Digital Commons Network^™

Probabilistic Short Term Solar Driver Forecasting With Neural Network Ensembles, Joshua Daniell

Graduate Theses, Dissertations, and Problem Reports (ETD)

Commonly utilized space weather indices and proxies drive predictive models for thermosphere density, directly impacting objects in low-Earth orbit (LEO) by influencing atmospheric drag forces. A set of solar proxies and indices (drivers), F_10.7, S_10.7, M_10.7, and Y_10.7, are created from a mixture of ground based radio observations and satellite instrument data. These solar drivers represent heating in various levels of the thermosphere and are used as inputs by the JB2008 empirical thermosphere density model. The United States Air Force (USAF) operational High Accuracy Satellite Drag Model (HASDM) relies on JB2008, and …

3d Hydrostratigraphic And Hydraulic Conductivity Modeling Using Supervised Machine Learning, T. A. Tilahun, Jesse T. Korus Dr.

Conservation and Survey Division: Faculty and Staff Publications

No abstract provided.

Short-Term Prediction Of Icu Admission For Covid-19 Inpatients, Yoon Sang Lee, Riyaz T. Sikora

Journal of International Technology and Information Management

Since the COVID-19 outbreak, many hospitals suffered from a surge of some high-risk inpatients needing to be admitted to the ICU. In this study, we propose a method

predicting the likelihood of COVID-19 inpatients’ admission to the ICU within a time frame of 12 hours. Four steps, the Bayesian Ridge Regression-based missing value imputation, the synthesis of training samples by the combination of two rows (the first and another row) of each patient, customized oversampling, and XGBoost classifier, are used for the proposed method. In the experiment, the AUC-ROC and F-score of our method is compared with those of other …

A Survey And Evaluation Of Android-Based Malware Evasion Techniques And Detection Frameworks, Parvez Faruki, Rhati Bhan, Vinesh Jain, Sajal Bhatia, Nour El Madhoun, Rajendra Pamula

School of Computer Science & Engineering Faculty Publications

Android platform security is an active area of research where malware detection techniques continuously evolve to identify novel malware and improve the timely and accurate detection of existing malware. Adversaries are constantly in charge of employing innovative techniques to avoid or prolong malware detection effectively. Past studies have shown that malware detection systems are susceptible to evasion attacks where adversaries can successfully bypass the existing security defenses and deliver the malware to the target system without being detected. The evolution of escape-resistant systems is an open research problem. This paper presents a detailed taxonomy and evaluation of Android-based malware evasion …

Using Machine Learning Classification And Esa Sentinel 2 Multispectral Imager Data To Delineate Marsh Vegetation And Measure Ecotone Movement In Coastal Georgia, Thomas A. Pudil

College of Graduate Studies: Theses & Dissertations

Tidal marshes are unique communities that are subjected to environmental stressors including sea level rise, salinity change, and drought, resulting in constant change. It is important to monitor these changing areas because of the ecosystem services they provide to us, such as protection from storms and carbon sequestration. The proposed work for this thesis project is focused on the study of tidal marshes and the dynamics between the vegetation species within them. The aim of this project is to use geospatial technology and analyses, along with machine learning classification methods, to monitor change in these valuable ecosystems. The Georgia coast …

Machine Learning Approach To Predict Tdcs-Induced Electric Current In The Human Brain, Chikako Olsen

Dissertations and Theses

Background: Transcranial direct current stimulation (tDCS) is a promising non-invasive method for treating neurological and psychiatric disorders and enhancing cognitive function. However, the underlying mechanisms of tDCS are not fully understood, and there is no ground truth for determining, non-invasively, where in the brain tDCS electrical currents flow. At the same time, effective neuronal engagement from tDCS requires accurate localization of the induced currents to specific brain target regions. Machine Learning (ML) has the potential to significantly improve the accuracy and precision of tDCS current prediction. This study aims to develop a novel approach using ML to predict the location …

Network Intrusion Detection With Two-Phased Hybrid Ensemble Learning And Automatic Feature Selection, Asanka Kavinda Mananayaka, Sunnie S. Chung

