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Full-Text Articles in Physical Sciences and Mathematics
Bullynet: Unmasking Cyberbullies On Social Networks, Aparna Sankaran
Bullynet: Unmasking Cyberbullies On Social Networks, Aparna Sankaran
Boise State University Theses and Dissertations
Social media has changed the way people communicate with each other, and consecutively affected people's ability to empathize in both positive and negative ways. One of the most harmful consequences of social media is the rise of cyberbullying, which tends to be more sinister than traditional bullying given that online records typically live on the internet for quite a long time and are hard to control. In this thesis, we present a three-phase algorithm, called BullyNet, for detecting cyberbullies on Twitter social network. We exploit bullying tendencies by proposing a robust method for constructing a cyberbullying signed network. BullyNet analyzes …
Adaptive Feature Engineering Modeling For Ultrasound Image Classification For Decision Support, Hatwib Mugasa
Adaptive Feature Engineering Modeling For Ultrasound Image Classification For Decision Support, Hatwib Mugasa
Doctoral Dissertations
Ultrasonography is considered a relatively safe option for the diagnosis of benign and malignant cancer lesions due to the low-energy sound waves used. However, the visual interpretation of the ultrasound images is time-consuming and usually has high false alerts due to speckle noise. Improved methods of collection image-based data have been proposed to reduce noise in the images; however, this has proved not to solve the problem due to the complex nature of images and the exponential growth of biomedical datasets. Secondly, the target class in real-world biomedical datasets, that is the focus of interest of a biopsy, is usually …
Feature Space Modeling For Accurate And Efficient Learning From Non-Stationary Data, Ayesha Akter
Feature Space Modeling For Accurate And Efficient Learning From Non-Stationary Data, Ayesha Akter
Doctoral Dissertations
A non-stationary dataset is one whose statistical properties such as the mean, variance, correlation, probability distribution, etc. change over a specific interval of time. On the contrary, a stationary dataset is one whose statistical properties remain constant over time. Apart from the volatile statistical properties, non-stationary data poses other challenges such as time and memory management due to the limitation of computational resources mostly caused by the recent advancements in data collection technologies which generate a variety of data at an alarming pace and volume. Additionally, when the collected data is complex, managing data complexity, emerging from its dimensionality and …
Applied Deep Learning In Intelligent Transportation Systems And Embedding Exploration, Xiaoyuan Liang
Applied Deep Learning In Intelligent Transportation Systems And Embedding Exploration, Xiaoyuan Liang
Dissertations
Deep learning techniques have achieved tremendous success in many real applications in recent years and show their great potential in many areas including transportation. Even though transportation becomes increasingly indispensable in people’s daily life, its related problems, such as traffic congestion and energy waste, have not been completely solved, yet some problems have become even more critical. This dissertation focuses on solving the following fundamental problems: (1) passenger demand prediction, (2) transportation mode detection, (3) traffic light control, in the transportation field using deep learning. The dissertation also extends the application of deep learning to an embedding system for visualization …
Phenomena Of Social Dynamics In Online Games, Essa Alhazmi
Phenomena Of Social Dynamics In Online Games, Essa Alhazmi
USF Tampa Graduate Theses and Dissertations
Online communities exhibit dynamic social phenomena that, if understood, can both influence the design of technical platforms and inform theories about general social dynamics. With increasing popularity, online games provide a rich recording of social dynamics that can contribute to understanding human behavior. This dissertation studies two phenomena of social dynamics at large scale using data traces from online games. The first phenomenon is team formation and the second is players mobility between gaming servers.
This dissertation first presents a framework for collecting data from online gaming through crawling. It includes the data sources and the tools used for data …
Statistical Machine Learning Methods For Mining Spatial And Temporal Data, Fei Tan
Statistical Machine Learning Methods For Mining Spatial And Temporal Data, Fei Tan
Dissertations
Spatial and temporal dependencies are ubiquitous properties of data in numerous domains. The popularity of spatial and temporal data mining has thus grown with the increasing prevalence of massive data. The presence of spatial and temporal attributes not only provides complementary useful perspectives, but also poses new challenges to the representation and integration into the learning procedure. In this dissertation, the involved spatial and temporal dependencies are explored with three genres: sample-wise, feature-wise, and target-wise. A family of novel methodologies is developed accordingly for the dependency representation in respective scenarios.
