Digital Commons Network^™

Assessment Of Temperature Correction Factors For Falling Weight Deflectometer Deflections, Pablo Orosa Iglesias, Jin Li, Bongsuk Park, Seonghwan Cho, John E. Haddock

JTRP Technical Reports

This study presents the development of an improved temperature correction methodology for falling weight deflectometer (FWD) deflections in full-depth asphalt pavements. The objective was to address the limitations of existing correction methods by integrating viscoelastic pavement behavior, structural properties, and realistic thermal gradients. Field temperature data from instrumented sites in Indiana were used to train a machine learning model capable of predicting pavement temperature profiles in flexible pavements. These predictions informed a series of finite element simulations that captured pavement responses to FWD loading under a range of thermal and structural conditions. Temperature correction factors were then derived and modeled …

Interpretable Battery Soh Prediction: A Comparative Interpretability Framework For Multi-Architecture Ml Models, Shafiyee Islam, Gon Namkoong

Electrical & Computer Engineering Faculty Publications

This work introduces a unified interpretability-efficiency framework for lithium-ion battery state of health (SOH) prediction using hybrid deep learning architectures. We comparatively analyze four hybrid models: CNN LSTM MultiHead, CNN Feature Extractor LSTM, DNN LSTM, and DNN BiLSTM to disentangle how network topology, feature composition, and computational design influence both predictive fidelity and physical interpretability. By integrating Monte Carlo Shapley (MC Shapley), background occlusion SHAP (BoSHAP), and ablation analysis, we quantify the contribution and robustness of five electrochemical feature groups: time, capacity, voltage, dQ/dV and peaks of dQ/dV from NASA battery dataset. The results reveal a consistent dominance of differential …

Performance Analysis Of Sparse Neural Networks In Brain Abnormality Detection, Megan Danh

Honors Undergraduate Theses

Neuroimages have held the capability of revealing to medical professionals patterns for brain abnormalities since their development. However, more recently, these professionals and researchers are looking to use neural networks to identify these brain abnormalities through neuroimages for early detection that would allow more effective treatment. Neuroimage datasets, specifically functional magnetic resonance imaging (fMRI), are extremely large in size. This would result in their processing and training to be computationally expensive, even with smaller neural networks. Fortunately, recent pruning methods have recently emerged, where network weights and neurons are pruned to reduce computational cost without compromising too much accuracy. By …

Evaluating Multi-Year Transferability Of Single- And Multi-Class Machine Learning For Remote Monitoring Of Beaver-Engineered Landscapes, Harris J. Sloan

Graduate Student Theses, Dissertations, & Professional Papers

Long-term satellite image archives enable ecological monitoring at large scales, but manual extraction of fine-grained features (e.g., beaver dams) remains time-consuming. Machine learning models may automate these tasks, but their temporal transferability for multiannual monitoring is still poorly resolved.

We evaluated the performance of two complementary neural-network frameworks across a 100,000-hectare watershed in Montana USA, using a decade of satellite imagery (2014-2024): (i) a single-class ResNet-50 beaver dam detector, and (ii) a multi-class ResU-Net landcover classifier.

In 2024, the dam detector identified 352 of 404 mapped dams, achieving balanced accuracy = 93.6%, precision = 79.8%, recall = 87.1%, F1 = …

The New York Times Company V. Microsoft Corporation Et Al.: Reforming The Fair Use Doctrine, Riya Dusara

Florida Atlantic University Undergraduate Law Journal

On December 27, 2023, The New York Times filed a complaint against OpenAI, one of the fastest growing artificial intelligence (AI) companies in the world. Due to allegations of copyright infringement, OpenAI, alongside its primary investor, Microsoft, has faced significant backlash in recent years for “stealing” articles from the New York Times. While facing this backlash, OpenAI and Microsoft responded by arguing that their use of the New York Times’ articles falls under fair use. The fair use doctrine, throughout its existence, has been a defining factor in many cases of copyright infringement. However, the doctrine has not yet been …

Interpretable Machine Learning For In-Home Mild Cognitive Impairment Detection, Budhitama Subagdja, Shanthoshigaa D, Ah-Hwee Tan, Iris Rawtaer

