Physical Sciences and Mathematics Commons^™

Precision Agriculture In The Age Of Ai: A Systematic Review Of Machine Learning Methods For Crop Disease Detection, Munir Majdalawieh, Carla Martins, Mohammed Radi, Maher Alaraj, Shafaq Khan

All Works

Artificial Intelligence (AI) has become a critical tool in modern precision agriculture, particularly in the detection of plant diseases and pests. This study provides a comprehensive review of current AI methodologies applied to crop disease detection, with a focus on machine learning models, dataset availability, and performance metrics. Our findings indicate that Convolutional Neural Networks (CNNs) are the most widely used and cost-effective approach, while Vision Transformers (ViTs) exhibit superior accuracy but require significantly higher computational resources. We identify key research gaps, including the geographic bias in dataset origins, the trade-off between data quality and quantity, and the limited exploration …

Llm-Driven Semantic Explanations For Soil Moisture Prediction Models, Bamory Ahmed Toru Koné, Khouloud Boukadi, Rima Grati, Emna Ben Abdallah, Massimo Mecella

All Works

Efficient soil moisture prediction is crucial for sustainable agricultural practices, especially in the face of climate change and increasing water scarcity. However, the adoption of machine learning (ML) models in this context is frequently limited by their lack of interpretability, particularly among non-expert users such as farmers. This study proposes a novel approach to soil moisture prediction that combines high predictive performance with enhanced explainability. We propose a framework that leverages large language models (LLMs) to generate textual explanations based on a proposed irrigation and soil moisture ontology, thus making the model's predictions more understandable to farmers. The ontology formalizes …

Physics-Guided Strategies For Enhancing Neural Networks Trained With Limited Data, Jose Guadalupe Perez Zamora

Open Access Theses & Dissertations

Deep neural networks excel at a wide range of processing tasks across various disciplines. However, the quantity and quality of data significantly impact network performance. In specialized domains, high-quality datasets are often difficult to gather, interpret, and curate for effective learning. Few-shot learning techniques, including transfer learning, data augmentation, and meta-learning, have emerged to address these constraints.

We propose three physics-guided strategies for enhancing neural networks trained with limited data: (1) combining existing models like LSTMs with Physics-Informed Neural Networks through two-branch architectures that merge their outputs, (2) deriving custom physics-informed data augmentation algorithms to expand limited datasets, and (3) …

Bias Testing And Mitigation In Llm-Based Code Generation, Dong Huang, Jie M. Zhang, Qingwen Bu, Xiaofei Xie, Junjie Chen, Heming Cui

Research Collection School Of Computing and Information Systems

As the adoption of LLMs becomes more widespread in software coding ecosystems, a pressing issue has emerged: does the generated code contain social bias and unfairness, such as those related to age, gender, and race? This issue concerns the integrity, fairness, and ethical foundation of software applications that depend on the code generated by these models but are underexplored in the literature. This paper presents a novel bias testing framework that is specifically designed for code generation tasks. Based on this framework, we conduct an extensive empirical study on the biases in code generated by five widely studied LLMs (i.e., …

Is Complexity Virtuous?, Ryan Elmore, Jack Strauss

Business Information and Analytics: Faculty Scholarship

(Kelly et al., 2024) show that increasing complexity in linear models, with potentially thousands of predictors, is "virtuous". Their work contradicts the dogma of model selection, including the Principles of Parsimony and Occam's Razor. They find that when the number of predictors far exceeds the number of observations, the bias-variance trade-off breaks down, the variance declines, and the Sharpe ratio increases. In the context of ridge regression, we find that very high complexity coupled with large penalty terms (excessive shrinkage) generate forecasts that converge to a rolling window of past returns. For example, we show the past twelve-month moving average …

Introduction To Machine Learning And Machine Learning Systems, Raffi T. Khatchadourian Ph.D.

