Physical Sciences and Mathematics Commons^™

Deciphering Trends And Tactics: Data-Driven Techniques For Forecasting Information Spread And Detecting Coordinated Campaigns In Social Media, Kin Wai Ng Lugo

USF Tampa Graduate Theses and Dissertations

The main objective of this dissertation is to develop models that predict and investigate the spread of information in social media over time. In this context, we consider topics of discussions as the information that spreads. Thus, we are interested in forecasting the number of messages per day in a future interval of time. We take a data-driven approach, in which we compare our results with real datasets from a multitude of socio-political contexts and from multiple social media platforms, specifically, Twitter and YouTube.

We identified a number of challenges related to forecasting social media time series per topic. First, …

Exploratory Data-Driven Models For Water Quality: A Case Study For Tampa Bay Water, Sandra Sekyere

USF Tampa Graduate Theses and Dissertations

Water, a crucial resource for sustaining life, covers approximately 70% of the earth's surface. Nonetheless, the quality of water is deteriorating rapidly due to the rapid growth of urban areas and industries, which is a worrying trend causing harm to human health and the ecosystem. Water quality forecasting has a key role in water resources management by enabling effective pollution control, ecosystem monitoring, and decision-making.

Previously, traditional statistical models were used to forecast water quality, but they were unable to examine the non-linear relationships between water quality parameters, and they assumed that all datasets were distributed normally. This study uses …

Insect Classification And Explainability From Image Data Via Deep Learning Techniques, Tanvir Hossain Bhuiyan

USF Tampa Graduate Theses and Dissertations

Since the dawn of the Industrial Revolution, humanity has always tried to make labor more efficient and automated, and this trend is only continuing in the modern digital age. With the advent of artificial intelligence (AI) techniques in the latter part of the 20th century, the speed and scale with which AI has been leveraged to automate tasks defy human imagination. Many people deeply entrenched in the technology field are genuinely intrigued and concerned about how AI may change many of the ways in which humans have been living for millennia. Only time will provide the answers. This dissertation is …

Generative Spatio-Temporal And Multimodal Analysis Of Neonatal Pain, Md Sirajus Salekin

USF Tampa Graduate Theses and Dissertations

Neonates can not express their pain like an adult person. Due to the lacking of proper muscle growth and inability to express non-verbally, it is difficult to understand their emotional status. In addition, if the neonates are under any treatment or left monitored after any major surgeries (post-operative), it is more difficult to understand their pain due to the side effect of medications and the caring system (i.e. intubated, masked face, covered body with blanket, etc.). In a clinical environment, usually, bedside nurses routinely observe the neonate and measure the pain status following any standard clinical pain scale. But current …

Social Media Time Series Forecasting And User-Level Activity Prediction With Gradient Boosting, Deep Learning, And Data Augmentation, Fred Mubang

USF Tampa Graduate Theses and Dissertations

In the overall history of technological innovations, social media has only existed for a brief time, however its influence is undeniable. Researchers have found that it can be used to influence elections, spread health misinformation, and aid with financial pump-and-dump schemes. Keeping all this in mind, it is clear that more research is needed to predict the spread of information on social media in order to combat its malicious use.

To that end, in this dissertation, we explore the use of Machine Learning algorithms to perform time series forecasting and user-level activity prediction in social media. We address the different …

Carbon And Other Low-Z Materials Under Extreme Conditions, Jonathan T. Willman

USF Tampa Graduate Theses and Dissertations

This work is focused on understanding material's behavior and response to extreme conditions. Under extreme conditions, which is categorized as regions of high pressures and temperatures in (P-T) space, materials can undergo multiple types of phase transitions as well as exhibit substantial damage as well as other exotic behaviors. By studying matter at these extreme conditions, we can elucidate a broad range of fundamental physics including a material's energetic, mechanical, and electronic responses. This thesis describes work that makes contributions to the growing body of knowledge within these subsets of condensed matter physics. In the first thrust, crystal structure prediction …

Online And Adjusted Human Activities Recognition With Statistical Learning, Yanjia Zhang

USF Tampa Graduate Theses and Dissertations

Wearable human activity recognition (HAR) is a widely application system for our daily life. It hasbeen built in many devices, such as smartphone, smartwatch, activity tracker, and health monitor. Many researchers try to develop a system which requires less memory space and power, but has fast and accurate classification results. Moreover, the objective of adjusting the classifier by the system self is also a study direction. In the present study, we introduced the machine learning methods to both smartphone data and smartwatch data and an adjusted model with the continuous generating data. Further, we also proposed a new HAR system …