Research Collection School Of Computing and Information Systems

This paper introduces a novel system for in-home cognitive health assessment using ambient sensors and a machine learning technology that can robustly detect mild cognitive impairment (MCI) despite limited available data. The learned model can explain the aspects of individuals’ daily lives led to the prediction, while reliably predicting MCI, providing more insights to healthcare workers for further clinical interventions. We developed the robust transparent machine learning model, based on fusion adaptive resonance theory (Fusion ART) neural network to learn individuals’ daily patterns of activity from continuous sensor data in terms of a suite of digital biomarkers reflecting four key …

Serum Igg N-Glycosylation Profiles Distinguish Primary Sjögren’S Disease, Rheumatoid Arthritis, And Systemic Lupus Erythematosus And Are Associated With Disease Activity In Sjögren’S Disease, Zhixian Chen, Xiaojia Xu, Manshu Song, Fengcai Shen, Haifeng Hou, Wei Wang, Ling Lin

Research outputs 2022 to 2026

Background: IgG N-glycosylation regulates immune effector functions and inflammation, but disease-specific patterns in primary Sjögren’s disease (SjD), rheumatoid arthritis (RA) and systemic lupus erythematosus (SLE) are insufficiently defined, particularly in SjD.

Objectives: To identify serum IgG N-glycan biomarkers that differentiate SjD, RA, and SLE and reflect disease activity in SjD.

Design: A clinical case-control study.

Methods: Serum IgG N-glycans from 88 SjD patients, 102 RA patients, 86 SLE patients and 91 healthy controls were profiled using ultra-performance liquid chromatography. Discriminative glycans were selected through machine learning-based feature selection and evaluated for their diagnostic performance using cross-validated logistic regression and receiver …

Applied Machine Learning In Mineral Prospectivity Mapping In The South Pass-Granite Mountains Of Wyoming, Carl D. York

Graduate Theses/Dissertations

The South Pass-Granite Mountains, comprised of Archean to Tertiary rocks embedded with an Archean greenstone belt in Central Wyoming, have historically been mined for gold, iron, steel, and silver. Rare earth elements have been of increasing interest in the region and thus, several datasets have become available. Knowledge driven and data driven models are used in mineral prospectivity mapping with input data including geological mapping, geochemical data, geophysical (USGS Critical Mineral magnetic and gravity data), radiometric (U, Th, K) data and multispectral remote sensing data. Mineral prospectivity has seen a rapid improvement in recent years using advancements in machine learning …

A Review On Underwater Beamforming: Techniques, Challenges, And Future Directions, Ruba Zaheer, Quoc Viet Phung, Iftekhar Ahmad, Asma Aziz, Daryoush Habibi, Yue Rong, Walid K. Hasan

Research outputs 2022 to 2026

This paper comprehensively reviews recent advancements in Underwater Beamforming (UWB) systems, highlighting its pivotal role in underwater communication, sensing, and environmental monitoring. It explores the various beamforming applications, ranging from maritime surveillance to marine life monitoring, and indicates its significance in enhancing signal clarity, spatial resolution, and noise suppression in underwater acoustic environments. The unique challenges posed by the underwater environment that introduce complexities into the beamforming process such as non-stationary noise interference, severe signal attenuation, multipath propagation, and dynamic environmental variability are thoroughly discussed. The review systematically discusses and examines conventional, adaptive, and learning-based beamforming techniques, analyzing their strengths, …

Advances And Challenges In Digitally Connected Point-Of-Care Biosensing, Abdellatif Ait Lahcen, Jegan Rajendran, Gymama Slaughter

Center for Bioelectronics Publications

Point-of-care (POC) biosensors are undergoing a paradigm shift from isolated diagnostic tools to digitally connected, intelligent platforms that enable continuous and decentralized healthcare delivery. This review critically examines recent advances in wearable, implantable, and portable biosensors, highlighting how integration with wireless communication, the Internet of Medical Things (IoMT), and artificial intelligence is transforming their functionality and clinical utility. Particular attention is given to innovations such as smartphone-enabled interfaces, cloud-based analytics, and machine learning-assisted analysis, which collectively enhance sensitivity, specificity, and user accessibility across diverse healthcare settings, from personalized home monitoring and bedside diagnostics to deployment in resource-limited regions. The review …

