Digital Commons Network^™

Machine Learning For Ecg Diagnosis And Risk Stratification Of Occlusion Myocardial Infarction, Salah S Al-Zaiti, Christian Martin-Gill, Jessica K Zègre-Hemsey, Zeineb Bouzid, Ziad Faramand, Mohammad O Alrawashdeh, Richard E Gregg, Stephanie Helman, Nathan T Riek, Karina Kraevsky-Phillips, Gilles Clermont, Murat Akcakaya, Susan M Sereika, Peter Van Dam, Stephen W Smith, Yochai Birnbaum, Samir Saba, Ervin Sejdic, Clifton W Callaway

Faculty, Staff and Students Publications

Patients with occlusion myocardial infarction (OMI) and no ST-elevation on presenting electrocardiogram (ECG) are increasing in numbers. These patients have a poor prognosis and would benefit from immediate reperfusion therapy, but, currently, there are no accurate tools to identify them during initial triage. Here we report, to our knowledge, the first observational cohort study to develop machine learning models for the ECG diagnosis of OMI. Using 7,313 consecutive patients from multiple clinical sites, we derived and externally validated an intelligent model that outperformed practicing clinicians and other widely used commercial interpretation systems, substantially boosting both precision and sensitivity. Our derived …

Dos/Ddos-Mqtt-Iot: A Dataset For Evaluating Intrusions In Iot Networks Using The Mqtt Protocol, Alaa Alatram, Leslie F. Sikos, Mike Johnstone, Patryk Szewczyk, James Jin Kang

Research outputs 2022 to 2026

Adversaries may exploit a range of vulnerabilities in Internet of Things (IoT) environments. These vulnerabilities are typically exploited to carry out attacks, such as denial-of-service (DoS) attacks, either against the IoT devices themselves, or using the devices to perform the attacks. These attacks are often successful due to the nature of the protocols used in the IoT. One popular protocol used for machine-to-machine IoT communications is the Message Queueing Telemetry Protocol (MQTT). Countermeasures for attacks against MQTT include testing defenses with existing datasets. However, there is a lack of real-world test datasets in this area. For this reason, this paper …

System-Characterized Artificial Intelligence Approaches For Cardiac Cellular Systems And Molecular Signature Analysis, Ziqian Wu

Dartmouth College Ph.D Dissertations

The dissertation presents a significant advancement in the field of cardiac cellular systems and molecular signature systems by employing machine learning and generative artificial intelligence techniques. These methodologies are systematically characterized and applied to address critical challenges in these domains. A novel computational model is developed, which combines machine learning tools and multi-physics models. The main objective of this model is to accurately predict complex cellular dynamics, taking into account the intricate interactions within the cardiac cellular system. Furthermore, a comprehensive framework based on generative adversarial networks (GANs) is proposed. This framework is designed to generate synthetic data that faithfully …

Continuous Time Dynamic Mode Decomposition In The Presence Of Partial Knowledge And Streaming Data, Efrain H. Gonzalez

USF Tampa Graduate Theses and Dissertations

Dynamic mode decomposition (DMD) is a physically interpretable data driven technique for modeling dynamical systems. The DMD method uses time series data where each data point is referred to as a snapshot and represents the value of the state at a particular moment in time. The important patterns found in the data are extracted and then used to create a model for the system. Once the important pieces have been obtained reduced order modeling can be used to reduce the complexity of the model.

But, DMD has evolved since its inception. After its association with the Koopman operator, DMD has …

Probing As A Technique To Understand Abstract Spaces, Ashlen A. Plasek

Scholarly Horizons: University of Minnesota, Morris Undergraduate Journal

Machine learning models, while very powerful, have their operation obfuscated behind millions of parameters. This obfuscation can make deriving a human meaningful process from a machine learning model very difficult. However, while the intermediate states of a machine learning model are similarly obfuscated, using probing, we can start to explore looking at possible structure in those intermediate states. Large language models are a prime example of this obfuscation, and probing can begin to allow novel experimentation to be performed.

