Digital Commons Network^™

Freqllm: Frequency-Aware Large Language Models For Time Series Forecasting, Shunan Wang, Min Gao, Zongwei Wang, Yibing Bai, Feng Jiang, Guansong Pang

Research Collection School Of Computing and Information Systems

Large Language Models (LLMs) have recently shown promise in Time Series Forecasting (TSF) by effectively capturing intricate time-domain dependencies. However, our preliminary experiments reveal that standard LLM-based approaches often fail to capture global correlations, limiting predictive performance. We found that embedding frequency-domain signals smooths weight distributions and enhances structured correlations by clearly separating global trends (low-frequency components) from local variations (high-frequency components). Building on these insights, we propose FreqLLM, a novel framework that integrates frequency-domain semantic alignment into LLMs to refine prompts for improved time series analysis. By bridging the gap between frequency signals and textual embeddings, FreqLLM effectively captures …

A Systematic Review Of Machine Learning In Groundwater Monitoring, Mrunmayee Dhapre, Shrikant Jadhav, Debanjana Das, Jehanzeb Khan, Youngsoo Kim, Sen Chiao, Thomas Danielson

Faculty Research, Scholarly, and Creative Activity

With increasing concerns about water scarcity, groundwater has become crucial since this resource provides most of the freshwater needs. However, various human and natural activities often contaminate the groundwater, making it unsuitable for use. Over the years, scientists and engineers have used many methods to predict and track groundwater contamination as part of environmental monitoring. Consequently, there is an urgent need for improved methods, particularly in the face of increasing contamination. Machine learning has sometimes been used to monitor groundwater, air quality, and climate. Traditional methods must be improved due to the complexity and large amount of environmental data. This …

Machine Learning-Guided Field Site Selection For River Classification, Zhihao Wang, Gregory Brian Pasternack, Yufang Jin, Costanza Rampini, Serena Alexander, Nikhil Kumar, Rune Storesund, K. Martin Perales, Christopher Lim, Stephanie Moreno, Igor Lacan

Faculty Research, Scholarly, and Creative Activity

Sufficient abundance and variety of field site sampling are crucial for obtaining an accurate reach-scale river classification of a regional stream network in support of scientific research and river management. However, many studies still randomly select field sites or only visit accessible streams. This leads to an inadequate exploration of stream characteristics, resulting in incomplete or inaccurate classification. Machine learning has been recognized for discovering and extracting streams’ geomorphic patterns efficiently and accurately from data, but its application in field site sampling design is still in its infancy. This study developed a general and practical field site selection framework by …

Developing A Neural Network For Prediction Of Interatomic Energies In Iron-Manganese Alloys, Robert D. H. Race, Doyl Dickel, Hala Ben Messaoud

Research Experiences for Undergraduates in Computational Methods with Applications in Materials Science

Machine-learned interatomic potentials (ML IAP) for alloy systems have been sought after due to their ability to reduce experimentation costs and time, accelerating alloy development and discovery. However, the explicit inclusion of magnetism in these potentials has been both a difficult and important problem to solve, due to the complexity of spin-lattice dynamics and its significance in the properties of magnetic alloys. We present here the development of an explicitly magnetic Fe-Mn ML IAP using a physics informed neural network (PINN) extension of the rapid artificial neural network (RANN) formalism. It is shown that the potential is capable of reproducing …

Potential Impact Of Artificial Intelligence In Cardiovascular Medicine, Katherine Konczak, Elizabeth G. Dieter, Robert S. Dieter Ii, Robert S. Dieter Md, Rvt, Alm, Aravinda Nanjundappa Md

Marshall Journal of Medicine

Artificial intelligence (AI), particularly machine learning (ML), is increasingly transforming cardiovascular medicine by enhancing diagnostic accuracy, imaging analysis, risk prediction, and treatment planning. AI applications in imaging, such as echocardiography, cardiac computed tomography (CT), and magnetic resonance imaging (MRI), improve detection, automate measurements, and reduce variability. Deep learning models interpret electrocardiograms (ECGs) with high accuracy in electrophysiology, aiding early diagnosis of arrhythmias and inherited syndromes. In cardio-oncology, AI identifies cardiovascular risk markers in cancer patients and monitors therapy-related cardiotoxicity. AI-driven tools also support real-time procedural guidance and individualized therapy using electronic health records and imaging data. Despite promising outcomes, concerns …

