Digital Commons Network^™

Tornado Outbreak False Alarm Probabilistic Forecasts With Machine Learning, Kirsten Reed Snodgrass

Theses and Dissertations

Tornadic outbreaks occur annually, causing fatalities and millions of dollars in damage. By improving forecasts, the public can be better equipped to act prior to an event. False alarms (FAs) can hinder the public’s ability (or willingness) to act. As such, a probabilistic FA forecasting scheme would be beneficial to improving public response to outbreaks.

Here, a machine learning approach is employed to predict FA likelihood from Storm Prediction Center (SPC) tornado outbreak forecasts. A database of hit and FA outbreak forecasts spanning 2010 – 2020 was developed using historical SPC convective outlooks and the SPC Storm Reports database. Weather …

Contributions To Auditory System Conduction Velocity: Insights With Multi-Modal Neuroimaging And Machine Learning In Children With Asd And Xyy Syndrome, Jeffrey I. Berman, Luke Bloy, Lisa Blaskey, Carissa R. Jackel, Judith S. Miller, Judith Ross, J. Christopher Edgar, Thimothy P.L. Roberts

Department of Pediatrics Faculty Papers

Introduction: The M50 electrophysiological auditory evoked response time can be measured at the superior temporal gyrus with magnetoencephalography (MEG) and its latency is related to the conduction velocity of auditory input passing from ear to auditory cortex. In children with autism spectrum disorder (ASD) and certain genetic disorders such as XYY syndrome, the auditory M50 latency has been observed to be elongated (slowed).

Methods: The goal of this study is to use neuroimaging (diffusion MR and GABA MRS) measures to predict auditory conduction velocity in typically developing (TD) children and children with autism ASD and XYY syndrome.

Results: Non-linear TD …

Towards A Reliable Machine Learning-Based Model Designed For Translating Sign Language Videos To Text, Maitha Essa Mohammad Ahli

Theses

Communication serves key roles in building relationships through sharing feelings, passing information, and connecting with others. Communication among the hearing impaired remains a significant stumbling block in today’s society since their communication means demands for an interpreter each moment. Various researchers agree that successful communication calls for the involvement of all individuals in a conversation and thus, deaf and hearing-impaired people require precise and welcoming communication to promote their working and learning relationships. Sign Language Recognition (SLR) is a critical and auspicious approach to promoting communication among hearing-impaired people. Sign languages greatly benefit from Machine Learning based translation techniques since …

Detection Of Crypto-Ransomware Attack Using Deep Learning, Muna Jemal

Master of Science in Computer Science Theses

The number one threat to the digital world is the exponential increase in ransomware attacks. Ransomware is malware that prevents victims from accessing their resources by locking or encrypting the data until a ransom is paid. With individuals and businesses growing dependencies on technology and the Internet, researchers in the cyber security field are looking for different measures to prevent malicious attackers from having a successful campaign. A new ransomware variant is being introduced daily, thus behavior-based analysis of detecting ransomware attacks is more effective than the traditional static analysis. This paper proposes a multi-variant classification to detect ransomware I/O …

Predicting Suicide Risk Among Youths Using Machine Learning Methods, Saswati Bhattacharjee

Master's Theses

Suicide is the second leading cause of death among youths in the USA. Although machine learning approaches have provided great potential for predicting suicide risk using survey data, prediction accuracy may not meet the need for clinical diagnosis due to the intrinsic characteristics of datasets. In this study, I perform a comparative study of six classification algorithms including naïve Bayes (NB), logistic regression (LR), multilayer perceptron (MLP), AdaBoost (Ada), random forest (RF), and bagging using YRBSS dataset and investigate the effectiveness of several data handling techniques to improve the overall performance of suicide risk prediction.

The dataset consists of 76 …

Artificial Intelligence Modelling To Assess The Risk Of Cardiovascular Disease In Oncology Patients., Samer S Al-Droubi, Eiman Jahangir, Karl M Kochendorfer, Marianna Krive, Michal Laufer-Perl, Dan Gilon, Tochukwu M Okwuosa, Christopher P Gans, Joshua H Arnold, Shakthi T Bhaskar, Hesham A Yasin, Jacob Krive

HPD Articles

AIMS: There are no comprehensive machine learning (ML) tools used by oncologists to assist with risk identification and referrals to cardio-oncology. This study applies ML algorithms to identify oncology patients at risk for cardiovascular disease for referrals to cardio-oncology and to generate risk scores to support quality of care.