Assessment Of E-Senses Performance Through Machine Learning Models For Colombian Herbal Teas Classification, Jeniffer Katerine Carrillo, Cristhian Manuel Durán, Juan Martin Cáceres, Carlos Alberto Cuastumal, Jordana Ferreira, José Ramos, Brian Bahder, Martin Oates, Antonio Ruiz

CCE Faculty Articles

This paper describes different E-Senses systems, such as Electronic Nose, Electronic Tongue, and Electronic Eyes, which were used to build several machine learning models and assess their performance in classifying a variety of Colombian herbal tea brands such as Albahaca, Frutos Verdes, Jaibel, Toronjil, and Toute. To do this, a set of Colombian herbal tea samples were previously acquired from the instruments and processed through multivariate data analysis techniques (principal component analysis and linear discriminant analysis) to feed the support vector machine, K-nearest neighbors, decision trees, naive Bayes, and random forests algorithms. The results of the E-Senses were validated using …

Numerical Simulation Of The Korteweg–De Vries Equation With Machine Learning, Kristina O. F. Williams, Benjamin F. Akers

Faculty Publications

A machine learning procedure is proposed to create numerical schemes for solutions of nonlinear wave equations on coarse grids. This method trains stencil weights of a discretization of the equation, with the truncation error of the scheme as the objective function for training. The method uses centered finite differences to initialize the optimization routine and a second-order implicit-explicit time solver as a framework. Symmetry conditions are enforced on the learned operator to ensure a stable method. The procedure is applied to the Korteweg–de Vries equation. It is observed to be more accurate than finite difference or spectral methods on coarse …

Enhancing Authentication In Online Distant Exams: A Proposed Method Utilizing Face And Voice Recognition, Ahmed T. Resha, Mohammed A. Azize A. Razek, Eslam E. El Maghraby

Information Sciences Letters

Due to COVID-19 pandemic, face-to-face teaching has been replaced by online education to reduce the risks of spreading the Coronavirus. Online examination is an important asset in the context of online learning to assess students, but observing students during testing and ensuring that they do not engage in misbehavior remains a major issue. Human observation is one of the most common methods when conducting an exam to ensure that students do not perform any unexpected behaviors, by entering the student in a laboratory or hall at the university and observing him throughout the exam period visually and soundly. However, this …

Machine Learning Data Feature Reduction And Model Optimization, Francisco P. Maturana, Phillip M. Lacasse

AFIT Patents

For machine learning data reduction and model optimization, a method randomly assigns each data feature of a training data set to a plurality of solution groups. Each solution group has no more than a solution group number k of data features and each data feature is assigned to a plurality of solution groups. The method identifies each solution group as a high-quality solution group or a low-quality solution group. The method further calculates data feature scores for each data feature comprising a high bin number and a low bin number. The method determines level data for each data feature from …

Towards An Experimental Bibliography Of Hemispheric Reconstruction Newspapers, Joshua Ortiz Baco, Benjamin Charles Germain Lee, Jim Casey, Sarah H. Salter

Criticism

Digital collections of newspapers have drawn broader attention to the fragmented and scattered print histories of minoritized communities. Attempts to survey these histories through bibliography, however, quickly meet with a fundamental problem: the practice of bibliographic description calls for creating a static record of social affiliations. Given the overwhelming scholarly consensus that categories such as race, ethnicity, and language are socially constructed, this article introduces an experimental bibliographic method for mapping the vast landscape of historical newspapers. This method extends the machine learning affordances of a recent project called Newspaper Navigator to enumerate the newspapers in Chronicling America according to …

Quantifying Implicit Bias In Judicial Legal Opinions: A Natural Language Processing Approach, Philip N. Surendran

Quantitative Social Science Undergraduate Senior Theses

Implicit bias and criminal justice are two concepts that have long been intertwined. There is no justice without neutrality, and yet how do we tell if the actors enforcing the system are actually impartial? In this thesis, I utilize recent advancements in machine learning to attempt to answer this question. Specifically, I use natural language processing to examine the text of opinions written by judges in appellate courts, and I leverage these findings to build quantifiable measures of implicit bias. In particular, I look at the over/under-representation of certain emotions, sentiments and linguistic styles as a proxy for disparate treatment …

Sarcasm Detection In English And Arabic Tweets Using Transformer Models, Rishik Lad

Computer Science Senior Theses

This thesis describes our approach toward the detection of sarcasm and its various types in English and Arabic Tweets through methods in deep learning. There are five problems we attempted: (1) detection of sarcasm in English Tweets, (2) detection of sarcasm in Arabic Tweets, (3) determining the type of sarcastic speech subcategory for English Tweets, (4) determining which of two semantically equivalent English Tweets is sarcastic, and (5) determining which of two semantically equivalent Arabic Tweets is sarcastic. All tasks were framed as classification problems, and our contributions are threefold: (a) we developed an English binary classifier system with RoBERTa, …