Streamlining Eligibility Assessment For Alzheimer’S Disease-Modifying Therapies: Prediction Of Mmse Scores Using The Digital Clock And Recall, Ali Jannati, Claudio Toro-Serey, Marissa Ciesla, Emma Chen, John Showalter, David Bates, Alvaro Pascual-Leone, Sean Tobyne

Faculty, Staff and Students Publications

           Introduction: The eligibility of anti-amyloid disease-modifying therapies (DMTs) and their integration into clinical practice in some institutions requires a specific range of Mini-Mental State Examination (MMSE) scores. Reliance on this pencil-and-paper psychometric instrument imposes operational burdens and risks of perpetuating health disparities, given the test's known educational and cultural biases. This study evaluates the efficacy of the Digital Clock and Recall (DCR™)-a rapid, FDA-listed digital cognitive assessment-to crosswalk to MMSE scores using machine learning, thereby offering a faster, scalable, and equitable mechanism for patient triage.

Methods: We conducted a retrospective analysis using data from the multi-site Bio-Hermes-001 (BH) study ( …

Saudi Stock Market Prediction Using Majority Voting, Mobeen W. Alhalabi, Aiiad Albishri

Journal of King Abdulaziz University: Computing and Information Technology Sciences

Predicting the stock market via data analysis is an important research topic. Because the system implementing it seems to think and act like humans, for the last two decades’ machine learning got a lot of attention from developers, programmers, scientists, even from the general public. Due to their advantages, machine learning techniques can be used in many areas, including finance and stock markets. Although many studies were performed with excellent results for the investors, many researchers are still looking for better results that will help minimize risk with higher percentage. In this study, the state of the techniques to predict …

Data-Driven Analysis And Atomistic Simulations Of Next-Generation Materials For Energy Conversion And Storage, Yuliang Shi

Dissertations

Metal-organic frameworks (MOFs), with their modular architectures and tunable properties, represent an especially rich domain for accelerated material design and discovery for a range of diverse applications. Within this class of multifunctional materials, two-dimensional (2D) electrically conductive MOFs (EC MOFs) are of particular interest, as their 7r-stacked layered structures combine permanent porosity with electronic conductivity, enabling potential breakthroughs in energy storage, energy conversion, and quantum sensing. But the discovery and design of new EC MOFs based on expensive experimental screening is increasingly impractical due to the infinite chemical space. Furthermore, the practical implementation of EC MOFs for specific tasks depends …

Applying Machine Learning Techniques For Early Detection Of Cyber Attacks On Iot Devices, Noor Adnan Allamy

Al-Esraa University College Journal for Engineering Sciences

This research designs, implements, and evaluates a machine learning-based framework for the early detection of cyber attacks targeting Internet of Things (IoT) devices, with a specific focus on the context and challenges present in Iraq. The study conducts a comparative analysis of three supervised learning algorithms—Support Vector Machine (SVM), Random Forest (RF), and Deep Neural Networks (DNN)—using a combination of benchmark datasets (NSL-KDD, CIC-IDS-2017, Bot-IoT) and a synthesized dataset adapted to simulate the Iraqi threat landscape. Key performance metrics, including accuracy, precision, recall, and F1-score, were used for evaluation. The proposed Random Forest model demonstrated superior performance, achieving an accuracy …

Asadh: A Meta Learning-Based Auto-Selective System For Anomaly Detection In Healthcare, Nadia Rashid, Rashid Mehmood, Fahad Alqurashi, Turki Alghamdi

Journal of King Abdulaziz University: Computing and Information Technology Sciences