Open Educational Resources

Lecture slides introducing machine learning and machine learning systems for an undergraduate software engineering course. Topics include what machine learning is and how it differs from traditional programming, foundation models, the major types of learning (supervised, unsupervised, reinforcement, and others), and applications across domains. Using a food-delivery time-prediction case study, the deck walks through a typical ML pipeline—data collection and cleaning, feature engineering, model training, and evaluation—and covers evaluation methods (precision and recall, confusion matrices, error measures) along with underfitting versus overfitting and the realities of learning and evaluation in production. Based on "Machine Learning in Production/AI Engineering" by Christian …

Fish-Spec: Fast Identification System For Handheld Spectroscopy And Species Classification, Mitchell Sueker, Nicholas Mackinnon, Gregory Bearman, Amanda Tabb, Diane Kim, Rosalee S. Hellberg, Alireza Akhbardeh, Hamid Reza Marateb, Jianwei Qin, Moon Kim, Fartash Vasefi, Hossein Kashani Zadeh

Food Science Faculty Articles and Research

Accurate fish species identification is critical to prevent mislabeling and fraud in the seafood industry. We present a handheld multi-mode point spectroscopy system that combines fluorescence (365 and 395 nm excitation) and reflectance measurements in the visible to near-infrared (∼350–900 nm) and short-wave infrared (∼900–1700 nm) regions for rapid, non-destructive classification of fish fillets. Tissue spectra were acquired at 25 positions on 68 fillets from 11 species, in both frozen and thawed states. Feature-level fusion across all four modes enabled higher classification accuracy than any single mode alone. A global machine-learning model classified all species with 85 ± 2.8 %, …

Archaeological Predictive Meta-Modeling In Pre-Columbian Mexico, Peter Stamm

Electronic Theses and Dissertations

Archaeological Predictive Modeling stands firmly as an important tool for Archaeologists to predict undiscovered sites from civilizations all across the globe. While powerful, this methodology is not without its own set of qualms. Striking a balance between pure a data-driven approach while also observing leading expert theories can be a complicated task. Going further, deciding on the specific domain of features to emphasize or overlook can be a challenge within itself, as one misstep can drastically change the output of model, sometimes for the worst. In addition, creating models that can expose their reasoning process can be rather difficult to …

Multiplexed Integrin Detection And Cancer Cell Classification Using Multicolor Gap-Enhanced Gold Nanorods And Machine Learning Algorithm, Suprava Shah, Reed Youngerman, Alberto Luis Rodriguez-Nieves, Mitchell Lee Taylor, William Rodney Bantom, David Thompson, Jingyi Chen, Yongmei Wang, Xiaohua Huang

Chemistry & Biochemistry Faculty Publications and Presentations

Integrins, cell-surface adhesion receptors involved in tumor progression, invasion, and metastasis, serve as crucial biomarkers for cancer diagnosis and therapeutic targeting. Multiplexed detection of integrins and cancer cell classification at the single-cell level allows for comprehensive profiling, facilitating precise identification and categorization of tumor cells that are heterogeneous in integrin expression and cell subtype. In this study, we developed a five-plex detection platform and demonstrated integrin profile for cancer cell classification leveraging surface-enhanced Raman scattering (SERS) with gap-enhanced gold nanorods (GENRs) in conjunction with advanced computational analysis. Specifically, we synthesized GENRs bearing five distinct Raman nanotags, each producing a unique …

Interpretable Machine Learning For Cardiovascular Risk Prediction: Insights From Nhanes Dietary And Health Data, Md Ahiduzzaman, Md Nahid Hasan

Faculty Publications

Background: Cardiovascular diseases (CVD) are one of the leading global causes of death, which requires an accurate early prediction. This study aimed to develop transparent machine learning (ML) models using National Health and Nutrition Examination Survey (NHANES) data from 2017–2023 to predict CVD risk based on dietary and health factors.

Methods: We analyzed data from 12,382 adults (aged 18 and older) from NHANES 2017–2023, including 41 dietary, anthropometric, clinical, and demographic variables. Recursive Feature Elimination (RFE) was used to select an optimal subset of 30 predictors. To address substantial class imbalance in the outcome, we applied the Random Over-Sampling Examples …

Image Captioning Through The Lens Of The Gricean Maxims: Generating Meaningful And Relevant Image Descriptions, Annika Lindh

Doctoral

Image captioning models enable us to automatically generate natural language image descriptions for previously unseen images. It combines the two fields of computer vision and natural language generation, allowing models to interpret the con tent of an image and communicate that knowledge through natural language text.

