Digital Commons Network^™

Machine Learning-Based Event Generator, Yasir Alanazi

Computer Science Theses & Dissertations

Monte Carlo-based event generators have been the primary source for simulating particle collision experiments for the study of interesting physics scenarios. Monte Carlo generators rely on theoretical assumptions, which limit their ability to capture the full range of possible correlations between particle’s momenta. In addition, the simulations of the complete pipeline often take minutes to generate a single event even with the help of supercomputers.

In recent years, much attention has been devoted to the development of machine learning event generators. They demonstrate attractive advantages, including fast simulations, data compression, and being agnostic of theoretical assumptions. However, most of the …

Enhanced Heart Rate Prediction Model Using Damped Least-Squares Algorithm, Angela An, Mohammad Al-Fawa’Reh, James Jin Kang

Research outputs 2022 to 2026

Monitoring a patient’s vital signs is considered one of the most challenging problems in telehealth systems, especially when patients reside in remote locations. Companies now use IoT devices such as wearable devices to participate in telehealth systems. However, the steady adoption of wearables can result in a significant increase in the volume of data being collected and transmitted. As these devices run on limited battery power, they can run out of power quickly due to the high processing requirements of the device for data collection and transmission. Given the importance of medical data, it is imperative that all transmitted data …

Study On Predictive Maintenance Of V-Belt In Milling Machines Using Machine Learning, Reza Aulia Rahman, Mohammad Faishol Erikyatna, Achmad Fauzan Hery Soegiharto

Journal of Mechanical Engineering Science and Technology (JMEST)

Towards industry 4.0, monitoring the degradation of machine tools’ components becomes a key feature so that smooth productivity is achieved. To preserve the functionality and performance of the machine tools, proper maintenance activities must be planned and carried out. V-belt is important component in machine tools that transmits power from the electric motor spindle in order to machine to work and cut desired material properly. The purpose of this research is to develop a predictive maintenance system for v-belt milling machine Krisbow 31N2F using machine learning. The machine learning algorithm models using multiple and simple linear regression algorithm was developed …

Sociodemographic Determinants Of Oral Anticoagulant Prescription In Patients With Atrial Fibrillations: Findings From The Pinnacle Registry Using Machine Learning, Zahra Azizi, Andrew T. Ward, Donghyun J. Lee, Sanchit S. Gad, Kanchan Bhasin, Robert J. Beetel, Tiago Ferreira, Sushant Shankar, John S. Rumsfeld, Salim S. Virani

Office of the Provost

Background: Current risk scores that are solely based on clinical factors have shown modest predictive ability for understanding of factors associated with gaps in real-world prescription of oral anticoagulation (OAC) in patients with atrial fibrillation (AF).
Objective: In this study, we sought to identify the role of social and geographic determinants, beyond clinical factors associated with variation in OAC prescriptions using a large national registry of ambulatory patients with AF.
Methods: Between January 2017 and June 2018, we identified patients with AF from the American College of Cardiology PINNACLE (Practice Innovation and Clinical Excellence) Registry. We examined associations between patient …

Predicting The Stability Of Open Stopes Using Machine Learning, Alicja Szmigiel, Derek B. Apel

Journal of Sustainable Mining

The Mathews stability graph method was presented for the first time in 1980. This method was developed to assess the stability of open stopes in different underground conditions, and it has an impact on evaluating the safety of underground excavations. With the development of technology and growing experience in applying computer sciences in various research disciplines, mining engineering could significantly benefit by using Machine Learning. Applying those ML algorithms to predict the stability of open stopes in underground excavations is a new approach that could replace the original graph method and should be investigated. In this research, a Potvin database …

A Review Of Risk Concepts And Models For Predicting The Risk Of Primary Stroke, Elizabeth Hunter, John D. Kelleher

Articles

Predicting an individual's risk of primary stroke is an important tool that can help to lower the burden of stroke for both the individual and society. There are a number of risk models and risk scores in existence but no review or classification designed to help the reader better understand how models differ and the reasoning behind these differences. In this paper we review the existing literature on primary stroke risk prediction models. From our literature review we identify key similarities and differences in the existing models. We find that models can differ in a number of ways, including the …

