Physical Sciences and Mathematics Commons^™

Predictive Modeling Of Cave Entrance Locations: Relationships Between Surface And Subsurface Morphology, William Blitch, Adia R. Sovie, Benjamin W. Tobin

International Journal of Speleology

Cave entrances directly connect the surface and subsurface geomorphology in karst landscapes. Understanding the spatial distribution of these features can help identify areas on the landscape that are critical to flow in the karst groundwater system. Sinkholes and springs are major locations of inflow and outflow from the groundwater system, respectively, however not all sinkholes and springs are equally connected to the main conduit system. Predicting where on the landscape zones of high connectivity exist is a challenge because cave entrances are difficult to detect and imperfectly documented. Wildlife research has a similar issue of understanding the complexities of where …

Measurement In Chemistry, Mathematics, And Physics Education: Student Explanations Of Organic Chemistry Reaction Mechanisms And Instructional Practices In Introductory Courses, Brandon J. Yik

USF Tampa Graduate Theses and Dissertations

The work in this dissertation is presented in two parts. The first part (Chapters 3 and 4) details work relating to students’ explanations of reaction mechanisms in organic chemistry. The second part (Chapters 5 and 6) details work relating to the evaluating the uptake of research-based instructional practices in introductory chemistry, mathematics, and physics courses.

To evaluate learning of organic chemistry reactions, instructors must ask students to construct written explanations of reaction mechanisms. Written assessments should focus on what is happening and why it is happening to promote deeper student understanding. However, for instructors to gain insight into students’ understanding, …

Using Fine-Scale Aquatic Habitat Data To Construct Dreissenid Sdms In The Laurentian Great Lakes, Grace C. Henderson

USF Tampa Graduate Theses and Dissertations

The invasion of the Laurentian Great Lakes by aquatic invasive species (AIS) has been the subject of investigation for decades, due to their dramatic alterations to the ecosystem and high economic costs. Two AIS with the largest impacts are dreissenid zebra and quagga mussels, and though these species have been studied extensively, questions remain about what factors control their distributions, and whether lake warming will alter these distributions. Species distribution models (SDMs) offer a powerful tool to examine the relationship between species presences and environmental variables, which are typically bioclimactic data. The creation of the Aquatic Habitat (AqHab) dataset containing …

Multi-Object Localization In Robotic Hand, Tsing Tsow

USF Tampa Graduate Theses and Dissertations

We have developed a machine learning approach to localized objects inside a robotic hand using only images from 2D cameras. Specifically, we used deep learning method (You Only Look Once, YOLO) and Iterative closest Point (ICP) to estimate the 3D coordinates of the objects in a robotic hand. This method will also output the number of objects inside the robotic hand in addition to the coordinates of the objects. We have demonstrated the performance with simulation and obtained typical accuracy within a few pixels (couple mm) and counting accuracy of about 76%. We have also applied it to real images, …

A Constitutive-Based Deep Learning Model For The Identification Of Active Contraction Parameters Of The Left Ventricular Myocardium, Igor Augusto Paschoalotte Nobrega

USF Tampa Graduate Theses and Dissertations

Modern breakthroughs in biomedical engineering, computer science, and data mining have created new opportunities for detecting important mechanical properties of soft tissues that can be employed to identify possible signs of diseases or physiological difficulties. However, the scarcity of different mechanical properties obtained through noninvasive testing emphasizes the importance of incorporating authentic biological data into computer models capable of replicating the behavior of soft tissues.

The field of continuum theory of large deformation hyperactivity permits the formulation of highly descriptive mathematical research and computational models capable of perfectly describing the minute mechanical characteristics of soft materials. By including features about …

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 …

Automatic Detection Of Vehicles In Satellite Images For Economic Monitoring, Cole Hill

USF Tampa Graduate Theses and Dissertations

With the growing supply of satellites capturing images of the planet, governments andinvestors are looking for ways in which these new images may be used to determine which businesses are struggling and thriving. Recent works have shown that parking lot fill rates can provide valuable information about businesses’ earnings, however, the task of manually annotating the number of vehicles in a parking lot is expensive and time-consuming. Systems which can automate this process are therefore valuable as they are faster and cheaper than human labor. In this thesis, the problem of detection of small objects in large low-resolution images is …

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 …

Gradient Boosting For Survival Analysis With Applications In Oncology, Nam Phuong Nguyen