Electrical and Computer Engineering Faculty Publications

The use of network connected devices has grown exponentially in recent years revolutionizing our daily lives. However, it has also attracted the attention of cybercriminals making the attacks targeted towards these devices increase not only in numbers but also in sophistication. To detect such attacks, a Network Intrusion Detection System (NIDS) has become a vital component in network applications. However, network devices produce large scale high-dimensional data which makes it difficult to accurately detect various known and unknown attacks. Moreover, the complex nature of network data makes the feature selection process of a NIDS a challenging task. In this study, …

Using Machine Learning To Predict Student Outcomes, Saba Fatima

Graduate Research Theses & Dissertations

Predicting students’ performance to identify which students are at risk of receiving aD/Fail/Withdraw (DFW) grade and ensuring their timely graduation is not just desirable but also necessary in most educational entities. In the US, not only is the Science, Technology, Engineering, and Mathematics (STEM) major becoming less popular among students, the graduation rate of STEM students is steadily declining. The lack of STEM graduates in the US is a serious problem that will place this country at a disadvantage as a competitor in international technological advancement. In order to secure its status as a technological leader internationally, the US institutions …

Using Machine Learning To Search For Vector Boson Scattering At The Cms Detector During Run 2, Mark Mekosh

Graduate Research Theses & Dissertations

This work reports on the use of different machine learning (ML) techniques in the search for vector boson scattering (VBS) events in the semileptonic $WV$ channel. VBS is an important process for studying electroweak symmetry breaking (EWSB), the Higgs mechanism, as well as for probing beyond the standard model physics. Boosted decision trees as well as deep neural networks were trained on Monte Carlo simulation samples and applied to 137 fb$^{-1}$ of proton-proton collision data taken from 2016 to 2018 by the Compact Muon Solenoid (CMS) experiment at the Large Hadron Collider (LHC) with a center of mass energy $\sqrt{s} …

Machine Learning Techniques For Credit Card Fraud Detection, Amal Alamri

Theses, Dissertations and Culminating Projects

Credit card fraudulent transactions are becoming an ever-growing problem in the financial market. There has been a rapid increase in the rate of fraudulent transactional activities in recent years producing considerable financial loss to many companies, organizations, and government agencies. These numbers are anticipated to increase in the near future and many scholars in this field are focused on detecting fraudulent transactions early on, using advanced Machine Learning techniques. However, credit card fraud transaction detection is not easy for two reasons: (I) fraudulent methods usually vary for each attempt, and (II) the dataset is extremely imbalanced with many more normal …

Measuring The Impact Of Youth Research In The Us Policy Documents, Miftahul Jannat Mokarrama

Graduate Research Theses & Dissertations

With the growing demand for aligning research with society, in recent years, researchers and policymakers have been unequivocal in increasing research use in the policy-making process. Integrating research into policy formulation not only results in more effective policies but also allows researchers to extend the reach and influence of their work to a broaderand more diverse audience. In the US, as youth comprise a substantial portion of the population, their perspectives, needs, and aspirations must be understood and addressed in policymaking. Therefore, in this thesis, we assessed the impact of youth-focused research on US policy documents using state-of-the-art pre-trained Large …

Navigating Missed Visits In Hiv Primary Care: Exploring Risk Factors Associated With Missed Visits, Jiaying Hao

All ETDs from UAB

The HIV care continuum is a public health model that outlines the steps or stages people with HIV go through from diagnosis to achieving and maintaining viral suppression. Retention in care, a key component of the HIV care continuum, is critical in achieving good health outcomes for people with HIV (PWH) and preventing HIV transmission. Consistent HIV care is essential for the health of PWH as a part of retention in care. De-spite no standard measure for retention in care to PWH, the missed (no-show) vis-its/appointments is one of the most commonly used methods for evaluating retention in HIV care. …

Facial Expression Processing In Autism Spectrum Disorder As A Function Of Alexithymia: An Eye Movement Study, Brian Escobar