Data-Driven Studies On Social Networks: Privacy And Simulation, Yasanka Sameera Horawalavithana

USF Tampa Graduate Theses and Dissertations

Social media datasets are fundamental to understanding a variety of phenomena, such as epidemics, adoption of behavior, crowd management, and political uprisings. At the same time, many such datasets capturing computer-mediated social interactions are recorded nowadays by individual researchers or by organizations. However, while the need for real social graphs and the supply of such datasets are well established, the flow of data from data owners to researchers is significantly hampered by privacy risks: even when humans’ identities are removed, or data is anonymized to some extent, studies have proven repeatedly that re-identifying anonymized user identities (i.e., de-anonymization) is doable …

Deep Learning Predictive Modeling With Data Challenges (Small, Big, Or Imbalanced), Renhao Liu

USF Tampa Graduate Theses and Dissertations

In the real world, data used to build machine learning models always has different sizes and characteristics. These size and characteristic features, including small datasets, big datasets, imbalanced datasets, often lead to different challenges when training machine learning models. Models trained on a small number of observations tend to overfit the training data and produce inaccurate results. When it comes to big data, efficiently learning from "huge" size data in a short time becomes important. With an imbalanced dataset, learning is usually biased towards the majority class in the data and appropriate measurements are needed to check model performance.

As …

Machine Learning For The Internet Of Things: Applications, Implementation, And Security, Vishalini Laguduva Ramnath

USF Tampa Graduate Theses and Dissertations

Artificial intelligence and ubiquitous sensor systems have seen tremendous advances in recent times, resulting in groundbreaking impact across domains such as healthcare, entertainment, and transportation through a collective ecosystem called the Internet of Things. The advent of 5G and improved wireless networks will further accelerate the research and development of tools in deep learning, sensor systems, and computing platforms by providing improved network latency and bandwidth. While tremendous progress has been made in the Internet of Things, current work has largely focused on building robust applications that leverage the data collected through ubiquitous sensor nodes to provide actionable rules and …

Fractional Random Weighted Bootstrapping For Classification On Imbalanced Data With Ensemble Decision Tree Methods, Sean Charles Carter

USF Tampa Graduate Theses and Dissertations

Ensemble methods are commonly used for building predictive models for classification. Models that are unstable to perturbations in the training set, such as the decision tree, often see considerable reductions in error when grouped, using bootstrapped resamples of the training data to train many models. The non-parametric bootstrap, however, has limited efficacy when used on severely imbalanced data, especially when the number of observations of one or more classes is exceptionally small. We explore the fractional random weighted bootstrap, which randomly assigns fractional weights to observations, as an alternative resampling pro cedure in training machine learning ensembles, particularly decision tree …

On The Feasibility Of Profiling, Forecasting And Authenticating Internet Usage Based On Privacy Preserving Netflow Logs, Soheil Sarmadi

USF Tampa Graduate Theses and Dissertations

Understanding Internet user behavior and Internet usage patterns is fundamental in developing future access networks and services that meet technical as well as Internet user needs. User behavior is routinely studied and measured, but with different methods depending on the research discipline of the investigator, and these disciplines rarely cross. We tackle this challenge by developing frameworks that the Internet usage statistics used as the main features in understanding Internet user behaviors, with the purpose of finding a complete picture of the user behavior and working towards a unified analysis methodology. In this dissertation we collected Internet usage statistics via …

Automatic Multimodal Assessment Of Neonatal Pain, Ghada Zamzmi

USF Tampa Graduate Theses and Dissertations

For several decades, pediatricians used to believe that neonates do not feel pain. The American Academy of Pediatrics (AAP) recognized neonates' sense of pain in 1987. Since then, there have been many studies reporting a strong association between repeated pain exposure (under-treatment) and alterations in brain structure and function. This association has led to the increased use of anesthetic medications. However, recent studies found that the excessive use of analgesic medications (over-treatment) can cause many side effects. The current standard for assessing neonatal pain is discontinuous and suffers from inter-observer variations, which can lead to over- or under-treatment. Therefore, it …

Machine Learning Methods For Network Intrusion Detection And Intrusion Prevention Systems, Zheni Svetoslavova Stefanova

USF Tampa Graduate Theses and Dissertations

Given the continuing advancement of networking applications and our increased dependence upon software-based systems, there is a pressing need to develop improved security techniques for defending modern information technology (IT) systems from malicious cyber-attacks. Indeed, anyone can be impacted by such activities, including individuals, corporations, and governments. Furthermore, the sustained expansion of the network user base and its associated set of applications is also introducing additional vulnerabilities which can lead to criminal breaches and loss of critical data. As a result, the broader cybersecurity problem area has emerged as a significant concern, with many solution strategies being proposed for both …

