Data Science Commons^™

Performing Holt-Winters Time Series Forecasting Using Neural Network Based Models, Kazeem Olanrewaju Bankole

College of Graduate Studies: Theses & Dissertations

We show how to create Artificial Neural Network based models for performing the well- known Holt-Winters time series analysis. Our work fares well compared to the well-known Holt-Winter time series prediction method while avoiding the burden of searching for the parameters of the model. We present the theoretical justification of the connection between the two models and experimental results showing the similarities of these models

Comparison Of Classification Methodologies Using Convolutional Neural Networks In A Dataset Of Plant Leaf Diseases., Ruairi O’Donohoe

ICT

This project investigates the impact of classification methodology selection on the performance of four Convolutional Neural Network (CNN) models applied to a multi-label image dataset. The dataset consists of plant leaf images with one or more diseases. Two classification methodologies—multi-label and multi-class—are compared based on their model performance metrics. It was hypothesised that multi-label classification would perform better, but the results show that although multi-label models performed better for Loss and Accuracy metrics, they underperformed in terms of the F1 score, which is considered a more appropriate metric for this task. This surprising result refutes the initial hypothesis. Transfer learning …

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, …

Editorial: Artificial Intelligence, Machine Learning, And Data-Mining Techniques To Increase Cost-Effectiveness In Healthcare., P Wilner Jeanty, Marie-Rachelle Narcisse, Romain Crastes Dit Sourd

Ambulatory and Primary Care Articles

No abstract provided.

Developing Machine Learning And Time-Series Analysis Methods With Applications In Diverse Fields, Muhammed Aljifri

Theses and Dissertations

This dissertation introduces methodologies that combine machine learning models with time-series analysis to tackle data analysis challenges in varied fields. The first study enhances the traditional cumulative sum control charts with machine learning models to leverage their predictive power for better detection of process shifts, applying this advanced control chart to monitor hospital readmission rates. The second project develops multi-layer models for predicting chemical concentrations from ultraviolet-visible spectroscopy data, specifically addressing the challenge of analyzing chemicals with a wide range of concentrations. The third study presents a new method for detecting multiple changepoints in autocorrelated ordinal time series, using the …

Large Language Models, Prompting, And Synthetic Data Generation For Continual Named Entity Recognition, Charles I. Cutler

Theses and Dissertations

With the ever-growing amount of textual data, the task of Named Entity Recognition (NER) is vital to Natural Language Processing (NLP), a field which focuses on enabling computers to understand and manipulate human language. NER enables the extraction of information from unstructured text. Accurate information extraction is crucial for applications ranging from information retrieval to systems for question-answering. To ensure that NER models are robust to changes in data distributions and capable of recognizing new entity types, one may consider expanding the capabilities of an existing model. Continual learning is a paradigm within machine learning. It studies the objective of …

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 …

Statistical And Machine Learning Techniques For Predicting Solar Power Generation In A Microgrid., Conor Dillon

ICT

This study investigates statistical and machine learning models for forecasting solar power generation in microgrids, focusing on the solar installation at Powell-Focht Bioengineering Hall, UC San Diego. Accurate predictions are critical due to the variability of solar energy, aiming to optimise microgrid operations and solar power efficiency. The research compares the performance of SARIMAX, LSTM, Random Forest, and ANN models using meteorological and solar power time series data. It finds that current meteorological inputs, especially solar radiation, enhance short-term forecasting accuracy over reliance on historical patterns.

The Random Forest Auto Regressor (RFAR) outperformed other models in 10-day-ahead solar power forecasting, …

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 …

Using Machine Learning To Identify Hate Speech And Offending Language On Twitter., Mayara Lorens, Thayene Lorens

ICT

This project focuses on applying Machine Learning (ML) techniques to detect hate speech and offensive language on Twitter, addressing ethical concerns like cyberbullying and fostering a safer online environment. The topic is chosen for its societal significance and business relevance, as hostile online behaviour negatively impacts user experiences and platform credibility.

To achieve this, the study implements four distinct ML models to develop an automated system capable of identifying and categorising content as offensive, non-offensive, or neutral. The system aims to contribute to mitigating harmful interactions on social media and improving user safety by effectively classifying potentially problematic content.

