activity was being measured using an electroencephalogram (EEG). For our project, the goal was to train a machine learning model to accurately classify an EEG event after training on past events. In greater detail, we focus on the brain signal when the participant hit the left or right button in response to the stimulus which are colored shapes.Item An analysis of electroencephalogram (EEG) with machine learning(2024) Emery, Jesse; Phan, Nhi; Jime, Isra; Cobzas, Dana; Hassall, Cameron D.Our capstone project was done in collaboration with Dr. Cameron Hassall from the Psychology department at MacEwan University. Our data was based on one of Dr. Hassall’s papers on “Task-level value affects trial-level reward processing” (Hassal, C, 2022), where he wanted to determine if the Anterior Cingulate Cortex was responsible or involved in decision making. To determine this, a task sequence was carried out 427 times using 12 participants over a 52 minute period. While the participants completed these tasks, brain activity was being measured using an electroencephalogram (EEG). For our project, the goal was to train a machine learning model to accurately classify an EEG event after training on past events. In greater detail, we focus on the brain signal when the participant hit the left or right button in response to the stimulus which are colored shapes.Item Analyzing factors impacting COVID-19 vaccination rates(2023) Cho, Dongseok; Driedger, Mitchell; Han, Sera; Khan, Noman; Elmorsy, Mohammed; El-Hajj, MohamadSince the approval of the COVID-19 vaccine in late 2020, vaccination rates have varied around the globe. Access to a vaccine supply, mandated vaccination policy, and vaccine hesitancy contribute to these rates. This study used COVID-19 vaccination data from Our World in Data and the Multilateral Leaders Task Force on COVID-19 to create two COVID-19 vaccination indices. The first index is the Vaccine Utilization Index (VUI), which measures how effectively each country has utilized its vaccine supply to doubly vaccinate its population. The second index is the Vaccination Acceleration Index (VAI), which evaluates how efficiently each country vaccinated their populations within their first 150 days. Pearson correlations were created between these indices and country indicators obtained from the World Bank. Results of these correlations identify countries with stronger Health indicators such as lower mortality rates, lower age dependency ratios, and higher rates of immunization to other diseases display higher VUI and VAI scores than countries with lesser values. VAI scores are also positively correlated to Governance and Economic indicators, such as regulatory quality, control of corruption, and GDP per capita. As represented by the VUI, proper utilization of the COVID-19 vaccine supply by country is observed in countries that display excellence in health practices. A country’s motivation to accelerate its vaccination rates within the first 150 days of vaccinating, as represented by the VAI, was largely a product of the governing body’s effectiveness and economic status, as well as overall excellence in health practises.Item 496 Capstone: AR.t(2020) Driedger, Andre; Ansorger, Anneliese; Galay, Chance; Lafitte, Chanelle; Cobzas, DanaOriginal artwork is often very expensive; being able to see how a painting will look on a wall before you buy is advantageous. As a collaborative project between the MacEwan Computer Science and Design departments, we set out to do develop an AR app that can be used by by consumers to shop for art on the walls of their homes and offices. Existing mobile AR applications cannot identify vertical surfaces, such as walls. Our solution is to implement a target image that can be posted onto vertical surfaces to be detected by our app. We developed an OpenCV prototype to test this method of using object-detection to set a starting point for subsequent tracking. The prototype was successful in rendering 3d objects, true to scale, onto walls. Next, we developed an Android version utilizing Google's ARCore toolkit. This also delivered good results. Ultimately, we were successful in showcasing art on walls using smartphones in real-time.Item AR.T deliver report(2020) Lafitte, Chanelle; Galay, Chance; Cobzas, DanaOriginal artwork is often very expensive; being able to see how a painting will look on a wall before you buy is advantageous. As a collaborative project between the MacEwan Computer Science and Design departments, we set out to do develop an AR app that can be used by by consumers to shop for art on the walls of their homes and offices. Existing mobile AR applications cannot identify vertical surfaces, such as walls. Our solution is to implement a target image that can be posted onto vertical surfaces to be detected by our app. We developed an OpenCV prototype to test this method of using object-detection to set a starting point for subsequent tracking. The prototype was successful in rendering 3d objects, true to scale, onto walls. Next, we developed an Android version utilizing Google's ARCore