Similarity Based Large Scale Malware Analysis: Techniques And Implications, Yuping Li

USF Tampa Graduate Theses and Dissertations

Malware analysis and detection continues to be one of the central battlefields for cybersecurity industry. For the desktop malware domain, we observed multiple significant ransomware attacks in the past several years, e.g., it was estimated that in 2017 the WannaCry ransomware attack affected more than 200,000 computers across 150 countries with hundreds of millions damages. Similarly, we witnessed the increased impacts of Android malware on global individuals due to the popular smartphone and IoT devices worldwide. In this dissertation, we describe similarity comparison based novel techniques that can be applied to achieve large scale desktop and Android malware analysis, and …

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 …

Lung Ct Radiomics: An Overview Of Using Images As Data, Samuel Hunt Hawkins

USF Tampa Graduate Theses and Dissertations

Lung cancer is the leading cause of cancer-related death in the United States and worldwide. Early detection of lung cancer can help improve patient outcomes, and survival prediction can inform plans of treatment. By extracting quantitative features from computed tomography scans of lung cancer, predictive models can be built that can achieve both early detection and survival prediction. To build these predictive models, first a detected lung nodule is segmented, then image features are extracted, and finally a model can be built utilizing image features to make predictions. These predictions can help radiologists improve cancer care.

Building predictive models based …

Real-Time Classification Of Biomedical Signals, Parkinson’S Analytical Model, Abolfazl Saghafi

USF Tampa Graduate Theses and Dissertations

The reach of technological innovation continues to grow, changing all industries as it evolves. In healthcare, technology is increasingly playing a role in almost all processes, from patient registration to data monitoring, from lab tests to self-care tools. The increase in the amount and diversity of generated clinical data requires development of new technologies and procedures capable of integrating and analyzing the BIG generated information as well as providing support in their interpretation.

To that extent, this dissertation focuses on the analysis and processing of biomedical signals, specifically brain and heart signals, using advanced machine learning techniques. That is, the …

Adaptive Region-Based Approaches For Cellular Segmentation Of Bright-Field Microscopy Images, Hady Ahmady Phoulady

USF Tampa Graduate Theses and Dissertations

Microscopy image processing is an emerging and quickly growing field in medical imaging research area. Recent advancements in technology including higher computation power, larger and cheaper storage modules, and more efficient and faster data acquisition devices such as whole-slide imaging scanners contributed to the recent microscopy image processing research advancement. Most of the methods in this research area either focus on automatically process images and make it easier for pathologists to direct their focus on the important regions in the image, or they aim to automate the whole job of experts including processing and classifying images or tissues that leads …

Ensemble Learning Method On Machine Maintenance Data, Xiaochuang Zhao

USF Tampa Graduate Theses and Dissertations

In the industry, a lot of companies are facing the explosion of big data. With this much information stored, companies want to make sense of the data and use it to help them for better decision making, especially for future prediction. A lot of money can be saved and huge revenue can be generated with the power of big data. When building statistical learning models for prediction, companies in the industry are aiming to build models with efficiency and high accuracy. After the learning models have been developed for production, new data will be generated. With the updated data, the …

Energy Efficient Context-Aware Framework In Mobile Sensing, Ozgur Yurur

USF Tampa Graduate Theses and Dissertations

The ever-increasing technological advances in embedded systems engineering, together with the proliferation of small-size sensor design and deployment, have enabled mobile devices (e.g., smartphones) to recognize daily occurring human based actions, activities and interactions. Therefore, inferring a vast variety of mobile device user based activities from a very diverse context obtained by a series of sensory observations has drawn much interest in the research area of ubiquitous sensing. The existence and awareness of the context provides the capability of being conscious of physical environments or situations around mobile device users, and this allows network services to respond proactively and intelligently …

Tagline: Information Extraction For Semi-Structured Text Elements In Medical Progress Notes, Dezon K. Finch

USF Tampa Graduate Theses and Dissertations

Text analysis has become an important research activity in the Department of Veterans Affairs (VA). Statistical text mining and natural language processing have been shown to be very effective for extracting useful information from medical documents. However, neither of these techniques is effective at extracting the information stored in semi-structure text elements. A prototype system (TagLine) was developed as a method for extracting information from the semi-structured portions of text using machine learning. Features for the learning machine were suggested by prior work, as well as by examining the text, and selecting those attributes that help distinguish the various classes …