The …

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 …

Judging Our New Judges: Why We Must Remove Artificial Intelligence From Our Courtrooms Now, Kieran Duffy Newcomb

Honors Theses and Capstones

In this paper, I explore some of the ways in which artificial intelligence might enhance the sentencing process through recidivism prediction technology. Notably, this technology can increase the accuracy of risk predictions and the speed with which sentencing decisions are reached. I then show, however, that the recidivism prediction technology is likely to turn into what data scientist Cathy O’Neil calls a Weapon of Math Destruction. The potential harmfulness of this technology is due not to the inherent nature of the technology, but the symbiotic relationship it will have with our already harmful criminal justice system. I argue that the …

A Deep Learning Model For Early Diagnosis Of Systemic Lupus Erythematosus From Facial Images, Shourav Bikash Dey

All Graduate Theses, Dissertations, and Other Capstone Projects

Systemic Lupus Erythematosus (SLE) poses significant challenges due to its complex and varied symptoms making diagnosis extremely challenging and time consuming. Symptoms of SLE often mimics other autoimmune or physical conditions and around 5 million people worldwide suffers from this condition, as reported by the Lupus Foundation of America during their study in 2019. However, diagnosis is much more difficult in developing countries with backdated clinical technology and setup therefore, making it virtually unknown the exact number of SLE patient count worldwide. Among all the heterogeneous symptoms presented by SLE, Butterfly Malar Rash (BMR) is one of the symptoms that …

Comparative Analysis Of Data Augmentation On Sentiment Analysis In Three Distinct Languages, Hyesu Lee

All Graduate Theses, Dissertations, and Other Capstone Projects

Machine learning in natural language processing analyzes datasets to make future predictions for various filed in the real world. By training machine algorithms on the datasets of text, the model can learn patterns and structure of the text in many different languages. Then the model enables to perform the text classification, sentiment analysis, and other tasks. A large and balanced dataset is required to develop an accurate machine learning model. However, the collection of a reliable, large, and equally distributed dataset is a challenging and requires significant resources and time. As a solution to this challenge, a data augmentation technique …

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.

Assessment Of The Impact Of Various Feature Extraction Techniques On The Effectiveness Of Music Genre Classification In Neural Network Models., Sabhdh Grace

ICT

This research focuses on Music Genre Classification (MGC) using Convolutional Neural Networks (CNNs) and various datasets, including raw audio files (WAV) and extracted features such as Mel Spectrograms (MS), Mel-Frequency Cepstral Coefficients (MFCC), and Chroma Features (CF). The study employs Explanatory Sequential Mixed Methods (ESMM), combining qualitative research and experimental analysis to explore different model inputs and their performance. Several CNN-based models, including 2D CNN, 2D CNN-LSTM, 1D CNN, and 1D CNN-LSTM, were tested. However, the models generally underperformed, with most achieving accuracy of 10% or lower, and the best model (raw audio 1D CNN) reaching only 20%. The research …

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 …

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 …

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 …

Evaluating The Performance Of Different Long Short-Term Memory Networks (Lstm’S) On Financial Timeseries Data Using Mean Squared Error In Order To Identify The Optimum Lstm Variant For Regression Performance On Financial Timeseries Data., Patrick O’ Connor

ICT

This study explores the use of Long Short Term Memory (LSTM) networks, a variant of Recurrent Neural Networks (RNNs), in the context of financial forecasting, specifically oil price prediction. The research follows the CRISP-DM (Cross-Industry Standard Process for Data Mining) methodology and tests six different LSTM variants. The models are evaluated based on Mean Squared Error (MSE), aiming to determine the optimal parameter settings for each LSTM type. Among the variants tested, the Gated Recurrent Unit (GRU) emerged as the highest performer, achieving an MSE of 0.100. This was surprising, as simpler variants outperformed more complex ones, suggesting that simpler …

Ml Predictive Model For Earthquakes Integrating Mass, Distance, Gravity, And Magnitude., Aadarsh Kushwaha

ICT

This research investigates the application of machine learning regression models to improve earthquake prediction by integrating geophysical and astronomical factors such as Earth-Moon gravitational forces, their varying distances, and localized gravity fluctuations. Using data from 2011 to 2024, sourced from the US Geological Survey (USGS) and web scraping, the study tested models across four dataset proportions (25%, 50%, 80%, and 100%) with a 70:30 train-test split. The XGBRegressor model emerged as the best performer, achieving an R² score of 0.8706 on training data and 0.8632 on test data, along with a Mean Squared Error (MSE) of 0.1114 and Mean Absolute …

Maize Crop Pests And Diseases Classification Using Hybrid Models., Diana Flora Namaemba

ICT

This research focuses on improving the detection and classification of maize crop pests and diseases to enhance agricultural yield and food security. A dataset comprising 5389 images of maize conditions (healthy, pest-affected, and disease-affected) across seven classes was used. The images underwent preprocessing, including resizing to 299x299, class balancing using augmentation techniques, and noise reduction with Gaussian filtering.

