Digital Commons Network^™

Evaluation Of A No-Code Ai Model For Detecting Periapical Radiolucencies: Impact Of Anatomical Region On Diagnostic Performance, Manal Hamdan, Kevin W. Yu, Marguerite Miller, Stephanie J. Sidow, Zaid Badr, Sergio E. Uribe

School of Dentistry Faculty Research and Publications

Background

To develop a no-code artificial intelligence (AI) model for the detection of apical radiolucent lesions and to assess how lesion location influences the model’s diagnostic performance.

Methods

312 periapical radiographs were retrospectively collected, each accompanied by a corresponding cone-beam computed tomography (CBCT) scan obtained within six months and a radiology report authored by board-certified oral and maxillofacial radiologists. These reports served as the reference standard, and all findings were cross-verified by the primary investigator through CBCT review. The dataset included 181 images with at least one apical radiolucent lesion and 131 lesion-free controls. Using the no-code AI platform LandingLens …

Validating A Predictive Model Of Nurse Burnout Using Integrated Data Sources, Shahin Davoudpour, Mahesh Vaidyanathan, Rinad S. Beidas, Hannah Mulroe, Elliott Brandon, Matthew Lloyd, Gabrielle M. Olson, Mallika Pandey, Daniel Blumenthal, Baljash Cheema, Gaurava Agarwal

Advances in Cancer Education and Quality Improvement

Objective: To evaluate an integrated machine learning model using routinely collected organizational data to identify nurses at increased risk for burnout.

Background: Health care worker burnout directly threatens patient safety, quality of care, and workforce stability. Although nurses experience significantly higher burnout rates than physicians and face distinct operational stressors (eg, staffing shortages, continuous bedside care demands), most predictive modeling efforts have focused on physicians. Current approaches to identifying burnout typically rely on voluntary surveys, but advances in data science and machine learning have created new opportunities to move beyond self-reported measures.

Methods: This retrospective observational study included 1,177 nurses …

Artificial Intelligence In Occupational Mental Health Assessment: Applications, Predictive Potential, Methodological Challenges, And Responsible Implementation, Julio Suárez Albanchez

Mesopotamian Journal of Artificial Intelligence in Healthcare

Background: Occupational mental health disorders represent a growing challenge for organizations worldwide. Artificial intelligence (AI) has emerged as a potential tool for improving the assessment and surveillance of psychosocial risks.
Methods: A critical narrative review of the literature on AI applications in occupational mental health was conducted, focusing on predictive models, methodological limitations, ethical risks, and governance challenges.
Results: Current evidence suggests that AI systems can support the prediction of burnout, stress, anxiety, and other mental health outcomes through machine learning, natural language processing, and behavioral analytics. However, important limitations remain regarding data quality, external validation, explainability, privacy, algorithmic bias, …

Growth, Methodological Evolution, And Clinical Maturity Of Artificial Intelligence Research In Major Medical Journals, 2018–2025: A Longitudinal Bibliometric Study, Abdulraoof Alsaeedi, Norah Alqarni, Ibrahim Rozi, Mohamed Ibrahim

Saudi Medical Journal

Objectives: To quantify changes in the volume, methods, and clinical maturity of artificial intelligence (AI) and machine learning (ML) research in major medical journals from 2018 through 2025.
Methods: We conducted a longitudinal bibliometric study of PubMed-indexed records from 99 leading clinical and general medical journals. The search was conducted on July 2026. Records were deduplicated and classified as empirical AI/ML research, evidence synthesis, AI discourse, or unclassified. Empirical studies were categorized by model family and clinical maturity. Journal-year Poisson generalized estimating equations used total substantive publications as an offset and clustered observations by journal.
Results: Among 6,891 unique publications, …

Artificial Intelligence In Gastrointestinal Precision Medicine: A Critical Appraisal Through The Lens Of Inflammatory Bowel Disease, Abdulrahman G. Alharbi, Abdulrahman A. Aljabri

