Digital Commons Network^™

Machine-Learning Approaches For Developing An Autograder For High School-Level Cs-For-All Initiatives, Sirazum Munira Tisha

LSU Doctoral Dissertations

Most existing autograders used for grading programming assignments are based on unit testing, which is tedious to implement for programs with graphical output and does not allow testing for other code aspects, such as programming style or structure. We present a novel autograding approach based on machine learning that can successfully check the quality of coding assignments from a high school-level CS-for-all computational thinking course. For evaluating our autograder, we graded 2,675 samples from five different assignments from the past three years, including open-ended problems from different units of the course curriculum. Our autograder uses features based on lexical analysis …

On The Accelerated Noise-Tolerant Power Method, Zhiqiang Xu

Machine Learning Faculty Publications

We revisit the acceleration of the noise-tolerant power method for which, despite previous studies, the results remain unsatisfactory as they are either wrong or suboptimal, also lacking generality. In this work, we present a simple yet general and optimal analysis via noise-corrupted Chebyshev polynomials, which allows a larger iteration rank p than the target rank k, requires less noise conditions in a new form, and achieves the optimal iteration complexity (Equation presented) for some q satisfying k ≤ q ≤ p in a certain regime of the momentum parameter. Interestingly, it shows dynamic dependence of the noise tolerance on the …

High-Dimensional Variable Selection Via Knockoffs Using Gradient Boosting, Amr Essam Mohamed

Dissertations

As data continue to grow rapidly in size and complexity, efficient and effective statistical methods are needed to detect the important variables/features. Variable selection is one of the most crucial problems in statistical applications. This problem arises when one wants to model the relationship between the response and the predictors. The goal is to reduce the number of variables to a minimal set of explanatory variables that are truly associated with the response of interest to improve the model accuracy. Effectively choosing the true influential variables and controlling the False Discovery Rate (FDR) without sacrificing power has been a challenge …

Application Of An Ontology For Model Cards To Generate Computable Artifacts For Linking Machine Learning Information From Biomedical Research, Muhammad Tuan Amith, Licong Cui, Kirk Roberts, Cui Tao

Faculty, Staff and Student Publications

Model card reports provide a transparent description of machine learning models which includes information about their evaluation, limitations, intended use, etc. Federal health agencies have expressed an interest in model cards report for research studies using machine-learning based AI. Previously, we have developed an ontology model for model card reports to structure and formalize these reports. In this paper, we demonstrate a Java-based library (OWL API, FaCT++) that leverages our ontology to publish computable model card reports. We discuss future directions and other use cases that highlight applicability and feasibility of ontology-driven systems to support FAIR challenges.

Convolutional Neural Network For Biomarker Discovery For Triple Negative Breast Cancer With Rna Sequencing Data, Xiangning Chen, Justin M Balko, Fei Ling, Yabin Jin, Anneliese Gonzalez, Zhongming Zhao, Jingchun Chen

Faculty, Staff and Student Publications

Triple negative breast cancers (TNBCs) are tumors with a poor treatment response and prognosis. In this study, we propose a new approach, candidate extraction from convolutional neural network (CNN) elements (CECE), for discovery of biomarkers for TNBCs. We used the GSE96058 and GSE81538 datasets to build a CNN model to classify TNBCs and non-TNBCs and used the model to make TNBC predictions for two additional datasets, the cancer genome atlas (TCGA) breast cancer RNA sequencing data and the data from Fudan University Shanghai Cancer Center (FUSCC). Using correctly predicted TNBCs from the GSE96058 and TCGA datasets, we calculated saliency maps …

Characterization Of Physical And Biochemical Traits In Wheat And Corn Plants Using High Throughput Image Analysis, Kantilata Thapa

Department of Agricultural and Biological Systems Engineering: Dissertations, Theses, and Student Research

Plant phenotyping has been recognized as a rapidly growing field of research due to the labor-intensive, destructive, and time-consuming nature of traditional phenotyping methods. These phenotyping bottlenecks can be addressed by advancements in image-based phenotyping like RGB and hyperspectral imaging for the assessment of plant traits important for breeding purposes. This study aims (1) to characterize the physical and biochemical traits of wheat and corn plants using RGB and hyperspectral imaging in the greenhouse, and (2) to estimate leaf nitrogen (N), phosphorus (P), and potassium (K) content using hyperspectral imaging and an analytical spectral device (ASD spectrometer) and compare the …

