With the rapid increase of technological capabilities, particularly in processing power and speed, the usage of machine learning is becoming increasingly widespread, especially in fields where real-time assessment of complex data is extremely valuable. This surge in popularity of machine learning gives rise to an abundance of potential research and projects on further broadening applications of artificial intelligence. From these opportunities comes the purpose of this thesis. Our work seeks to meaningfully increase our understanding of current capabilities of machine learning and the problems they can solve. One extremely popular application of machine learning is in data prediction, as machines are capable of finding trends that humans often miss. Our effort to this end was to examine the CVE dataset and attempt to predict future entries with Random Forests. The second area of interest lies within the great promise being demonstrated by neural networks in the field of autonomous driving. We sought to understand the research being put out by the most prominent bodies within this field and to implement a model on one of the largest standing datasets, Berkeley DeepDrive 100k. This thesis describes our efforts to build, train, and optimize a Random Forest model on the CVE dataset and a convolutional neural network on the Berkeley DeepDrive 100k dataset. We document these efforts with the goal of growing our knowledge on (and usage of) machine learning in these topics.

Quantum computing is an emerging and promising alternative to classical computing due to its ability to perform rapidly complex computations in a parallel manner. In this thesis, we aim to design an audio classification algorithm using a hybrid quantum-classical neural network. The thesis concentrated on healthcare applications and focused specifically on COVID-19 cough sound classification. All machine learning algorithms developed or implemented in this study were trained using features from Log Mel Spectrograms of healthy and COVID-19 coughing audio. Results are first presented from a study in which an ensemble of a VGG13, CRNN, GCNN, and GCRNN are utilized to classify audio using classical computing. Then, improved results attained using an optimized VGG13 neural network are presented. Finally, our quantum-classical hybrid neural network is designed and assessed in terms of accuracy and number of quantum layers and qubits. Comparisons are made to classical recurrent and convolutional neural networks.

For my Honors Thesis, I decided to create an Artificial Intelligence Project to predict Fantasy NFL Football Points of players and team's defense. I created a Tensorflow Keras AI Regression model and created a Flask API that holds the AI model, and a Django Try-It Page for the user to use the model. These services are hosted on ASU's AWS service. In my Flask API, it actively gathers data from Pro-Football-Reference, then calculates the fantasy points. Let’s say the current year is 2022, then the model analyzes each player and trains on all data from available from 2000 to 2020 data, tests the data on 2021 data, and predicts for 2022 year. The Django Website asks the user to input the current year, then the user clicks the submit button runs the AI model, and the process explained earlier. Next, the user enters the player's name for the point prediction and the website predicts the last 5 rows with 4 being the previous fantasy points and the 5th row being the prediction.