Due to the COVID-19 pandemic, declared in March of 2020, there have been many lifestyle changes which have likely influenced tobacco smoking behavior. Such lifestyle changes include lockdowns, stay at home orders, reduction in social cues related to smoking, increased stress, and boredom among other things. This study utilized a cross-sectional survey which looked into these behaviors, primarily perceived risk to COVID-19, and determined if there is an association between perceived risk and education level/race. Education level is a proxy for income and material resources, therefore making it more likely that people with lower levels of education have fewer resources and higher perceived risk to negative effects of COVID-19. Additionally, people of color are often marginalized in the medical community along with being the target of heavy advertising by tobacco companies which have likely impacted risk to COVID-19 as well.

The mental health of ASU students has been negatively affected by the pandemic. Our research looks to prove that COVID-19 has caused an increase in stress levels while uncovering other relationships to stress. We obtained our data by conducting a survey through Google Forms that was exclusively accessible to ASU students. Stress levels were measured with the use of the Perceived Stress Scale (PSS). We find that the stress of ASU students from before the pandemic to during rises from 15 to 22 points, a 50% increase (n = 228). We discovered that women are more stressed than men before and during the pandemic. We also discovered that there is no difference between stresses among different races. We notice that there is a parabolic relationship between enrollment time and stress levels with the peak occurring during semesters 2-6. We also conclude that students who attended more than 5 events during the pandemic had lower stress scores, and those who had their videos on for at least 3 events had lower stress scores. Furthermore, students who utilized campus resources to manage their stress had higher stress levels than those who did not.

The mental health of ASU students has been negatively affected by the pandemic. Our research looks to prove that COVID-19 has caused an increase in stress levels while uncovering other relationships to stress. We obtained our data by conducting a survey through Google Forms that was exclusively accessible to ASU students. Stress levels were measured with the use of the Perceived Stress Scale (PSS). We find that the stress of ASU students from before the pandemic to during rises from 15 to 22 points, a 50% increase (n = 228). We discovered that women are more stressed than men before and during the pandemic. We also discovered that there is no difference between stresses among different races. We notice that there is a parabolic relationship between enrollment time and stress levels with the peak occurring during semesters 2-6. We also conclude that students who attended more than 5 events during the pandemic had lower stress scores, and those who had their videos on for at least 3 events had lower stress scores. Furthermore, students who utilized campus resources to manage their stress had higher stress levels than those who did not.

The mental health of ASU students has been negatively affected by the pandemic. Our research looks to prove that COVID-19 has caused an increase in stress levels while uncovering other relationships to stress. We obtained our data by conducting a survey through Google Forms that was exclusively accessible to ASU students. Stress levels were measured with the use of the Perceived Stress Scale (PSS). We find that the stress of ASU students from before the pandemic to during rises from 15 to 22 points, a 50% increase (n = 228). We discovered that women are more stressed than men before and during the pandemic. We also discovered that there is no difference between stresses among different races. We notice that there is a parabolic relationship between enrollment time and stress levels with the peak occurring during semesters 2-6. We also conclude that students who attended more than 5 events during the pandemic had lower stress scores, and those who had their videos on for at least 3 events had lower stress scores. Furthermore, students who utilized campus resources to manage their stress had higher stress levels than those who did not.

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.