Beyond News Values On Twitter: Predicting Factors That Drive User Engagement In News, Zhiyan Zhong

Dartmouth College Master’s Theses

When deciding on what news stories to cover, traditional journalism determines news values by following several elements of newsworthiness, such as impact, timeliness, and prominence. However, these guidelines do not always seem to correspond with the success of content on social media. As people are increasingly turning to social media for news, our research aims to understand and predict factors that drive user engagement for news on social media. In this study, we analyze news content published on Twitter, and examine a diverse set of characteristics like metrics retrieved from the Twitter API and semantics by natural language processing, including …

Defining Safe Training Datasets For Machine Learning Models Using Ontologies, Lynn C. Vonder Haar

Doctoral Dissertations and Master's Theses

Machine Learning (ML) models have been gaining popularity in recent years in a wide variety of domains, including safety-critical domains. While ML models have shown high accuracy in their predictions, they are still considered black boxes, meaning that developers and users do not know how the models make their decisions. While this is simply a nuisance in some domains, in safetycritical domains, this makes ML models difficult to trust. To fully utilize ML models in safetycritical domains, there needs to be a method to improve trust in their safety and accuracy without human experts checking each decision. This research proposes …

Socially Aware Natural Language Processing With Commonsense Reasoning And Fairness In Intelligent Systems, Sirwe Saeedi

Dissertations

Although Artificial Intelligence (AI) promises to deliver ever more user-friendly consumer applications, recent mishaps involving fake information and biased treatment serve as vivid reminders of the pitfalls of AI. AI can harbor latent biases and flaws that can cause harm in diverse and unexpected ways. It is crucial to understand the reasons for, mechanisms behind, and circumstances under which AI can fail. For instance, a lack of commonsense reasoning can lead to biased or unfair decisions made by Machine Learning (ML) systems. For example, if an ML system is trained on data that is biased or unrepresentative of the real …

The Rise Of Machine Learning In Polymer Discovery, Cheng Yan, Guoqiang Li

Faculty Publications

In the recent decades, with rapid development in computing power and algorithms, machine learning (ML) has exhibited its enormous potential in new polymer discovery. Herein, the history of ML is described and the basic process of ML accelerated polymer discovery is summarized. Next, the four steps in this process are reviewed, that is, dataset selection, fingerprinting, ML framework, and new polymer generation. Finally, a couple of main challenges for ML accelerated polymer discovery is presented and the outlooks in this field are prospected. It is expected that this review can service as a useful tool for the people who just …

Analysis Of U.S. Housing Markets Using Advanced Econometric Models And Machine Learning Algorithms, Cody Kirby

Dissertations

The Great Recession is the most recent reminder in U.S. history of the important relationship that exists between monetary policy and housing market activity. Since World War II, eight U.S. recessions have followed downturns in housing. A decline in the housing market results in the balance sheet deterioration of homeowners, financial institutions, private organizations, central banks, governing bodies, and foreign countries. The ability to understand the relationship between monetary policy and housing market activity, and to better forecast housing prices, allows for better planning by all economic agents.

This study answers the following research questions. (1) How have monetary policy …

Investigating Collaborative Problem Solving Temporal Dynamics Using Interactions Within A Digital Whiteboard, Hua Leong Fwa

Research Collection School Of Computing and Information Systems

Collaborative Problem Solving, the resolution of complex problems with the collaboration of multiple peoplepooling their knowledge, skills and effort is postulated as an essential 21st century skills for the futureworkforce. Collaborative Problem Solving has been embraced in schools where both online and face-to-face collaboration are afforded through the proliferation of educational technology tools. Assessing the amount of collaboration that has taken place among the students has however been challenging. In this research, we seek to identify the collaboration patterns of our students by mining the temporal sequence of their actions logs captured within a digital whiteboard tool. With the use …

Machine Learning Methods For Inferring The Number Of Uav Emitters Via Massive Mimo Receive Array, Yifan Li, Feng Shu, Jinsong Hu, Shihao Yan, Haiwei Song, Weiqiang Zhu, Da Tian, Yaoliang Song, Jiangzhou Wang