Electronic Theses and Dissertations 2020 - Present

The perception and interpretation of faces provides individuals with a wealth of knowledge that enables them to navigate their social environments more successfully. Prior research has hypothesized that the decreased facial expression recognition (FER) abilities observed in autism spectrum disorder (ASD) may be better explained by comorbid alexithymia, the alexithymia hypothesis. The present study sought to further examine the alexithymia hypothesis by collecting data from 59 participants and examining FER performance and eye movement patterns for ASD and neurotypical (NT) individuals while controlling for alexithymia severity. Eye movement-related differences and similarities were examined via eye tracking in conjunction with statistical …

Persuasive Communication Systems: A Machine Learning Approach To Predict The Effect Of Linguistic Styles And Persuasion Techniques, Annye Braca, Pierpaolo Dondio

Articles

Prediction is a critical task in targeted online advertising, where predictions better than random guessing can translate to real economic return. This study aims to use machine learning (ML) methods to identify individuals who respond well to certain linguistic styles/persuasion techniques based on Aristotle’s means of persuasion, rhetorical devices, cognitive theories and Cialdini’s principles, given their psychometric profile.

Machine Learning For Electronic And Atomistic Simulations, Jun Yang

Dartmouth College Ph.D Dissertations

The demand for accurate and efficient atomistic simulations and electronic structure calculations in materials science and quantum chemistry has motivated the development of novel computational methodologies. The rapid evolution of machine learning has brought new techniques for advancing the accuracy, efficiency, and predictive power of atomistic simulations and electronic structure calculations.

In this thesis, we explore the symmetry requirements and physics intuitions needed for developing machine-learning interatomic potentials, which are the most critical component in atomistic simulations. Specifically, we introduce a novel physics-inspired graph neural network interatomic potential that enables accurate and efficient atomistic simulations of complex materials. The machine …

Binary Classification Of The Endocrine Disrupting Chemicals By Artificial Neural Networks, Moustafa Ali, Adam T Szafran, Fabio Stossi, Michael A Mancini, Efstratios N Pistikopoulos, Burcu Beykal

Faculty, Staff and Students Publications

We develop a machine learning framework that integrates high content/high throughput image analysis and artificial neural networks (ANNs) to model the separation between chemical compounds based on their estrogenic receptor activity. Natural and man-made chemicals have the potential to disrupt the endocrine system by interfering with hormone actions in people and wildlife. Although numerous studies have revealed new knowledge on the mechanism through which these compounds interfere with various hormone receptors, it is still a very challenging task to comprehensively evaluate the endocrine disrupting potential of all existing chemicals and their mixtures by pure

Machine Learning-Based Jet And Event Classification At The Electron-Ion Collider With Applications To Hadron Structure And Spin Physics, Kyle Lee, James Mulligan, Mateusz Płoskoń, Felix Ringer, Feng Yuan

Physics Faculty Publications

We explore machine learning-based jet and event identification at the future Electron-Ion Collider (EIC). We study the effectiveness of machine learning-based classifiers at relatively low EIC energies, focusing on (i) identifying the flavor of the jet and (ii) identifying the underlying hard process of the event. We propose applications of our machine learning-based jet identification in the key research areas at the future EIC and current Relativistic Heavy Ion Collider program, including enhancing constraints on (transverse momentum dependent) parton distribution functions, improving experimental access to transverse spin asymmetries, studying photon structure, and quantifying the modification of hadrons and jets in …

Optimizing Constraint Selection In A Design Verification Environment For Efficient Coverage Closure, Vanessa Cooper

CCAC Theses and Dissertations

No abstract provided.

Graph Neural Networks For Ethereum Fraud Detection, Charity Mwanza

Theses

Detecting fraudulent transactions on the Ethereum network can help cryptocurrency companies that operate on the Ethereum platform protect their users from exposure to fraudsters. The most common fraudulent activities in the cryptocurrency network include phishing and smart Ponzi schemes. Since cryptocurrency technology is still young, most investors lack knowledge of how the smart contacts used in the Ethereum platform operate; hence, they cannot evaluate the risks they are exposed to when carrying out cryptocurrency transactions. The key role that this paper looks at is the application of graph neural networks in the extraction of features of users in the Ethereum …

Leveraging Machine Learning To Analyze Sentiment From Covid-19 Tweets: A Global Perspective, Md Mahbubar Rahman, Nafiz Imtiaz Khan, Iqbal H. Sarker, Mohiuddin Ahmed, Muhammad Nazrul Islam