Research into image captioning has the potential benefit of reducing the gap in digital information availability between fully sighted individuals and those who are visually impaired. However, automatically generated captions often fail to provide the required level of detail and specificity to achieve this goal. Furthermore, current standard evaluation methods are insufficient at measuring …

Computational Nanoscale Investigations Of Structure, Dynamics, And Reactivity At Solid–Liquid Interfaces, Golam Azom

LSU Doctoral Dissertations

Interfaces where two or more condensed phases meet govern phenomena central to catalysis, electrochemical energy conversion, separations, and environmental remediation. Their structural heterogeneity and dynamic nature make them challenging to probe experimentally, but computer simulations provide molecular-level insights into processes that remain inaccessible to experiments. This dissertation employs ab initio molecular dynamics (AIMD), classical molecular dynamics, and machine-learned interatomic potentials to investigate the structure, dynamics, and proton transport at graphene oxide (GO)–liquid interfaces.

The first project explores aqueous GO interfaces across a spectrum of oxidation states that extends from highly oxidized sheets to fully reduced graphene. Deep neural network force …

Investigating Characteristic Droplet Size Distributions In Large Eddy Simulations Of Stratocumulus Clouds, Nithin Allwayin, Daniel J. Miller, Kamal Kant Chandrakar, Michael L. Larsen, Raymond A. Shaw

Michigan Tech Publications

Cloud processes relevant to radiative and precipitation properties depend on the shape of the cloud droplet size distribution. Recent holographic observations revealed that cloud droplet populations do not have the same size distribution shapes throughout but form regions of characteristic distributions with similar microphysical properties. We investigate the existence and properties of these characteristic distributions within Large-Eddy Simulations of stratocumulus clouds using Lagrangian and bin microphysics schemes. Distribution types are identified, revealing localized characteristic distributions that vary on the scale of the largest convective cell for simulations with bin microphysics. The results from the Lagrangian microphysics scheme hint at similar …

Dramatic Biases In Terrestrial Nitrogen Fixation In Earth System Models Revealed By Natural Isotope Signatures, Maoyuan Fang, Shushi Peng, Philippe Ciais, Daniel S. Goll, Benjamin Z. Houlton, Ying-Ping Wang, Yilong Wang, Pan Liu, Joshua B. Fisher, Pierre Regnier

Biology, Chemistry, and Environmental Sciences Faculty Articles and Research

Biological nitrogen fixation (BNF) is the primary input of new reactive nitrogen to natural terrestrial ecosystems. However, this flux is poorly constrained due to its unclear drivers and associated control mechanisms. Here, we extend the existing theory of nitrogen (N) isotope mass balance to estimate BNF rates and then use a Bayesian approach to constrain the BNF rates in natural terrestrial ecosystems by using measurements of natural N-isotope ratios (δ¹⁵N) in plants (δ_P) and soil (δ_S). Together with pairwise δ_P and δ_S measurements from 18 forest sites covering diverse climates and thousands …

Improved Streamflow Forecasting Through Swe-Augmented Spatio-Temporal Graph Neural Networks, Akhila Akkala, Soukaina Filali Boubrahimi, Shah Muhammad Hamdi, Pouya Hosseinzadeh, Ayman Nassar

Computer Science Student Research

Streamflow forecasting in snowmelt-dominated basins is essential for water resource planning, flood mitigation, and ecological sustainability. This study presents a comparative evaluation of statistical, machine learning (Random Forest), and deep learning models (Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), and Spatio-Temporal Graph Neural Network (STGNN)) using 30 years of data from 20 monitoring stations across the Upper Colorado River Basin (UCRB). We assess the impact of integrating meteorological variables—particularly, the Snow Water Equivalent (SWE)—and spatial dependencies on predictive performance. Among all models, the Spatio-Temporal Graph Neural Network (STGNN) achieved the highest accuracy, with a Nash–Sutcliffe Efficiency (NSE) of 0.84 …

