Digital Commons Network^™

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 …

Terrain Characterization Via Machine Vs. Deep Learning Using Remote Sensing, Jordan Ewing, Thomas Oommen, Jobin Thomas, Anush Kasaragod, Richard Dobson, Colin Brooks, Paramsothy Jayakumar, Michael Cole, Tulga Ersal

Michigan Tech Publications, Part 1

Terrain traversability is critical for developing Go/No-Go maps for ground vehicles, which significantly impact a mission’s success. To predict the mobility of terrain, one must understand the soil characteristics. In-situ measurements performed in the field are the current method of collecting this information, which is time-consuming, costly, and can be lethal for military operations. This paper investigates an alternative approach using thermal, multispectral, and hyperspectral remote sensing from an unmanned aerial vehicle (UAV) platform. Remotely sensed data combined with machine learning (linear, ridge, lasso, partial least squares (PLS), support vector machines (SVM), and k nearest neighbors (KNN)) and deep learning …

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 …

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 …

Cropland Mapping In Tropical Smallholder Systems With Seasonally Stratified Sentinel-1 And Sentinel-2 Spectral And Textural Features, Manushi B. Trivedi, Michael Marshall, Lyndon Estes, C.A.J.M. De Bie, Ling Chang, Andrew Nelson

Geography

Mapping arable field areas is crucial for assessing agricultural productivity but poses challenges in sub-Saharan agroecosystems because of diverse crop calendars, small and irregularly shaped fields, persistent cloud cover, and lack of high-quality model training data. This study proposes several methodological improvements to overcome these challenges. Specifically, it utilizes long-term MODIS data to stratify finer Sentinel-2 reflectance and Sentinel-1 backscatter image features on a per-pixel basis. It also incorporates texture features and employs a machine learning approach with over 300,000 samples. The eastern region of Ghana was stratified into seven seasonal strata exhibiting distinct vegetation seasonality, capturing diversity in crop …

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 …

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 …

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 …

Issue Of Data Imbalance On Low Birthweight Baby Outcomes Prediction And Associated Risk Factors Identification: Establishment Of Benchmarking Key Machine Learning Models With Data Rebalancing Strategies, Yang Ren, Dezhi Wu, Yan Tong, Ana Lopéz-De Fede, Sarah Gareau

Publications

Background: Low birthweight (LBW) is a leading cause of neonatal mortality in the United States and a major causative factor of adverse health effects in newborns. Identifying high-risk patients early in prenatal care is crucial to preventing adverse outcomes. Previous studies have proposed various machine learning (ML) models for LBW prediction task, but they were limited by small and imbalanced data sets. Some authors attempted to address this through different data rebalancing methods. However, most of their reported performances did not reflect the models’ actual performance in real-life scenarios. To date, few studies have successfully benchmarked the performance of ML …

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 …

Artificial Intelligence And Machine Learning In The Diagnosis And Management Of Stroke: A Narrative Review Of United States Food And Drug Administration-Approved Technologies, Anirudha S. Chandrabhatla, Elyse A. Kuo, Jennifer D. Sokolowski, Ryan T. Kellogg, Min Park, Panagiotis Mastorakos

Department of Neurosurgery Faculty Papers

Stroke is an emergency in which delays in treatment can lead to significant loss of neurological function and be fatal. Technologies that increase the speed and accuracy of stroke diagnosis or assist in post-stroke rehabilitation can improve patient outcomes. No resource exists that comprehensively assesses artificial intelligence/machine learning (AI/ML)-enabled technologies indicated for the management of ischemic and hemorrhagic stroke. We queried a United States Food and Drug Administration (FDA) database, along with PubMed and private company websites, to identify the recent literature assessing the clinical performance of FDA-approved AI/ML-enabled technologies. The FDA has approved 22 AI/ML-enabled technologies that triage brain …

Sentiment Analysis Of Text And Emoji Data For Twitter Network, Paramita Dey, Soumya Dey

Al-Bahir

Twitter is a social media platform where users can post, read, and interact with 'tweets'. Third party like corporate organization can take advantage of this huge information by collecting data about their customers' opinions. The use of emoticons on social media and the emotions expressed through them are the subjects of this research paper. The purpose of this paper is to present a model for analyzing emotional responses to real-life Twitter data. The proposed model is based on supervised machine learning algorithms and data on has been collected through crawler “TWEEPY” for empirical analysis. Collected data is pre-processed, pruned and …