METHODS AND RESULTS: De-identified patient data were obtained from Vanderbilt University Medical Center. Patients with breast, kidney, and B-cell lymphoma cancers were targeted. Additionally, the study included patients who received immunotherapy drugs for treatment of melanoma, lung cancer, or kidney cancer. Random forest (RF) and artificial neural network (ANN) ML models …

Pixel-Wise Machine Learning And Deep Learning Methods Implementation On Multi-Class Wildfire Mapping, Mingda Wu

Honors Capstones

Wildfires are destructive natural hazards. Artificial Intelligence (AI) has been a trendy topic in recent years due to its powerful applicability. This study focuses on the use of artificial intelligence (AI) in hazard management, specifically in the field of wildfire mapping. Machine learning and Deep learning are two subsets of AI. This study applied pixel-wise machine learning and deep learning methods to do multi-class mapping on two wildfire events in California, USA. The purpose of this research is to demonstrate the usefulness and advantages of using AI in the field of hazard management. The machine learning methods selected are Random …

Unobtrusive Data Collection In Clinical Settings For Advanced Patient Monitoring And Machine Learning, Walker Arce

Department of Electrical and Computer Engineering: Dissertations, Theses, and Student Research

When applying machine learning to clinical practice, a major hurdle that will be encountered is the lack of available data. While the data collected in clinical therapies is suitable for the types of analysis that are needed to measure and track clinical outcomes, it may not be suitable for other types of analysis. For instance, video data may have poor alignment with behavioral data, making it impossible to extract the videos frames that directly correlate with the observed behavior. Alternatively, clinicians may be exploring new data modalities, such as physiological signal collection, to research methods of improving clinical outcomes that …

Detecting Medication-Taking Gestures Using Machine Learning And Accelerometer Data Collected Via Smartwatch Technology: Instrument Validation Study, Chrisogonas Odero Odhiambo, Lukacs Ablonczy, Pamela J. Wright, Cynthia F. Corbett

Publications

BACKGROUND: Medication adherence is a global public health challenge, as only approximately 50% of people adhere to their medication regimens. Medication reminders have shown promising results in terms of promoting medication adherence. However, practical mechanisms to determine whether a medication has been taken or not, once people are reminded, remain elusive. Emerging smartwatch technology may more objectively, unobtrusively, and automatically detect medication taking than currently available methods. OBJECTIVE: This study aimed to examine the feasibility of detecting natural medication-taking gestures using smartwatches. METHODS: A convenience sample (N=28) was recruited using the snowball sampling method. During data collection, each participant recorded …

Dense & Attention Convolutional Neural Networks For Toe Walking Recognition, Junde Chen, Rahul Soangra, Marybeth Grant-Beuttler, Y. A. Nanehkaran, Yuxin Wen

Physical Therapy Faculty Articles and Research

Idiopathic toe walking (ITW) is a gait disorder where children’s initial contacts show limited or no heel touch during the gait cycle. Toe walking can lead to poor balance, increased risk of falling or tripping, leg pain, and stunted growth in children. Early detection and identification can facilitate targeted interventions for children diagnosed with ITW. This study proposes a new one-dimensional (1D) Dense & Attention convolutional network architecture, which is termed as the DANet, to detect idiopathic toe walking. The dense block is integrated into the network to maximize information transfer and avoid missed features. Further, the attention modules are …

Acat 2.0: An Ai Transformer-Based Approach To Predictive Speech Generation, Kairan Quazi

Computer Science and Engineering Senior Theses

While constituting a rare family of diseases that afflicts 268,000 people worldwide, motor neuron diseases carry a high fatality rate with one-third of people dying within a year of diagnosis and 50% of people dying within two years (MND Association, 2022). MNDs rapidly and progressively impair muscle movement, making everyday activities like walking, chewing, and speaking almost impossible. In collaboration with famed physicist Dr. Stephen Hawking, Intel Labs developed an assistive communications platform known as ACAT to simulate speech and facilitate electronic tasks. However, the original ACAT can be slow to use, leading to awkward pauses in conversations. This paper …