Fast, Slow, And Metacognitive Thinking In Ai, M. Bergamaschi Ganapini, M. Campbell, F. Fabiano, L. Horesh, J. Lenchner, A. Loreggia, N. Mattei, F. Rossi, Biplav Srivastava, K. B. Venable

Faculty Publications

Inspired by the ”thinking fast and slow” cognitive theory of human decision making, we propose a multi-agent cognitive architecture (SOFAI) that is based on ”fast”/”slow” solvers and a metacognitive module. We then present experimental results on the behavior of an instance of this architecture for AI systems that make decisions about navigating in a constrained environment. We show that combining the two decision modalities through a separate metacognitive function allows for higher decision quality with less resource consumption compared to employing only one of the two modalities. Analyzing how the system achieves this, we also provide evidence for the emergence …

Explainabledetector: Exploring Transformer-Based Language Modeling Approach For Sms Spam Detection With Explainability Analysis, Mohammad Amaz Uddin, Muhammad Nazrul Islam, Leandros Maglaras, Helge Janicke, Iqbal H. Sarker

Research outputs 2022 to 2026

Short Message Service (SMS) is a widely used and cost-effective communication medium that has unfortunately become a frequent target for unsolicited messages - commonly known as SMS spam. With the rapid adoption of smartphones and increased Internet connectivity, SMS spam has emerged as a prevalent threat. Spammers have recognized the critical role SMS plays in today's modern communication, making it a prime target for abuse. As cybersecurity threats continue to evolve, the volume of SMS spam has increased substantially in recent years. Moreover, the unstructured format of SMS data creates significant challenges for SMS spam detection, making it more difficult …

Leveraging Machine Learning And Causal Inference For Loan Default Prediction, Luca Guida

Doctoral Dissertations and Master's Theses

This research explores a systematic application of machine learning techniques combined with causal inference to predict loan defaults in peer-to-peer lending. Accurately forecasting loan defaults is crucial for mitigating financial risk and optimizing lending strategies. This analysis is based on multiple datasets of loan applications spanning over a decade, containing detailed financial and credit information about borrowers. Beginning with extensive Exploratory Data Analysis (EDA) coupled with scaling strategies, the research identifies key trends in loan performance across a large number of factors, such as interest rates or borrower creditworthiness, and one objective is to determine from the many available predictors …

Deep Learning For Hate Speech Detection: A Comparative Study, Jitendra Singh Malik, Hezhe Qiao, Guansong Pang, Anton Van Den Hengel

Research Collection School Of Computing and Information Systems

Automated hate speech detection is an important tool in combating the spread of hate speech, particularly in social media. Numerous methods have been developed for the task, including a recent proliferation of deep-learning based approaches. A variety of datasets have also been developed, exemplifying various manifestations of the hate-speech detection problem. We present here a largescale empirical comparison of deep and shallow hate-speech detection methods, mediated through the three most commonly used datasets. Our goal is to illuminate progress in the area, and identify strengths and weaknesses in the current state-of-the-art. We particularly focus our analysis on measures of practical …

Non-Invasive Detection Of Choroidal Melanoma Via Tear-Derived Protein Corona On Gold Nanoparticles: A Machine Learning Approach, Hakimeh Rakhshandeh, Ahmad Nasiraei, Hamid Riazi-Esfahani, Babak Masoomian, Fariba Ghassemi, Mojtaba Arjmand, Saeed Heidari Keshel, Fatemeh Atyabi, Rassoul Dinarvand

Wills Eye Hospital Papers

This study investigates the feasibility of using tear sample analysis, based on protein corona formation on gold nanoparticles combined with electrospray ionization mass spectrometry (ESI-MS) and machine learning techniques, as a non-invasive approach for the detection of choroidal melanoma. The aim is to assess whether protein-nanoparticle interactions can support early and reliable identification of this ocular condition. Tear samples were collected using Schirmer strips from six healthy individuals and six patients diagnosed with choroidal melanoma, with subsequent augmentation to 18 samples per group. Gold nanoparticles (AuNPs, ~ 20 nm) were synthesized via citrate reduction and incubated with tear samples to …