Gene Regulatory Network Prediction Using Machine Learning, Deep Learning, And Hybrid Approaches, Sai Teja Mummadi, Md Khairul Islam, Victor Busov, Hairong Wei

Michigan Tech Publications

Construction of gene regulatory networks (GRNs) is essential for elucidating the regulatory mechanisms underlying metabolic pathways, biological processes, and complex traits. In this study, we developed and evaluated machine learning, deep learning, and hybrid approaches for constructing GRNs by integrating prior knowledge and large-scale transcriptomic data from Arabidopsis thaliana, poplar, and maize. Among these, hybrid models that combined convolutional neural networks and machine learning consistently outperformed traditional machine learning and statistical methods, achieving over 95% accuracy on the holdout test datasets. These models not only identified a greater number of known transcription factors regulating the lignin biosynthesis pathway but also …

A Survey Of Image Forensics: Exploring Forgery Detection In Image Colorization, Saurabh Agarwal, Deepak Sharma, Nancy Girdhar, Cheonshik Kim, Ki-Hyun Jung

School of Engineering, Computing and Mathematics

In today’s digital era, the rapid evolution of image editing technologies has brought about a significant simplification of image manipulation. Unfortunately, this progress has also given rise to the misuse of manipulated images across various domains. One of the pressing challenges stemming from this advancement is the increasing difficulty in discerning between unaltered and manipulated images. This paper offers a comprehensive survey of existing methodologies for detecting image tampering, shedding light on the diverse approaches employed in the field of contemporary image forensics. The methods used to identify image forgery can be broadly classified into two primary categories: classical machine …

Comparative Analysis Of Linear And Non-Linear Feature Selection For Breast Cancer Detection With Shap Analysis, Hamza Sabo Maccido

AUIQ Technical Engineering Science

Breast cancer remains one of the leading causes of mortality worldwide, emphasizing the critical need for accurate and efficient diagnostic tools. This study investigates the effectiveness of combining linear and non-linear feature selection methods-Principal Component Analysis (PCA), Pearson Correlation Coefficient (PCC), and Backpropagation Neural Networks (BNN) to improve breast cancer classification using machine learning models. We utilized the Wisconsin Breast Cancer Dataset to evaluate the performance of five classifiers-Support Vector Machine (SVM), K-Nearest Neighbor (KNN), Logistic Regression (LR), Decision Tree (DT), Naïve Bayes (NB) and Artificial Neural Network (ANN). The results demonstrated that BNN-selected features consistently outperformed PCA and PCC …

Comparative Study Of Machine Learning Models For Predicting The Market Value Of Professional Football Players, Álvaro Salvador López

Master's Theses or Doctor of Nursing Practice

The market value of professional football players is a critical factor in decision-making for clubs, agents, and analysts. Accurate player valuation impacts transfers, contract negotiations, and financial planning. In recent years, data-driven approaches have emerged to support traditional scouting with predictive analytics. This thesis presents a comparative study of machine learning models to estimate the market value of football players based on historical performance and personal attributes.

This thesis presents a comparative study of two independently developed machine learning systems designed to predict the market value of football players for the 2020–2021 season. Both systems were trained using real data …

Implementing A Health Recommendation System From Wearable Data, Priti Pramanick

Doctoral Theses

The rising interest in personalized health monitoring has created a demand for intelligent systems that not only evaluate an individual’s health status but also offer actionable recommendations. This dissertation presents a data-driven approach to assess overall health by calculating a weekly health score using multi-dimensional data sources such as sleep patterns, nutrition, cardiovascular activity, fitness levels, and metabolic parameters. The system integrates and processes data stored in MongoDB using Python, applies scoring logic tailored to each health domain, and aggregates them into a unified health score. Addi- tionally, the system generates a detailed summary and leverages a language model to …

