Use Of Interlaboratory Studies For The Development Of Consensus-Based Criteria For The Elemental Analysis Of Electrical Tapes, Lacey M. Leatherland

Graduate Theses, Dissertations, and Problem Reports (ETD)

Tape evidence is often used in criminal cases involving violent crimes, kidnappings, improvised explosive devices (IEDs), and drug trafficking. This evidence can reveal potential links between suspects, items, or scenes. The forensic examination of electrical tape can provide investigative leads or offer support to alternative hypotheses evaluated in the courtroom. A conventional analytical scheme includes microscopic examination, Fourier Transform Infrared Spectroscopy (FTIR), Scanning Electron Microscopy Energy Dispersive Spectrometry (SEM-EDS), and Pyrolysis Gas Chromatography Mass Spectrometry (Py-GC/MS). Elemental analysis of electrical tapes is commonly achieved using SEM-EDS; however, recent scientific literature suggests that this analysis can evolve from using SEM-EDS to …

Development Of Probabilistic Dynamic Model Building And Bayesian Machine Learning Approaches, Samuel Oladayo Adeyemo

Graduate Theses, Dissertations, and Problem Reports (ETD)

Abstract

Development of Probabilistic Dynamic Model Building and Bayesian Machine Learning Approaches

Samuel Adeyemo

The recent years have seen a tremendous increase in the use of artificial intelligence (AI) and machine learning (ML) for the development of data-driven mathematical models needed for performing real-time optimization, model-based control, performance optimization, dynamic data reconciliation, and process performance monitoring. However, the development of data-driven models is faced with some challenges including lack of model interpretability, sensitivity of algorithm to noise in training data, limited extrapolation capabilities and violation of conservation laws. Drawing motivation from these existing gaps, this work aims to develop robust …

Advanced Techniques In Time Series Forecasting: From Deterministic Models To Deep Learning, Xue Bai

Graduate Theses, Dissertations, and Problem Reports (ETD)

This dissertation discusses three instances of temporal prediction, applied to population dynamics and deep learning.

In population modeling, dynamic processes are frequently represented by systems of differential equations, allowing for the analysis of various phenomena. The first application explores modeling cloned hematopoiesis in chronic myeloid leukemia (CML) via a nonlinear system of differential equations. By tracking the evolution of different cell compartments, including cycling and quiescent stem cells, progenitor cells, differentiated cells, and terminally differentiated cells, the model captures the transition from normal hematopoiesis to the chronic and accelerated-acute phases of CML. Three distinct non-zero steady states are identified, representing …

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 …

A Comparative Analysis Of A Family Of Advanced Iterative Optimization Methods In Nonlinear Regression, Tanmoy Kumar Debnath

College of Graduate Studies: Theses & Dissertations

Classical statistical supervised learning optimization techniques like the Gauss-Newton Iterative Method (GNIM), Weighted Gauss-Newton Iterative Method (WGNIM), Reweighted Gauss-Newton Iterative Method (RGNIM), and Levenberg-Marquart (LM) algorithm extend the nonlinear least squares method. The WGNIM improves model fitting by controlling heteroscedasticity in the linear and nonlinear models. A comparative analysis of the GNIM, WGNIM, RGNIM, and LM methods for fitting nonlinear models is presented. A step-wise diagnosis for structural multicollinearity in the reweighted linearized model is investigated via the Variance Inflation Factor (VIF) to determine variance inflation in the sequence of estimators for the model parameters. Under restricted multicollinearity levels in …

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 …

Title: I: L1-Norm Matrix Completion For Recommender Systems Ii: Conjecturing-Based Classification, Fatemeh Valizadeh Gamchi

Theses and Dissertations

Recommendation systems are essential for providing personalized user experiences, but their performance can be affected by outliers especially in traditional collaborative filtering methods that use the L2-norm. To address this challenge, we developed two new algorithms, SharpEl1rs and SharpEl1rs-Impute, based on the L1-norm to improve resistance against extreme values and effectively handle missing data. Our experimental setting was designed to compare these proposed methods with existing techniques. Then our algorithms are applied to real datasets to assess their performance, with findings indicating that our proposed models offer improved accuracy in some cases and solid performance in others for industrial-scale recommendation …

Molecular Understanding And Design Of Deep Eutectic Solvents And Proteins Using Computer Simulations And Machine Learning, Usman Lame Abbas

Theses and Dissertations--Chemical and Materials Engineering

Hydrophobic deep eutectic solvents (DESs) have emerged as excellent extractants. A major challenge is the lack of an efficient tool to discover DES candidates. Currently, the search relies heavily on the researchers’ intuition or a trial-and-error process, which leads to a low success rate or bypassing of promising candidates. DES performance depends on the heterogeneous hydrogen bond environment formed by multiple hydrogen bond donors and acceptors. Understanding this heterogeneous hydrogen bond environment can help develop principles for designing high performance DESs for extraction and other separation applications. This work investigates the structure and dynamics of hydrogen bonds in hydrophobic DESs …