Evaluating The Effect Of Public Health Governance Structure And Public Opinion On Covid-19 Disease Control Interventions, Daniel Chacreton

USF Tampa Graduate Theses and Dissertations

Background

Since the start of the COVID-19 outbreak, the responses to the pandemic varied greatly from nation to nation. In the US, state-level variance in responses has likely contributed to the disparate COVID-19 infection-related state-level outcomes such as incidence and case fatality rates. The large variances in national and sub-national responses to COVID-19 when combined with the abundance of factors that influence disease transmission rates has created a number of important research opportunities.

Methods

In this vain, this research study aims to describe the relationship that exists between public health governance structure and public sentiment and COVID-19 control measure effectiveness …

Open-Source Clinical Machine Learning Models: Critical Appraisal Of Feasibility, Advantages, And Challenges, Keerthi B. Harish, W. Nicholson Price Ii, Yindalon Aphinyanaphongs

Articles

Machine learning applications promise to augment clinical capabilities and at least 64 models have already been approved by the US Food and Drug Administration. These tools are developed, shared, and used in an environment in which regulations and market forces remain immature. An important consideration when evaluating this environment is the introduction of open-source solutions in which innovations are freely shared; such solutions have long been a facet of digital culture. We discuss the feasibility and implications of open-source machine learning in a health care infrastructure built upon proprietary information. The decreased cost of development as compared to drugs and …

Investigating Students' Interpretations Of Ecological Food Webs, Christopher Neil Grissett

USF Tampa Graduate Theses and Dissertations

To better prepare undergraduate students for current and future biological challenges, scientists, educators and researchers in the Vision and Change report recommended five core conceptual areas essential for the improvement of biological literacy, one of which is biological systems. Systems are an identified core concept that may help promote biological literacy. One example of a system that students have difficulty understanding is an ecological food web, which consists of parts or components interacting with one another to perform a given phenomenon. The intricacies of this system tend to confuse students and can produce naïve conceptions that could hinder future learning …

Anti-Phishing Tools: State Of The Art And Detection Efficiencies, M. F. Alghenaim, N. A. Abu Bakar, F. Abdul Rahim

Applied Mathematics & Information Sciences

A phishing attack is a process of obtaining a customers private data, whether by using phishing emails or fake websites. With every new development on the Internet, the attackers means of phishing attacks develop, requiring more powerful phishing tools to counter these attacks. The Internet has become an essential part of the personal and social life of the public and governments, institutions, and companies all over the world. This means that Internet users need tools to protect against phishing attack types and web risks, which may cause personal, institutional, financial, and informational damage. Hence, this study reviews anti-phishing attack tools …

Anti-Phishing Tools: State Of The Art And Detection Efficiencies, M. F. Alghenaim, N. A. Abu Bakar, F. Abdul Rahim

Applied Mathematics & Information Sciences

A phishing attack is a process of obtaining a customers private data, whether by using phishing emails or fake websites. With every new development on the Internet, the attackers means of phishing attacks develop, requiring more powerful phishing tools to counter these attacks. The Internet has become an essential part of the personal and social life of the public and governments, institutions, and companies all over the world. This means that Internet users need tools to protect against phishing attack types and web risks, which may cause personal, institutional, financial, and informational damage. Hence, this study reviews anti-phishing attack tools …

Using Artificial Intelligence In The Law Review Submissions Process, Brenda M. Simon

Faculty Scholarship

The use of artificial intelligence to help editors examine law review submissions may provide a way to improve an overburdened system. This Article is the first to explore the promise and pitfalls of using artificial intelligence in the law review submissions process. Technology-assisted review of submissions offers many possible benefits. It can simplify preemption checks, prevent plagiarism, detect failure to comply with formatting requirements, and identify missing citations. These efficiencies may allow editors to address serious flaws in the current selection process, including the use of heuristics that may result in discriminatory outcomes and dependence on lower-ranked journals to conduct …