What Drives Weight Status Among Female University Students? A Machine Learning Analysis Of Sociodemographic, Dietary, And Lifestyle Determinants, Radwan Qasrawi, Abir Ajab, Leila Cheikh Ismail, Ayesha Al Dhaheri, Sharifa Alblooshi, Razan Abu Ghoush, Stephanny Vicuna Polo, Malak Amro, Suliman Thwib, Ghada Issa, Haleama Al Sabbah

All Works

Background: Obesity and underweight are increasingly common among young adult women, often resulting from complex interactions between diet, lifestyle, and socioeconomic factors. This study addresses that gap by applying machine learning to a wide range of behavioral, dietary, and demographic data. The main research question asks: What are the key factors influencing weight status among female university students, and how accurately can machine learning models identify them? We hypothesize that different factors are significantly associated with underweight, overweight, and obesity, and that machine learning can reliably detect these patterns. The aim is to identify the strongest predictors and support more …

Detecting Android Malware Based On Static Analysis Using Classification And Modified Clustering Techniques, Abdullah Allawi Al-Sraratee, Ahmed Habeeb Al-Azawei

Journal of Intelligent Informatics, Networking, and Cybersecurity

Because Android malware harms internet security, prior research proposes several different approaches to detect it accurately. However, such proposed models depend on numerous number of features to attain high accuracy. This could lead to high computation cost and potential overfitting. Furthermore, manual data labeling is labor-intensive, requiring significant human effort and skills. This research aims to: 1) extend previous literature on Android malware detection, 2) improve the accuracy of Android malware detection based on a low number of features, and 3) modify a clustering technique to group data into two different clusters to address the issue of unlabeled data. To …

Real-Time Fraud Detection Using Big Data, Abdulla Matar Alketbi

Theses

In today’s digital world, fraud detection has become an important part of financial security. This study explores and compares the performance of different machine learning models in identifying fraudulent transactions using the IEEE-CIS Fraud Detection dataset. Techniques such as Random Forest, Gradient Boosting, Deep Neural Networks, and Logistic Regression were evaluated. The dataset was pre-processed using SMOTE to balance the classes and improve model sensitivity to fraud cases. Each performance of the model was assessed using accuracy, precision, recall, and F1-score. The Random Forest model achieved the highest overall performance with an F1-score of 99.23

Predicting Sleep And Sleep Stage In Children Using Actigraphy And Heartrate Via A Long Short-Term Memory Deep Learning Algorithm: A Performance Evaluation, Robert Weaver Med, Phd, James White, Olivia Finnegan, Hongpeng Yang, Zifei Zhong, Keagan Kiely, Catherine Jones, Yan Tong, Srihari Nelakuditi, Rahul Ghosal, David E. Brown, Russell R. Pate Ph.D., Gregory J. Welk, Massimiliano De Zambotti, Yuan Wang, Sarah Burkart, Elizabeth L. Adams Phd, Bridget Armstrong, Michael Beets Med, Mph, Phd

Faculty Publications

Children's ambulatory sleep is commonly measured via actigraphy. However, traditional actigraphy measured sleep (e.g., Sadeh algorithm) struggles to predict wake (i.e., specificity, values typically < 70) and cannot predict sleep stages. Long short-term memory (LSTM) is a machine learning algorithm that may address these deficiencies. This study evaluated the agreement of LSTM sleep estimates from actigraphy and heartrate (HR) data with polysomnography (PSG). Children (N = 238, 5–12 years,52.8% male, 50% Black 31.9% White) participated in an overnight laboratory polysomnography. Participants were referred be-cause of suspected sleep disruptions. Children wore an ActiGraph GT9X accelerometer and two of three consumer wearables(i.e., Apple Watch Series 7, Fitbit Sense, Garmin Vivoactive 4) on their non-dominant wrist during the polysomnogram. LSTM estimated sleep versus wake and sleep stage (wake, not-REM, REM) using raw actigraphy and HR data for each 30-s epoch. Logistic regression and random forest were also estimated as a benchmark for performance with which to compare the LSTM results. A 10-fold cross-validation technique was employed, and confusion matrices were constructed. Sensitivity and specificity were calculated to assess the agreement between research-grade and consumer wearables with the criterion polysomnography. For sleep versus wake classification, LSTM outperformed logistic regression and random forest with accuracy ranging from 94.1to 95.1, sensitivity ranging from 94.9 to 95.9 across different devices, and specificity ranging from 84.5 to 89.6. The addition of HR improved the prediction of sleep stages but not binary sleep versus wake. LSTM is promising for predicting sleep and sleep staging from actigraphy data, and HR may improve sleep stage prediction.

