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Full-Text Articles in Library and Information Science

Improving Collection Understanding For Web Archives With Storytelling: Shining Light Into Dark And Stormy Archives, Shawn M. Jones Jul 2021

Improving Collection Understanding For Web Archives With Storytelling: Shining Light Into Dark And Stormy Archives, Shawn M. Jones

Computer Science Theses & Dissertations

Collections are the tools that people use to make sense of an ever-increasing number of archived web pages. As collections themselves grow, we need tools to make sense of them. Tools that work on the general web, like search engines, are not a good fit for these collections because search engines do not currently represent multiple document versions well. Web archive collections are vast, some containing hundreds of thousands of documents. Thousands of collections exist, many of which cover the same topic. Few collections include standardized metadata. Too many documents from too many collections with insufficient metadata makes collection understanding …


Designing Targeted Mobile Advertising Campaigns, Kimia Keshanian Jun 2021

Designing Targeted Mobile Advertising Campaigns, Kimia Keshanian

USF Tampa Graduate Theses and Dissertations

With the proliferation of smart, handheld devices, there has been a multifold increase in the ability of firms to target and engage with customers through mobile advertising. Therefore, not surprisingly, mobile advertising campaigns have become an integral aspect of firms’ brand building activities, such as improving the awareness and overall visibility of firms' brands. In addition, retailers are increasingly using mobile advertising for targeted promotional activities that increase in-store visits and eventual sales conversions. However, in recent years, mobile or in general online advertising campaigns have been facing one major challenge and one major threat that can negatively impact the …


Using Machine Learning To Predict Super-Utilizers Of Healthcare Services, Kevin Paul Buchan Jr. May 2021

Using Machine Learning To Predict Super-Utilizers Of Healthcare Services, Kevin Paul Buchan Jr.

Legacy Theses & Dissertations (2009 - 2024)

In this dissertation, I aim to forecast high utilizers of emergency care and inpatient Medicare services (i.e., healthcare visits). Through a literature review, I demonstrate that accurate and reliable prediction of these future high utilizers will not only reduce healthcare costs but will also improve the overall quality of healthcare for patients. By identifying this population at risk before manifestation, I propose that there is still time to reverse undesirable healthcare trajectories (i.e., individuals whose clinical risk increases an excessive healthcare and treatment burden) through timely attention and proper care coordination. My dissertation culminates in the delivery of state-of-the-art predictive …


Multimodal Data Fusion And Attack Detection In Recommender Systems, Mehmet Aktukmak Nov 2020

Multimodal Data Fusion And Attack Detection In Recommender Systems, Mehmet Aktukmak

USF Tampa Graduate Theses and Dissertations

The commercial platforms that use recommender systems can collect relevant information to produce useful recommendations to the platform users. However, these sources usually contain missing values, imbalanced and heterogeneous data, and noisy observations. Such characteristics render the process of exploiting the information nontrivial, as one should carefully address them during the data fusion process. In addition to the degenerative characteristics, some entries can be fake, i.e., they can be the outcomes of malicious intents to manipulate the system. These entries should be eliminated before incorporation to any recommendation task. Detecting such malicious attacks quickly and accurately and then mitigating them …


Clinical Information Extraction From Unstructured Free-Texts, Mingzhe Tao Jan 2018

Clinical Information Extraction From Unstructured Free-Texts, Mingzhe Tao

Legacy Theses & Dissertations (2009 - 2024)

Information extraction (IE) is a fundamental component of natural language processing (NLP) that provides a deeper understanding of the texts. In the clinical domain, documents prepared by medical experts (e.g., discharge summaries, drug labels, medical history records) contain a significant amount of clinically-relevant information that is crucial to the overall well-being of patients. Unfortunately, in many cases, clinically-relevant information is presented in an unstructured format, predominantly consisting of free-texts, making it inaccessible to computerized methods. Automatic extraction of this information can improve accessibility. However, the presence of synonymous expressions, medical acronyms, misspellings, negated phrases, and ambiguous terminologies make automatic extraction …


Novel Machine Learning Methods For Modeling Time-To-Event Data, Bhanukiran Vinzamuri Jan 2016

Novel Machine Learning Methods For Modeling Time-To-Event Data, Bhanukiran Vinzamuri

Wayne State University Dissertations

Predicting time-to-event from longitudinal data where different events occur at different time points is an extremely important problem in several domains such as healthcare, economics, social networks and seismology, to name a few. A unique challenge in this problem involves building predictive models from right censored data (also called as survival data). This is a phenomenon where instances whose event of interest are not yet observed within a given observation time window and are considered to be right censored. Effective models for predicting time-to-event labels from such right censored data with good accuracy can have a significant impact in these …


Bus Stop Usage Evaluation And Brt Station Selection Strategy By Machine Learning Methods, Tianchi Zhang Jan 2014

Bus Stop Usage Evaluation And Brt Station Selection Strategy By Machine Learning Methods, Tianchi Zhang

Legacy Theses & Dissertations (2009 - 2024)

According to Commuting in the United States 2009, 86.1% of Americans commuted by car, light truck, or van, and about three-quarters of these individuals were driving alone, causing traffic congestion and raising environmental and energy-saving concerns in society. Therefore, transportation experts encourage the public to take public transportation and recommend the development of Bus Rapid Transit (BRT). Currently, bus service restructuring and BRT plans are based on rider surveys, community meetings and on-street interviews. However, these methods require large investments in manpower and material resources, and produce potentially biased results. In this research, the author used the machine learning method, …


An Investigation Of Digital Reference Interviews: A Dialogue Act Approach, Keisuke Inoue May 2013

An Investigation Of Digital Reference Interviews: A Dialogue Act Approach, Keisuke Inoue

School of Information Studies - Dissertations

The rapid increase of computer-mediated communications (CMCs) in various forms such as micro-blogging (e.g. Twitter), online chatting (e.g. digital reference) and community- based question-answering services (e.g. Yahoo! Answers) characterizes a recent trend in web technologies, often referred to as the social web. This trend highlights the importance of supporting linguistic interactions in people's online information-seeking activities in daily life - something that the web search engines still lack because of the complexity of this hu- man behavior. The presented research consists of an investigation of the information-seeking behavior of digital reference services through analysis of discourse semantics, called dialogue acts, …


Automated Classification Of The Narrative Of Medical Reports Using Natural Language Processing, Ira J. Goldstein Jan 2011

Automated Classification Of The Narrative Of Medical Reports Using Natural Language Processing, Ira J. Goldstein

Legacy Theses & Dissertations (2009 - 2024)

In this dissertation we present three topics critical to the document level classification of the narrative in medical reports: the use of preferred terminology in light of the presence of synonymous terms, the less than optimal performance of classification systems when presented with a non-uniform distribution of classes, and the problems associated with scarcity of labeled data when presented with an imbalance of classes in the data sets.


A Comparative Study On Text Categorization, Aditya Chainulu Karamcheti May 2010

A Comparative Study On Text Categorization, Aditya Chainulu Karamcheti

UNLV Theses, Dissertations, Professional Papers, and Capstones

Automated text categorization is a supervised learning task, defined as assigning category labels to new documents based on likelihood suggested by a training set of labeled documents. Two examples of methodology for text categorizations are Naive Bayes and K-Nearest Neighbor.

In this thesis, we implement two categorization engines based on Naive Bayes and K-Nearest Neighbor methodology. We then compare the effectiveness of these two engines by calculating standard precision and recall for a collection of documents. We will further report on time efficiency of these two engines.