Machine Learning Reveals Lipidome Remodeling Dynamics In A Mouse Model Of Ovarian Cancer, Olatomiwa O Bifarin, Samyukta Sah, David A Gaul, Samuel G Moore, Ruihong Chen, Murugesan Palaniappan, Jaeyeon Kim, Martin M Matzuk, Facundo M Fernández

Faculty, Staff and Students Publications

Ovarian cancer (OC) is one of the deadliest cancers affecting the female reproductive system. It may present little or no symptoms at the early stages and typically unspecific symptoms at later stages. High-grade serous ovarian cancer (HGSC) is the subtype responsible for most ovarian cancer deaths. However, very little is known about the metabolic course of this disease, particularly in its early stages. In this longitudinal study, we examined the temporal course of serum lipidome changes using a robust HGSC mouse model and machine learning data analysis. Early progression of HGSC was marked by increased levels of phosphatidylcholines and phosphatidylethanolamines. …

An Investigation Into Machine Learning Techniques For Designing Dynamic Difficulty Agents In Real-Time Games, Ryan Adare Dunagan

Electronic Theses and Dissertations

Video games are an incredibly popular pastime enjoyed by people of all ages world wide. Many different kinds of games exist, but most games feature some elements of the player overcoming some challenge, usually through gameplay. These challenges are insurmountable for some people and may turn them off to video games as a pastime. Games can be made more accessible to players of little skill and/or experience through the use of Dynamic Difficulty Adjustment (DDA) systems that adjust the difficulty of the game in response to the player’s performance. This research seeks to establish the effectiveness of machine learning techniques …

Why Softmax? Because It Is The Only Consistent Approach To Probability-Based Classification, Anatole Lokshin, Vladik Kreinovich

Departmental Technical Reports (CS)

In many practical problems, the most effective classification techniques are based on deep learning. In this approach, once the neural network generates values corresponding to different classes, these values are transformed into probabilities by using the softmax formula. Researchers tried other transformation, but they did not work as well as softmax. A natural question is: why is softmax so effective? In this paper, we provide a possible explanation for this effectiveness: namely, we prove that softmax is the only consistent approach to probability-based classification. In precise terms, it is the only approach for which two reasonable probability-based ideas -- Least …

Predicting Hiv Vaccine-Mediated Protection Level To Identify Immune Correlates Using Positive Unlabeled Learning, Shiwei Xu

Dartmouth College Ph.D Dissertations

The development of a vaccine for Human Immunodeficiency Virus type 1 (HIV-1) is a crucial step in preventing the global spread of AIDS. To ensure the progress and effectiveness of this vaccine, it is essential to establish efficient biotechnology platforms and data mining methods. These methods would help identify immune characteristics that distinguish individuals with varying levels of vaccine-induced protection and determine the underlying factors that contribute to protection against HIV acquisition. While previous studies have focused on identifying immune markers associated with infection outcomes among vaccinated patients, it is important to acknowledge the limitations of traditional case-control analytical protocols …

Learning The Game: Implementations Of Convolutional Networks In Automated Strategy Identification, Cameron Klig

Master's Theses

Games can be used to represent a wide variety of real world problems, giving rise to many applications of game theory. Various computational methods have been proposed for identifying game strategies, including optimized tree search algorithms, game-specific heuristics, and artificial intelligence. In the last decade, systems like AlphaGo and AlphaZero have significantly exceeded the performance of the best human players in Chess, Go, and other games. The most effective game engines to date employ convolutional neural networks (CNNs) to evaluate game boards, extract features, and predict the optimal next move. These engines are trained on billions of simulated games, wherein …

A Survey On Security Analysis Of Machine Learning-Oriented Hardware And Software Intellectual Property, Ashraful Tauhid, Lei Xu, Mostafizur Rahman, Emmett Tomai

Computer Science Faculty Publications

Intellectual Property (IP) includes ideas, innovations, methodologies, works of authorship (viz., literary and artistic works), emblems, brands, images, etc. This property is intangible since it is pertinent to the human intellect. Therefore, IP entities are indisputably vulnerable to infringements and modifications without the owner’s consent. IP protection regulations have been deployed and are still in practice, including patents, copyrights, contracts, trademarks, trade secrets, etc., to address these challenges. Unfortunately, these protections are insufficient to keep IP entities from being changed or stolen without permission. As for this, some IPs require hardware IP protection mechanisms, and others require software …

Artificial Intelligence Cad Tools In Trauma Imaging: A Scoping Review From The American Society Of Emergency Radiology (Aser) Ai/Ml Expert Panel, David Dreizin, Pedro V Staziaki, Garvit D Khatri, Nicholas M Beckmann, Zhaoyong Feng, Yuanyuan Liang, Zachary S Delproposto, Maximiliano Klug, J Stephen Spann, Nathan Sarkar, Yunting Fu

Faculty, Staff and Student Publications

BACKGROUND: AI/ML CAD tools can potentially improve outcomes in the high-stakes, high-volume model of trauma radiology. No prior scoping review has been undertaken to comprehensively assess tools in this subspecialty.