Saudi Medical Journal

Artificial intelligence (AI) now supports many facets of gastrointestinal and hepatic care, from lesion recognition during endoscopy to the interpretation of large molecular datasets. This review first outlines AI's broader role across gastroenterology and hepatology, and then uses inflammatory bowel disease (IBD) as a case study for AI-assisted multiomics integration. The review applies a consistent evidentiary framework that distinguishes exploratory, retrospective, and prospective study designs. Retrospective genomic and image-based studies report promising discriminative performance for risk classification and lesion detection, but this evidence remains largely exploratory or retrospective, whereas prospective randomized evidence is currently strongest for AI-assisted endoscopic polyp detection. …

Machine Learning Models For Estimating The Consumed And Remaining Useful Life Of Haul Trucks In An Open-Pit Mine In Peru, Marco Cotrina, Jairo Marquina, Mario Sandoval, Jose Mamani, Johnny Ccatamayo

Journal of Sustainable Mining

The purpose of this study was to develop a machine learning-based model to predict the consumed useful life and estimate the remaining useful life of haul trucks in an open-pit mining operation in Peru. A comparative analysis of multiple machine learning models was conducted, including multiple linear regression (MLR), random forest + PSO, support vector regression (SVR), gradient boosting machine (GBM), decision tree + PSO, and artificial neural networks (ANN-MLP). The models were evaluated using performance metrics such as R², RMSE, and MAE, selecting the optimal model to estimate the remaining useful life based on a theoretical lifespan …

Understanding The Role Of Data Science Applications In Soil And Water Health, Payton Davis, Debabrata Sahoo, Dara Park, Brook Russell

Forestry and Natural Resources

Data science is an emerging field that can be incorporated into many disciplines, including environmental science. Data science can provide valuable information from data, enhancing the understanding of systems and environments. This publication is intended to give an overview of the different aspects of data science and how data science can be leveraged into and applied to soil and water health.

Intelligent Identification Of Deep Coal Macrolithotypes Based On Weighted Ensemble In Machine Learning: A Case Study Of The Shenmu Area, Ordos Basin, Yao Yuhao, Yang Zhaobiao, Huang Daojun, Wang Jianan, Dong Guodong, Chen Yilin, He Jinxian, Zhang Shang

Coal Geology & Exploration

Objective Coal macrolithotype represents a key geological property in the exploration and recovery of deep coalbed methane (CBM) resources, and its accurate identification is of great significance for selecting the optimal geological and engineering sweet-spot intervals. However, conventional coal macrolithotype identification relies heavily on core observation, suffering from prohibitively high costs since core data are extremely valuable and thus feature a small sample size. Meanwhile, conventional log-based threshold methods face challenges in effectively characterizing the complex boundaries between coal macrolithotypes.Methods This study investigated the No. 8 coal seam of the Benxi Formation, Shenmu area, eastern Ordos Basin. Based on …

Hybrid Deep-Machine Learning For Butterfly Species Image Classification, Maha A. Rajab, Wisal Hashim Abdulsalam, Firas A. Abdullatif, Tole Sutikno

Baghdad Science Journal

Classifying butterfly species is crucial in biodiversity studies and environmental monitoring. However, manual classification is often a laborious process that requires specialized expertise and is prone to error, especially when species have similar visual characteristics. To address these drawbacks, this paper presents a hybrid approach that combines machine learning with deep learning for feature extraction. To enhance the visibility of important features, preprocessing techniques such as background removal and binarization are applied to butterfly images. Feature extraction was performed using the SqueezeNet convolutional neural network, pretrained on the ImageNet dataset. By discarding the final classification layer, the network produced discriminative …

Quantum-Enhanced And Machine Learning Framework For High-Accuracy Fatigue Life Prediction And Structural Analysis Of Connecting Rod, P. H. J. Venkatesh, P. Seema Rani

Babylonian Journal of Mechanical Engineering

The connecting rod is a critical mechanical component that transmits reciprocating motion from the piston to rotational motion in the crankshaft. Although connecting rods are traditionally manufactured from aluminum alloys, titanium alloys, or alloy steels, the demand for higher power-to-weight ratios has motivated the exploration of alternative materials supported by advanced computational validation methods. This study proposes a multilayer optimization framework for identifying the most suitable material for the connecting rod of a 110 cc four-stroke petrol engine. The component was modelled in Pro/ENGINEER, and high-fidelity Finite Element Analysis (FEA) was performed in ANSYS to evaluate the von Mises stress, …

