Digital Commons Network^™

Evolving Solutions For Red Blood Cell Preservation, Ali Alkafaji, Charles A. Elder, Mohammad Zaidi, Kavin Parthiv, Michael A. Menze

The Cardinal Edge

In emergencies such as natural disasters, armed conflicts, or during outer space missions, the availability of transfusable blood can mean the difference between life and death. Red blood cells (RBCs) must be stored at +4 ± 2 °C and have a shelf life of just 42 days, which makes maintaining a stable blood supply during adverse conditions extraordinarily challenging. This challenge was especially apparent during the COVID-19 pandemic when hospitals faced severe blood shortages. Freeze-drying, or lyophilization, offers a promising avenue to extend the shelf life of RBCs for transfusion during crises. However, a significant hurdle in dry preservation is …

Applications Of Artificial Intelligence In Nanotechnology, Shahad Eabd Alrida, Ola Obed, Elaf Taha, Thamer Abdullah, Mustafa Hathal, Viola Somogyi

Engineering and Technology Journal

Artificial intelligence (AI) is emerging as a prominent technological advancement. It is the act of replicating human intelligence for many purposes. In contrast to conventional methodologies, artificial intelligence (AI) is undergoing tremendous advancements. The present state of artificial intelligence (AI) technology enables them to effectively address numerous intricate difficulties with proficiency comparable to a human's. The significance of advancements in AI is particularly evident in machine learning, where the techniques and algorithms are effectively applied to address many problems, including those in nanotechnology. In contemporary nanotechnology, it is crucial to expedite the search for the most favorable synthesis parameters while …

State-Of-The-Art Review Into Signal Processing And Artificial Intelligence-Based Approaches Applied In Gearbox Defect Diagnosis, Asaad Dubaish, Alaa Jaber

Engineering and Technology Journal

Various industrial applications, including rotating and reciprocating machinery, depend on gears. Therefore, a sudden breakdown of the gears could result in substantial financial losses. Due to this, extensive studies have focused on defect diagnosis. Both machinery maintenance decisions and preventive maintenance techniques have been aided by vibration analysis. An increased vibration is a warning sign that a machine is about to malfunction or break down. Observing and evaluating the machine's vibration pulses can identify the nature and extent of the issue and, as a result, predict when the machine will fail. The vibration signal may identify gearbox defects early on …

Enhancing Wind Tunnel Computational Simulations Of Finite Element Analysis Using Machine Learning-Based Algorithms, Luttfi Al-Haddad, Alaa Jaber, Latif Ibraheem, Sinan Al-Haddad, Naseem Ibrahim, Fawaz Abdulwahed

Engineering and Technology Journal

Wind tunnels are essential for examining aircraft model aerodynamics, accurately simulating real-world conditions, and enhancing design and performance evaluations. This study introduces a novel technique to improve the time and accuracy of stress distribution forecasts in wind tunnel simulations. This method combines Finite Element Analysis (FEA) with two regression models: Support Vector Machine (SVM) and k-Nearest Neighbors (kNN). The investigation begins with a thorough analysis of ANSYS fluent flow data, which reveals intricate fluid dynamics details within the wind tunnel. A comparative analysis of stress projections, supplemented by Root Mean Square Error (RMSE) metric, demonstrates the proposed methodology’s viability. High …

Using Artificial Neural Networks To Predict The Compressive Strength Of Cement And Sawdust Ash-Treated Lateritic Soil, James Ukpai, Ugochukwu Okonkwo

Engineering and Technology Journal

Sawdust industrial residue could be hazardous to the environment, but it becomes pozzolanic when incinerated. Thus, harnessing sawdust ash (SDA) and lateritic soil as road construction materials for low-cost roads is apt. However, modeling and prediction of the properties of soils treated with SDA have received very little attention. This study predicted the compressive strength of lateritic soil treated with cement and SDA using an Artificial Neural Network (ANN). The soil was found to belong to A-2-4(0) in the AASHTO rating and clayey sand (SC) in the Unified Soil Classification System. The soil minerals composition was conducted using an x-ray …