Anomaly detection plays a crucial role in healthcare, supporting early disease identification, process improvement, and the reliability of medical equipment. Traditional machine learning (ML) and deep learning (DL) models often fail to generalize across heterogeneous medical datasets due to variations in feature distributions, class imbalance, and high dimensionality. To address this, we extend our earlier ASAD framework and propose ASADH (Auto-Selective Anomaly Detection in Healthcare), designed specifically for medical anomaly detection. ASADH applies meta-learning over a unified set of cross-modal meta-features spanning tabular, time-series, and medical image data to characterize each dataset and automatically recommend the most suitable anomaly detection …

Deep Learning Approaches For License Plate Detection In Saudi Arabia, Reemah M. Alhebshi, Abeer Almakky, Lujain Waleed Aljahdali, Raghad Bakr Hawsawi

Journal of King Abdulaziz University: Computing and Information Technology Sciences

The most important and challenging tasks in an intelligent traffic monitoring system are vehicle detection and classification. Conventional approaches are significantly computationally intensive and create limitations when the data-collecting modality alters. This study evaluates the effectiveness of deep learning models in extracting and classifying key vehicle attributes, such as license plates, models, and colors, through four AI-driven components for vehicle analysis. The YOLOv8m model was used for detecting the vehicle license plate and character recognition, achieving accuracy ranging from 95.10% to 99.24%. For vehicle model recognition, the Xception model also showed high performance with a precision of 96.79%. The EfficientNetB3 …

Generative Ai For Method Development In Analytical Chemistry: A New Paradigm In Experimental Design And Optimization, Yasir Fathi Mahmood

Al-Esraa University College Journal for Engineering Sciences

The fast development of artificial intelligence (AI), especially generative AI models, is changing the environment of analytical chemistry. As classical method generation in analytical methods relies on manual trial-and-error methodology as well as statistical methods, generative AI is a new paradigm with automated generation of experimental methodology and optimization. In this paper, the authors discuss the use of generative AI-based technologies, including large language models (LLMs) and neural network-based generators, to create new, efficient, and customized methods of analysis. The paper examines existing applications, technology frameworks, and issues and offers a roadmap with regards to the future incorporation of generative …

Phishing Detection Using Data-Driven Intelligence, Ahood Al Darmaki, Ajmal Khan, Muhammad Rizwan Mughal, Mohammed M. Bait-Suwailam

The Journal of Engineering Research

Since the early days of the Internet, phishing attacks have been a serious threat to the security of users and their personal information on the web. This problem raises the need to find a strict solution to minimize the cyber-attacks. This paper explores the application of machine learning models to detect phishing websites by analyzing multiple datasets containing phishing and legitimate website features. In order to evaluate machine learning models, in this study three publicly available phishing datasets were utilized. These datasets have different sizes and features. The performance of the model was evaluated on the basis of accuracy, precision, …

Towards Computational Methods In Medical Data Analysis: From Speech And Text To Imaging, Kristin Qi

Graduate Doctoral Dissertations

Early detection of cognitive decline and efficient medical image analysis remain critical challenges in healthcare. Traditional clinical assessments are infrequent and resource-intensive, while everyday speech data and unlabeled medical images remain largely unexploited. This dissertation develops computational methods integrating machine learning and artificial intelligence across speech, text, and imaging modalities to address challenges in medical data processing. For cognitive monitoring, this work first introduces methods using voice assistant systems to collect longitudinal speech data in home environments, demonstrating that incorporating historical session patterns significantly enhances detection of mild cognitive impairment. Building on this foundation, a framework combining large language model-driven …

The Role Of Ai In Addressing The United Nations Sustainable Development Goals (Sdgs): A Comprehensive Survey, Walid Abdullah, Muhammad Nawaz, Basma Nasir, Muhammad Javed Ramzan

Sustainable Machine Intelligence Journal

Artificial Intelligence (AI) emerges as a transformative tool in addressing global challenges, particularly in achieving the United Nations Sustainable Development Goals (SDGs). This paper provides a comprehensive survey of how AI technologies contribute to addressing global challenges across social, economic, and environmental domains. By enabling advanced data analysis, predictive modeling, and automation, AI offers innovative solutions across sectors such as poverty reduction, healthcare, quality education, sustainable cities, climate action, and the protection of life on land and below water. Special attention is given to practical, real-world examples where AI enables more efficient resource management, predictive analytics, environmental monitoring, and data-driven …