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, Angela 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

Food Science Faculty Articles and Research

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 …

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 …

Development Of A Modular Agricultural Robotic Sprayer, Paolo Rommel P. Sanchez

Theses and Dissertations

Precision Agriculture (PA) increases farm productivity, reduces pollution, and minimizes input costs. However, the wide adoption of existing PA technologies for complex field operations, such as spraying, is slow due to high acquisition costs, low adaptability, and slow operating speed. In this study, we designed, built, optimized, and tested a Modular Agrochemical Precision Sprayer (MAPS), a robotic sprayer with an intelligent machine vision system (MVS). Our work focused on identifying and spraying on the targeted plants with low cost, high speed, and high accuracy in a remote, dynamic, and rugged environment. We first researched and benchmarked combinations of one-stage convolutional …

An Optimized Bagging Ensemble Learning Approach Using Bestrees For Predicting Students’ Performance, Edmund Evangelista

All Works

Every academic institution's goal is to identify students who require additional assistance and take appropriate actions to improve their performance. As such, various research studies have focused on developing prediction models that can detect correlated patterns influencing students' performance, dropout, collaboration, and engagement. Among the influential predictive models available, the bagging ensemble has captured the interest of researchers seeking to improve prediction accuracy over single classifiers. However, prior work in this area has focused mainly on selecting single classifiers as the base classifier of the bagging ensemble, with little to no further optimization of the proposed framework. This study aims …

Learning–Assisted Constraint Filtering To Enhance Power System Optimization Performance, Fouad Hasan

LSU Doctoral Dissertations

Machine learning (ML) is a powerful tool that provides meaningful insights for operators to make fast and efficient decisions by analyzing data from power systems. ML techniques have great potential to assist in solving optimization problems within a shorter time frame and with less computational burden. AC optimal power flow (ACOPF), dynamic economic dispatch (D-ED), and security-constrained unit commitment (SCUC) are the three energy management optimization functions studied in this dissertation. ACOPF is solved every 5~15 minutes. Because of the nonconvex and complex nature of ACOPF, solving this problem for large systems is computationally expensive and time-consuming. Classification and regression …

High-Throughput Phenotyping Of Plant Leaf Morphological, Physiological, And Biochemical Traits On Multiple Scales Using Optical Sensing, Huichun Zhang, Lu Wang, Xiuliang Jin, Liming Bian, Yufeng Ge

Department of Agricultural and Biological Systems Engineering: Faculty Publications

Acquisition of plant phenotypic information facilitates plant breeding, sheds light on gene action, and can be applied to optimize the quality of agricultural and forestry products. Because leaves often show the fastest responses to external environmental stimuli, leaf phenotypic traits are indicators of plant growth, health, and stress levels. Combination of new imaging sensors, image processing, and data analytics permits measurement over the full life span of plants at high temporal resolution and at several organizational levels from organs to individual plants to field populations of plants. We review the optical sensors and associated data analytics used for measuring morphological, …

Early Detection Of Autism Spectrum Disorder (Asd) Using Machine Learning Techniques, Mahra Musabbah Bin Beyat Alfalasi

Theses

ASD is a neurological disorder that affects over 1 in 44 chilren and this rate has increased. The diagnosis process can be timely and costly. This will make it difficult for the patients to adhere to the prescribed treatments and will hinder the progress of the patient. This project is focused on increasing the efficiency of this diagnosis process through machine learning techniques. The proposed datasets are; ASD Screening Data for Adult, ASD screening Data for Children and ASD Screening Data for Adolescent. These datasets are a categorical, continuous, and binary data type. They have 21 common attributes and a …

Study To Improve The Employee Experiences And Reducing The Employee Attrition, Mahra Essa Mohammed Belarai Alfalasi

