Data Science Commons^™

Supply Chain Optimisation With Machine Learning And Neural Networks: Applications To Demand Planning, Supply Planning, And Inventory Planning., Laurence Cully

ICT

This thesis explores the impact of machine learning (ML) on supply chain planning, particularly in demand forecasting, supply planning, and inventory optimisation. By analysing literature on supply chain management, data flow, and the intersection of ML and competitive advantage, the author contextualises the research within a globalised market's demands. Case studies, interviews with industry professionals, and raw data collection provide empirical support for evaluating the research objectives and documenting the integration of ML in supply chain processes.

The findings reveal that optimised ML models, particularly those using model stacking (autoregressors, GRUs, and Random Forests), significantly outperform traditional demand forecasting methods, …

Machine Learning-Based Analysis Of Dna Methylation Patterns In E.Lenta, Tsion Tsegaye Sherbeza

All Graduate Theses, Dissertations, and Other Capstone Projects

Methylation patterns in bacterial genomes, such as those found in Eggerthella lenta, take roles in mediating microbial interactions with their environment, host, and external stressors. These patterns are formed by DNA methylation with diverse sequence specificities that provide insights into the DNA sequence regulation and defense against bacteriophages. We utilize computational approaches and machine learning models to identify and analyze 5mC and 6mA methylation motifs in E. lenta genomes. By integrating host characteristics such as age, birth country, gender, and medication history, we explore 1) the predictive relationships between 5mC & 6mA methylation types in E. lenta strains and …

How Can A Cloud Computing It Framework Be Created And Applied Effectively In The Online Printing Industry?, Stefan Meissner

Dissertations

This research aims to design a cloud computing IT framework for the online printing industry based on a detailed literature review, the development of proof of concepts (PoC), and the conduction of a focus group. The framework can be adopted by the online printing industry or by vendors of print-specific applications to optimize their products for the online printing industry. The author has been working in the online printing process optimization and automation since 2007. During this time, he got deep insight into many industry-specific applications, their architectural design, and their challenges being used in the context of online printing. …

Organizational Identity Management Policies, Alexander Rieger, Tamara Roth, Johannes Sedlmeir, Gilbert Fridgen, Amber Young

Information Systems Faculty Publications and Presentations

Effective identity management is essential for secure organizational processes, but organizations often do not approach it strategically. To break this trajectory, organizational policymakers need to define a clear and sustainable identity management strategy. This paper presents an overview and guidelines to help shape such strategy. It analyzes the key characteristics and trade-offs of today’s identity management models. Moreover, it offers practical recommendations for organizational policymakers when choosing among these models.

Building A Human Digital Twin (Hdtwin) Using Large Language Models For Cognitive Diagnosis: Algorithm Development And Validation, Gina Sprint, Maureen Schmitter-Edgecombe, Diane Cook

Computer Science Faculty Scholarship

Background: Human digital twins have the potential to change the practice of personalizing cognitive health diagnosis because these systems can integrate multiple sources of health information and influence into a unified model. Cognitive health is multifaceted, yet researchers and clinical professionals struggle to align diverse sources of information into a single model. Objective: This study aims to introduce a method called HDTwin, for unifying heterogeneous data using large language models. HDTwin is designed to predict cognitive diagnoses and offer explanations for its inferences. Methods: HDTwin integrates cognitive health data from multiple sources, including demographic, behavioral, ecological momentary assessment, n-back test, …

Social Networks And Large Language Models For Division I Basketball Game Winner Prediction, Gina Sprint

Computer Science Faculty Scholarship

Sporting event outcome prediction is a well-established and actively researched domain, with a particular focus on college basketball’s March Madness tournament. Researchers, fans, and gamblers alike seek accurate game-level predictions using features such as tournament seeds, season performance, and expert opinions. While machine learning algorithms have been harnessed to build prediction models, no perfect model or human-created bracket has emerged. This paper explores a novel approach to basketball game outcome prediction by utilizing the power of social networks and large language models (LLMs). LLMs are trained to understand and generate text, often eliminating the need for a feature engineering step. …

Investigation Of Space Charge Effects On Co2 Electrocatalytic Reduction On Gd-Doped Ceria Via Scanning Kelvin Probe And Model-Based Bayesian Analysis, Alejandro Mejia

Graduate Theses, Dissertations, and Problem Reports (ETD)

In studying novel energy conversion and storage systems, such as high-temperature electrolysis, numerous underlying fundamental physical processes remain unclear or inadequately understood. Among these, the modeling and comprehension of surface reaction mechanisms, coupled with the intricate effects of space‑charge interfaces, remains an unclear and challenging area of research.