Deep Learning Approaches For Anti-Money Laundering On Mobile Transactions: Review, Framework, And Directions, Jiani Fan, Lwin Khin Shar, Ruichen Zhang, Ziyao Liu, Wenzhuo Yang, Dusit Niyato, Kwok-Yan Lam

Research Collection School Of Computing and Information Systems

Money laundering is a financial crime that obscures the origin of illicit funds, necessitating the development and enforcement of anti-money laundering (AML) policies by governments and organizations. The proliferation of mobile payment platforms and smart IoT devices has significantly complicated AML investigations. As payment networks become more interconnected, there is an increasing need for efficient real-time detection to process large volumes of transaction data on heterogeneous payment systems by different operators such as digital currencies, cryptocurrencies, and account-based payments. Most of these mobile payment networks are supported by connected devices, many of which are considered loT devices in the FinTech …

Improving Public Transport Through Machine Learning Influence Flow Analysis (Mifa): Southern England Bus Case Study, Benjamin Lee, Wolfgang Garn, Masoud Fakhimi, Nick F. Ryman-Tubb

Research Collection School Of Accountancy

Public transport (PT) is crucial for enhancing the quality of life and enabling sustainable urban development. As part of the UK Transport Investment Strategy, increasing PT usage is critical to achieving efficient and sustainable mobility. This paper introduces Machine Learning Influence Flow Analysis (MIFA), a novel framework for identifying the key influencers of PT usage. Using survey data from bus passengers in Southern England, we evaluate machine learning models. Subsequently, MIFA uncovers that easy payments, e-ticketing, and mobile applications can substantially improve the PT service. MIFA’s implementation demonstrates that strength and importance lead to specific insights into how service characteristics …

Key Predictors Of Postpartum Depression And Anxiety Symptoms Among Mothers In Kilifi, Kenya: A Machine Learning Approach, Faith Benson, Rachel Odhiambo, Willie Brink, Anthony Ngugi, Akbar Waljee, Eileen Weinheimer-Haus, Cheryl Moyer, Ji Zhu, Amina Abubakar

Institute for Human Development, East Africa

Background:

The burden of maternal postpartum depression and anxiety is disproportionately high in sub-Saharan Africa (SSA), yet the use of advanced analytical methods to capture the complex interplay of variables influencing these conditions remains underexplored.

Objective:

To apply machine learning (ML) methods to predict depressive and anxiety symptoms in postpartum mothers and to identify key and actionable predictors.

Methods:

This cross-sectional study included 1,995 biological mothers of singleton infants aged 0–6 months, using survey data collected between March 2023 and March 2024 in Kaloleni and Rabai sub-counties, Kilifi County, Kenya, within the Kaloleni–Rabai Health and Demographic Surveillance …

Automated Machine Learning For High-Resolution Daily And Hourly Methane Emission Mapping For Rice Paddies Over South Korea: Integrating Modis, Era5-Land, And Soil Data, Jiah Jang, Seung Hee Kim, Menas Kafatos, Jaeil Cho, Gayoung Yoo, Sujong Jeong, Yangwon Lee

Institute for ECHO Articles and Research

Agriculture is a major global source of methane (CH4), and accurate emission estimates are essential for refining national greenhouse gas inventories and supporting climate-resilient policies. This study develops a high-resolution estimation framework for CH4 emissions from Korean rice paddies by integrating multi-source datasets, including Moderate Resolution Imaging Spectroradiometer (MODIS) vegetation indices, European Centre for Medium-Range Weather Forecasts (ECMWF) Reanalysis Version 5 (ERA5)-Land meteorological variables, and Harmonized World Soil Database (HWSD) soil properties. Using CH4 flux observations from four global rice ecosystems (Italy, Japan, South Korea, and USA), we constructed parallel daily and hourly machine learning models using an automated machine …

Development Of A Framework For Identifying Asphalt Pavement Cracking Distresses Using Machine Learning, Dingxin Cheng