Machine Learning Crime Prediction Models And The Gap Between Research And Implementation: A Systematic Review, Ricardo Huamantingo, Miguel Cano-Lengua, Ciro Rodriguez

Karbala International Journal of Modern Science

A crime is an illegal or violent act committed by one individual against another. The increasing crime rate has become a major concern as it negatively affects people's quality of life and generates significant social and economic costs. This study aims to identify the most widely used machine learning (ML) models for crime prediction, determine evaluation metrics for assessing model performance, and analyze key data characteristics to enhance real-world implementation. The study follows the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) methodology. A search string was formulated using the population, intervention, comparison, and outcomes (PICO) framework and applied …

Prediction, Comparison And Visualization Of Premium Pricing Using Machine Learning Methods, Lal S Jan, Farzan Tashfeen, Mohid Hussain, Nadi M Alresheedi, Syed Muhammad Hassan Ahmer, Nahla M Albarkati

Yanbu Journal of Engineering and Science

This study investigates the efficacy of machine learning in forecasting and comparing health insurance premiums. The all-inclusive goal line is to improve the accuracy of premium pricing and minimize the manual labor associated with the traditional methods and present data trends in a logical manner. An open-source dataset of US medical premiums has been utilized and a systematic machine learning model in python programming language has been developed. This model took into account factors like age, bmi, smoking habits, and geographical location. The XGBoost model excels other models with an RMSE value of 0.349 and an adjusted R2 of 90.1% …

Leveraging Weekly Snow Cover Time Series For Improved Glacier Monitoring And Modeling, Rainey Aberle, Ellyn M. Enderlin, David R. Rounce, Shad O'Neel, Brandon Tober, Alexandra Friel

Boise State University Publications and Presentations

Seasonal snow and ice melt strongly influence glacier mass balance, yet sparse sub-annual observations limit our understanding of seasonal dynamics. Here we construct and analyze weekly snow cover time series for 200 glaciers across western North America from 2013 to 2023 using an automated image processing pipeline. Snow cover varied widely across the region: snow minima timing varied with latitude — from August from 62 to 64N to October from 48 to 50N—and accumulation area ratios ranged from near-zero to 0.92 (median of 0.52). A comparison of snowlines from observations and the PyGEM glacier mass balance model revealed seasonally evolving …

Towards Leveraging Social Media Data For Fostering Collaborations Among Non-Profits, Monazil Chowdhury

LSU Doctoral Dissertations

Nonprofit organizations serve a crucial role in tackling a wide range of significant social, environmental, and economic issues. But it is often hard to get a clear picture of their work because their information is spread out and it is difficult to see how they are collaborating. To address this issue we developed a web-based tool to collect scattered data—from a variety of sources, such as the IRS, social media, and the Census, into one easy-to-use resource. The tool begins by taking IRS records and geocoding each nonprofit’s physical address With its coordinates. It then retrieves census tract information from …

A Machine Learning Approach To Quantitative X-Ray Diffraction Analysis, Spencer Snow Chandler

Theses and Dissertations

X-ray Powder Diffraction (XRPD) is a powerful method in material sciences that gives insights into the atomical and crystallographic structure of a material, revealing information into the material's properties and suitability for industrial and scientific application. In geology, XRPD analysis is frequently leveraged to identify and quantify the present mineral phases in an unknown mixture. Despite it's widespread use, interpreting XRPD patterns requires highly-specialized knowledge, making the analysis largely dependent upon the background experience of the analyst. To assist experts, computational methods have been developed over the years. Some of these techniques involve fitting diffraction patterns using pseudo-Voigt functions, which …