USF Tampa Graduate Theses and Dissertations

Cancer is one of the most deadly diseases that the world has been fighting against over decades. An enormous number of research has been conducted, via a wide scale of approaches, raging from genetic analysis to mathematical modeling. Survival analysis is a well-performed methodology frequently used to estimate the survival probability of a patient. Although there has been a large number of methods for survival analysis, efficient exploration of a high-dimensional feature space has been challenging due to its computational cost and complexity. This thesis adapts the component-wise gradient boosting algorithms for cancer survival analysis, and also proposes a new …

Probabilistic Machine Learning Using Bayesian Inference, Mayank Pandey

Undergraduate Journal of Mathematical Modeling: One + Two

Machine Learning is a branch of AI (Artificial Intelligence) which expands on the idea of a computational system extending its knowledge about set methodical behaviors from the data that is fed to it to essentially develop analytical skills that can help in identifying patterns and making decisions with little to no participation of a real human being. Computer algorithms help in gaining experience to improve the facility over time for use by both consumers and corporations. In today’s technologically advanced world, Machine Learning has given us self-driving cars, speech recognition software, and AI agents like Siri and Google assistant. This …

Uncovering Host-Microbiome Interactions In Global Systems With Collaborative Programming: A Novel Approach Integrating Social And Data Sciences [Version 1; Peer Review: Awaiting Peer Review], Jenna Oberstaller, Swamy Rakesh Adapa, Guy Dayhoff Ii, Justin Gibbons, Gregory S. Herbert

School of Geosciences Faculty and Staff Publications

Microbiome data are undergoing exponential growth powered by rapid technological advancement. As the scope and depth of microbiome research increases, cross-disciplinary research is urgently needed for interpreting and harnessing the unprecedented data output. However, conventional research settings pose challenges to much-needed interdisciplinary research efforts due to barriers in scientific terminologies, methodology and research-culture. To breach these barriers, our University of South Florida OneHealth Codeathon was designed to be an interactive, hands-on event that solves real-world data problems. The format brought together students, postdocs, faculty, researchers, and clinicians in a uniquely cross-disciplinary, team-focused setting. Teams were formed to encourage equitable distribution …

Detecting Digitally Forged Faces In Online Videos, Neilesh Sambhu

USF Tampa Graduate Theses and Dissertations

We use Rossler’s FaceForensics dataset of 1004 online videos and their corresponding forged counterparts [1] to investigate the ability to distinguish digitally forged facial images from original images automatically with deep learning. The proposed convolutional neural network is much smaller than the current state-of-the-art solutions. Nevertheless, the network maintains a high level of accuracy (99.6%), all while using the entire FaceForensics dataset and not including any temporal information. We implement majority voting and show the impact on accuracy (99.67%), where only 1 video of 300 is misclassified. We examine why the model misclassified this one video. In terms of tuning …

Evaluating Beach Water Quality And Dengue Fever Risk Factors By Satellite Remote Sensing And Artificial Neural Networks, Abdiel Elias Laureano-Rosario

USF Tampa Graduate Theses and Dissertations

Climatic variations, together with large-scale environmental forces and human development affect the quality of coastal recreational waters, creating potential risks to human health. These environmental forces, including increased temperature and precipitation, often promote specific vector-borne diseases in the Caribbean and Gulf of Mexico. Human activities affect water quality through discharges from urban areas, including nutrient and other pollutants derived from wastewater systems. Both water quality of recreational beaches and vector-borne diseases can be better managed by understanding their relationship with local environmental forces.

I evaluated how changes in vector-borne diseases and poor recreational water quality were related to specific environmental …

Diagnosing Breast Cancer With A Neural Network, John Cullen

Undergraduate Journal of Mathematical Modeling: One + Two

Fine needle aspiration (FNA) is a minimally invasive biopsy technique that can be used to successfully diagnose types of cancer, including breast cancer. Immediately, it is difficult for a human to spot any trends in the cell level data gathered during a fine needle aspiration procedure. One way to predict the type of tumor a patient has, is to use a computer to develop a mathematical model based on known data. This project utilizes the Diagnostic Wisconsin Breast Cancer Database (DWBCDB) to create an accurate mathematical model that predicts the type of a patient’s tumor (Malignant or Benign). A neural …

Evaluating Machine Learning Classifiers For Defensive Cyber Operations, Michael D. Rich, Robert F. Mills, Thomas E. Dube, Steven K. Rogers

Military Cyber Affairs

Today’s defensive cyber sensors are dominated by signature-based analytical methods that require continuous maintenance and lack the ability to detect unknown threats. Anomaly detection offers the ability to detect unknown threats, but despite over 15 years of active research, the operationalization of anomaly detection and machine learning for Defensive Cyber Operations (DCO) is lagging. This article provides an introduction to machine learning concepts with a focus on the unique challenges to using machine learning for DCO. Traditional machine learning evaluation methods are challenged in favor of a value-focused evaluation method that incorporates evaluator-specific weights for classifier and sensitivity threshold selection …