Non-Gradient Quaternion Training Matrix Modifications For Color-Image Distillation, Tahsin Shahnewaz, Megdam Ahmed Chowdhury, Nikolay Metodiev Sirakov

Student Publications

This paper develops a new multi-stage image distillation method that combines two well known techniques. In the first stage, our method creates a matrix from all training images. In the next stage, it adapts a modified principal component analysis (M-PCA) approach to transform the training matrix. In the third stage, Singular Value Decomposition (SVD) fur ther refines the training-image matrix through low-rank reconstruction and controlled row selection. In the fourth stage, rotation of small 2 ×2 matrix blocks on the entire left singular matrix is conducted. The upper m (user-selected number) rows of the reconstructed matrix are selected and transformed …

Leakage-Safe Boosting Ensembles With Shap Explainability For Alzheimer's Disease Risk Prediction: A Rigorous Multi-Classifier Benchmark On Global Structured Clinical Data, Hadeer Mahmoud, Fady Salama

Sustainable Machine Intelligence Journal

Alzheimer's disease (AD) is a major public health challenge, and scalable non-invasive risk-stratification tools are needed. We benchmarked 11 machine-learning classifiers on a publicly available global structured Alzheimer's prediction dataset (N = 74,283; 24 predictors) using leakage-safe sklearn/imblearn pipelines. Four resampling strategies were assessed with stratified cross-validation; preprocessing included imputation, IQR-based outlier capping, feature engineering, one-hot encoding, and standardization. The leading models underwent HalvingRandomSearchCV optimization, followed by soft-voting and stacking ensembles. Performance was evaluated on a held-out 20% test set and interpreted with SHAP, permutation importance, and built-in feature importance. No resampling was selected in Stage A (composite score = …

From Data To Victory: The Race For Analytic Superiority In Warfare, Robert Grossman, Emily Goldman

Joint Force Quarterly

Artificial intelligence technologies have reached a tipping point after decades of development. They are diffusing widely across defense and national security applications. Twenty-first century warfighters rely on analytic models in all systems, at all echelons, and in all domains. As more powerful models built on ever larger data sets become ubiquitous, militaries are in a new competition to deploy artificial intelligence. Operational art must embrace “analytic superiority.” This is the operational advantage from collecting and ingesting data, building robust models and computing infrastructure, deploying the models into operational systems, and denying adversaries' ability to do the same

This article explains …

Ai-Based Community Pharmacy Antibiotic Use Misuse Analysis: A Community-Based Study, Yasameen Riyadh Saeed Al-Azzawi

Muthanna Medical Journal

Background: The misuse of antibiotics in the community is a significant health issue of concern since it directly leads to antimicrobial resistance, treatment failure, and health care burden. Aim: determine the trend of antibiotic misuse in community pharmacies and to use analytical models based on artificial intelligence. Methods: The current research was carried out in a sample of the community pharmacies in the city of Hilla, Babylon, Iraq, between November 2025 and February 2026. There were 200 encounters of antibiotics dispensing. The structured recording form was used as the data collection instrument to record the sociodemographic data, presenting …

Artificial Intelligence In Food Engineering For Food Safety, Quality Assurance, And Human Health, Ayad Abbood Abdulhasan, A. M. Mustafa, F. F. Sayyid, Marwan B. Hussein, Mohanad Muzahem Khalaf

AUIQ Technical Engineering Science

Artificial intelligence (AI) is increasingly reshaping food engineering by providing data-driven tools for safeguarding food safety, ensuring quality assurance, and improving human health outcomes. This review synthesizes current evidence on the application of machine learning, deep learning, computer vision, and the Internet of Things (IoT) across the food production continuum, from raw material inspection to consumer-facing nutrition guidance. We examine how convolutional neural networks, hyperspectral imaging, and sensor-fusion approaches enable rapid, non-destructive detection of contaminants and pathogens, and how predictive models support shelf-life estimation and quality grading. We further discuss AI-enabled traceability systems, including blockchain-integrated supply chains, and the growing …

