Computer Sciences Commons^™

Homln-Sd: Substructure Discovery In Homogeneous Multilayer Networks, Arshdeep Singh

Computer Science and Engineering Theses

Substructure discovery is a process in data analysis and data mining that involves identifying and extracting meaningful patterns, structures, or components within a larger dataset. These substructures can be of various types, such as frequent patterns, motifs, or any other relevant features within the data. The growth of the internet and the proliferation of mobile devices have led to the generation of enormous amounts of data. Companies like Facebook and Twitter can generate large datasets from user interactions on their websites, such as connections between users and user generated content. Moreover, advances in processing power and storage capacity have made …

Cannabidiol Tweet Miner: A Framework For Identifying Misinformation In Cbd Tweets., Jason Turner

Electronic Theses and Dissertations

As regulations surrounding cannabis continue to develop, the demand for cannabis-based products is on the rise. Despite not producing the psychoactive effects commonly associated with THC, products containing cannabidiol (CBD) have gained immense popularity in recent years as a potential treatment option for a range of conditions, particularly those associated with pain or sleep disorders. However, due to current federal policies, these products have yet to undergo comprehensive safety and efficacy testing. Fortunately, utilizing advanced natural language processing (NLP) techniques, data harvested from social networks have been employed to investigate various social trends within healthcare, such as disease tracking and …

Campus Safety Data Gathering, Classification, And Ranking Based On Clery-Act Reports, Walaa F. Abo Elenin

Electronic Theses and Dissertations

Most existing campus safety rankings are based on criminal incident history with minimal or no consideration of campus security conditions and standard safety measures. Campus safety information published by universities/colleges is usually conceptual/qualitative and not quantitative and are based-on criminal records of these campuses. Thus, no explicit and trusted ranking method for these campuses considers the level of compliance with the standard safety measures. A quantitative safety measure is important to compare different campuses easily and to learn about specific campus safety conditions.

In this thesis, we utilize Clery-Act reports of campuses to automatically analyze their safety conditions and generate …