Research outputs 2022 to 2026

To provide important prior knowledge for the direction of arrival (DOA) estimation of UAV emitters in future wireless networks, we present a complete DOA preprocessing system for inferring the number of emitters via a massive multiple-input multiple-output (MIMO) receive array. Firstly, in order to eliminate the noise signals, two high-precision signal detectors, the square root of the maximum eigenvalue times the minimum eigenvalue (SR-MME) and the geometric mean (GM), are proposed. Compared to other detectors, SR-MME and GM can achieve a high detection probability while maintaining extremely low false alarm probability. Secondly, if the existence of emitters is determined by …

Short-Term Energy Load Forecasting Model With Sample Filtering, Ruthbetha Kateule, Lucas A. Sakala, Mahadia Tunga

Tanzania Journal of Science

Short-term energy load forecasting is a crucial task in the power smart grid, which enables the power utilities to understand the future energy demands and plans to attain the demand and supply equilibrium, thereby optimizing power deployment and reducing power losses. Several techniques have been implemented to enhance energy load forecasting. However, the nonlinear nature of the data collected in the smart grid makes it difficult to attain 100% energy load forecasting accuracy. For instance, the Deep Feedforward Neural Networks model based on Input Attention Mechanism and Hidden Connection Mechanism has a mean absolute percentage error of 3.17%; model based …

Brainwave-Based Authentication Using Features Fusion, Mahyar Tajdini, Volodymyr Sokolov, Ievgeniia Kuzminykh, Bogdan Ghita

School of Engineering, Computing and Mathematics

This article investigates the use of human brainwaves for user authentication. We used data collected from 50 volunteers and leveraged the Support Vector Machine (SVM) as a classification algorithm for the case study. User recognition patterns are taken from a combination of blinking, attention concentration, and picture recognition emotion sequences. These actions impact alpha, beta, gamma, and theta brain waves, which are measured using several electrodes. Ten different electrode placement patterns are explored, with varied positioning on the head. For each placement position, four features are examined, for a total of 40 extracts in the learning model. Features are: 1) …

An Advanced Deep Learning Models-Based Plant Disease Detection: A Review Of Recent Research, Muhammad Shoaib, Babar Shah, Shaker Ei-Sappagh, Akhtar Ali, Asad Ullah, Fayadh Alenezi, Tsanko Gechev, Tariq Hussain, Farman Ali

All Works

Plants play a crucial role in supplying food globally. Various environmental factors lead to plant diseases which results in significant production losses. However, manual detection of plant diseases is a time-consuming and error-prone process. It can be an unreliable method of identifying and preventing the spread of plant diseases. Adopting advanced technologies such as Machine Learning (ML) and Deep Learning (DL) can help to overcome these challenges by enabling early identification of plant diseases. In this paper, the recent advancements in the use of ML and DL techniques for the identification of plant diseases are explored. The research focuses on …

Machine Intelligence Framework For Predictive Modeling Of Co2 Concentration: A Path To Sustainable Environmental Management, Mohamed Abouhawwash, Mohammed Jameel, Sameh S. Askar

Sustainable Machine Intelligence Journal

Climate change poses critical challenges, necessitating accurate and timely monitoring of CO2 concentrations for sustainable environmental management. Traditional methods for CO2 prediction exhibit limitations in precision and scalability. This paper introduces a novel Machine Intelligence Framework (MIF) specifically designed for predictive modeling of CO2 concentration levels. Leveraging advanced machine learning algorithms and data processing techniques, MIF aims to address the existing research gap by offering enhanced accuracy and adaptability in CO2 forecasting. Motivated by the urgency to combat climate change, this research develops a comprehensive framework integrating predictive modeling with machine intelligence. The methodology involves algorithm design, data integration, and …

Discriminating Stay-Green Grasses Using Hyperspectral Imaging And Chemometrics, J. Taylor, B. Moore, J. J. Rowland, H. Thomas, H. J. Ougham

IGC Proceedings (1977-2023)

Screening of plant collections for traits can be expensive, in terms of the number of plants to be screened, the duration of the plant lifecycle and the required observations. This study describes the application of a non-invasive method, hyperspectral imaging, combined with multivariate analysis, to distinguish between homozygous wild-type (YY) Lolium multiflorum and Lolium multiflorum F2 back cross plants heterozygous for y, a recessive Festuca pratensis stay-green gene (Thomas et al., 1997).