Mineta Transportation Institute

Asphalt pavement cracking is one of the most critical distresses affecting pavement performance and service life. When pavement deteriorates, it can lead to safety hazards, higher vehicle maintenance costs, and expensive repairs for cities and states—making early detection essential for everyone who relies on the roadway system. To address this challenge, the research team developed a prototype cracking identification system that integrates a customized machine learning model with computer vision algorithms. High-resolution images collected from drones or ground-based cameras are processed within the system to automatically detect and classify major cracking types. The core of the framework utilizes the You …

Machine Learning–Driven Prediction Of Dementia From Mri And Clinical Features: A Comparative Analysis Of Ensemble And Baseline Models, Sarah Raad Hameed, Zainab Muhannad Nahid, Rawan Ahmed Abdulmahdi

AUIQ Technical Engineering Science

Early detection of dementia remains a pressing challenge in clinical neuroscience, as delayed diagnosis limits therapeutic impact and healthcare planning. Leveraging the Open Access Series of Imaging Studies (OASIS) cross-sectional dataset of 436 participants, this study developed a robust machine learning pipeline integrating sociodemographic, clinical, and neuroimaging-derived features. Preprocessing included removal of highly sparse variables (Delay), median imputation of partially missing but clinically essential measures (SES, MMSE, CDR, Educ), Winsorization of extreme values, and skewness correction. The target Clinical Dementia Rating (CDR) was binarized (0 = no dementia, ≥ 0.5 = dementia) to align with clinically actionable screening. Categorical features …

Exploring Music Representation Learning For Detection Of Finer-Grained Details, Vishnu Pendyala, Samhita Konduri, Kriti Pendyala

Faculty Research, Scholarly, and Creative Activity

The foundational tonic in Indian classical music presents a crucial element for algorithmic understanding. This research investigates the efficacy of modern machine learning techniques for detecting this fine-grained musical attribute from diverse audio representations. Addressing limitations in prior work that used spe-cialized techniques, this study pioneers the integration of audio representations with non-linear dimensionality reduction and machine learning classifiers for tonic classification. The methodology employs various machine learning algorithms on music represented by audio features such as Mel-frequency cepstral coefficients and Mel spectrograms. Spectral analysis using Uniform Manifold Approximation and Projection (UMAP) offers novel insights into music representation learning. The …

Honey Badger Algorithm-Based Feature Selection For Ddos Attack Detection In Iot Networks, Mustafa Azeez Al-Mayyahi, Ahmed Raad Al-Sudani

Baghdad Science Journal

By quick computer and communication technology improvement, Distributed Denial of Service (DDoS) attack harm is getting more important. DDoS attacks research is the important study domain; a number of methods exist which have been presented like the algorithm of evolutionary as well as artificial intelligence in literature to diagnose attacks of DDoS. Unfortunately, new popular models of DDoS diagnosis are deteriorating for validating DDoS attacks objective and prior identification. Because of DDoS attack modes diversity as well as various attack traffic amount, still there is not the technique of diagnosis with promising accuracy of diagnosis currently. By choosing the best …

Structure–Activity Modeling And Hybrid Machine Learning-Based Prediction Of Bioactivity In Pyrazole Derivatives For Drug Discovery Applications, Kader Şahi̇n, Serhat Kiliçarslan, Serdar Durdaği, Emi̇n Saripinar

Turkish Journal of Biology

Background/aim: Pyrazole derivatives are of growing interest due to their diverse pharmacological activities. However, their biological activity is often highly sensitive to subtle structural modifications. Existing quantitative structure–activity relationships (QSAR) approaches frequently fail to capture the conformational flexibility and nonlinear structure–activity relationships (SAR) of such heterocyclic scaffolds, creating a gap in the accurate prediction of their biological profiles. Therefore, there is a strong need for more robust and predictive computational frameworks. This study addresses this gap by integrating four-dimensional (4D)-QSAR descriptors with hybrid machine learning (ML) techniques to improve predictive accuracy and provide a more reliable tool for structure-based drug …