A Bibliometric Analysis Of Machine Learning Econometrics In Asset Pricing, Hector O. Zapata, Supratik Mukhopadhyay

Faculty Publications

Machine learning (ML) is a novel method that has applications in asset pricing and that fits well within the problem of measurement in economics. Unlike econometrics, ML models are not designed for parameter estimation and inference, but similar to econometrics, they address, and may be better suited for, problems of prediction. While some ML methods have been applied in econometrics for decades, their success in prediction has been limited, and examples of this abound in the asset pricing literature. In recent years, the ML literature has advanced new, more efficient, computation methods for regularization, modeling nonlinearity, and improved out-of-sample prediction. …

Discredited Data, Ngozi Okidegbe

Faculty Scholarship

Jurisdictions are increasingly employing pretrial algorithms as a solution to the racial and socioeconomic inequities in the bail system. But in practice, pretrial algorithms have reproduced the very inequities they were intended to correct. Scholars have diagnosed this problem as the biased data problem: pretrial algorithms generate racially and socioeconomically biased predictions, because they are constructed and trained with biased data.
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/="/">This Article contends that biased data is not the sole cause of algorithmic discrimination. Another reason pretrial algorithms produce biased results is that they are exclusively built and trained with data from carceral knowledge sources – the …

Societal Impact Assessment Of Depression, Mohammed Salah Almazrouei

Theses

Depression is a mental problem characterised by unusual changes in the mood of a person, accompanied by a temporary different emotional response to everyday changes in the phenomena (WHO, 2020). Affected people suffer greatly, hence functioning poorly in the workplace, school, or at home in a family setup. Even though the public is well aware of its existence, there is little going on to combat this condition. Amongst the various stumbling blocks towards achieving an effective approach to address the problem include limited resources and associated well-trained personnel to help the victims.

A mental disorder associated with social stigma and …

Identifying Early-Life Behavior To Predict Mothering Ability In Swine Utilizing NuTrack System, Savannah Gabrielle Millburn

Department of Animal Science: Dissertations, Theses, and Student Research

Early recognition of indicator traits for swine reproduction and longevity supports economical selection decision making. Gilt activity is a key variable impacting a sow’s herd life and productivity. The purpose of this study was to examine early- life behaviors contributing to farrowing traits including gestation length (GL), number born alive (NBA), number weaned (NW), and herd life (HL). Herd life was a binary trait representing if a gilt was culled after one parity. Beginning at approximately 20 weeks of age, video recordings were taken on 480 gilts for 7 consecutive days and processed using the NUtrack system. Activity traits …

Personal Comfort Models Based On A 6‐Month Experiment Using Environmental Parameters And Data From Wearables, Federico Tartarini, Stefano Schiavon, Matias Quintana, Clayton Miller

Research Collection College of Integrative Studies

Personal thermal comfort models are a paradigm shift in predicting how building occupants perceive their thermal environment. Previous work has critical limitations related to the length of the data collected and the diversity of spaces. This paper outlines a longitudinal field study comprising 20 participants who answered Right-Here-Right-Now surveys using a smartwatch for 180 days. We collected more than 1080 field-based surveys per participant. Surveys were matched with environmental and physiological measured variables collected indoors in their homes and offices. We then trained and tested seven machine learning models per participant to predict their thermal preferences. Participants indicated 58% of …

Generating Realistic Cyber Data For Training And Evaluating Machine Learning Classifiers For Network Intrusion Detection Systems, Marc W. Chalé, Nathaniel D. Bastian

Faculty Publications

No abstract provided.