The Feasibility Of Passively Tracking Children’S Tv Viewing And Mobile Device Use In Naturalistic Settings, Teresia M O'Connor, Tatyana Garza, Uzair Alam, Anil Kumar Vadathya, Jennette P Moreno, Alicia Beltran, Samah Haidar, Nimah Haidar, Sheryl O Hughes, Debbe Thompson, Salma M A Musaad, Tom Baranowski, Jason A Mendoza, Joseph Young, Akane Sano, Ashok Veeraraghavan

Faculty, Staff and Students Publications

Research on children's technology and digital media (TDM) is hampered by a lack of robust approaches for assessing TDM use. This study assessed the feasibility of passively measuring children's TV screens and mobile devices (TDM) in a naturalistic setting. In the three-day feasibility study, FLASH-TV was set up on one to two TVs the child (5-12 year olds) typically used in the home (n=20). Children's mobile device use was assessed with either the Chronicle App or ScreenTime screenshots. Parents completed three TDM diaries. An exit interview with the parent explored their perceptions of the assessments and the child's TDM use …

Advances In The Application Of Machine Learning To Food Nutrition And Health, Zhou Xin, Li Dapeng

Food and Machinery

A balanced diet is fundamental to maintaining human health and preventing or reducing the risk of chronic non -communicable diseases.Efficient and accurate analysis of nutritional and functional components in food,followed by the provision of personalized nutritional guidance,is a research hotspot in the field of food nutrition and health.This article reviews the research progress on artificial intelligence technologies,such as machine learning,in the analysis of food nutritional components,functional component analysis and screening,and personalized nutrition,and highlights the pressing issues that need to be addressed in this field.

Academic Year 2025–26 Annual Estimate Of The Strategic Security Environment, Us Army War College

Books, Monographs & Collaborative Studies

Maintaining strategic advantage demands professional discourse from across the force. The Academic Year 2025–26 Annual Estimate of the Strategic Security Environment provides a framework for those aspiring to tackle the Department of Defense’s most pressing challenges. This year’s authors highlight trending challenges and identify potential tension points across 15 sections organized into four enduring themes. This survey of regional, domestic, and institutional challenges represents the collective expertise of the US Army War College. The narrative is supplemented by a tailored list of 100 command-sponsored questions from 43 different Army and Joint organizations from across the Department of Defense. Combined, the …

Benford's Law In Basic Rnn And Long Short-Term Memory And Their Associations, Farshad Ghassemi Toosi

Department of Computer Science Publications

Benford's Law describes the distribution of numerical patterns, specifically focusing on the frequency of the leading digit in a set of natural numbers. It divides these numbers into nine groups based on their first digit, with the largest category comprising numbers beginning with 1, followed by those starting with 2, and so on. Each neuron within a neural network (NN) is associated with a numerical value called a weight, which is updated according to specific functions. This research examines the Degree of Benford's Law Existence (DBLE) across two language model methodologies: (1) recurrent neural networks (RNNs) and (2) long short-term …

A Non-Invasive Mental Health Risk Predictor Using Machine Learning Models Utilising Music Listening Habits

Communications of the IIMA

Globally, there has been a rise in mental health issues such as insomnia, anxiety, and depression. However, the stigma that is associated with such a diagnosis makes individuals not want to seek help. Recent research has explored the relationship between music listening habits and mental health status, offering promising insights into the potential of leveraging this data for predictive modelling. This research proposes a non-invasive approach that integrates features extracted from music listening patterns including demographic and lifestyle data to build machine learning models that detect mental health conditions such as insomnia, depression and anxiety levels The results show that …

From Imaging To Computational Domains For Physics-Driven Molecular Biology Simulations: Hindered Diffusion In Platelet Masses, Catherine House, Ziyi Huang, Kaushik Shankar, Sandra Young, Meghan Roberts, Scott Diamond, Maurizio Tomaiuolo, Timothy Stalker, Lu Lu, Talid Sinno

Cardeza Foundation for Hematologic Research

When formed in vivo, murine hemostatic thrombi exhibit a heterogeneous architecture comprised of distinct regions of densely and sparsely packed platelets. In this study, we utilize high-resolution electron microscopy alongside machine learning and physics-based simulations to investigate how such clot microstructure impacts molecular diffusivity. We used Serial Block Face - Scanning Electron Microscopy (SBF-SEM) to image select volumes of hemostatic masses formed in a mouse jugular vein, producing high-resolution 2D images. Images were segmented using machine learning software (Cellpose), whose training was augmented by manually segmented images. The segmented images were then utilized as 2D computational domains for Lattice Kinetic …