Research outputs 2022 to 2026

Since the advent of the worldwide COVID-19 pandemic, analyzing public sentiment has become one of the major concerns for policy and decision-makers. While the priority is to curb the spread of the virus, mass population (user) sentiment analysis is equally important. Though sentiment analysis using different state-of-the-art technologies has been focused on during the COVID-19 pandemic, the reasons behind the variations in public sentiment are yet to be explored. Moreover, how user sentiment varies due to the COVID-19 pandemic from a cross-country perspective has been less focused on. Therefore, the objectives of this study are: to identify the most effective …

Artificial Neural Networks: An Overview, Roheen Qamar, Baqar Ali Zardari

Mesopotamian Journal of Computer Science

Neural networks, also known as artificial neural networks (ANNs) or artificially generated neural networks (SNNs) are a subset of machine learning that provide the foundation of deep learning techniques. Their name and form are inspired by the human brain, and they replicate the way real neurons communicate with one another. Artificial neural networks (ANNs) are massively parallel systems comprised of a huge number of interconnected basic processors. This paper discuss about the artificial neural network and its basic types. This article explains the ANN and its basic outlines the fundamental neuron and the artificial computer model. It describes network structures …

Evolving Privacy Protections For Emerging Machine Learning Data Under Carpenter V. United States, Emily Nicolella

FIU Law Review

The Fourth Amendment’s third-party doctrine eliminates an individual’s reasonable expectation of privacy in information they willingly turn over to third parties. Government scrutiny of this information is not considered a search under the Fourth Amendment and is therefore not given constitutional protections. In the 2018 case Carpenter v. United States, the Supreme Court created an exception to the third-party doctrine. In Carpenter, a case involving the warrantless use of cell site location information (CSLI) in a criminal investigation, the Court held that individuals do have a reasonable expectation of privacy regarding CSLI. According to Chief Justice Roberts, despite the necessary …

Heart Disease Prediction Using Stacking Model With Balancing Techniques And Dimensionality Reduction, Ayesha Noor, Nadeem Javaid, Nabil Alrajeh, Babar Mansoor, Ali Khaqan, Safdar Hussain Bouk

School of Cybersecurity Faculty Publications

Heart disease is a serious worldwide health issue with wide-reaching effects. Since heart disease is one of the leading causes of mortality worldwide, early detection is crucial. Emerging technologies like Machine Learning (ML) are currently being actively used by the biomedical, healthcare, and health prediction industries. PaRSEL, a new stacking model is proposed in this research, that combines four classifiers, Passive Aggressive Classifier (PAC), Ridge Classifier (RC), Stochastic Gradient Descent Classifier (SGDC), and eXtreme Gradient Boosting (XGBoost), at the base layer, and LogitBoost is deployed for the final predictions at the meta layer. The imbalanced and irrelevant features in the …

Predictions Of The Dynamic Complex Modulus Of Non-Conventional Asphalt Concrete Using Machine Learning Techniques, Annie Benson

College of Graduate Studies: Theses & Dissertations

The complex dynamic modulus (|E*|) is a characterization property that defines the stiffness of an asphalt mixture. The dynamic modulus can be found through lab testing or predictions. Since lab testing can be time-consuming and expensive, the prediction method can be used as an alternative method. While a statistical method has been traditionally used for the |E*| prediction such as the Witczak’s predictive equations, machine learning (ML) is recently emerging as an alternative way that |E*| predictions can be made. This research attempted to predict the |E*| using several ML techniques including linear regression, support vector machines (SVM), decision trees, …

Mobilenetv1-Based Deep Learning Model For Accurate Brain Tumor Classification, Maad M. Mijwil, Ruchi Doshi, Kamal Kant Hiran, Omega John Unogwu, Indu Bala

Mesopotamian Journal of Computer Science

Brain tumors are among the most dangerous diseases that lead to mortality after a period of time from injury. Therefore, physicians and healthcare professionals are advised to make an early diagnosis of brain tumors and follow their instructions. Magnetic resonance imaging (MRI) is operated to provide sufficient and practical data in detecting brain tumors. Applications based on artificial intelligence contribute a very large role in disease detection, provide incredible accuracy and assist in creating the right decisions. In particular, deep learning models, which are a significant part of artificial intelligence, have the ability to diagnose and process medical image datasets. …

