Purpose: This qualitative research aimed to create a developmentally and gender-appropriate game-based intervention to promote Human Papillomavirus (HPV) vaccination in adolescents. <br/>Background: Ranking as the most common sexually transmitted infection, about 80 million Americans are currently infected by HPV, and it continues to increase with an estimated 14 million new cases yearly. Certain types of HPV have been significantly associated with cervical, vaginal, and vulvar cancers in women; penile cancers in men; and oropharyngeal and anal cancers in both men and women. Despite HPV vaccination being one of the most effective methods in preventing HPV-associated cancers, vaccination rates remain suboptimal in adolescents. Game-based intervention, a novel medium that is popular with adolescents, has been shown to be effective in promoting health behaviors. <br/>Methods: Sample/Sampling. We used purposeful sampling to recruit eight adolescent-parent dyads (N = 16) which represented both sexes (4 boys, 4 girls) and different racial/ethnic groups (White, Black, Latino, Asian American) in the United States. The inclusion criteria for the dyads were: (1) a child aged 11-14 years and his/her parent, and (2) ability to speak, read, write, and understand English. Procedure. After eligible families consented to their participation, semi-structured interviews (each 60-90 minutes long) were conducted with each adolescent-parent dyad in a quiet and private room. Each dyad received $50 to acknowledge their time and effort. Measure. The interview questions consisted of two parts: (a) those related to game design, functioning, and feasibility of implementation; (b) those related to theoretical constructs of the Health Belief Model (HBM) and the Theory of Planned Behavior (TPB). Data analysis. The interviews were audio-recorded with permission and manually transcribed into textual data. Two researchers confirmed the verbatim transcription. We use pre-developed codes to identify each participant’s responses and organize data and develop themes based on the HBM and TPB constructs. After the analysis was completed, three researchers in the team reviewed the results and discussed the discrepancies until a consensus is reached.<br/>Results: The findings suggested that the most common motivating factors for adolescents’ HPV vaccination were its effectiveness, benefits, convenience, affordable cost, reminders via text, and recommendation by a health care provider. Regarding the content included in the HPV game, participants suggested including information about who and when should receive the vaccine, what is HPV and the vaccination, what are the consequences if infected, the side effects of the vaccine, and where to receive the vaccine. The preferred game design elements were: 15 minutes long, stories about fighting or action, option to choose characters/avatars, motivating factors (i.e., rewards such as allowing users to advance levels and receive coins when correctly answering questions), use of a portable electronic device (e.g., tablet) to deliver the education. Participants were open to multiplayer function which assists in a facilitated conversation about HPV and the HPV vaccine. Overall, the participants concluded enthusiasm for an interactive yet engaging game-based intervention to learn about the HPV vaccine with the goal to increase HPV vaccination in adolescents. <br/>Implications: Tailored educational games have the potential to decrease the stigma of HPV and HPV vaccination, increasing communication between the adolescent, parent, and healthcare provider, as well as increase the overall HPV vaccination rate.

In this thesis, several different methods for detecting and removing satellite streaks from astronomic images were evaluated and compared with a new machine learning based approach. Simulated data was generated with a variety of conditions, and the performance of each method was evaluated both quantitatively, using Mean Absolute Error (MAE) against a ground truth detection mask and processing throughput of the method, as well as qualitatively, examining the situations in which each model performs well and poorly. Detection methods from existing systems Pyradon and ASTRiDE were implemented and tested. A machine learning (ML) image segmentation model was trained on simulated data and used to detect streaks in test data. The ML model performed favorably relative to the traditional methods tested, and demonstrated superior robustness in general. However, the model also exhibited some unpredictable behavior in certain scenarios which should be considered. This demonstrated that machine learning is a viable tool for the detection of satellite streaks in astronomic images, however special care must be taken to prevent and to minimize the effects of unpredictable behavior in such models.

This project aims to incorporate the aspect of sentiment analysis into traditional stock analysis to enhance stock rating predictions by applying a reliance on the opinion of various stocks from the Internet. Headlines from eight major news publications and conversations from Yahoo! Finance’s “Conversations” feature were parsed through the Valence Aware Dictionary for Sentiment Reasoning (VADER) natural language processing package to determine numerical polarities which represented positivity or negativity for a given stock ticker. These generated polarities were paired with stock metrics typically observed by stock analysts as the feature set for a Logistic Regression machine learning model. The model was trained on roughly 1500 major stocks to determine a binary classification between a “Buy” or “Not Buy” rating for each stock, and the results of the model were inserted into the back-end of the Agora Web UI which emulates search engine behavior specifically for stocks found in NYSE and NASDAQ. The model reported an accuracy of 82.5% and for most major stocks, the model’s prediction correlated with stock analysts’ ratings. Given the volatility of the stock market and the propensity for hive-mind behavior in online forums, the performance of the Logistic Regression model would benefit from incorporating historical stock data and more sources of opinion to balance any subjectivity in the model.

This project aims to incorporate the aspect of sentiment analysis into traditional stock analysis to enhance stock rating predictions by applying a reliance on the opinion of various stocks from the Internet. Headlines from eight major news publications and conversations from Yahoo! Finance’s “Conversations” feature were parsed through the Valence Aware Dictionary for Sentiment Reasoning (VADER) natural language processing package to determine numerical polarities which represented positivity or negativity for a given stock ticker. These generated polarities were paired with stock metrics typically observed by stock analysts as the feature set for a Logistic Regression machine learning model. The model was trained on roughly 1500 major stocks to determine a binary classification between a “Buy” or “Not Buy” rating for each stock, and the results of the model were inserted into the back-end of the Agora Web UI which emulates search engine behavior specifically for stocks found in NYSE and NASDAQ. The model reported an accuracy of 82.5% and for most major stocks, the model’s prediction correlated with stock analysts’ ratings. Given the volatility of the stock market and the propensity for hive-mind behavior in online forums, the performance of the Logistic Regression model would benefit from incorporating historical stock data and more sources of opinion to balance any subjectivity in the model.

This paper serves to report the research performed towards detecting PD and the effects of medication through the use of machine learning and finger tapping data collected through mobile devices. The primary objective for this research is to prototype a PD classification model and a medication classification model that predict the following: the individual’s disease status and the medication intake time relative to performing the finger-tapping activity, respectively.

This paper serves to report the research performed towards detecting PD and the effects of medication through the use of machine learning and finger tapping data collected through mobile devices. The primary objective for this research is to prototype a PD classification model and a medication classification model that predict the following: the individual’s disease status and the medication intake time relative to performing the finger-tapping activity, respectively.