Predicting Malignant Nodules From Screening Ct Scans, Samuel Hawkins, Hua Wang, Ying Liu, Alberto Garcia, Olya Stringfield, Henry Krewer, Qiang Li, Dmitry Cherezov, Matthew Schabath, Lawrence O. Hall, Robert J. Gillies

Computer Science and Engineering Faculty Publications

Objectives

The aim of this study was to determine whether quantitative analyses (“radiomics”) of low-dose computed tomography lung cancer screening images at baseline can predict subsequent emergence of cancer.

Methods

Public data from the National Lung Screening Trial (ACRIN 6684) were assembled into two cohorts of 104 and 92 patients with screen-detected lung cancer and then matched with cohorts of 208 and 196 screening subjects with benign pulmonary nodules. Image features were extracted from each nodule and used to predict the subsequent emergence of cancer.

Results

The best models used 23 stable features in a random forests classifier and could …

Determining The Effectiveness Of Soil Treatment On Plant Stress Using Smart-Phone Cameras, Anurag Panwar

USF Tampa Graduate Theses and Dissertations

Plants are vital to the health of our biosphere, and effectively sustaining their growth is fundamental to the existence of life on this planet. A critical aspect, which decides the sustainability of plant growth is the quality of soil. All other things being fixed, the quality of soil greatly impacts the plant stress, which in turn impacts overall health. Although plant stress manifests in many ways, one of the clearest indicators are colors of the leaves. In this thesis, we conducted an experimental study in a greenhouse for detecting plant stress caused by nutrient deficienceies in soil using smartphone cameras, …

Human Intention Recognition Based Assisted Telerobotic Grasping Of Objects In An Unstructured Environment, Karan Hariharan Khokar

USF Tampa Graduate Theses and Dissertations

In this dissertation work, a methodology is proposed to enable a robot to identify an object to be grasped and its intended grasp configuration while a human is teleoperating a robot towards the desired object. Based on the detected object and grasp configuration, the human is assisted in the teleoperation task. The environment is unstructured and consists of a number of objects, each with various possible grasp configurations. The identification of the object and the grasp configuration is carried out in real time, by recognizing the intention of the human motion. Simultaneously, the human user is assisted to preshape over …

On The Automatic Recognition Of Human Activities Using Heterogeneous Wearable Sensors, Oscar David Lara Yejas

USF Tampa Graduate Theses and Dissertations

Delivering accurate and opportune information on people's activities and behaviors has become one of the most important tasks within pervasive computing. Its wide spectrum of potential applications in medical, entertainment, and tactical scenarios, motivates further

research and development of new strategies to improve accuracy, pervasiveness, and eciency.

This dissertation addresses the recognition of human activities (HAR) with wearable sensors in three main regards: In the rst place, physiological signals have been incorporated as a new source of information to improve the recognition accuracy achieved by conventional approaches, which rely on accelerometer signals solely. A new HAR system, Centinela, was born …

Combining Natural Language Processing And Statistical Text Mining: A Study Of Specialized Versus Common Languages, Jay Jarman

USF Tampa Graduate Theses and Dissertations

This dissertation focuses on developing and evaluating hybrid approaches for analyzing free-form text in the medical domain. This research draws on natural language processing (NLP) techniques that are used to parse and extract concepts based on a controlled vocabulary. Once important concepts are extracted, additional machine learning algorithms, such as association rule mining and decision tree induction, are used to discover classification rules for specific targets. This multi-stage pipeline approach is contrasted with traditional statistical text mining (STM) methods based on term counts and term-by-document frequencies. The aim is to create effective text analytic processes by adapting and combining individual …

Automatic Red Tide Detection Using Modis Satellite Images, Wijian Cheng

USF Tampa Graduate Theses and Dissertations

Red tides pose a significant economic and environmental threat in the Gulf of Mexico. Detecting red tide is important for understanding this phenomenon. In this thesis, machine learning approaches based on Random Forests, Support Vector Machines and K-Nearest Neighbors have been evaluated for red tide detection from MODIS satellite images. Detection results using machine learning algorithms were compared to ship collected ground truth red tide data. This work has three major contributions. First, machine learning approaches outperformed two of the latest thresholding red tide detection algorithms based on bio-optical characterization by more than 10% in terms of F measure and …