PURPOSE: To map the evolution and current state of trauma radiology CAD tools along key dimensions of technology readiness.

METHODS: Following a search of databases, abstract screening, and full-text document review, CAD tool maturity was charted using elements of data curation, performance validation, outcomes research, explainability, user acceptance, and funding patterns. Descriptive statistics were used to illustrate key trends.

RESULTS: A total of 4052 records were screened, and 233 full-text …

Experimental And Machine Learning Studies On Chitosan-Polyacrylamide Copolymers For Selective Separation Of Metal Sulfides In The Froth Flotation Process, Keitumetse Monyake, Taihao Han, Danish Ali, Lana Z. Alagha, Aditya Kumar

Mining Engineering Faculty Research & Creative Works

The froth flotation process is extensively used for the selective separation of valuable base metal sulfides from uneconomic associated minerals. However, in this complex multiphase process, various parameters need to be optimized to ensure separation selectivity and peak performance. In this study, two machine learning (ML) models, artificial neural network (ANN) and random forests (RF), were used to predict the efficiency of in-house synthesized chitosan-polyacrylamide copolymers (C-PAMs) in the depression of iron sulfide minerals (i.e., pyrite) while valuable base metal sulfides (i.e., galena and chalcopyrite) were floated using nine flotation variables as inputs to the models. The prediction performance of …

Statistical And Biological Analyses Of Acoustic Signals In Estrildid Finches, Moises Rivera

Dissertations, Theses, and Capstone Projects

Acoustic communication is a process that involves auditory perception and signal processing. Discrimination and recognition further require cognitive processes and supporting mechanisms in order to successfully identify and appropriately respond to signal senders. Although acoustic communication is common across birds, classical research has largely disregarded the perceptual abilities of perinatal altricial taxa. Chapter 1 reviews the literature of perinatal acoustic stimulation in birds, highlighting the disproportionate focus on precocial birds (e.g., chickens, ducks, quails). The long-held belief that altricial birds were incapable of acoustic perception in ovo was only recently overturned, as researchers began to find behavioral and physiological evidence …

Automatic Scoring Of Speeded Interpersonal Assessment Center Exercises Via Machine Learning: Initial Psychometric Evidence And Practical Guidelines, Louis Hickman, Christoph N. Herde, Filip Lievens, Louis Tay

Research Collection Lee Kong Chian School Of Business

Assessment center (AC) exercises such as role-plays have established themselves as valuable approaches for obtaining insights into interpersonal behavior, but they are often considered the “Rolls Royce” of personnel assessment due to their high costs. The observation and rating process comprises a substantial part of these costs. In an exploratory case study, we capitalize on recent advances in natural language processing (NLP) by developing NLP-based machine learning (ML) models to investigate the possibility of automatically scoring AC exercises. First, we compared the convergent-related validity and contamination with word count of ML scores based on models that used different NLP methods …

Continuum Modeling Of Active Nematics Via Data-Driven Equation Discovery, Connor Robertson

Dissertations

Data-driven modeling seeks to extract a parsimonious model for a physical system directly from measurement data. One of the most interpretable of these methods is Sparse Identification of Nonlinear Dynamics (SINDy), which selects a relatively sparse linear combination of model terms from a large set of (possibly nonlinear) candidates via optimization. This technique has shown promise for synthetic data generated by numerical simulations but the application of the techniques to real data is less developed. This dissertation applies SINDy to video data from a bio-inspired system of mictrotubule-motor protein assemblies, an example of nonequilibrium dynamics that has posed a significant …

Connecting Linguistic Expressions And Pain Relief Through Transformer Model Construction And Analysis, Sarah M. Chacko