Insect Monitoring Without Pitfalls: Seven Steps For Robust Insect Sensing Systems, Jamie Alison, Luca Pegoraro, Jarrett Blair, Yuval Cohen, Birgen Haest, Jacob Idec, Jacob Kamminga, Jenna Lawson, Meng Li, Leandro Aparecido Do Nascimento, Charlotte L. Outhwaite, Benjamin Rutschmann, Maximilian Sittinger, Mariana Abarca, Et Al

Biological Sciences: Faculty Publications

1. Data shortages fuel controversy about an ongoing insect biodiversity crisis. Insects are immensely diverse and functionally critical for ecosystems, yet data on their trends remain patchy and biased. Sensors, ranging from camera traps and acoustic recorders to weather radar stations, are set to transform data collection in entomol- ogy. Meanwhile, AI models that extract biological information from sensors are improving at a startling rate.

2. Realising the potential of automated monitoring means progressing from proof- of-concept studies to scalable insect sensing systems. However, stakeholders face severe operational challenges when adopting a growing suite of sensors, models and protocols for …

Enhancing Implementation Of Balanced Mix Design For Asphalt Mixtures Through Improved Material Selection, Cracking Characterization, And Performance Prediction, Shubham Harishbhai Modi

Doctoral Dissertations

Balanced Mix Design (BMD) has emerged as a promising approach for improving asphalt mixture performance by balancing rutting and cracking resistance through performance tests rather than relying solely on volumetric criteria. Therefore, its application is increasingly being explored by transportation agencies for both highway and airfield domains. Despite its advantages, several challenges still limit the widespread implementation of BMD. These challenges include uncertainties associated with the compatibility of asphalt mixture components, the limited practicality of existing cracking performance tests, and the lack of performance prediction tools suitable for process control during mixture production. This dissertation addresses these critical challenges by …

Machine Learning-Based Gene Signature Detection Highlights Cxcl12 As A Key Marker For Aml Prediction, Selahattin Alperen Uysal, Burçin Kaymaz

Chulalongkorn Medical Journal

Background: Acute myeloid leukemia (AML) is a severe hematologic malignancy marked by uncontrolled proliferation and impaired differentiation of myeloid cells, disrupting normal hematopoiesis and resulting in poor clinical outcomes. Conventional diagnostic approaches often lack the precision to accurately classify AML subtypes, necessitating the integration of advanced computational methods to improve diagnostic and therapeutic strategies.

Objectives: This study aimed to apply machine learning (ML) techniques to transcriptomic data in order to identify a concise and informative gene signature capable of distinguishing AML cases from normal samples. Additionally, the study sought to evaluate the performance of predictive models in supporting AML prediction …

Exposing And Addressing Machine Learning Brittleness Through Constraint Solving, Muyeed Ahmed

Dissertations

Machine Learning (ML) implementations are fundamentally brittle: nondeterministic, inconsistent, and prone to overfitting; however, constraint solving can be used to systematically expose, quantify, and address this brittleness.

This dissertation first establishes that widely-used implementations of popular ML algorithms are nondeterministic (producing different outputs on the same input, across different runs) and inconsistent (different implementations of the same algorithm producing different outputs on the same input). This is more prevalent in Unsupervised Learning (UL) implementations where, due to the lack of a ground truth, subtle execution errors can go unnoticed and are difficult to verify. Nondeterminism and inconsistency also introduce security …

A Web-Based Machine Learning Application For Classifying Post-Covid Risk Among Middle-Aged Adults, Himasha Amarathunga, Dhishan Dhammearatchi, Nimesh Pollwaththage

Journal of Innovation in Science, Engineering and Technology

Post-COVID syndrome (Long-COVID) represents a significant public health challenge, yet accessible tools for individual risk assessment among middle-aged adults remain limited. This study describes the development and evaluation of a web-based machine learning application for the classification of post-COVID risk in adults aged 30–60 years. A publicly available secondary dataset comprising 9,637 patient records was used, of which 5,871 (61%) were classified as high-risk and 3,766 (39%) as standard recovery, reflecting an imbalanced class distribution. The web application incorporates key demographic and clinical features including age, sex, vaccination status and comorbidities to produce a binary risk classification (High/Low). Five machine …

Machine Learning For Functional Outcome Prediction After Vestibular Schwannoma Surgery: A Systematic Review And Diagnostic Test Accuracy Meta-Analysis, Shiva Nischal, Shaan Patel, Musa China, Kush Kale, Yi Hein Chai, Santosh Guru, William Muirhead, Patrick Grover

Department of Neurosurgery Faculty Papers

PURPOSE: Machine learning (ML) models have been increasingly applied to predict postoperative facial nerve dysfunction and hearing preservation after vestibular schwannoma (VS) surgery. However, reported performance varies substantially, and the overall diagnostic accuracy and clinical reliability of these models remain uncertain. We conducted a systematic review and diagnostic test accuracy meta-analysis to characterise the current state and methodological readiness of ML-based prediction of these outcomes.