Context-Aware Gaze-Based Interface For Smart Wheelchair, Tien Pham

Computer Science and Engineering Theses - Archive

Human-Computer Interfaces (HCI) is an essential aspect of modern technology that has revolutionized the way we interact with machines. With the revolution of computers and smart devices and the advent of autonomous vehicles and other machines, there has been a significant advancement in this area that brings convenience to users to interact with technology intuitively and efficiently. However, the importance of HCI goes beyond the convenience of everyday technology. It has become crucial in the development of assistive technologies that empower people with disabilities to live more independently. Person with disabilities, who lack control of one or more parts of …

Toward Digital Phenotyping: Human Activity Representation For Embodied Cognition Assessment, Mohammad Zakizadehghariehali

Computer Science and Engineering Dissertations - Archive

Cognition is the mental process of acquiring knowledge and understanding through thought, experience and senses. Based on Embodied Cognition theory, physical activities are an important manifestation of cognitive functions. As a result, they can be employed to both assess and train cognitive skills. In order to assess various cognitive measures, the ATEC system has been proposed. It consists of physical exercises with different variations and difficulty levels, designed to provide assessment of executive and motor functions. This thesis focuses on obtaining human activity representation from recorded videos of ATEC tasks in order to automatically assess embodied cognition performance. Representation learning …

A Survey On Digital Agriculture In Five West African Countries, Degila Jules, Ida Sèmévo Tognisse, Anne-Carole Honfoga, Sèton Calmette Ariane Houetohossou, Fréjus Ariel Kpedetin Sodedji, Souand Peace Gloria Tahi, Achille Ephrem Assogbadjo

AGriDI

This study focuses on agriculture, which is the main source of economic growth in many West African countries. In recent years, conventional agriculture has undergone a remarkable evolution and digital technologies are widely used for different purposes. While the world is rapidly using advanced digital technologies to grow their agriculture, Africa seems to be lagging behind, especially West Africa. To know how to contribute effectively, it is important to know what is being performed about this issue. The objective of this study is to examine the state of digital agriculture in five countries, namely, Benin, Burkina Faso, Côte d’Ivoire, Ghana, …

Automated Detection And Prediction Of Electrical Disturbances In A Power Transmission System, Jonathan Boyd

Masters Theses and Doctoral Dissertations

As power quality becomes a higher priority in the electric utility industry, utilities simply do not have the required personnel to analyze the ever-growing amount of data by hand. This thesis presents an automated approach for the analysis of power quality phenomena within a power transmission system by leveraging rule-based analytics as well as machine learning to analyze the characteristics of the recorded data. Waveform signatures analyzed within this thesis include: various faults, motor starting, and incipient instrument transformer failure. The developed analytics were tested on 160 waveform files and yielded an average accuracy of 99%. Machine learning techniques are …

A Comprehensive Review On Machine Learning In Healthcare Industry: Classification, Restrictions, Opportunities And Challenges, Qi An, Saifur Rahman, Jingwen Zhou, James Jin Kang

Research outputs 2022 to 2026

Recently, various sophisticated methods, including machine learning and artificial intelligence, have been employed to examine health-related data. Medical professionals are acquiring enhanced diagnostic and treatment abilities by utilizing machine learning applications in the healthcare domain. Medical data have been used by many researchers to detect diseases and identify patterns. In the current literature, there are very few studies that address machine learning algorithms to improve healthcare data accuracy and efficiency. We examined the effectiveness of machine learning algorithms in improving time series healthcare metrics for heart rate data transmission (accuracy and efficiency). In this paper, we reviewed several machine learning …

Development And Evaluation Of Machine Learning Models For Fugitive Methane Detection And Intensity Prediction, Jacquan Pollard

All Theses

The environmental impacts of global warming driven by fugitive methane (CH4) emissions have catalyzed significant research initiatives in developing novel technologies that enable proactive and rapid detection of CH4 emissions. This study evaluated the performance of data-driven machine learning (ML) models using support vector machines (SVM) to detect the presence of trace CH4 emissions and the corresponding intensity amongst various meteorological conditions. The author used simulation data comprising various meteorological parameters such as temperature, relative humidity, wind speed, water vapor, pressure, precipitation rate, and a parameter possessing trace concentrations of CH4 emissions. The novelty of the SVM models developed in …

Open Source Intelligence For Cybersecurity Events Via Twitter Data, Dakota Dale

Graduate Theses and Dissertations

Open-Source Intelligence (OSINT) is largely regarded as a necessary component for cybersecurity intelligence gathering to secure network systems. With the advancement of artificial intelligence (AI) and increasing usage of social media, like Twitter, we have a unique opportunity to obtain and aggregate information from social media. In this study, we propose an AI-based scheme capable of automatically pulling information from Twitter, filtering out security-irrelevant tweets, performing natural language analysis to correlate the tweets about each cybersecurity event (e.g., a malware campaign), and validating the information. This scheme has many applications, such as providing a means for security operators to gain …