Applications Of Generative Adversarial Networks In Single Image Datasets, Dylan E. Cramer

Scholarly Horizons: University of Minnesota, Morris Undergraduate Journal

One of the main difficulties faced in most generative machine learning models is how much data is required to train it, especially when collecting a large dataset is not feasible. Recently there have been breakthroughs in tackling this issue in SinGAN, with its researchers being able to train a Generative Adversarial Network (GAN) on just a single image with a model that can perform many novel tasks, such as image harmonization. ConSinGAN is a model that builds upon this work by concurrently training several stages in a sequential multi-stage manner while retaining the ability to perform those novel tasks.

Using Mds Preference Plot As Visual Analytics Of Data: A Machine Learning Approach, Yan Zhang, Cody Ding

Education Sciences and Professional Programs Faculty Works

The primary purpose of this paper is to advocate the use of multidimensional scaling (MDS) preference plot to study relationships among variables and individual differences in these variables. MDS preference plot is not a new visual technique; nevertheless, its application to visualize individual differences in variables for high-dimensional data is rare, particularly in education and social sciences. We illustrated its application using a real example in an educational setting. The results indicate that the MDS preference plot is a viable visualization technique for data mining and analytics. Traditional statistical methods, such as the analysis of variance, can be used to …

Design, Determination, And Evaluation Of Gender-Based Bias Mitigation Techniques For Music Recommender Systems, Sunny Shrestha

Electronic Theses and Dissertations

The majority of smartphone users engage with a recommender system on a daily basis. Many rely on these recommendations to make their next purchase, download the next game, listen to the new music or find the next healthcare provider. Although there are plenty of evidence backed research that demonstrates presence of gender bias in Machine Learning (ML) models like recommender systems, the issue is viewed as a frivolous cause that doesn’t merit much action. However, gender bias poses to effect more than half of the population as by default ML systems are designed to cater to a cisgender man. This …

Solving Fjssp With A Genetic Algorithm, Michael John Srouji

Computer Science and Software Engineering

The Flexible Job Shop Scheduling Problem is an NP-Hard combinatorial problem. This paper aims to find a solution to this problem using genetic algorithms, and discuss the effectiveness of this. Initially, I did exploratory work on whether neural networks would be effective or not, and found a lot of trade offs between using neural networks and chromosome sequencing. In the end, I decided to use chromosome sequencing over neural networks, due to the scope of my problem being on a small scale rather than on a large scale.

Therefore, the genetic algorithm was implemented using chromosome sequencing. My chromosomes were …

The Use Of Artificial Intelligence To Detect Students Sentiments And Emotions In Gross Anatomy Reflections, Krzysztof J. Rechowicz, Carrie A. Elzie

VMASC Publications

Students' reflective writings in gross anatomy provide a rich source of complex emotions experienced by learners. However, qualitative approaches to evaluating student writings are resource heavy and timely. To overcome this, natural language processing, a nascent field of artificial intelligence that uses computational techniques for the analysis and synthesis of text, was used to compare health professional students' reflections on the importance of various regions of the body to their own lives and those of the anatomical donor dissected. A total of 1365 anonymous writings (677 about a donor, 688 about self) were collected from 132 students. Binary and trinary …

Characterizing Location-Based Electromagnetic Leakage Of Computing Devices Using Convolutional Neural Networks To Increase The Effectiveness Of Side-Channel Analysis Attacks, Ian C. Heffron

Theses and Dissertations

SCA attacks aim to recover some sort of secret information, often in the form of a cipher key, from a target device. Some of these attacks focus on either power-based leakage, or EM-based leakage. Neural networks have recently gained in popularity as tools in SCA attacks. Near-field EM probes with high-spatial resolution enable attackers to isolate physical locations above a processor. This enables attackers to exploit the spatial dependencies of algorithms running on said processor. These spatial dependencies result in different physical locations above a chip emanating different signal strengths. The strengths of different locations can be mapped using the …

Spectral Material Classification Of Orbital Objects - Applying Machine Learning To Visible And Near-Infrared Spectral Scenes, Stephen M. Stumpf