METHODS: PubMed, Embase, and CENTRAL were searched from inception to February 2026. Studies evaluating ML-based prediction of facial nerve function or hearing preservation following VS surgery were included. Diagnostic performance metrics were pooled using random-effects …

Harnessing Artificial Intelligence For Real-Time Innovation In Upstream Oil And Gas: A Data-Driven Framework For Early Detection Of Lost Circulation Incidents, Gnana Allan Whinney Gnanapragasam

Journal of Global Management Research

Artificial intelligence (AI) and machine learning are reshaping upstream oil and gas operations, yet their value depends less on algorithmic sophistication than on converting heterogeneous sensor streams into timely operational decisions. This study develops and evaluates an AI-enabled framework for early detection of lost circulation incidents (LCIs) during drilling, positioning this safety-critical use case within the broader innovation architecture of upstream operations. Uncontrolled drilling-fluid losses generate non-productive time, well-control risk, and substantial cost. The empirical component uses historical surface drilling data from approximately 200 wells that experienced severe or total losses, recorded at an average frequency of 0.2 Hz (one …

Predicting Cesarean Section Delivery In The United States Using Machine Learning: Population-Based Retrospective Study, Yanchao Yang

Business Analytics Faculty Publications

Background:
Cesarean section (C-section) is the most common surgical procedure in the United States, yet its use varies widely across regions and institutions. Although clinical risk factors are central to delivery decisions, geographic context, health system capacity, and local practice patterns may also influence C-section use. Understanding both the determinants and predictability of C-section delivery is important for improving obstetric quality and equity.

Objective:
This study aims to document geographic variation in C-section use across the United States, identify maternal and county-level factors associated with C-section delivery, and evaluate the predictive performance of machine learning models across clinically defined risk …

Data-Driven Staging Of Neurodegenerative Pathology Related To Alzheimer’S Disease, Thomas Earnest

McKelvey School of Engineering Graduate Student Theses & Dissertations

Alzheimer’s Disease (AD), the most common cause of dementia, is a dual proteinopathy exhibiting deposits of extracellular amyloid-beta (Aβ) and intracellular tau in brain tissue. Models which describe the spatial and temporal trajectories of these pathologies have been invaluable for understanding AD and have the potential to support diagnosis, disease monitoring, and treatment development. Disease staging systems, which provide protocols for assessing individual disease severity based on their extent of brain pathology, have been crucial for explaining and diagnosing AD. However, these systems have been primarily informed by post-mortem histological assessments and are not directly applicable to disease assessment in …

Research Status And Prospects Of Monitoring Technology For Large-Span Cable-Stayed Bridges Based On Machine Learning, Liu Guoliang, Liu Guokun, Yan Donghuang, Wang Wenxi, Wang Qishun

Journal of China & Foreign Highway

Machine learning and intelligent optimization algorithms have been increasingly applied to construction and health monitoring of long-span cable-stayed bridges. Based on the construction history of cable-stayed bridges both domestically and internationally, an overview of the origin and development process of cable-stayed bridges was provided. Firstly, from the perspective of the entire life cycle of bridges, bridge monitoring was divided into construction period monitoring and operation period monitoring. The applications of mainstream construction monitoring methods in large cable-stayed bridge projects were elaborated, and the specific composition of bridge health monitoring systems was clarified. Secondly, the basic principles of several machine learning …

Real-Time Two-Stage Machine Sound Monitoring For Wire-Edm State Recognition, Yuseop Sim, Yonghyun Lee, Edgar James Yother, Duju Lee, Hojun Lee, Byoungkwon Yoon, Asif Tanveer, Martin Byung-Guk Jun