Hip Osteoarthritis: A Novel Network Analysis Of Subchondral Trabecular Bone Structures, Andrew Allison

Faculty, Staff and Student Publications

Hip osteoarthritis (HOA) is a degenerative joint disease that leads to the progressive destruction of subchondral bone and cartilage at the hip joint. Development of effective treatments for HOA remains an open problem, primarily due to the lack of knowledge of its pathogenesis and a typically late-stage diagnosis. We describe a novel network analysis methodology for microcomputed tomography (micro-CT) images of human trabecular bone. We explored differences between the trabecular bone microstructure of femoral heads with and without HOA. Large-scale automated extraction of the network formed by trabecular bone revealed significant network properties not previously reported for bone. Profound differences …

A Machine Learning Approach For Improved Thermal Comfort Prediction In Sustainable Built Environments, Waleed Abd El-Khalik

Sustainable Machine Intelligence Journal

Thermal comfort prediction within sustainable built environments stands as a pivotal challenge intertwining human well-being and environmental sustainability. This paper presents a pioneering framework leveraging machine learning methodologies to advance predictive models for thermal comfort. Drawing upon a comprehensive dataset sourced from ASHRAE field studies and the RP-884 database, comprising 107,463 entries, our study unfolds a novel approach to enhancing thermal comfort predictions. The integration of diverse physiological parameters, environmental data, and occupant preferences forms the foundation of our machine learning-driven framework. Through meticulous analysis and model development, our approach not only refines predictive accuracy but also underscores adaptability across …

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, …

Tntdetectai: A Deep Learning Model For Automated Detection And Counting Of Tunneling Nanotubes In Microscopy Images, Yasin Ceran, Hamza Ergüder, Katherine Ladner, Sophie Korenfeld, Karina Deniz, Phillip Wong, Thomas Pengo, Emil Lou, Chirag B Patel

Faculty, Staff and Student Publications

BACKGROUND: Tunneling nanotubes (TNTs) are cellular structures connecting cell membranes and mediating intercellular communication. TNTs are manually identified and counted by a trained investigator; however, this process is time-intensive. We therefore sought to develop an automated approach for quantitative analysis of TNTs.

METHODS: We used a convolutional neural network (U-Net) deep learning model to segment phase contrast microscopy images of both cancer and non-cancer cells. Our method was composed of preprocessing and model development. We developed a new preprocessing method to label TNTs on a pixel-wise basis. Two sequential models were employed to detect TNTs. First, we identified the regions …

A Machine Learning Model For The Identification Of The Holy Quran Reciter Utilizing K-Nearest Neighbor And Artificial Neural Networks, Meshal Mohammed Al Anazi, Osama R. Shahin

Information Sciences Letters

The method of identification of the Holy Quran reciter, which is entered on the various features of the acoustic wave, is referred to as the Holy Quran Reciter Identification. The Muslim communitys Holy Book is the Holy Quran. Listening to or reading the Holy Quran is one of the obligatory activities for Muslims. This research proposes a machine learning model for identifying the Holy Quran reciter using a machine learning language. Here, the presented system comprises the essential phases for a voice recognition system encompassing the processes of classification, extraction of features, preprocessing, and data acquisition. Moreover, the voices of …

Multi-Label Prediction For Political Text-As-Data, Aaron Erlich, Stefano G. Dantas, Benjamin E. Bagozzi, Daniel Berliner, Brian Palmer-Rubin

Political Science Faculty Research and Publications

Political scientists increasingly use supervised machine learning to code multiple relevant labels from a single set of texts. The current “best practice” of individually applying supervised machine learning to each label ignores information on inter-label association(s), and is likely to under-perform as a result. We introduce multi-label prediction as a solution to this problem. After reviewing the multi-label prediction framework, we apply it to code multiple features of (i) access to information requests made to the Mexican government and (ii) country-year human rights reports. We find that multi-label prediction outperforms standard supervised learning approaches, even in instances where the correlations …

Fraudulent Insurance Claims Detection Using Machine Learning, Arif Ismail Alrais

Theses

As the different countries around the world evolve into a more economical-based and stimulating their economy is the goal. The main purpose of most of these countries is to fight off money launderers and fraudsters for better economic growth. A popular fraud topic in this regard is insurance fraud since it costs the companies and the public billions. Applying data analysis and machine learning are great ways used to address many problems regarding any automated system. To address this problem, first extensive research should be made to check out what has been applied and what the most promising solution using …

