This thesis project focuses on the creation and assessment of the "Simple Stocks" app, a straightforward investment tool specifically developed for people who are new to investing and find it challenging to comprehend the complexities of the stock market. We identified a significant gap in the availability of easy-to-understand resources and information for beginner investors, which led us to design an app that provides clear and simple data, professional advice from financial analysts, and an advanced machine learning feature to predict stock trends. The "Simple Stocks" app also incorporates a voting feature, allowing users to see what other investors think about specific stocks. This functionality not only helps users make informed decisions but also encourages a sense of community, as users can learn from each other's experiences and opinions. By creating a supportive environment, the app promotes a more approachable and enjoyable experience for those who are new to investing. Following the successful release of the "Simple Stocks'' app on the App Store, our current objectives include expanding the user base and looking into various ways to generate income. One possible approach is to collaborate with other companies and establish an advertising-based revenue model, which would benefit both parties by attracting more users and increasing profits.

This honors thesis explores using machine learning technology to assist a patient's return to activity following a significant injury, specifically an anterior cruciate ligament (ACL) tear. The goal of the project was to determine if a machine learning model trained with ACL reconstruction (ACLR) applicable injury data would be able to correctly predict which phase of return to sport a patient would be classified in when introduced to a new data set.

Although relatively new technology, machine learning has rapidly demonstrated its many uses. One potential application of machine learning is the diagnosis of ailments in medical imaging. Ideally, through classification methods, a computer program would be able to identify different medical conditions when provided with an X-ray or other such scan. This would be very beneficial for overworked doctors, and could act as a potential crutch to aid in giving accurate diagnoses. For this thesis project, five different machine-learning algorithms were tested on two datasets containing 5,856 lung X-ray scans labeled as either “Pneumonia” or “Normal”. The goal was to determine which algorithm achieved the highest accuracy, as well as how preprocessing the data affected the accuracy of the models. The following supervised-learning methods were tested: support vector machines, logistic regression, decision trees, random forest, and a convolutional neural network. Each model was adjusted independently in order to achieve maximum performance before accuracy metrics were generated to pit the models against each other. Additionally, the effect of resizing images on model performance was investigated. Overall, a convolutional neural network proved to be the superior model for pneumonia detection, with a 91% accuracy. After resizing to 28x28, CNN accuracy decreased to 85%. The random forest model performed second best. The 28x28 PneumoniaMNIST dataset achieved higher accuracy using traditional machine learning models than the HD Chest X-Ray dataset. Resizing the Chest X-ray images had minimal effect on traditional model performance when resized to 28x28 or larger.

Following a study conducted in 1991 supporting that kinesthetic information affects visual processing information when moving an arm in extrapersonal space, this research aims to suggest utilizing virtual-reality (VR) technology will lead to more accurate and faster data acquisition (Helms Tillery, et al.) [1]. The previous methods for conducting such research used ultrasonic systems of ultrasound emitters and microphones to track distance from the speed of sound. This method made the experimentation process long and spatial data difficult to synthesize. The purpose of this paper is to show the progress I have made in the efforts to capture spatial data using VR technology to enhance the previous research that has been done in the field of neuroscience. The experimental setup was completed using the Oculus Quest 2 VR headset and included hand controllers. The experiment simulation was created using Unity game engine to build a 3D VR world which can be used interactively with the Oculus. The result of this simulation allows the user to interact with a ball in the VR environment without seeing the body of the user. The VR simulation is able to be used in combination with real-time motion capture cameras to capture live spatial data of the user during trials, though spatial data from the VR environment has not been able to be collected.

The importance of nonverbal communication has been well established through several theories including Albert Mehrabian's 7-38-55 rule that proposes the respective importance of semantics, tonality and facial expressions in communication. Although several studies have examined how emotions are expressed and preceived in communication, there is limited research investigating the relationship between how emotions are expressed through semantics and facial expressions. Using a facial expression analysis software to deconstruct facial expressions into features and a K-Nearest-Neighbor (KNN) machine learning classifier, we explored if facial expressions can be clustered based on semantics. Our findings indicate that facial expressions can be clustered based on semantics and that there is an inherent congruence between facial expressions and semantics. These results are novel and significant in the context of nonverbal communication and are applicable to several areas of research including the vast field of emotion AI and machine emotional communication.