MMRL-XMO Industrial Seminar

Operational state monitoring is critical for assessing machine utilization and reducing downtime in smart manufacturing. However, many Wire Electrical Discharge Machining (Wire EDM) systems remain legacy machines with limited access to controller-level data, making non-invasive sensing approaches necessary. This study proposes a lightweight two-stage sound-based operational state monitoring framework using an Internal Sound Sensor (ISS) and a 1D convolutional neural network (CNN). The ISS, built around a stethoscope-inspired bell structure, captures machine-specific acoustic signatures while suppressing ambient machine-shop noise and spark-related acoustic interference. The proposed framework classifies machine states in two stages: Stage 1 identifies coarse states such as beep, …

Machine Learning-Driven Prediction And Design Guidance For Asphalt Concrete Using Marshall Stability And Indirect Tensile Strength, Jianglei Xing, Xiao Tan, Mu Guo, Pengwei Guo, Yuhuan Wang, Dongzhao Jin

Michigan Tech Publications

This work addresses the simultaneous prediction of Marshall Stability (MS) and Indirect Tensile Strength (ITS) by integrating machine learning models with multi-objective optimization for the preliminary design of asphalt concrete. Based on 389 experimental samples, 15 variables were selected to describe asphalt properties, aggregate gradation, volumetric parameters and fiber characteristics, and four dual-output prediction models were developed. The models were evaluated using 50 Monte Carlo splits. TabICLv2 performed slightly better for MS prediction, with an RMSE of 1.49 ± 0.22 kN and an R of 0.85 ± 0.04, whereas TabPFN showed a slight advantage for ITS prediction, achieving an RMSE …

Reducing Mr Acquisition Times Using Artificial Intelligence: A Systematic Review, Fatin M. Arief

Radiologic Sciences

Purpose To synthesize existing evidence on artificial intelligence (AI) applications for reducing magnetic resonance (MR) imaging acquisition time, evaluate their impact on image quality, and identify challenges in clinical implementation.

Method A systematic literature review was conducted using PubMed, CINAHL, and Scopus including peer reviewed, English-language articles published between 2020 and 2026. Both qualitative and quantitative primary research were eligible and had to be on AI use in MR processes. Findings were analyzed through thematic synthesis, and the quality of each study was appraised using relevant critical appraisal tools.

Results Three major themes emerged: AI methods in MR procedures, perceived …

Archaeal Signal Peptide Prediction Through Integration Of Proteomics And Machine Learning, Anjali Kaur

Theses

Signal peptides are short N-terminal amino acid sequences that direct proteins through cellular secretion pathways and play an essential role in protein localization and function. Computational tools such as SignalP are widely used to predict signal peptides. However, prediction accuracy for archaeal proteins remains lower than for bacterial and eukaryotic proteins, likely because relatively few archaeal proteins were available for model training. The goal of this study was to determine whether expanding the archaeal training data used by SignalP 6.0 could improve archaeal signal peptide prediction. Archaeal proteomics datasets from five archaeal species were analyzed to identify proteins with experimentally …

An Interpretable Remote Sensing–Machine Learning Framework For Wildfire Soil Burn Severity Prediction In The Northwestern United States, Subhankar Das, Mariana Dobre, Roger Lew, Sarah A. Lewis, Erin S. Brooks, Peter R. Robichaud, Anurag Srivastava, Mary E. Miller, Alex M. Watanabe, Marta Basso

Michigan Tech Publications

Background: Although soil burn severity (SBS) is traditionally mapped post-fire, pre-fire predictive approaches remain limited despite their potential to identify areas at risk of soil degradation, runoff and erosion. Aim: To develop a large-scale model for predicting post-fire SBS from pre-fire conditions at ~30 m spatial resolution. Methods: A harmonized dataset of 62 wildfires (2018–2024) was used to build machine learning (ML) frameworks based on Random Forest, eXtreme Gradient Boosting (XGBoost), and Light Gradient Boosting Machine (LightGBM), using pre-fire vegetation, climate, soil–terrain and anthropogenic predictors. Performance was evaluated using five-fold cross-validation (CV), leave-one-fire-out (LOFO) validation and independent testing on 2025 …