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ASU Electronic Theses and Dissertations


This collection includes most of the ASU Theses and Dissertations from 2011 to present. ASU Theses and Dissertations are available in downloadable PDF format; however, a small percentage of items are under embargo. Information about the dissertations/theses includes degree information, committee members, an abstract, supporting data or media.

In addition to the electronic theses found in the ASU Digital Repository, ASU Theses and Dissertations can be found in the ASU Library Catalog.

Dissertations and Theses granted by Arizona State University are archived and made available through a joint effort of the ASU Graduate College and the ASU Libraries. For more information or questions about this collection contact or visit the Digital Repository ETD Library Guide or contact the ASU Graduate College at gradformat@asu.edu.


Contributor
Date Range
2010 2019


Fraud is defined as the utilization of deception for illegal gain by hiding the true nature of the activity. While organizations lose around $3.7 trillion in revenue due to financial crimes and fraud worldwide, they can affect all levels of society significantly. In this dissertation, I focus on credit card fraud in online transactions. Every online transaction comes with a fraud risk and it is the merchant's liability to detect and stop fraudulent transactions. Merchants utilize various mechanisms to prevent and manage fraud such as automated fraud detection systems and manual transaction reviews by expert fraud analysts. Many proposed solutions …

Contributors
Yildirim, Mehmet Yigit, Davulcu, Hasan, Bakkaloglu, Bertan, et al.
Created Date
2019

Networks naturally appear in many high-impact applications. The simplest model of networks is single-layered networks, where the nodes are from the same domain and the links are of the same type. However, as the world is highly coupled, nodes from different application domains tend to be interdependent on each other, forming a more complex network model called multi-layered networks. Among the various aspects of network studies, network connectivity plays an important role in a myriad of applications. The diversified application areas have spurred numerous connectivity measures, each designed for some specific tasks. Although effective in their own fields, none of …

Contributors
Chen, Chen, Tong, Hanghang, Davulcu, Hasan, et al.
Created Date
2019

In the artificial intelligence literature, three forms of reasoning are commonly employed to understand agent behavior: inductive, deductive, and abductive. More recently, data-driven approaches leveraging ideas such as machine learning, data mining, and social network analysis have gained popularity. While data-driven variants of the aforementioned forms of reasoning have been applied separately, there is little work on how data-driven approaches across all three forms relate and lend themselves to practical applications. Given an agent behavior and the percept sequence, how one can identify a specific outcome such as the likeliest explanation? To address real-world problems, it is vital to understand …

Contributors
Shaabani, Elham, Shakarian, Paulo, Davulcu, Hasan, et al.
Created Date
2019

Due to large data resources generated by online educational applications, Educational Data Mining (EDM) has improved learning effects in different ways: Students Visualization, Recommendations for students, Students Modeling, Grouping Students, etc. A lot of programming assignments have the features like automating submissions, examining the test cases to verify the correctness, but limited studies compared different statistical techniques with latest frameworks, and interpreted models in a unified approach. In this thesis, several data mining algorithms have been applied to analyze students’ code assignment submission data from a real classroom study. The goal of this work is to explore and predict students’ …

Contributors
Tian, Wenbo, Hsiao, Ihan, Bazzi, Rida, et al.
Created Date
2019

Visual processing in social media platforms is a key step in gathering and understanding information in the era of Internet and big data. Online data is rich in content, but its processing faces many challenges including: varying scales for objects of interest, unreliable and/or missing labels, the inadequacy of single modal data and difficulty in analyzing high dimensional data. Towards facilitating the processing and understanding of online data, this dissertation primarily focuses on three challenges that I feel are of great practical importance: handling scale differences in computer vision tasks, such as facial component detection and face retrieval, developing efficient …

Contributors
Zhou, Xu, Li, Baoxin, Hsiao, Sharon, et al.
Created Date
2018

The spatial databases are used to store geometric objects such as points, lines, polygons. Querying such complex spatial objects becomes a challenging task. Index structures are used to improve the lookup performance of the stored objects in the databases, but traditional index structures cannot perform well in case of spatial databases. A significant amount of research is made to ingest, index and query the spatial objects based on different types of spatial queries, such as range, nearest neighbor, and join queries. Compressed Spatial Bitmap Index (cSHB) structure is one such example of indexing and querying approach that supports spatial range …

Contributors
Gadkari, Ashish, Candan, Kasim Selçuk, Davulcu, Hasan, et al.
Created Date
2018

Cyber-systems and networks are the target of different types of cyber-threats and attacks, which are becoming more common, sophisticated, and damaging. Those attacks can vary in the way they are performed. However, there are similar strategies and tactics often used because they are time-proven to be effective. The motivations behind cyber-attacks play an important role in designating how attackers plan and proceed to achieve their goals. Generally, there are three categories of motivation are: political, economical, and socio-cultural motivations. These indicate that to defend against possible attacks in an enterprise environment, it is necessary to consider what makes such an …

Contributors
Alshamrani, Adel, Huang, Dijiang, Doupe, Adam, et al.
Created Date
2018

This research start utilizing an efficient sparse inverse covariance matrix (precision matrix) estimation technique to identify a set of highly correlated discriminative perspectives between radical and counter-radical groups. A ranking system has been developed that utilizes ranked perspectives to map Islamic organizations on a set of socio-cultural, political and behavioral scales based on their web site corpus. Simultaneously, a gold standard ranking of these organizations was created through domain experts and compute expert-to-expert agreements and present experimental results comparing the performance of the QUIC based scaling system to another baseline method for organizations. The QUIC based algorithm not only outperforms …

Contributors
Kim, Nyunsu, Davulcu, Hasan, Sen, Arunabha, et al.
Created Date
2018

Machine learning models convert raw data in the form of video, images, audio, text, etc. into feature representations that are convenient for computational process- ing. Deep neural networks have proven to be very efficient feature extractors for a variety of machine learning tasks. Generative models based on deep neural networks introduce constraints on the feature space to learn transferable and disentangled rep- resentations. Transferable feature representations help in training machine learning models that are robust across different distributions of data. For example, with the application of transferable features in domain adaptation, models trained on a source distribution can be applied …

Contributors
Eusebio, Jose Miguel Ang, Panchanathan, Sethuraman, Davulcu, Hasan, et al.
Created Date
2018

Social Computing is an area of computer science concerned with dynamics of communities and cultures, created through computer-mediated social interaction. Various social media platforms, such as social network services and microblogging, enable users to come together and create social movements expressing their opinions on diverse sets of issues, events, complaints, grievances, and goals. Methods for monitoring and summarizing these types of sociopolitical trends, its leaders and followers, messages, and dynamics are needed. In this dissertation, a framework comprising of community and content-based computational methods is presented to provide insights for multilingual and noisy political social media content. First, a model …

Contributors
Alzahrani, Sultan, Davulcu, Hasan, Corman, Steve R., et al.
Created Date
2018