Predicting Delays In The Supply Chain With The Use Of Machine Learning, Raghad Al-Saghir

Theses

The supply chain includes all the steps in an item's life cycle starting from raw material and all the way to the customers. Delays in these steps have always been a problem faced by businesses due to the huge financial loss these delays account for. These delays frequently cause significant financial losses, irritate customers, and loss of customer confidence. The aim of this study is to construct a prediction model that predicts late deliveries before its occurrence with the use of big data and machine learning. The study used Dataco Supply Chain dataset and cleaned, visualized, and trained it using …

A Simpler Machine Learning Model For Acute Kidney Injury Risk Stratification In Hospitalized Patients, Yirui Hu, Kunpeng Liu, Kevin Ho, David Riviello, Jason Brown, Alex R. Chang, Gurmukteshwar Singh, H. Lester Kirchner

Computer Science Faculty Publications and Presentations

Background: Hospitalization-associated acute kidney injury (AKI), affecting one-in-five inpatients, is associated with increased mortality and major adverse cardiac/kidney endpoints. Early AKI risk stratification may enable closer monitoring and prevention. Given the complexity and resource utilization of existing machine learning models, we aimed to develop a simpler prediction model. Methods: Models were trained and validated to predict risk of AKI using electronic health record (EHR) data available at 24 h of inpatient admission. Input variables included demographics, laboratory values, medications, and comorbidities. Missing values were imputed using multiple imputation by chained equations. Results: 26,410 of 209,300 (12.6%) inpatients developed AKI during …

Search For Triple-Proton Decay Using Machine Learning With Cuore, Douglas Adams

Theses and Dissertations

A framework to search for a triple-proton decay of ¹³⁰Te in the CUORE detector against a background of muons is presented. We use machine learning to classify different kinds of energy depositing events. We use the classification information to improve our detection or non-detection limits of a triple-proton decay process. We derive and use a methodology of combining Poisson counting statistics with supervised classification machine learning tools. Additionally, a sensitivity calculation is provided which uses the classification counting likelihood. Using our analysis technique, we achieve an lower 2σ half-life bound of 7.43×10²⁴yrs for triple-proton decay of …

Predicting Electrical Faults In Power Distribution Network, Ahmad Adel Bin Sulaiman

Theses

Electricity is becoming increasingly important in modern civilization, and as a result, the emphasis on and use of power infrastructure is gradually expanding. Simultaneously, investment and distribution modes are shifting from the large-scale centralized generation of electricity and sheer consumption to decentralized generators and extremely sophisticated clients. This transformation puts further strain on old infrastructure, necessitating significant expenditures in future years to ensure a consistent supply. Subsequent technical and prediction technologies can help to maximize the use of the current grid while lowering the probability of faults. This study discusses some of the local grid difficulties as well as a …

Tntdetect.Ai: A Deep Learning Model For Automated Detection And Counting Of Tunneling Nanotubes In Microscopy Images, Yasin Ceran, Hamza Ergüder, Katherine Ladner, Sophie Korenfeld, Karina Deniz, Sanyukta Padmanabhan, Phillip Wong, Murat Baday, Thomas Pengo, Emil Lou, Chirag B. Patel

Faculty Research, Scholarly, and Creative Activity

Background: Tunneling nanotubes (TNTs) are cellular structures connecting cell membranes and mediating intercellular communication. TNTs are manually identified and counted by a trained investigator; however, this process is time-intensive. We therefore sought to develop an automated approach for quantitative analysis of TNTs. Methods: We used a convolutional neural network (U-Net) deep learning model to segment phase contrast microscopy images of both cancer and non-cancer cells. Our method was composed of preprocessing and model development. We developed a new preprocessing method to label TNTs on a pixel-wise basis. Two sequential models were employed to detect TNTs. First, we identified the regions …