A Survey Of Using Machine Learning In Iot Security And The Challenges Faced By Researchers, Khawlah M. Harahsheh, Chung-Hao Chen

Electrical & Computer Engineering Faculty Publications

The Internet of Things (IoT) has become more popular in the last 15 years as it has significantly improved and gained control in multiple fields. We are nowadays surrounded by billions of IoT devices that directly integrate with our lives, some of them are at the center of our homes, and others control sensitive data such as military fields, healthcare, and datacenters, among others. This popularity makes factories and companies compete to produce and develop many types of those devices without caring about how secure they are. On the other hand, IoT is considered a good insecure environment for cyber …

Comparison Of Machine Learning Methods For Classification Of Alexithymia In Individuals With And Without Autism From Eye-Tracking Data, Furkan Iigin, Megan A. Witherow, Khan M. Iftekharuddin

Electrical & Computer Engineering Faculty Publications

Alexithymia describes a psychological state where individuals struggle with feeling and expressing their emotions. Individuals with alexithymia may also have a more difficult time understanding the emotions of others and may express atypical attention to the eyes when recognizing emotions. This is known to affect individuals with Autism Spectrum Disorder (ASD) differently than neurotypical (NT) individuals. Using a public data set of eye-tracking data from seventy individuals with and without autism who have been assessed for alexithymia, we train multiple traditional machine learning models for alexithymia classification including support vector machines, logistic regression, decision trees, random forest, and multilayer perceptron. …

Artificial Intelligence-Enabled Exploratory Cyber-Physical Safety Analyzer Framework For Civilian Urban Air Mobility, Md. Shirajum Munir, Sumit Howlader Dipro, Kamrul Hasan, Tariqul Islam, Sachin Shetty

VMASC Publications

Urban air mobility (UAM) has become a potential candidate for civilization for serving smart citizens, such as through delivery, surveillance, and air taxis. However, safety concerns have grown since commercial UAM uses a publicly available communication infrastructure that enhances the risk of jamming and spoofing attacks to steal or crash crafts in UAM. To protect commercial UAM from cyberattacks and theft, this work proposes an artificial intelligence (AI)-enabled exploratory cyber-physical safety analyzer framework. The proposed framework devises supervised learning-based AI schemes such as decision tree, random forests, logistic regression, K-nearest neighbors (KNN), and long short-term memory (LSTM) for predicting and …

Design Of Robust Blockchain-Envisioned Authenticated Key Management Mechanism For Smart Healthcare Applications, Siddhant Thapiyal, Mohammad Wazid, Devesh Pratap Singh, Ashok Kumar Das, Sachin Shetty

VMASC Publications

The healthcare sector is a very crucial and important sector of any society, and with the evolution of the various deployed technologies, like the Internet of Things (IoT), machine learning and blockchain it has numerous advantages. However, in this section, the data is much more vulnerable than others, because the data is strictly private and confidential, and it requires a highly secured framework for the transmission of data between entities. In this article, we aim to design a blockchain-envisioned authentication and key management mechanism for the IoMT-based smart healthcare applications (in short, we call it SBAKM-HS). We compare the various …

A Genome-Wide Association Study Coupled With Machine Learning Approaches To Identify Influential Demographic And Genomic Factors Underlying Parkinson’S Disease, Md Asad Rahman, Jinling Liu

Engineering Management and Systems Engineering Faculty Research & Creative Works

Background: Despite the recent success of genome-wide association studies (GWAS) in identifying 90 independent risk loci for Parkinson's disease (PD), the genomic underpinning of PD is still largely unknown. At the same time, accurate and reliable predictive models utilizing genomic or demographic features are desired in the clinic for predicting the risk of Parkinson's disease. Methods: To identify influential demographic and genomic factors associated with PD and to further develop predictive models, we utilized demographic data, incorporating 200 variables across 33,473 participants, along with genomic data involving 447,089 SNPs across 8,840 samples, both derived from the Fox Insight online study. …