The work of this dissertation involves the development of a 2D finite element analysis model, leveraging the robust MOOSE framework from INL. This model, featuring inhomogeneous defect thermodynamics for near-surface chemistry, formulated through Poisson‑Cahn variational theory, has been exploited for studying the electrocatalytic reduction of CO₂ on gadolinia doped ceria. The …

Data-Driven Modeling Of Oxygen Kinetics In La0.6sr0.4co0.2fe0.8o3−Δ (Lscf) For High-Temperature Reduction Of Co2 In An Electrolysis Cell, Ferron Campbell

Graduate Theses, Dissertations, and Problem Reports (ETD)

Electrolysis systems are critical to several societal applications, particularly energy storage and conversion. Developing these systems requires a detailed knowledge of the chemistry and thermodynamics of the materials used in the electrolysis cell. This work focuses on using embedded scientific machine learning as an efficient way to build an interpretable model for the reaction and transport kinetics in the LSCF electrode, whose performance directly influences the electrolysis system’s performance. The models developed in this study are trained using the publicly available machine learning package, FoKL-GP. This package incorporates a robust Gibbs sampler that employs a forward variable selection process to …

Efficient Classification Of Very High Resolution Images, Mohammad I. Nouyed

Graduate Theses, Dissertations, and Problem Reports (ETD)

In recent decades, deep learning approaches have shown significant improvement in various image understanding tasks. However, analysis of high-resolution images remains a major challenge. In this work, we address the challenge of very high-resolution histopathological image (VHRHI) classification using a new information-theoretic discriminative patch selection approach. We show results on a high-resolution image dataset, namely, gigapixel whole slide tissue images for cancer tumors. Then we address how to efficiently classify challenging histopathology images, such as gigapixel whole-slide images for cancer diagnostics with image-level annotation. These ``weak labels'' are applied throughout the image but describe tumor regions of variable sizes and …

Statistical Modeling Of Bankruptcy Data, Andrew Elsfelder

Williams Honors College, Honors Research Projects

My project uses a dataset of bankrupt and non-bankrupt companies in Taiwan from 1999 to 2009. This data was collected from the Taiwan Economic Journal. The statistical methods I used to model the data are CHAID, CART, and logistic regression. The models created are tools that can predict if a company is bankrupt, or not-bankrupt based on other data about the company. I created multiple models for each of the methods to find the best model for each method. I then analyzed the output from each method. Lastly, I determined which model was the best for this data based on …

Classification Models Using Python In Industrial/Organizational Psychology, Beyza Ceylan

Williams Honors College, Honors Research Projects

Companies, industries, and places of business use artificial intelligence and statistics to predict the characteristics of their employees and staff. Data collected from these individuals is also used to make decisions about them regarding their work life, such as promotions, salaries, or within the hiring process. Two models that are commonly used throughout the field of psychology and specifically in industrial/organizational psychology are the linear regression and the logistic regression. Examining different classification models using Python shows the potential that there may be different models that are more accurate in their predictions of employee success, including a Random Forest model …

A Hybrid Bi-Lstm And Rbm Approach For Advanced Underwater Object Detection, Manimurugan S, Karthikeyan P, Narmatha C, Majed M. Aborokbah, Anand Paul, Subramaniam Ganesan, Rajendran T, Mohammad Ammad-Uddin

School of Public Health Faculty Publications

This research addresses the imperative need for efficient underwater exploration in the domain of deep-sea resource development, highlighting the importance of autonomous operations to mitigate the challenges posed by high-stress underwater environments. The proposed approach introduces a hybrid model for Underwater Object Detection (UOD), combining Bi-directional Long Short-Term Memory (Bi-LSTM) with a Restricted Boltzmann Machine (RBM). Bi-LSTM excels at capturing long-term dependencies and processing sequences bidirectionally to enhance comprehension of both past and future contexts. The model benefits from effective feature learning, aided by RBMs that enable the extraction of hierarchical and abstract representations. Additionally, this architecture handles variable-length sequences, …

Using Unsupervised Learning Methods In Extracting Features For Classifying Rice Varieties From Rice Grains Images., Kevin Anthony Martinez

ICT

Rice, a staple food for nearly half of the global population, requires accurate classification of its varieties to ensure food quality, support agricultural trade, and enhance yield optimisation. Traditional manual classification methods are time-intensive and error-prone, prompting this study's exploration of unsupervised learning for feature extraction from rice grain images. The research tested classifiers on 75,000 rice samples across five classes, with 15,000 samples per class.