Improving Self-Diagnostic Methods Of Flow Measurement Systems Based On Artificial Intelligence, Elbek Ortikov

Chemical Technology, Control and Management

The article examines methods for improving self-diagnostics of consumption measurement systems based on artificial intelligence in the context of industry digitalization and the development of cyber-physical systems. It has been shown that traditional flow meters used to measure the flow rate of liquids and gases are subject to mechanical, hydraulic, electronic, and hidden failures, which reduce the accuracy and reliability of measurements. A justification for the need to transition from classical maintenance methods to intelligent self-control methods that ensure the detection of anomalies and hidden malfunctions in real time is presented. A multi-level architecture of intelligent self-diagnosis is proposed, including …

Empirical Analysis Of Machine Learning Models For Predicting Equipment Failures Using Iot Sensor Data, Yusuf Shodiyevich Avazov

Chemical Technology, Control and Management

This article examines the problem of detecting and predicting industrial equipment faults using IoT sensor data through machine learning techniques. Sensor readings such as temperature, vibration, pressure, voltage, and current, as well as FFT-based features, were statistically analyzed. Class imbalance and low signal informativeness were identified as key factors limiting model accuracy. Results obtained from Logistic Regression, Random Forest, and XGBoost models were comparatively evaluated, showing that when ROC-AUC values remain around 0.5, distinguishing fault and non-fault states becomes challenging. Correlation and feature-importance analyses confirmed the absence of strong dominant indicators. The findings highlight the need to improve sensor architecture …

Enhanced Network Anomaly Detection Using Hybrid Deep Learning Network Based On Interactive Threshold, Maythem S. Derweesh, Sundos A. Hameed Alazawi, Anwar H. Al-Saleh

Baghdad Science Journal

In recent years, the growing use of the internet by both governments and private companies has led to a major increase in individual online activity. This expand lead to make the systems more effected to the threats and cyber attacks, and need more strong solutions to cyber security. Recently, deep learning (DL) and machine learning (ML) have become powerful tools in the cybersecurity field, especially for tasks such as detecting malware and filtering spam. This study present new multi layer method to detect the abnormal activities by busing advanced deep learning techniques. The proposed system work in tow main steps. …

Hate Speech Detection Using Optimized Feature Representation Via Spiral-Grey Wolf Optimizer-Based Machine Learning Approaches, Noor S. Farhan, Matheel E. Abdulmunim, Hasanen S. Abdullah

Journal of Soft Computing and Computer Applications

Hate speech detection is crucial as social media diversifies. This research present a lightweight, scalable system using traditional machine learning methods along with a new approach called Spiral-Grey Wolf Optimizer (S-GWO).

S-GWO effectively selects key features that consider both meaning and content from the Term Frequency Inverse Document Frequency (TF-IDF) space, leading to high-quality representation without excessive computing power.

The propoused system was tested on Arabic and another English datasets using six machine learning methods: SVM, RF, LR, KNN, NB, and SGD. It achieved 92% accuracy and F1 score on the Arabic dataset, while reaching 100% accuracy on the English …

Real-Time Production Of High-Resolution, Gap-Free, 3-Hourly Aod Over South Korea: A Machine Learning Approach Using Model Forecasts, Satellite Products, And Air Quality Data, Seoyeon Kim, Youjeong Youn, Menas Kafatos, Jaejin Kim, Wonsik Choi, Seung Hee Kim, Yangwon Lee

Institute for ECHO Articles and Research

Aerosol optical depth (AOD) is essential for air quality monitoring and climate research. However, satellite-based retrievals suffer from cloud-related data gaps, and reanalysis products are limited by coarse spatial resolution and substantial production latency. This study develops a real-time, gap-free, high-resolution (1.5 km) AOD retrieval system for South Korea. The system integrates Copernicus Atmosphere Monitoring Service (CAMS) forecasts, high-resolution meteorological fields, and ground-based air quality observations within a machine learning framework. Three models with varying training periods were systematically evaluated using cross-validation and independent validation with 2024 Aerosol Robotic Network (AERONET) data. The optimal model, trained on 2015–2023 data, achieved …