Penalized Interpolating B-Splines And Their Applications, Kylee L. Hartman-Caballero

Theses and Dissertations

One of the most studied data analysis techniques in Numerical Analysis is interpolation. Interpolation is used in a variety of fields, namely computer graphic design and biomedical research. Among interpolation techniques, cubic splines have been viewed as the standard since at least the 1960s, due to their ease of computation, numerical stability, and the relative smoothness of the interpolating curve. However, cubic splines have notable drawbacks, such as their lack of local control and necessary knowledge of boundary conditions. Arguably a more versatile interpolation technique is the use of B-splines. B-splines, a relative of Bézier curves, allow local control through …

Intelligent Traffic Management Systems, Mohammad Mazhar

All Graduate Theses, Dissertations, and Other Capstone Projects

With the increase in population and in particular urban population. The traffic and travel times in between cities and inside cities has increased due to more and more people using private means of transportation. Due to this need arose for tackling the increase in traffic by managing it using various means. For this we look towards The Intelligent Traffic Management System (ITMS). ITMS is an AI-powered solution designed to optimize traffic flow, reduce congestion, and improve overall road safety. The system will monitor real-time traffic data using a combination of cameras and sensors, identify traffic jams, and send alerts to …

Developing A Snow Detection Algorithm Using Spatial Attention For Pedestrian Safety, Ricardo De Deijn

All Graduate Theses, Dissertations, and Other Capstone Projects

SNOW-COVERED SIDEWALKS POSE SIGNIFICANT SAFETY HAZARDS, ESPECIALLY FOR VULNERABLE POPULATIONS SUCH AS THE ELDERLY AND VISUALLY IMPAIRED. THE DEVELOPMENT OF EFFECTIVE SNOW DETECTION SYSTEMS IS CRUCIAL FOR ENHANCING PEDESTRIAN SAFETY. THIS RESEARCH AIMS TO ADDRESS THESE CHALLENGES BY DEVELOPING A SNOW DETECTION ALGORITHM SPECIFICALLY DESIGNED FOR SIDEWALKS. THE PROPOSED ALGORITHM USES A CONVOLUTIONAL NEURAL NETWORK (CNN) ARCHITECTURE INCORPORATING A 2-DIMENSIONAL SPATIAL ATTENTION MECHANISM TO FOCUS ON RELEVANT FEATURES IN IMAGES, IMPROVING SNOW DETECTION ACCURACY. DUE TO THE SEASONAL AND GEOGRAPHIC LIMITATIONS OF SNOW DATA COLLECTION, SYNTHETIC DATA GENERATION USING INVERSE DIFFUSION MODELS WAS EMPLOYED TO AUGMENT THE REAL-WORLD DATASET. ALTHOUGH …

Metaheuristics For White-Box Path Attraction Attacks In Hidden Markov Models, Brandon Koch

All Graduate Theses, Dissertations, and Other Capstone Projects

Hidden Markov Models (HMMs) play a pivotal role in fields such as speech recognition, spam detection, and autonomous vehicles, where reliable predictive capabilities are essential. However, the rapid adoption of HMMs has heightened their susceptibility to adversarial attacks. This research investigates inherent weaknesses in traditional HMMs by examining how adversarial manipulation of observable data impacts model performance. We address three core questions: How does varying HMM parameters influence a path attraction problem? Which metaheuristic methods most effectively optimize these attacks in a white-box scenario? What key vulnerabilities emerge in HMMs under adversarial manipulation? To explore these questions, we design HMMs …

A Bayesian Inversion For Emissions And Export Productivity Across The End-Cretaceous Boundary, Alexander A. Cox

Dartmouth College Master’s Theses

The end-Cretaceous mass extinction was marked by both the Chicxulub impact and the ongoing emplacement of the Deccan Traps flood basalt province. Both of these events perturbed the environment by the emission of climate-active volatiles, primarily CO2 and SO2. To understand the mechanism of extinction, we must disentangle the timing, duration, and intensity of volcanic and meteoritic environmental forcings. In this thesis, we used a parallel Markov chain Monte Carlo approach to invert for the aforementioned volatile emissions, export productivity, and remineralization from 67 to 65 million years ago using the LOSCAR (Long-term Ocean-atmosphere-Sediment CArbon cycle Reservoir) model. The parallel …

Leveraging Redundancy As A Link Between Spreading Dynamics On And Of Networks, Felipe Xavier Costa

Electronic Theses & Dissertations (2024 - present)

A constant quest in network science has been in the development of methods to identify the most relevant components in a dynamical system solely via the interaction structure amongst its subsystems. This information allows the development of control and intervention strategies in biochemical signaling and epidemic spreading. We highlight the relevant components in heterogeneous dynamical system by their patterns of redundancy, which can connect how dynamics affect network topology and which pathways are necessary to spreading phenomena on networks. In order to measure the redundancies in a large class of empirical systems, we develop the backbone of directed networks methodology, …