Computer Science Senior Theses

Chronic pain is a widespread problem that significantly impacts quality of life. Overprescription and abuse of pain medication continues to be a major public health issue and can further burden patients due to a fragmented health care system. Previous research has suggested a possible psychological basis to pain and the potential for safer, non-pharmacological alternatives for pain relief. This project leverages language models to study chronic pain development and relief through psychological treatments, which will be assessed through responses to post-treatment interviews. A transformer-based natural language processing model is employed to identify connections between language expressions and pain on a …

Investigating English-Language Dialect-Adjusted Models, Samiha Datta

Computer Science Senior Theses

This thesis describes several approaches to better understand how large language models interpret different dialects of the English language. Our goal is to consider multiple contexts of textual data and to analyze how English-language dialects are realized in them, as well as how a variety of machine learning techniques handle these differences. We focus on two genres of text data: news and social media. In the news context, we establish a dataset covering news articles from five countries and four US states and consider language modeling analysis, topic and sentiment distributions, and manual analysis before performing nine experiments and evaluating …

Data-Optimized Spatial Field Predictions For Robotic Adaptive Sampling: A Gaussian Process Approach, Zachary Nathan

Computer Science Senior Theses

We introduce a framework that combines Gaussian Process models, robotic sensor measurements, and sampling data to predict spatial fields. In this context, a spatial field refers to the distribution of a variable throughout a specific area, such as temperature or pH variations over the surface of a lake. Whereas existing methods tend to analyze only the particular field(s) of interest, our approach optimizes predictions through the effective use of all available data. We validated our framework on several datasets, showing that errors can decline by up to two-thirds through the inclusion of additional colocated measurements. In support of adaptive sampling, …

Deep Learning For Skin Photoaging, Gokul Srinivasan

Computer Science Senior Theses

Skin photoaging is the premature aging of skin that results from ultraviolet light exposure. It is a major risk factor for the development of skin cancer, among other malignant skin pathologies. Accordingly, understanding its etiology is important for both preventative and reparative clinical action. In this study, skin samples obtained from patients with ranging solar elastosis grades – a proxy for skin photoaging – were sequenced using next-generation sequencing techniques to further understand the genomic, epigenomic, and histological signs and signals of skin photoaging. The results of this study suggest that tissues with severe photoaging exhibit increases in the frequency …

Towards Generalizable Machine Learning Models For Computer-Aided Diagnosis In Medicine, Yiyang Wang

College of Computing and Digital Media Dissertations

Hidden stratification represents a phenomenon in which a training dataset contains unlabeled (hidden) subsets of cases that may affect machine learning model performance. Machine learning models that ignore the hidden stratification phenomenon--despite promising overall performance measured as accuracy and sensitivity--often fail at predicting the low prevalence cases, but those cases remain important. In the medical domain, patients with diseases are often less common than healthy patients, and a misdiagnosis of a patient with a disease can have significant clinical impacts. Therefore, to build a robust and trustworthy CAD system and a reliable treatment effect prediction model, we cannot only pursue …

Rapid Assessment Of Fish Freshness For Multiple Supply-Chain Nodes Using Multi-Mode Spectroscopy And Fusion-Based Artificial Intelligence, Hossein Kashani Zadeh, Mike Hardy, Mitchell Sueker, Yicong Li, Angelis Tzouchas, Nicholas Mackinnon, Gregory Bearman, Simon A Haughey, Alireza Akhbardeh, Insuck Baek, Chansong Hwang, Jianwei Qin, Amanda M Tabb, Rosalee S Hellberg, Shereen Ismail, Hassan Reza, Fartash Vasefi, Moon Kim, Kouhyar Tavakolian, Christopher T Elliott

Faculty, Staff and Student Publications

This study is directed towards developing a fast, non-destructive, and easy-to-use handheld multimode spectroscopic system for fish quality assessment. We apply data fusion of visible near infra-red (VIS-NIR) and short wave infra-red (SWIR) reflectance and fluorescence (FL) spectroscopy data features to classify fish from fresh to spoiled condition. Farmed Atlantic and wild coho and chinook salmon and sablefish fillets were measured. Three hundred measurement points on each of four fillets were taken every two days over 14 days for a total of 8400 measurements for each spectral mode. Multiple machine learning techniques including principal component analysis, self-organized maps, linear and …

Building Explainable Machine Learning Lifecycle: Model Training, Selection, And Deployment With Explainability, Vidit Singh, Yonas Kassa, Brian Ricks, Robin Gandhi

Information Systems and Quantitative Analysis Faculty Proceedings & Presentations

No abstract provided.