capstone project university of alberta

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Any CS courses that require a capstone project?

I saw engineering people showing their capstone projects today

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Spring 2024 Capstone Projects

Company sponsors.

Some of our spring 2024 company sponsors for capstone projects included:

• The U.S. Army Combat Capabilities Development Command - BRL-CAD
• American Airlines
• Boom Boards
• Program of Excellence for Mothers, Children and Families
• Detectachem

BRL-CAD Graphics Acceleration

BRL-CAD's existing ray tracer is not up to industry standards. Our project was aimed at researching an existing high performance ray tracer and implementing it in place of BRL-CAD's current ray tracer to improve the user's workflow. Research Area: Graphics, Visualization and Computational Fabrication.

Chatbot for American Airlines which offers a unified resource for information to increase customers' ease of navigation. The three main personas for this app included first-time travelers, non-native speakers, and assistance-dependents. Research areas: Human-Computer Interaction, Databases, Data Mining, Information Retrieval Systems.

Autonomously Driving through Imitation Learning

Machine learning can address transportation disparities in rural communities through a relatively new autonomous driving approach known as imitation learning. This method involves training a model on collected human driving behaviors, enabling cars to navigate autonomously. Research Area: AI, ML, NLP

Interactive cornhole

Boom Boards uses interactivity of a smartphone app with catchy voice narration to engage players in cornhole. It offers a fun new way for experienced and new players to interact with cornhole, elevating the normal game experience by adding real time narration and score boards. Research area: Embedded Systems.

Oliva-NAVIGATOR

Olivia-Navigator is a public health record database. It contains the patient's medical history, diagnoses, medications, treatment plans, immunization dates, allergies, radiology images, and laboratory test results. The user can log in and view past submissions of forms along with submitting new ones. Research Areas: Software and Software Engineering; Health.

SEEKER Pro Algorithm Enhancement

Team DetectaChem was tasked with augmenting an existing chemical reaction detection algorithm. The goal was to reduce false negatives and maintain or reduce false positives. Our solution approach included image processing and machine learning to discover avenues DetectaChem could further explore to improve detection accuracy. Research areas: Computer Vision, AI, ML, NLP.

Quantum Cryptography

As quantum computing becomes more viable, we need a way to easily implement quantum-safe cryptography into everyday applications. In doing so, we need to also know how this will affect our network performance. Research area: Cybersecurity.

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SCS Technologies CEO Part of Winning Hydrogen Capstone Project Team at University of Houston

Cody Johnson, Along With Hydrogen Economy Cohorts, Wins Spring 2024 Capstone Project With ‘Business Roadmap for Utilizing Hydrogen in Houston’

Today, SCS Technologies (SCS), a leading provider of CO 2 measurement systems, methane vapor recovery units and petroleum LACT units, announced that Cody Johnson, the company’s CEO, was on the winning 2024 Spring Capstone Project team in the University of Houston Hydrogen Economy Program. The “ Business Roadmap for Utilizing Hydrogen in Houston ” project outlines potential profits of $1.8 billion over the contract life with $180 million in green H2 investments.

“It was an honor to collaborate with my Hydrogen Economy Program teammates to explore business opportunities using existing technologies to produce clean hydrogen and reinvest profits to further advance decarbonization efforts in the future,” said Johnson. “I extend my gratitude to the University of Houston for assembling top-notch resources on the critical topic of clean hydrogen production. By bringing together students, corporate leaders, engineers, and scientists, we are able to join forces to accelerate the renewable hydrogen economy.”

The winning capstone project demonstrates the implementation of decarbonization processes and the enhancement of capacity utilization in existing industrial hydrogen production along the Houston Ship Channel through amine capture technology. Additionally, the team identified business opportunities in producing ammonia as a liquid carrier using the Haber-Bosch process and leveraging the existing maritime ammonia tanker fleet to ship to markets in Western Europe and Northeast Asia.

“Congratulations to Cody and team for designing an innovative yet feasible clean hydrogen business plan that leverages technologies and logistics already in place, making this a viable business opportunity for the oil and gas industry,” said Alan Rossiter, Executive Director, External Relations/Educational Program Development UH Energy at the University of Houston. “At the University of Houston, we are committed to advancing the energy transition by bringing diverse skills and knowledge together. The Hydrogen Economy Program is one of the many ways we achieve this. With the new cohort beginning in August and registration now open, we look forward to working with a new group of passionate, curious, and intelligent energy professionals and students.”

The energy industry faces a massive transition spurred by environmental and economic catalysts. The University of Houston’s Hydrogen Economy Program provides a forum within Houston, the world’s energy hub, for energy professionals and students to stay informed on the evolving energy industry from faculty and industry leaders.

Participants in the University of Houston Hydrogen Economy Program have the option to develop a capstone project using knowledge from the completed course to showcase a business plan for a clean hydrogen start-up venture. The individuals or teams then present their project to the class and a panel of judges, who evaluate the ideas and determine the first- through third-place winners. Program details, including this year's winners and winners from previous years, can be found here .