Sparse Representation Learning For Temporal Networks, Maxwell Mcneil

Electronic Theses & Dissertations (2024 - present)

Temporal networks arise in many domains including activity of social network users, sensor network readings over time, and time course gene expression within the interaction network of a model organism. Data of this type contains a wealth of prior information such as the connectivity among nodes (e.g., a friendship graph), and prior knowledge of expected temporal patterns (e.g., periodicity). Modeling these temporal and network patterns jointly is essential for state-of-the-art performance in temporal network data analysis and mining. Sparse dictionary encoding is one modeling approach for such underlying patterns. However, most classical approaches consider only one dimension of the data …

Innovative Solutions In Medical Diagnostics, Seafood Industry, Environmental, And Forensic Applications Through Integrating Raman Spectroscopy And Machine Learning, Luis Perez Almodovar

Electronic Theses & Dissertations (2024 - present)

There is an urgent need for rapid and on-site analytical methods to address various environmental and forensic challenges. This dissertation explores the application of Raman spectroscopy combined with machine learning for three critical areas: evaluating perfluoroalkyl or polyfluoroalkyl substances (PFAS) exposure in wildlife, authenticating tuna species in the seafood industry, identifying body fluids at crime scenes, and medical diagnostics.

First, Raman spectroscopy and support vector machine discriminant analysis (SVMDA) were employed to detect PFAS levels in the blood plasma of smallmouth bass from two different tributaries, rivers, or streams that flow into larger bodies of water. The method achieved 100% …

Optimal Network Analysis Through Vertex Order Coloring Of Intuitionistic Fuzzy Graph Operations, A. Meenakshi, S. Dhanushiya, Hong Qin, Maniyandy Elangovan

Data Science Faculty Publications

Intuitionistic fuzzy graphs IFGs are a powerful tool for modeling uncertainty and complex relationships. They offer versatile frameworks for addressing real-world challenges. In this research, we have introduced intuitionistic fuzzy vertex order coloring IFVOC and analyzed the alpha-strong (alpha str), beta-strong (beta str), and gamma-strong (gamma str) vertices through their degree. We explored important theorems based on the types of strong vertices, broadening the scope of our study. We analyzed multiple IFG products to determine the most optimal network based on some important metrics, including the weight and total number of alpha str vertices, the chromatic number, and the weight …

Exacfs - A Cil Method To Mitigate Catastrophic Forgetting, S. Balasubramanian, Sai Subramaniam M., Sai Sriram Talasu, Manepalli Pranav Phanindra Sai, Yedu P. Krishna, Darshan Gera, Ravi Mukkamala

Computer Science Faculty Publications

Deep neural networks (DNNs) excel at learning from static datasets but struggle with continual learning, where data arrives sequentially. Catastrophic forgetting, the phenomenon of forgetting previously learned knowledge, is a primary challenge. This paper introduces EXponentially Averaged Class-wise Feature Significance (EXACFS) to mitigate this issue in the class incremental learning (CIL) setting. By estimating the significance of model features for each learned class using loss gradients, gradually aging the significance through the incremental tasks and preserving the significant features through a distillation loss, EXACFS effectively balances remembering old knowledge (stability) and learning new knowledge (plasticity). Extensive experiments on CIFAR-100 and …

Learning Optimal Inter-Class Margin Adaptively For Few-Shot Class-Incremental Learning Via Neural Collapse-Based Meta-Learning, Hang Ran, Weijun Li, Lusi Li, Songsong Tian, Xin Ning, Prayag Tiwari

Computer Science Faculty Publications

Few-Shot Class-Incremental Learning (FSCIL) aims to learn new classes incrementally with a limited number of samples per class. It faces issues of forgetting previously learned classes and overfitting on few-shot classes. An efficient strategy is to learn features that are discriminative in both base and incremental sessions. Current methods improve discriminability by manually designing inter-class margins based on empirical observations, which can be suboptimal. The emerging Neural Collapse (NC) theory provides a theoretically optimal inter-class margin for classification, serving as a basis for adaptively computing the margin. Yet, it is designed for closed, balanced data, not for sequential or few-shot …

Osfs-Vague: Online Streaming Feature Selection Algorithm Based On A Vague Set, Jie Yang, Zhijun Wang, Guoyin Wang, Yanmin Liu, Yi He, Di Wu

Computer Science Faculty Publications

Online streaming feature selection (OSFS), as an online learning manner to handle streaming features, is critical in addressing high-dimensional data. In real big data-related applications, the patterns and distributions of streaming features constantly change over time due to dynamic data generation environments. However, existing OSFS methods rely on presented and fixed hyperparameters, which undoubtedly lead to poor selection performance when encountering dynamic features. To make up for the existing shortcomings, the authors propose a novel OSFS algorithm based on vague set, named OSFS-Vague. Its main idea is to combine uncertainty and three-way decision theories to improve feature selection from the …