Theses

Employee attrition is a significant issue that occurs in every company today, regardless of external environment changes. According to the definition of attrition, the number of employees gradually decreases due to retirement, death, and resignations (Marais, 2022). Attrition can occur when a well-trained and well-adjusted talented person leaves the company for any reason, leaving a gap in the workplace (BasuMallick, 2021). It is extremely difficult for HR employees to fill the gap that has been created. For today's managers, minimizing turnover rates is a major concern, and modern HR managers do this in several ways, the employee's decision was motivated …

Ptmtorrent: A Dataset For Mining Open-Source Pre-Trained Model Packages, Wenxin Jiang, Nicholas Synovic, Purvish Jajal, Taylor R. Schorlemmer, Arav Tewari, Bhavesh Pareek, George K. Thiruvathukal, James C. Davis

Computer Science: Faculty Publications and Other Works

Due to the cost of developing and training deep learning models from scratch, machine learning engineers have begun to reuse pre-trained models (PTMs) and fine-tune them for downstream tasks. PTM registries known as “model hubs” support engineers in distributing and reusing deep learning models. PTM packages include pre-trained weights, documentation, model architectures, datasets, and metadata. Mining the information in PTM packages will enable the discovery of engineering phenomena and tools to support software engineers. However, accessing this information is difficult — there are many PTM registries, and both the registries and the individual packages may have rate limiting for accessing …

The Development Of Metabolic Models And Machine Learning Methods For Biofuel-Producing Bacteria, Garrett Roell

McKelvey School of Engineering Graduate Student Theses & Dissertations

Humans have harnessed the power of microbiology for the creation of fermented foods for over 10,000 years. Synthetic biology now allows chemicals and materials that historically were derived from fossil fuels to be produced via fermentation. These technologies will lessen our dependence on nonrenewable resources and reduce the rate CO2 is released into the atmosphere. Non-model microbial species with unique metabolic traits are promising solutions for bioproduction from renewable feedstocks including lignin, agricultural waste, and CO2. Systems biology characterizations, metabolic models, and data-driven approaches are essential for deciphering and optimizing cell metabolism for these applications.Rhodococcus opacus, R. opacus, is an …

An Empirical Study Of Pre-Trained Model Reuse In The Hugging Face Deep Learning Model Registry, Wenxin Jiang, Nicholas Synovic, Matt Hyattt, Taylor R. Schorlemmer, Rohan Sethi, Yung-Hisang Lu, George K. Thiruvathukal, James C. Davis

Computer Science: Faculty Publications and Other Works

Deep Neural Networks (DNNs) are being adopted as components in software systems. Creating and specializing DNNs from scratch has grown increasingly difficult as state-of-the-art architectures grow more complex. Following the path of traditional software engineering, machine learning engineers have begun to reuse large-scale pre-trained models (PTMs) and fine-tune these models for downstream tasks. Prior works have studied reuse practices for traditional software packages to guide software engineers towards better package maintenance and dependency management. We lack a similar foundation of knowledge to guide behaviors in pre-trained model ecosystems.

In this work, we present the first empirical investigation of PTM reuse. …

Identifying Key Activity Indicators In Rats' Neuronal Data Using Lasso Regularized Logistic Regression, Avery Woods

Honors Theses

This thesis aims to identify timestamps of rats’ neuronal activity that best determine behavior using a machine learning model. Neuronal data is a complex and high-dimensional dataset, and identifying the most informative features is crucial for understanding the underlying neuronal processes. The Lasso regularization technique is employed to select the most relevant features of the data to the model’s prediction. The results of this study provide insights into the key activity indicators that are associated with specific behaviors or cognitive processes in rats, as well as the effect that stress can have on neuronal activity and behavior. Ultimately, it was …

Anomaly Detection In Scada Systems Using Machine Learning, Eric Kudjoe Fiah

Theses and Dissertations

In this thesis, different Machine learning (ML) algorithms were used in the detection of anomalies using a dataset from a Gas pipeline SCADA system which was generated by Mississippi State University’s SCADA laboratory. This work was divided into two folds: Binary Classification and Categorized classification. In the binary classification, two attack types namely: Command injection and Response injection attacks were considered. Eight Machine Learning Classifiers were used and the results were compared. The Light GBM and Decision tree classifiers performed better than the other algorithms. In the categorical classification task, Seven (7) attack types in the dataset were analyzed using …

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 …