First, dependencies among discrete, continuous and repeated observations are studied …
Knowing Without Knowing: Real-Time Usage Identification Of Computer Systems, Leila Mohammed Hawana
Knowing Without Knowing: Real-Time Usage Identification Of Computer Systems, Leila Mohammed Hawana
Dissertations and Theses
Contemporary computers attempt to understand a user's actions and preferences in order to make decisions that better serve the user. In pursuit of this goal, computers can make observations that range from simple pattern recognition to listening in on conversations without the device being intentionally active. While these developments are incredibly useful for customization, the inherent security risks involving personal data are not always worth it. This thesis attempts to tackle one issue in this domain, computer usage identification, and presents a solution that identifies high-level usage of a system at any given moment without looking into any personal data. …
A Data Mining Framework For Improving Student Outcomes On Step 1 Of The United States Medical Licensing Examination, James Clark
A Data Mining Framework For Improving Student Outcomes On Step 1 Of The United States Medical Licensing Examination, James Clark
CCE Theses and Dissertations
Identifying the factors associated with medical students who fail Step 1 of the United States Medical Licensing Examination (USMLE) has been a focus of investigation for many years. Some researchers believe lower scores on the Medical Colleges Admissions Test (MCAT) are the sole factor used to identify failure. Other researchers believe lower course outcomes during the first two years of medical training are better indicators of failure. Yet, there are medical students who fail Step 1 of the USMLE who enter medical school with high MCAT scores, and conversely medical students with lower academic credentials who are expected to have …
Citationally Enhanced Semantic Literature Based Discovery, John David Fleig
Citationally Enhanced Semantic Literature Based Discovery, John David Fleig
CCE Theses and Dissertations
We are living within the age of information. The ever increasing flow of data and publications poses a monumental bottleneck to scientific progress as despite the amazing abilities of the human mind, it is woefully inadequate in processing such a vast quantity of multidimensional information. The small bits of flotsam and jetsam that we leverage belies the amount of useful information beneath the surface. It is imperative that automated tools exist to better search, retrieve, and summarize this content. Combinations of document indexing and search engines can quickly find you a document whose content best matches your query - if …
Learning From Heterogeneous Data, Lu Wang
Learning From Heterogeneous Data, Lu Wang
Wayne State University Dissertations
Data with both heterogeneity and homogeneity is now ubiquitous due to the development of multitudinous data collection techniques. To encode the data heterogeneity and homogeneity, we focus on unsupervised and supervised learning approaches. In unsupervised learning, to consider both data heterogeneity and homogeneity, we develop three clustering frameworks to maximize the heterogeneity among data sub-groups and homogeneity within each data sub-group for over-dispersed data in three different data types, i.e., alphabetic, network and mixed feature types data. In supervised learning, the traditional approaches, however, either build a global model for a whole group including all sub-groups, which fail to consider …
Efficient Algorithms For Mining Healthcare Data :, Yan Hu
Efficient Algorithms For Mining Healthcare Data :, Yan Hu
Legacy Theses & Dissertations (2009 - 2024)
Data-Driven Healthcare (DDH) is defined as the usage of available medical big data to provide the best and most personalized care, which is believed to be one of the most promising directions for transforming healthcare. The healthcare data includes claims and cost data, clinical data, pharmaceutical R&D data, patient behavior and sentiment data, and health data on the web. There has been a remarkable upsurge in the adoption of healthcare data over the past several years. In particular, it has been used for medical concept extraction, patient trajectory modeling, disease inference, etc.
Predictive Analysis Of Real-Time Strategy Games Using Graph Mining, Isam Abdulmunem Alobaidi
Predictive Analysis Of Real-Time Strategy Games Using Graph Mining, Isam Abdulmunem Alobaidi
Doctoral Dissertations
"Machine learning and computational intelligence have facilitated the development of recommendation systems for a broad range of domains. Such recommendations are based on contextual information that is explicitly provided or pervasively collected. Recommendation systems often improve decision-making or increase the efficacy of a task. Real-Time Strategy (RTS) video games are not only a popular entertainment medium, they also are an abstraction of many real-world applications where the aim is to increase your resources and decrease those of your opponent. Using predictive analytics, which examines past examples of success and failure, we can learn how to predict positive outcomes for such …