Unveiling The Interplay Of Electronic And Phononic Excitations In Laser-Induced Oxygen Activation On Ru(0001), Xiangrui Wang, Jiamin Wang, Paul Spiering, Liping Liu, Jörg Meyer, Jerry L. Larue, Hongliang Xin

Biology, Chemistry, and Environmental Sciences Faculty Articles and Research

Understanding laser-induced dynamics on metal surfaces poses significant challenges due to the intricate interplay between electronic and phononic degrees of freedom, which evolve on distinct timescales. In this study, we introduce a machine learning-accelerated approach to molecular dynamics simulations that incorporates anisotropic electronic friction, providing deeper insights into these complex processes. Our framework extends the accessible time and length scales for nonadiabatic dynamics simulations, enabling a detailed investigation of the laser-induced activation of oxygen on the Ru(0001) surface. Statistical analysis reveals that strong electronic excitation dominates the first 800 fs after laser exposure. Beyond this timescale, energy deposited by electronic …

Artificial Intelligence In Head And Neck Cancer: Towards Precision Medicine, Jacob Hagen, Logan Hornung, William Barham, Supratik Mukhopadhyay, Adam Bess, Kevin Contrera, Devraj Basu, Vlad Sandulache, Guillaume Spielmann, Sagar Kansara

School of Medicine Faculty Publications

Over the past 20 years, the capabilities of artificial intelligence (AI) have gained significant interest. While AI has been implemented to various degrees in several disciplines, its unique applications in head and neck cancer (HNC) remain underdeveloped. This narrative review examines the existing body of literature regarding the use of AI in HNC. Studies to date have demonstrated AI’s utility across multiple phases of the HNC treatment continuum. Despite its promise, integrating AI into clinical practice faces several challenges, including concerns about system integrity, generalizability, privacy, and bias. In this review, we address these challenges and offer insights into future …

Intelligence Architectures And Machine Learning Applications In Contemporary Spine Care, Rahul Kumar, Conor Dougherty, Kyle Sporn, Akshay Khanna, Puja Ravi, Pranay Prabhakar, Nasif Zaman

SKMC Student Presentations and Publications

The rapid evolution of artificial intelligence (AI) and machine learning (ML) technologies has initiated a paradigm shift in contemporary spine care. This narrative review synthesizes advances across imaging-based diagnostics, surgical planning, genomic risk stratification, and post-operative outcome prediction. We critically assess high-performing AI tools, such as convolutional neural networks for vertebral fracture detection, robotic guidance platforms like Mazor X and ExcelsiusGPS, and deep learning-based morphometric analysis systems. In parallel, we examine the emergence of ambient clinical intelligence and precision pharmacogenomics as enablers of personalized spine care. Notably, genome-wide association studies (GWAS) and polygenic risk scores are enabling a shift from …

Deep Learning-Driven Proteomics Analysis For Gene Annotation In The Renin-Angiotensin System, Mortaza Eivazi, Kamran Hosseini, Shahin Alipanahi, Huijing Xia, Luke Restivo, Ayushi Patel, Mahdieh Gozali, Tahereh Ebrahimi, Amy Scarborough, Vahideh Tarhriz, Eric Lazartigues

School of Medicine Faculty Publications

The renin-angiotensin system (RAS) is central to cardiovascular diseases such as hypertension and cardiomyopathy, yet the functions of many RAS genes remain unclear. This study developed a multi-label deep learning model to systematically annotate RAS gene functions and elucidate their roles in biological pathways. A total of 39,463 RAS-related publications from PubMed and PMC were processed into text format. Feature matrices were generated using TF-IDF and token processing, followed by dimensionality reduction via Principal Component Analysis (PCA). A Multi-Layer Perceptron (MLP) was applied for multi-label classification, with performance evaluated using Precision, F1-Score, Ranking Loss, and ROC-AUC metrics. The model outperformed …

The “Founder’S Gaze”: How The Fourth Amendment Is A Surveillance Technology That Enables Ai To Scale Control Over The Subaltern, Diego H. Alcalá Laboy