toolkit. This also delivered good results. Ultimately, we were successful in showcasing art on walls using smartphones in real-time.Item OpenCV laptop demo(2020) Driedger, Andre; Ansorger, Anneliese; Galay, Chance; Lafitte, Chanelle; Cobzas, DanaWhen we started the project, we had decided to make a program that would use feature matching to recognize a specific image (eg. a poster or sticker), find it’s orientation, and then display some kind of useful AR artifacts in the 3D space of our recognized image. We have implemented this in OpenCV, to show that we have an in-depth understanding of how AR works.Item Android app demo(2020) Driedger, Andre; Ansorger, Anneliese; Galay, Chance; Lafitte, Chanelle; Cobzas, DanaFor the app, we developed an Android version utilizing Google's ARCore toolkit. The Design students prototyped screens for user profiles, buying art, as well as filtering and browsing functionality. This functionality has not yet been implemented, and we instead chose to focus on the AR screens. The user can browse through and preview different paintings and frames.Item Visual assistance software for the visually impaired(2020) Gupta, VasuThe proposed project involves the creation of a visual assistance software for the visually impaired to identify people in their surroundings to avoid potential collisions. Object detection technique using TensorFlow Lite Object Detection API is performed to identify the people in the view of a visually impaired person. Vibrations of different lengths were used to notify the visually impaired about the location of the person in the view. The software is tested in various conditions to verify its working.Item Promoting game flow in the design of a hyper-casual mobile game(2020) Dorokhine, Andrew; Bratt, SharonThis capstone research project describes the application and evaluation of elements from the GameFlow model (Sweetser et al., 2017; Sweetser & Wyeth, 2005) to make a hyper-casual tile-matching mobile game more responsive, engaging, challenging while providing the player with a sense of agency and flow. Mobile game design research, as well as game design from a more general perspective frame the design decisions. Contemporary game design research provided a reference to guide the design decisions. The GameFlow model provided assessment criteria. Results show that many of the elements of game flow are promoted. Several recommendations emerged, both situational and generalizable, which could enhance the redesign and provide guidance for game designers who use game flow as a core driver. Future research is encouraged to address issues of immersion, social interaction and user interface. Contributions include a new evaluation methodology that combines design science research and action research.Item Hello, world: an internationalization at home project for computing for social good(2019) Aheer, Komal; Macdonell, CameronWe have developed and piloted a cross-institution activity as part of an Internationalization at Home (IaH) initiative to expose first year computer science students to the concept of computing for social good in an international context. We explore how differences in culture can influence students’ perceptions and approaches to computing for social good.Item The influence of noise in cytoxicity assessment(2016) Yong, Alan; Anton, CristinaIndustrial activity produces many chemicals that may be hazardous to human health or the environment. Traditionally, experiments can be carried out on live subjects (in vivo), but this is both expensive and raises significant ethical concerns. Rather than conducting these assays, we might try using mathematical or computational models to assess the effect of these toxicants (1). With in vitro assays at the Alberta Centre of Toxicology using the xCelligence Real-Time Cell Analysis HT system, time-dependent response curves (TCRCs) were generated. These experimentally-derived curves reflect the response of human cells to these toxicants. The goal was to find a mathematical model that could accurately reproduce these curves (2). Depending on the value of various parameters of the toxicant – such as its toxicity and how fast cells absorb it – there are generally two possible equilibria dependent on the toxicant's initial external concentration: a cell line may persevere and survive; or the concentration may be large enough to cause extinction of the cell population. Data from the TCRCs were used to generate a deterministic model. That is, a given set of parameter values will always generate the same cell fate. However, there is inherently uncertainty in the value of these parameters. To assess the influence of this noise on the external concentration of toxicant at which some population of cells would reach survival or extinction equilibria, a new model was created with additional variables for this uncertainty. Several simulations were run with this extended model. The information generated will be useful for planning further experiments regarding cytotoxicity, and for numerically generating TCRCs for clustering and classification.