Machine Learning Based Framework For Smart Contract Vulnerability Detection In Ethereum Blockchain, Qusai Omar Mustafa Hasan

Theses

Abstract

Blockchain technology is a disruptive technology that revolutionized digital payments and transactions of digital assets. Blockchain transactions operate using smart contracts which are automated software code that facilitates transactions between parties without the need for intermediary systems. Smart contracts have become an increasingly popular means of conducting transactions and executing code in a decentralized manner. As it can be written in various languages which have their flaws in terms of logic and vulnerabilities, also the immutability and autonomy of smart contracts also make them vulnerable to various security threats. Security for smart contracts is essential as exploiting bad logic …

Deep Learning Based Predictive Models For Performance Enhancement In Bio Medical Applications, Sajila Dimagi Wickramaratne

Doctoral Dissertations

The medical field produces large quantities of multidimensional, complex, and often unstructured data. The timely interpretation of medical data is crucial for medical diagnostics. Machine learning applications are increasingly becoming popular with the ever-increasing computational ability and data availability. Machine learning methods can potentially find complicated relationships that may not be apparent in traditional clinical decision rule systems used in medical applications. Models deployed in medical applications need to be reliable, time efficient, and easy to interpret, thus making working with biosignals challenging in some cases.

The first part of the dissertation focuses on building predictive systems using existing data …

Automated Segmentation Of Autofluorescence Lifetime Images Using A Convolutional Neural Network, Nicole Hooten

Biomedical Engineering Undergraduate Honors Theses

Chronic wounds affect nearly 1 out of every 50 people in the United States, decreasing quality of life and putting people with potential comorbidities at high risk for obtaining an infection. Wound healing progress in clinical settings is measured by tracking wound size and there are currently no non-invasive, quantitative measurement techniques. Previous studies have proposed nicotinamide adenine dinucleotide (NADH) autofluorescence lifetime (FLIM) imaging of the wound edge as a method to quantify the wound healing process by connecting the role of NADH in cellular metabolism to wound healing stage. However, evaluation of FLIM images is heavily subjective, as a …

Investigation The Plugging Behavior Of Virus Filters, Iman Abdulqader Saab

Graduate Theses and Dissertations

A virus filtration step is integral to the manufacturing of biopharmaceuticals to ensure viral safety. A virus filter is a single-use device that uses a size-based separation process and has a unique structure with minimal defects, so that contaminating virus particles cannot pass through the membrane pores, while therapeutic molecules can. The development of novel antibodies (Abs), including significant increases in product titers, is frequently challenged by virus filter fouling, making a better understanding of the underlying mechanisms essential. This thesis focused investigating of the effect of prefilter types, buffer type and salt content on virus filtration performance. The impact …

Effectiveness Variation Of Different Census Outreach Activities: An Empirical Analysis From The State Of Illinois Using Machine Learning And User Interface Technologies For Participatory Data Collection, Anton Rozhkov, Ahoura Zandiatashbar, Dean Massey, Jaeyong Shin, Janet Smith, Moira Zellner

Faculty Research, Scholarly, and Creative Activity

Major social changes stem from policies that their developments heavily rely on public participation in the decennial U.S. Census. For individual states, the population counts determine representation in the U.S. House of Representatives and the amount of federal funding each state receives, leading to state outreach efforts to encourage participation in the census. The present empirical study uses advanced data collection technologies based on a user interface (UI) and machine learning techniques (k-means clustering, random forest, gradient boosting) to analyze the effectiveness of the outreach activities organized by the State of Illinois. As a result, we assess various types of …

A Machine Learning Specklegram Wavemeter (Maswave) Based On A Short Section Of Multimode Fiber As The Dispersive Element, Ogbole C. Inalegwu, Rex E. Gerald, Jie Huang

Electrical and Computer Engineering Faculty Research & Creative Works

Wavemeters are very important for precise and accurate measurements of both pulses and continuous-wave optical sources. Conventional wavemeters employ gratings, prisms, and other wavelength-sensitive devices in their design. Here, we report a simple and low-cost wavemeter based on a section of multimode fiber (MMF). The concept is to correlate the multimodal interference pattern (i.e., speckle patterns or specklegrams) at the end face of an MMF with the wavelength of the input light source. Through a series of experiments, specklegrams from the end face of an MMF as captured by a CCD camera (acting as a low-cost interrogation unit) were analyzed …