Quantitative Evaluation Of Tunnel Rock Mass Integrity Based On Mwd Technology, Zhang Kunmu, Peng Hao, Liang Ming, Han Yu, Song Guanxian

Journal of China & Foreign Highway

In tunnel construction,the quantitative evaluation of rock mass integrity heavily relies on information from the exposed face,and there are challenges when drilling data is used for integrity evaluation.To this end,this study introduced a novel method for quantitative evaluation of rock mass integrity during drilling,integrating numerical statistics with machine learning.A substantial dataset of digital drilling data was collected,covering three common types of rock mass integrity:relatively intact,relatively fractured,and fractured.Subsequently,a high-performance random forest model for the classification of rock mass integrity was developed through data preprocessing and hyperparameter optimization.The interpretability of the model ’s predictive results was enhanced using Shapley additive explanations (SHAP …

Advancing Skin Cancer Diagnostics: A Comparative Analysis Of Convolutional Neural Network Models For Skin Lesion Classification, Jesse Osuji, Tania Malik

SURE Journal: Science Undergraduate Research Experience Journal

For many years, skin cancer diagnosis and cancer research have been major focal points in the healthcare sector. Accurate classification of different types of skin lesions, including cancerous ones, is essential for proper diagnostics. Many machine learning (ML) techniques and models are used to solve various problems across various domains. One of such techniques is called neural networks. Convolutional Neural Networks (CNNs) are primarily used in computer vision-based problems, allowing computer systems to process and understand images. CNNs offer a potential tool for diagnosing skin lesions from images, occasionally demonstrating superior accuracy compared to trained dermatologists. This project explores various …

Pixel-Perfect Segmentation Of Solar Filaments, Jamie Harris

Undergraduate Research Symposium

The observation and classification of solar filaments has a drastic impact on the ability to predict solar-magnetic weather phenomena that threatens to put both satellite infrastructure and astronauts at risk. Using the Hɑ filter provided by the Global Oscillations Network Group (GONG), a network of six telescopes around the world dedicated to 24/7 surveillance of the sun, we are able to get images that clearly and prominently display filament activity. With the vast amount of images the GONG takes, it is not possible to manually analyze every image. Using the U-Net model for computer vision, we were able to train …

Rapid Detection Of Aflatoxin B 1 Contamination Levels In Peanuts Based On Dual-Band Hyperspectral Imaging And Machine Learning, Lu Ren, Shuyue Shen, Wanrong Shi, Chuang Rui, Maofeng Dong, Tingting Li

Journal of Food Science and Biotechnology

[Objective ] This study aims to achieve rapid detection of AFB 1 contamination levels in peanuts through dual-band hyperspectral imaging and machine learning.[Method ] A peanut AFB 1 contamination level warning model was constructed via a dual-band hyperspectral imaging system consisting of visible near-infrared spectroscopy at 400~1 000 nm and short-wave near-infrared spectroscopy at 900~1 700 nm,combined with machine learning.Peanut samples with four contamination levels were collected,in which the mass fraction of AFB 1 was determined by ultra-high performance liquid chromatography-tandem mass spectrometry.The effects of four pretreatment methods [first derivative,second derivative,multiplicative scatter correction,and standard normal variate (SNV )],two characteristic wavelength …

Studying Causal Inferences For Student Success In Higher Education, Prabesh Shrestha

Theses and Dissertations

Higher education institutions strive to support students in achieving their academic goals through targeted interventions and data-driven strategies. This study analyzed 19 variables, spanning student demographics, enrollment patterns, and institutional interventions, to identify predictors of students completing all enrolled courses in a semester. Using random forest, logistic regression, and bootstrap forest models, we assessed variable importance and derived causal estimates to determine their impact. Results from causal analysis indicate positive effects from session type, term load, referrals, kudos, flags, and scholarships, while negative impacts were associated with location, course delivery method, enrollment status, advisor contact, and registration interval. These findings …