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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


Given the process of tumorigenesis, biological signaling pathways have become of interest in the field of oncology. Many of the regulatory mechanisms that are altered in cancer are directly related to signal transduction and cellular communication. Thus, identifying signaling pathways that have become deregulated may provide useful information to better understanding altered regulatory mechanisms within cancer. Many methods that have been created to measure the distinct activity of signaling pathways have relied strictly upon transcription profiles. With advancements in comparative genomic hybridization techniques, copy number data has become extremely useful in providing valuable information pertaining to the genomic landscape of …

Contributors
Trevino, Robert, Kim, Seungchan, Ringner, Markus, et al.
Created Date
2011

Crises or large-scale emergencies such as earthquakes and hurricanes cause massive damage to lives and property. Crisis response is an essential task to mitigate the impact of a crisis. An effective response to a crisis necessitates information gathering and analysis. Traditionally, this process has been restricted to the information collected by first responders on the ground in the affected region or by official agencies such as local governments involved in the response. However, the ubiquity of mobile devices has empowered people to publish information during a crisis through social media, such as the damage reports from a hurricane. Social media …

Contributors
Kumar, Shamanth, Liu, Huan, Davulcu, Hasan, et al.
Created Date
2015

Source selection is one of the foremost challenges for searching deep-web. For a user query, source selection involves selecting a subset of deep-web sources expected to provide relevant answers to the user query. Existing source selection models employ query-similarity based local measures for assessing source quality. These local measures are necessary but not sufficient as they are agnostic to source trustworthiness and result importance, which, given the autonomous and uncurated nature of deep-web, have become indispensible for searching deep-web. SourceRank provides a global measure for assessing source quality based on source trustworthiness and result importance. SourceRank's effectiveness has been evaluated …

Contributors
Jha, Manishkumar, Kambhampati, Subbarao, Liu, Huan, et al.
Created Date
2011

A community in a social network can be viewed as a structure formed by individuals who share similar interests. Not all communities are explicit; some may be hidden in a large network. Therefore, discovering these hidden communities becomes an interesting problem. Researchers from a number of fields have developed algorithms to tackle this problem. Besides the common feature above, communities within a social network have two unique characteristics: communities are mostly small and overlapping. Unfortunately, many traditional algorithms have difficulty recognizing these small communities (often called the resolution limit problem) as well as overlapping communities. In this work, two enhanced …

Contributors
Wang, Ran, Liu, Huan, Sen, Arunabha, et al.
Created Date
2015

Computer vision technology automatically extracts high level, meaningful information from visual data such as images or videos, and the object recognition and detection algorithms are essential in most computer vision applications. In this dissertation, we focus on developing algorithms used for real life computer vision applications, presenting innovative algorithms for object segmentation and feature extraction for objects and actions recognition in video data, and sparse feature selection algorithms for medical image analysis, as well as automated feature extraction using convolutional neural network for blood cancer grading. To detect and classify objects in video, the objects have to be separated from …

Contributors
Cao, Jun, Li, Baoxin, Liu, Huan, et al.
Created Date
2018

The rapid growth of social media in recent years provides a large amount of user-generated visual objects, e.g., images and videos. Advanced semantic understanding approaches on such visual objects are desired to better serve applications such as human-machine interaction, image retrieval, etc. Semantic visual attributes have been proposed and utilized in multiple visual computing tasks to bridge the so-called "semantic gap" between extractable low-level feature representations and high-level semantic understanding of the visual objects. Despite years of research, there are still some unsolved problems on semantic attribute learning. First, real-world applications usually involve hundreds of attributes which requires great effort …

Contributors
Chen, Lin, Li, Baoxin, Turaga, Pavan, et al.
Created Date
2016

Discriminative learning when training and test data belong to different distributions is a challenging and complex task. Often times we have very few or no labeled data from the test or target distribution, but we may have plenty of labeled data from one or multiple related sources with different distributions. Due to its capability of migrating knowledge from related domains, transfer learning has shown to be effective for cross-domain learning problems. In this dissertation, I carry out research along this direction with a particular focus on designing efficient and effective algorithms for BioImaging and Bilingual applications. Specifically, I propose deep …

Contributors
Sun, Qian, Ye, Jieping, Ye, Jieping, et al.
Created Date
2015

Ranking is of definitive importance to both usability and profitability of web information systems. While ranking of results is crucial for the accessibility of information to the user, the ranking of online ads increases the profitability of the search provider. The scope of my thesis includes both search and ad ranking. I consider the emerging problem of ranking the deep web data considering trustworthiness and relevance. I address the end-to-end deep web ranking by focusing on: (i) ranking and selection of the deep web databases (ii) topic sensitive ranking of the sources (iii) ranking the result tuples from the selected …

Contributors
Balakrishnan, Raju, Kambhampati, Subbarao, Chen, Yi, et al.
Created Date
2012

Twitter is a micro-blogging platform where the users can be social, informational or both. In certain cases, users generate tweets that have no "hashtags" or "@mentions"; we call it an orphaned tweet. The user will be more interested to find more "context" of an orphaned tweet presumably to engage with his/her friend on that topic. Finding context for an Orphaned tweet manually is challenging because of larger social graph of a user , the enormous volume of tweets generated per second, topic diversity, and limited information from tweet length of 140 characters. To help the user to get the context …

Contributors
Vijayakumar, Manikandan, Kambhampati, Subbarao, Liu, Huan, et al.
Created Date
2014

With the rise of social media, hundreds of millions of people spend countless hours all over the globe on social media to connect, interact, share, and create user-generated data. This rich environment provides tremendous opportunities for many different players to easily and effectively reach out to people, interact with them, influence them, or get their opinions. There are two pieces of information that attract most attention on social media sites, including user preferences and interactions. Businesses and organizations use this information to better understand and therefore provide customized services to social media users. This data can be used for different …

Contributors
Abbasi, Mohammad Ali, Liu, Huan, Davulcu, Hasan, et al.
Created Date
2014