Michigan Journal of Race and Law

Much has been written about the rise of artificial intelligence and machine learning applications and how the current Fourth Amendment law has been unable to mitigate the privacy harm that these tools produce. This article explores how the development and usage of AI and machine learning models is dependent on the originalism principles of Fourth Amendment Law. Utilizing Critical Surveillance Studies and Anticolonial Theory, I posit that the Fourth Amendment is a surveillance technology that categorizes conduct, persons, and places to impose the material conditions for the subjugation of historically minoritized communities within the United States. Furthermore, this article explores …

Exact And Approximate Conformal Inference For Multi-Output Regression, Chancellor Johnstone, Eugene Ndiaye

Faculty Publications

It is common in machine learning to estimate a response y given covariate information x . However, these predictions alone do not quantify any uncertainty associated with said predictions. One way to overcome this deficiency is with conformal inference methods, which construct a set containing the unobserved response with a prescribed probability. Unfortunately, even with a one-dimensional response, conformal inference is computationally expensive despite recent encouraging advances. In this paper, we explore multi-output regression, delivering exact derivations of conformal inference p-values when the predictive model can be described as a linear function of y . Additionally, we introduce a multivariate …

Multi-Fidelity Machine Learning Modeling For Aerodynamic Response Prediction Of Aerospace Vehicles, Ethan S. Jackman

Theses and Dissertations

Hypersonic vehicle design requires understanding complex aerodynamic phenomena across the full flight regime. This study presents a novel MF surrogate modeling methodology that enables the prediction the full field response across a vehicle’s surface. A Space-Filling Curve (SFC) is used to convert unstructured data into 1D vectors. The a Convolutional Autoencoder is used with transfer learning to reduce the dimensionality of the data. An Emulator-Embedded Neural Network (E2NN) combines multi-fidelity data for fast, accurate predictions. A benchmark analytical example and hypersonic application are used to evaluate the methodology. Using various numbers of samples and sampling strategies it is found that …

Trends And Predictive Modeling Of Real Estate Prices In Major Saudi Arabia Cities, Meshal S. Aldahas

Theses and Dissertations

his research examines historical trends and explanatory modeling of real estate prices in major Saudi cities, with a focus on Riyadh, Jeddah, and Dammam. Using a mixed-methods approach, the study integrates quantitative data from 2010–2023, including housing and macroeconomic indicators, with qualitative insights drawn from over 320 survey responses that captured consumer sentiment on affordability, job security, and housing policies. A combination of descriptive statistics, ARIMA and Exponential Smoothing techniques was applied to detect long-term patterns, seasonal variations, and market shocks. Predictive modeling was conducted using Linear Regression, Decision Trees, and Neural Networks, with results showing that job security consistently …

Novel Machine Learning Methods For Quasar Variability And Strong Gravitational Lensing, Joshua Fagin

Dissertations, Theses, and Capstone Projects

Machine learning methods are well suited to handle the unprecedented data volume expected from upcoming wide-field surveys. The European Space Agency's Euclid telescope was launched in July 2023 and is expected to observe billions of galaxies at high resolution, including tens of thousands of strongly lensed galaxies. The Rubin Observatory Legacy Survey of Space and Time (LSST) will monitor tens of millions of quasars throughout its ten-year lifetime, thousands of which will be strongly lensed. This flood of data will enable measuring lens galaxy mass distributions and substructure, as well as probing quasar accretion disk structure and black hole properties …

Harnessing Graphs For Knowledge Representation In Natural Language Processing, Uras Varolgunes

Dissertations

This work proposes innovative methods for integrating domain-specific knowledge into natural language processing tasks through the use of graphs, aiming to enhance the performance of models across various domains, including finance and healthcare. Several novel approaches are proposed that fuse graph structures with modern deep learning techniques, addressing the challenges of missing word embeddings, label prediction, and graph representation learning for large language models.

First, a powerful embedding method built on top of the recent advances in latent graph learning is introduced to address the critical problem of word embedding imputation. Second, a graph-enhanced label attention model designed for medical …