Exploring System Identification Of Non-Linear Dynamics Using The Weighted Composition Operator And The Liouville Operator, Chukwuebuka Amagwula

USF Tampa Graduate Theses and Dissertations

System identification is the process of determining mathematical models that describe the dynamics of a system from data. Dynamic Mode Decomposition (DMD) and Sparse Identification of Nonlinear Dynamical Systems (SINDy) are two distinct approaches used for this purpose.

DMD identifies dominant spatiotemporal modes and eigenvalues that describe the evo lution of a system. It assumes a near-linear representation of dynamics and is closely linked to the Koopman operator, making it ideal for analyzing fluid flows, oscillatory systems, and modal structures. The DMD method uses time series data where each data point is referred to as a snapshot and represents the …

Machine Learning Prediction Of Urban Heat Island Severity In The Midwestern United States, Ali Mansouri, Abdolmajid Erfani

Michigan Tech Publications

Rapid population growth and urbanization have greatly impacted the environment, causing a sharp rise in city temperatures—a phenomenon known as the Urban Heat Island (UHI) effect. While previous research has extensively examined the influence of land use characteristics on urban heat islands, their impact on community demographics and UHI severity remains unexplored. Moreover, most previous studies have focused on specific locations, resulting in relatively homogeneous environmental data and limiting understanding of variations across different areas. To address this gap, this paper develops ensemble learning models to predict UHI severity based on demographic, meteorological, and land use/land cover factors in Midwestern …

Internet Of Things In Sustainable Agriculture Systems, Ataguba E. Hillary, Ayodeji A. Okubanjo, Nurudeen. S. Lawal, Abisola A. Olayiwola

AUIQ Technical Engineering Science

The agriculture industry has evolved toward intelligent, data-driven processes due to the growing need for food worldwide, environmental sustainability, and effective resource use. A thorough analysis of smart agriculture as a game-changing element of the industry 4.0 revolution is provided in this study, with a focus on the incorporation of Internet of Things (IoT)-based technologies for sustainable farming. To optimize agricultural processes including irrigation, crop health monitoring, climate and weather tracking, animal management, and disease detection, it investigates the functions and uses of smart sensors and IoT devices. The study demonstrates how smart agriculture may meet important issues like food …

Internet Of Things For Sustainable Transportation Systems, Ayodeji Akinsoji Okubanjo, Ignatius Kema Okakwu, Oluyinka Esther Olaifa, Matthew Babatunde Olajide, Olufemi Peter Alao, Olayiwola Abisola

Al-Mustaqbal Journal of Sustainability in Engineering Sciences

This paper highlights the opportunities for the Internet of Things in the transportation industry. The need for the Internet of Things and its architecture to address various complex challenges in the transportation sector are discussed. Various smart applications of the Internet of Things and its noticeable benefits over the existing technology are well articulated. In addition, the role of new and emerging technology such as artificial intelligence, machine learning, big data, cloud, data storage, and analysis for future sustainable transportation are highlighted with specific cases. Furthermore, smart areas of the Internet of Things in transportation are pictorially discussed. In addition, …

Artificial Intelligence For Detecting Non-Alcoholic Steatohepatitis (Nash), Muhammad Wildan Gifari, Yogi Ramadhani, Dhadhang Wahyu Kurniawan

Pharmaceutical Sciences and Research

Non-alcoholic steatohepatitis (NASH), an inflammatory disease of the liver, has recently raised concern among healthcare professionals worldwide due to its asymptomatic features, making early diagnosis challenging. If left unnoticed, NASH often progresses to lethal diseases such as liver fibrosis or hepatocellular carcinoma. Recent developments in the field of artificial intelligence (AI) might facilitate the early diagnosis of NASH in a more efficient manner, forming a promising strategy to diagnose patients. In simple terms, AI is any machine that is capable of human-level intelligence, including visual perception, speech recognition, or decision making. A subclass of AI, which particularly deals with knowledge-based …