Demand Prediction And Inventory Management Of Surgical Supplies, Rajon Paul Pantha

Graduate Theses and Dissertations

Effective supply chain management is critical to operations in various industries, including healthcare. Demand prediction and inventory management are essential parts of healthcare supply chain management for ensuring optimal patient outcomes, controlling costs, and minimizing waste. The advances in data analytics and technology have enabled many sophisticated approaches to demand forecasting and inventory control. This study aims to leverage these advancements to accurately predict demand and manage the inventory of surgical supplies to reduce costs and provide better services to patients. In order to achieve this objective, a Long Short-Term Memory (LSTM) model is developed to predict the demand for …

Quantum Chemistry–Machine Learning Approach For Predicting Properties Of Lewis Acid–Lewis Base Adducts, Hieu Huynh, Thomas J. Kelly, Linh Vu, Tung Hoang, Phuc An Nguyen, Tu C. Le, Emily Jarvis, Hung Phan

Chemistry and Biochemistry Faculty Works

Synthetic design allowing predictive control of charge transfer and other optoelectronic properties of Lewis acid adducts remains elusive. This challenge must be addressed through complementary methods combining experimental with computational insights from first principles. Ab initio calculations for optoelectronic properties can be computationally expensive and less straightforward than those sufficient for simple ground-state properties, especially for adducts of large conjugated molecules and Lewis acids. In this contribution, we show that machine learning (ML) can accurately predict density functional theory (DFT)-calculated charge transfer and even properties associated with excited states of adducts from readily obtained molecular descriptors. Seven ML models, built …

Self-Supervised Representation Learning For Motion Time Series: A Case Study In Activity Recognition, Luis Carlos Garza Perez

Theses and Dissertations

In this thesis we will learn about what contrastive learning and time series are and understand the differences between supervised and self-supervised frameworks in machine learning. In addition, we will describe how the newest and most efficient self-supervised learning framework for visual representations to this date works, called SimCLR, which was originally developed to obtain useful vector representations from static images. We will also explain what TS2Vec is, and how a combination of both approaches can be applied to the concept of a time series, and still be able to extract a vector representation of the subject described by the …

Hardware Accelerators And Their Use In Computer Vision, Cameron Villone

Theses

The evolution of technology is impressive. Before digital, there was analog; before software, there needed hardware. This evolution is natural as we try and optimize technology for our needs. The shift to digital was fueled by the space saved from using digital systems compared to analog. When it came to software, the ability to use generic hardware in the forms of cen- tral processing units; CPUs, Graphics Processing Units; GPUs, and Random Access Memory; RAM allowed for complex software solutions to be able to run on many different devices with- out much need for translations. With software development getting so …

Quantum Acceleration Of Linear Regression For Artificial Neural Networks, Martin A. Hoffnagle

Theses

Through proofs and small scale implementations, quantum computing has shown potential to provide significant speedups in certain applications such as searches and matrix calculations. Recent library developments have introduced the concept of hybrid quantum-classical compute models where quantum processor units could be used as additional hardware accelerators by classical computers. While these developments have opened the prospect of applying quantum computing to machine learning tasks, there are still many limitations of near and midterm quantum computing. If implemented carefully, the advantages of quantum algorithms could be used to accelerate current machine learning models. In this work, a hybrid quantum-classical model …

Toward Accelerometer Recording Of Pit Viper Foraging Behavior In Nature: Validation And Case Study With Timber Rattlesnakes (Crotalus Horridus), Morgan L. Thompson

Biology Theses

Accelerometer dataloggers are becoming increasingly common in field studies of animal behavior. Among the most difficult study subjects, and consequently, the most ideal candidates for accelerometer applications, are those for which simultaneous observation of key fitness-determining behaviors, such as foraging, across a sample of individuals in the wild is traditionally impossible or restricted to the use of proxy measures. This is the case for many solitary vertebrate predators, such as all pit vipers (Viperidae; Crotalinae). Large-bodied pit vipers are ambush (sit-and-wait) specialists that represent uniquely challenging and intriguing study subjects in predator ecology. Unlike many comparable avian or mammalian terrestrial …