Physical Sciences and Mathematics Commons^™

Constructing Frameworks For Task-Optimized Visualizations, Ghulam Jilani Abdul Rahim Quadri

USF Tampa Graduate Theses and Dissertations

Visualization is crucial in today’s data-driven world to augment and enhance human understanding and decision-making. Effective visualizations must support accuracy in visual task performance and expressive data communication. Effective visualization design depends on the visual channels used, chart types, or visual tasks. However, design choices and visual judgment are co-related, and effectiveness is not one-dimensional, leading to a significant need to understand the intersection of these factors to create optimized visualizations. Hence, constructing frameworks that consider both design decisions and the task being performed enables optimizing visualization design to maximize efficacy. This dissertation describes experiments, techniques, and user studies to …

Developing Agent-Based Models To Study Financial Markets, Saurav Chakraborty

USF Tampa Graduate Theses and Dissertations

This dissertation presents research that employs agent-based modelling to provide a framework to support simulation as a complement to traditional economic models for policy evaluation. It consists of three studies. The first study employs cluster analysis to capture the different types of banks and the associated business models that define their decision-making. The results from study one will help us get an understanding of how different banks behave and provide an insight into their lending practices. Hence, it would be very helpful in evaluating and analyzing the impact of future policies. Study two develops a fine-grained interbank lending model based …

Graph-Based Latent Embedding, Annotation And Representation Learning In Neural Networks For Semi-Supervised And Unsupervised Settings, Ismail Ozsel Kilinc

USF Tampa Graduate Theses and Dissertations

Machine learning has been immensely successful in supervised learning with outstanding examples in major industrial applications such as voice and image recognition. Following these developments, the most recent research has now begun to focus primarily on algorithms which can exploit very large sets of unlabeled examples to reduce the amount of manually labeled data required for existing models to perform well. In this dissertation, we propose graph-based latent embedding/annotation/representation learning techniques in neural networks tailored for semi-supervised and unsupervised learning problems. Specifically, we propose a novel regularization technique called Graph-based Activity Regularization (GAR) and a novel output layer modification called …

Statistical Modeling Of Carbon Dioxide And Cluster Analysis Of Time Dependent Information: Lag Target Time Series Clustering, Multi-Factor Time Series Clustering, And Multi-Level Time Series Clustering, Doo Young Kim

USF Tampa Graduate Theses and Dissertations

The current study consists of three major parts. Statistical modeling, the connection between statistical modeling and cluster analysis, and proposing new methods to cluster time dependent information.

First, we perform a statistical modeling of the Carbon Dioxide (CO₂) emission in South Korea in order to identify the attributable variables including interaction effects. One of the hot issues in the earth in 21st century is Global warming which is caused by the marriage between atmospheric temperature and CO₂ in the atmosphere. When we confront this global problem, we first need to verify what causes the problem then we …

Automatic Identification Of Points Of Interest In Global Navigation Satellite System Data: A Spatial Temporal Approach, Khoa Anh Tran

USF Tampa Graduate Theses and Dissertations

In addition to the emergence of smartphones and tablets in recent years, the rise of Global Navigation Satellite Systems (GNSS) has allowed mobile tracking applications to become popular and be put into many uses. Analyzing tracking records to identify points of interest (POIs) is useful for both prediction applications and research such as human behavior analysis, transportation planning, and especially travel surveys. Past research in travel surveys has shown that a GPS mobile phone-based survey is a useful tool for collecting information about individuals. Moreover, a passive travel survey collection is preferred to an active travel survey method by the …

Prevention And Detection Of Intrusions In Wireless Sensor Networks, Ismail Butun

USF Tampa Graduate Theses and Dissertations

Wireless Sensor Networks (WSNs) continue to grow as one of the most exciting and challenging research areas of engineering. They are characterized by severely constrained computational and energy

resources and also restricted by the ad-hoc network operational

environment. They pose unique challenges, due to limited power

supplies, low transmission bandwidth, small memory sizes and limited energy. Therefore, security techniques used in traditional networks cannot be directly adopted. So, new ideas and approaches are needed, in order to increase the overall security of the network. Security applications in such resource constrained WSNs with minimum overhead provides significant challenges, and is the …

Detecting Surface Oil Using Unsupervised Learning Techniques On Modis Satellite Data, Joshua Kidd

USF Tampa Graduate Theses and Dissertations

The release of crude oil or other petroleum based products into marine habitats can have a devastating impact on the environment as well as the local economies that rely on these waters for commercial fishing and tourism. The Deepwater Horizon catastrophe that started on April 20th 2010 leaked an estimated 4.4 million barrels of crude oil into the Gulf of Mexico over a 3 month period threatening thousands of species and crippling the gulf coast. The National Oceanic and Atmospheric Administration (NOAA) used several satellite remote sensing technologies to manually track and predict the extent and location of oil on …

Mining Associations Using Directed Hypergraphs, Ramanuja N. Simha

USF Tampa Graduate Theses and Dissertations

This thesis proposes a novel directed hypergraph based model for any database. We introduce the notion of association rules for multi-valued attributes, which is an adaptation of the definition of quantitative association rules known in the literature. The association rules for multi-valued attributes are integrated in building the directed hypergraph model. This model allows to capture attribute-level associations and their strength. Basing on this model, we provide association-based similarity notions between any two attributes and present a method for finding clusters of similar attributes. We then propose algorithms to identify a subset of attributes known as a leading indicator that …