Feature extraction utilised EfficientNetB0 and InceptionV3 architectures, with PCA employed for feature selection. Classification was conducted using a Support Vector Machine (SVM) with a One-vs-One strategy, alongside a baseline 2D CNN model. Data engineering included label encoding, standardisation, and an 80:10:10 train-test-validation …

The Hazard Prediction Problem, Mary E. Helander, Brendan Smith, Sylvia Charchut, Erika Swiatowy, Calvin Nau, Gregory Cavaretta, Timothy Schuler, Adam Schunk, Héctor Ortiz-Peña

Social Science - All Scholarship

This work formulates the hazard prediction problem while addressing the research question: Can machine learning create a model to automatically recognize patterns that correspond to hazard state conditions during a mission-critical operation? Supervised learning models were trained and tested on data observed from mission simulators, which allowed for safe observation of dynamic system states and undesirable casualty events. The prediction task was formulated as a binary classification problem, producing the probability of being in a hazard state at time t and providing situational awareness of a possible imminent loss. Several modeling architectures were investigated: neural networks, logistic regression, a support …

Machine Learning Approaches For Cyberbullying Detection, Roland Fiagbe

Data Science and Data Mining

Cyberbullying refers to the act of bullying using electronic means and the internet. In recent years, this act has been identifed to be a major problem among young people and even adults. It can negatively impact one’s emotions and lead to adverse outcomes like depression, anxiety, harassment, and suicide, among others. This has led to the need to employ machine learning techniques to automatically detect cyberbullying and prevent them on various social media platforms. In this study, we want to analyze the combination of some Natural Language Processing (NLP) algorithms (such as Bag-of-Words and TFIDF) with some popular machine learning …

Data Science In Finance: Challenges And Opportunities, Xianrong Zheng, Elizabeth Gildea, Sheng Chai, Tongxiao Zhang, Shuxi Wang

Information Technology & Decision Sciences Faculty Publications

Data science has become increasingly popular due to emerging technologies, including generative AI, big data, deep learning, etc. It can provide insights from data that are hard to determine from a human perspective. Data science in finance helps to provide more personal and safer experiences for customers and develop cutting-edge solutions for a company. This paper surveys the challenges and opportunities in applying data science to finance. It provides a state-of-the-art review of financial technologies, algorithmic trading, and fraud detection. Also, the paper identifies two research topics. One is how to use generative AI in algorithmic trading. The other is …

A Novel K-Nearest Neighbors Method Based On Generalized Feature Optimization For Precipitation Forecasting, Sean Guidry Stanteen

Mathematics Dissertations - Archive

This study introduces a novel k-nearest neighbors (kNN) method of forecasting precipitation at weather-observing stations. The method identifies numerous monthly temporal patterns to produce precipitation forecasts for a specific month. Compared to climatological forecasts, which average the observed precipitation over the prior thirty years, and other existing contemporary iterations of kNN, the proposed novel kNN method produces more accurate forecasts on a consistent basis. Specifically, the novel kNN method produces improved root mean square errors (RMSE), mean relative errors, and Nash-Sutcliffe coefficients when compared to climatological and other kNN forecasts at five weather …

Integrating Machine Learning With Cure Models And Associated Inference, Wisdom Aselisewine

Mathematics Dissertations - Archive

Recent advancements in medical treatments have significantly enhanced the rates of recovery for numerous chronic illnesses. This progress has sparked growing interest in developing suitable statistical models capable of handling survival data that includes substantial cure fractions. The mixture cure model finds extensive application in analyzing survival data when there exists a cured subgroup. Standard logistic regression-based approaches for modeling the incidence part of the mixture cure model may suffer from poor predictive accuracy, especially in the presence of high dimensional covariates and/or non-linear covariate effects. To overcome this limitation, we propose the integration of distinct machine learning algorithms with …

Language Models For Rare Disease Information Extraction: Empirical Insights And Model Comparisons, Shashank Gupta

Theses and Dissertations--Computer Science

End-to-end relation extraction (E2ERE) is a crucial task in natural language processing (NLP) that involves identifying and classifying semantic relationships between entities in text. This thesis compares three paradigms for end-to-end relation extraction (E2ERE) in biomedicine, focusing on rare diseases with discontinuous and nested entities. We evaluate Named Entity Recognition (NER) to Relation Extraction (RE) pipelines, sequence-to-sequence models, and generative pre-trained transformer (GPT) models using the RareDis information extraction dataset. Our findings indicate that pipeline models are the most effective, followed closely by sequence-to-sequence models. GPT models, despite having eight times as many parameters, perform worse than sequence-to-sequence models and …

Three Essays On Energy Related To State Policies And Low Carbon Transitions, Pinky Thomas

Graduate Theses, Dissertations, and Problem Reports (ETD)

This dissertation consists of three essays on energy-related state policies and energy transition. Each paragraph below refers to the three abstracts for the three chapters in this dissertation, respectively.

The first essay is entitled: “Impacts of State Tax and Resource Ownership Policies on Extraction: Evidence from U.S. Natural Gas Production”. The innovation of combined use of horizontal drilling and hydraulic fracturing technologies during the 2000s has allowed natural gas producers in the United States to extract natural gas and liquids from deep shale formations in a cost-efficient manner. This essay evaluates whether unconventional gas production responds to tax changes, and …

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 …