Theses and Dissertations

MSI and HSI techniques allow users to determine the material composition of an object at range. To avoid labor-intensive manual classification, ML is used to determine the most likely material contained in a given pixel of a target image. Previous work primarily focuses on terrestrial applications; this paper extends these techniques into the low-illumination space situational awareness domain, which is of critical importance to national security. HSI datacubes are preprocessed with RL deconvolution as a means of reducing the effects of the optical PSF; then, statistical ML techniques, including k-NN, LDA, QDA, and SVMs are implemented as means of assigning …

Classification And Analysis Of Twitter Bot And Troll Accounts, Callan P. Mccormick

Theses and Dissertations

This research trains, tests, and analyzes bot and troll classification models using publicly available, open source datasets. Specifically, it applies decision tree, random forest, feed forward neural networks, and long-short term memory neural networks with hyperparameters tuned via designed experiment to five labeled bot datasets created between 2011 and 2020 and one dataset labeling state-sponsored disinformation accounts or trolls. The first three models utilize account profile features, while the last model applies natural language processing techniques, specifically GloVe embedding, to analyze a user’s Tweet history. Results indicate that the random forest model outperforms the other three models with an average …

Synchronization Of The Training Of Specialists In The Automation Of Technological Processes In Accordance With The Dynamics Of The Taxonomy Of Learning Goals, Yusuf Shodievich Avazov, Kamola Abdullaeva

Chemical Technology, Control and Management

Changes in the characteristics of modern ones, reflecting them as the "digital generation", inevitably necessitate electronic interactive, mobile and mixed, machine learning, especially in the field of automation and control of technological processes and production. As a consequence of reflecting these realities, there have been parallel changes in the taxonomy of learning objectives - from the classical to the revised rethought and digital.

In order to study the problem of synchronizing the training of future specialists in the field of automation and control of technological processes and production and funny knowledge in accordance with the dynamics of the taxonomy of …

Ambient Electromagnetic Radiation As A Predictor Of Honey Bee (Apis Mellifera) Traffic In Linear And Non-Linear Regression: Numerical Stability, Physical Time And Energy Efficiency, Vladimir Kulyukin, Daniel Coster, Anastasiia Tkachenko, Daniel Hornberger, Aleksey V. Kulyukin

Computer Science Faculty and Staff Publications

Since bee traffic is a contributing factor to hive health and electromagnetic radiation has a growing presence in the urban milieu, we investigate ambient electromagnetic radiation as a predictor of bee traffic in the hive’s vicinity in an urban environment. To that end, we built two multi-sensor stations and deployed them for four and a half months at a private apiary in Logan, Utah, U.S.A. to record ambient weather and electromagnetic radiation. We placed two non-invasive video loggers on two hives at the apiary to extract omnidirectional bee motion counts from videos. The time-aligned datasets were used to evaluate 200 …

Machine Learning Methods For Computational Phenotyping Using Patient Healthcare Data With Noisy Labels, Praveen Kumar

Computer Science ETDs

Positive and Unlabeled (PU) learning problems abound in many real-world applications. In healthcare informatics, diagnosed patients are considered labeled positive for a specific disease, but being undiagnosed does not mean they can be labeled negative. PU learning can improve classification performance, and estimate the positive fraction, α, among unlabeled samples. However, algorithms based on the Selected Completely At Random (SCAR) assumption are inadequate when the SCAR assumption fails (e.g., severe cases overrepresented), and when class imbalance is substantial. This dissertation presents and evaluates new algorithms to overcome these limitations. The proposed methods outperform the state-of-art for α-estimation, enhance classification performance, …

Human Saliency-Driven Patch-Based Matching For Interpretable Post-Mortem Iris Recognition, Aidan Boyd, Daniel Moreira, Andrey Kuehlkamp, Kevin Bowyer, Adam Czajka

Computer Science: Faculty Publications and Other Works

Forensic iris recognition, as opposed to live iris recognition, is an emerging research area that leverages the discriminative power of iris biometrics to aid human examiners in their efforts to identify deceased persons. As a machine learning-based technique in a predominantly human-controlled task, forensic recognition serves as “back-up” to human expertise in the task of post-mortem identification. As such, the machine learning model must be (a) interpretable, and (b) post-mortem-specific, to account for changes in decaying eye tissue. In this work, we propose a method that satisfies both requirements, and that approaches the creation of a post-mortem-specific feature extractor in …