The study's DCGAN-CNN model achieved the highest classification accuracy of 99.67%. However, the PCA-CNN model underperformed, with only 20% accuracy, due to implementation errors. Recommendations for improvement include optimising model parameters such as learning …

Deep Learning Model Compression For Resource-Constrained Environments., Stephen Burke

ICT

This study examines the effects of three Deep Neural Network compression techniques—Quantisation, Pruning, and Weight Sharing/Clustering—on CNN and ANN models trained for image classification tasks. The models were tested on the CIFAR-10 dataset for multiclass classification and a binary classification task using a dataset derived from COCO. The best validation accuracy achieved was 74.7% with a CNN on CIFAR-10 and 53% with the best ANN. On the COCO dataset, a modified CIFAR-10 CNN model achieved 75%. The models were compressed using the three techniques and benchmarked on a ThinkPad laptop and Raspberry Pi 3B+ based on metrics relevant for resource-constrained …

Applying Neural Networks To Predict Factors Affecting Harmful Algal Blooms For Timely Alerting And Implementing Preventive Measures In Ireland's Marine Ecosystem., Nikolai Potapov

ICT

This study applies neural networks to predict harmful algal blooms (HABs) along the Irish coast, addressing ecological, health, and economic risks. Using primary interviews and secondary data on HAB species like Alexandrium and Karenia mikimotoi, the research incorporated Exploratory Data Analysis and tested three neural models: LSTM, Ensemble Stacking LSTM, and CNN-LSTM. Key factors influencing HABs, such as sea surface temperature and euphotic zone depth, were identified.

Results demonstrate the potential of neural networks to improve HAB prediction and monitoring, despite limitations. Future work aims to enhance model accuracy and integrate them into HAB warning systems.

Development And Optimisation Of Convolutional Neural Networks (Cnns) To Predict The Nutrition And Sustainability Scores Of Foods From Crowd Sourced Images., Cormac Mcelhinney

ICT

This research explores the use of Convolutional Neural Networks (CNNs) for the automated classification and profiling of food products based on publicly sourced data. With the vast array of food products available worldwide and the complexity of labelling regulations, food business operators face challenges in ensuring compliance, while regulators struggle to verify adherence. This study addresses the need for efficient and accurate methods for food classification and eco/nutritional profiling. It begins with a comprehensive literature review on the application of CNNs in food product classification, followed by the collection of a large-scale dataset from Open Food Facts. A CNN architecture …

Evaluation And Implementation Of Machine Learning Models To Predict Customer Churn In The Telecommunications Sector., Stephen Hasson

ICT

This research addresses customer churn in the Telecom industry by utilizing Machine Learning (ML) models to predict customers at risk of leaving and provide data-driven retention strategies. The study highlights the effectiveness of ML, particularly in churn prediction, while noting the need for further exploration into the ethical implications of AI, such as potential biases towards vulnerable groups. Using the CRISP-DM framework, the study develops and compares three Supervised Learning (SL) models: Random Forests (RF), LightGBM (LGBM), and XGBoost (XGB), incorporating class resampling techniques to manage data imbalance.

The findings identified five key features as the most significant predictors of …

Data Analysis Of Twitter’S Nasdaq100 Sentiments And Topics As Indicators For News Articles Retrieval: Fine-Tuning Roberta And Rag., Kagan Timur

ICT

This study investigates the combination of sentiment analysis using the VADER lexicon and semantic analysis through Latent Dirichlet Allocation (LDA) to identify real-life events, focusing on Twitter datasets. The research shows that while sentiment analysis alone may be insufficient, combining it with semantic analysis improves the process, particularly for identifying relevant news articles and understanding brand perception on social media. The study also fine-tunes the RoBERTa model for question-answering tasks, yielding significant improvements in the SQuAD evaluation metric. The exact match (EM) score rose dramatically from 2.06% to 62%, and the F1 score improved from 9.41% to 65%. A retrieval …

Responsible Natural Language Processing To Aid Employee Performance Reviews., Grace Rubinger

ICT

This research explores the use of Natural Language Processing (NLP) techniques in assessing evaluators' written appraisals during Employee Performance Reviews (EPRs), aiming to address biases inherent in traditional methods. By integrating Responsible Artificial Intelligence (AI) and foundational Large Language Models (LLMs), the study seeks to enhance the objectivity, fairness, and ethical transparency of performance evaluations. It highlights the potential of AI systems to ensure comprehensive assessments while promoting trust, ethical standards, and employee retention.

The research also aims to advance the field of AI Ethics in practical Human Resources Management (HRM) applications, particularly through NLP-driven tools. These tools are designed …

Pneumothorax Detection And Segmentation From Chest X-Ray Radiographs Using A Patch-Based Fully Convolutional Encoder-Decoder Network, Jakov Ivan S. Dumbrique, Reynan Hernandez, Juan Miguel L. Cruz, Ryan M. Pagdanganan, Prospero C. Naval

Mathematics Faculty Publications

Pneumothorax, a life-threatening condition characterized by air accumulation in the pleural cavity, requires early and accurate detection for optimal patient outcomes. Chest X-ray radiographs are a common diagnostic tool due to their speed and affordability. However, detecting pneumothorax can be challenging for radiologists because the sole visual indicator is often a thin displaced pleural line. This research explores deep learning techniques to automate and improve the detection and segmentation of pneumothorax from chest X-ray radiographs. We propose a novel architecture that combines the advantages of fully convolutional neural networks (FCNNs) and Vision Transformers (ViTs) while using only convolutional modules to …