About SCS Technologies SCS Technologies designs, fabricates, and installs CO2 measurement systems, methane vapor recovery units, and petroleum LACT units for the energy, industrial and environmental sectors. SCS Technologies has committed to net zero emissions by 2050 as part of the company’s strategic initiative to mitigate CO2 emissions within its operations. Based in Big Spring, Texas, with a Strategy & Innovation Office in the Ion Houston, SCS Technologies is partnered with Black Bay Energy Capital, an energy-focused private equity fund based in New Orleans, Louisiana. www.scstechglobal.com ; www.blackbayenergy.com

Media contact: Carrie Ward [email protected] 832-407-5347

View source version on businesswire.com: https://www.businesswire.com/news/home/20240814511959/en/

Market Updates

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Hypothesized Surface: ISRU for Hypothesized Surfaces – Additive Manufacturing for Metallic and Silicate Resources

Institution.

University of Texas – Tyler (UTT)

Tungsten Class (2023 – 2024)

STUDENT TEAM

Academic guidance.

Dr. Hussain Rizvi, Assistant Professor of Instruction, Department of Mechanical Engineering, University of Texas at Tyler

PROJECT DESCRIPTION

This work was created in partial fulfillment of the University of Texas – Tyler Capstone Course “MENG 4215”. The work is a result of the Psyche Student Collaborations component of NASA’s Psyche Mission (https://psyche.asu.edu). “Psyche: A Journey to a Metal World” [Contract number NNM16AA09C] is part of the NASA Discovery Program mission to solar system targets. Trade names and trademarks of ASU and NASA are used in this work for identification only. Their usage does not constitute an official endorsement, either expressed or implied, by Arizona State University or National Aeronautics and Space Administration. The content is solely the responsibility of the authors and does not necessarily represent the official views of ASU or NASA.

Predicting Pediatric Malpractice Risks with AI

Miguel Nunez

Aug 13, 2024, 8:45 PM

Preverity – Predicting Pediatric Malpractice Risks with AI

In collaboration with Vanderbilt Data Science, Preverity launched a project aimed at developing a predictive model to assess malpractice risks in pediatric healthcare. This initiative was driven by the need to enhance patient safety in a particularly vulnerable population. By employing advanced logistic regression models with Lasso regularization, the project sought to create a tool that healthcare providers could use to identify potential risks before they manifest, ultimately improving the quality of care.

Predictive Modeling for Pediatric Malpractice

The project was spearheaded by a student who undertook the challenge of building a model capable of predicting the likelihood of malpractice occurrences in pediatric medicine. The student applied a combination of statistical techniques and machine learning algorithms to develop a model that not only predicts risk but also provides insights into the factors that contribute to these risks. The ultimate goal was to create a data-driven tool that could be integrated into healthcare systems to support better decision-making and reduce the incidence of malpractice.

Project Highlights:

Throughout the course of the project, the student focused on several areas essential to the development of an accurate and reliable predictive model. These highlights include:

• Purpose: The primary aim of the project was to develop a logistic regression model enhanced by Lasso regularization, capable of effectively predicting malpractice occurrences in pediatric healthcare. By identifying these risks early, healthcare providers can take proactive measures to prevent adverse outcomes and improve patient safety.
• Data Collection and Preparation: The project began with the collection of a comprehensive dataset, which included a variety of variables such as patient demographics, treatment types, outcomes, and historical malpractice claims. The student undertook extensive data cleaning and normalization processes to ensure the dataset was of high quality and suitable for modeling. This stage was crucial in laying the foundation for an accurate and reliable predictive model.
• Feature Selection and Model Development: The student used logistic regression with Lasso regularization to manage the high-dimensional dataset. This technique was particularly effective in selecting the most relevant features that influence malpractice risks, helping to improve the model’s accuracy and interpretability. By focusing on the most significant variables, the model was able to provide clear and actionable insights.
• Addressing Class Imbalance: One of the significant challenges the student faced was the issue of class imbalance, where the majority of cases did not involve malpractice, making it difficult for the model to learn from the minority class. To address this, the student implemented Synthetic Minority Over-sampling Technique (SMOTE) and adjusted class weights to balance the dataset. These techniques were essential in ensuring that the model could accurately identify and predict instances of malpractice.
• Applications: The predictive model developed through this project is intended to serve as a decision-support tool for healthcare providers. By incorporating this model into clinical practice, providers can better identify patients at higher risk of adverse outcomes, allowing them to implement targeted interventions and reduce the likelihood of malpractice. This proactive approach has the potential to significantly improve patient care and safety in pediatric healthcare settings.

Session Insights:

• The project highlighted the importance of rigorous data preparation and feature selection in developing predictive models for healthcare. The approach to data cleaning, normalization, and feature selection ensured that the model was both accurate and interpretable.
• Addressing class imbalance was a critical component of the project’s success. By implementing rebalancing techniques like SMOTE, the student was able to create a model that could reliably predict malpractice risks despite the inherent challenges posed by imbalanced data.
• The use of logistic regression with Lasso regularization was particularly effective in handling the complex, high-dimensional dataset. This approach not only improved the model’s predictive power but also provided insights into the most significant factors contributing to malpractice risks, offering valuable information for healthcare providers.
• Overall, the project underscored the potential of AI-driven predictive modeling in healthcare, particularly in high-stakes environments like pediatric medicine, where the ability to anticipate and mitigate risks can have profound implications for patient safety and care quality.

The collaboration between Preverity and Vanderbilt Data Science illustrates how data science can be applied to healthcare, particularly in assessing pediatric malpractice risks. The project contributed to healthcare analytics and underscored the role of predictive modeling in enhancing patient safety and outcomes.

What is a capstone project?

Explore story topics.

• - Generative AI
• Capstone Case Study
• Completed Research
• Medical Sciences
• Natural and Life Sciences
• Vanderbilt Generative AI Research