Uranium Chemical Compound Classification Using Sub-Images And Statistical Machine Learning For Nuclear Forensics, Lee C. Lambert, Brett J. Borghetti, Abigail A. Bickley

Faculty Publications

Uranium particle analysis from Scanning Electron Microscope (SEM) imagery is a crucial tool in nuclear forensics. The particle morphology lexicon proposed by Tamasi et al. in J Radioanal Nucl Chem 307, 1611–1619 (2016) follows a standardized, manual identification process to identify particle morphology features. The present work seeks to mirror this methodology using computer feature selection from the scikit-image Python library rather than human classification. Using a random forest classifier, a 56% overall uranium true positive classification accuracy (a 39.6% balanced classification accuracy) was achieved on a test set outperforming a naïve (chance) model by 48%. The methodology introduced splits …

Hybrid 3d Modelling Framework For Indoor Navigation Using Federated Learning And Internet Of Things-Enabled Edge Devices, Noopur Tyagi, Jaiteg Singh, Saravjeet Singh, Ahmad Ali Alzubi, Farman Ali, Sukhjit Singh Sehra, Babar Shah

All Works

Background There has been a recent trend towards using three-dimensional (3D) models to enhance spatial awareness and maximize resource utilization in complex environments. 3D building model can be used in various applications, such as real-time guidance and tracking the positions of individuals in multi storied buildings. Cities are now being modeled and studied in three dimensions as an improved method of urban planning. Method This study proposes an advanced indoor navigation framework that combines 3D modelling, federated learning (FL), and Internet of Things (IoT) integration to deliver reliable floor-level localization and real-time guidance. In Phase 1, highly accurate 3D models …

Optimizing Traffic Signal Timings Using Real Time Data Analytics, Hamad Abdulla Binsultan

Theses

City traffic jams have become a major challenge for contemporary cities, causing delays, increased fuel consumption, and other environmental impacts. Conventional traffic signal controlling systems usually depend on fixed or preset signal plans that are incompetent to adjust themselves with changing traffic conditions. Traffic signal timings are optimized across the intersection health of the system by processing real-time data analytics. Through real-time data analytics, traffic flow efficiency is improved while waiting time at signalized intersections is reduced. Using traffic real-time data like vehicle counts, vehicle types, time of day and day of the week to see howtraffic behaves under different …

An Ai Approach To Lunar Phase Detection: Enhancing The Identification Of The New Crescent With Astronomical Data Integration, Murad Al-Rajab, Samia Loucif, Raed Abu Zitar, Mubarak Gwaza Abdu-Aguye

All Works

Introduction: The observation of the lunar crescent is significant in astronomy, cultural traditions, and religious lunar calendar determinations. However, earth-based imaging that captures all lunar phases, particularly the new crescent across multiple months, remains limited. This study explores the feasibility of using artificial intelligence (AI) techniques to detect and analyze the birth of the new lunar crescent using space-borne imagery from NASA’s Lunar Reconnaissance Orbiter (LRO), spanning over 13 years. Methods: This study evaluates both deep learning and traditional machine learning approaches for new crescent detection. Convolutional Neural Networks (CNN), Random Forests (RF), and Support Vector Machines (SVM) were applied …

Artificial Intelligence In Radiology: Hidden Fragilities And The Path To Resilience, Mustafa S. Alhasan

Saudi Medical Journal

Radiology artificial intelligence (AI) is advancing however its adoption faces fragile foundations that threaten sustainability. Despite bold promises of efficiency and accuracy, current deployment is undermined by weaknesses in economics, evidence, infrastructure, human factors, regulation, security, and environmental impact. Nearly 90% of radiology AI studies report process metrics rather than patient outcomes, while hidden costs elevate ownership to 400% to 500% of subscription fees. Technical fragilities include 25% or greater performance loss with routine protocol or scanner shifts, compounded by vendor consolidation that has eliminated 63% of companies since 2020, creating migration costs averaging 180,000 dollars per exit. Human factor …

Flaplet: A Full-Stack Web Platform For End-To-End Time Series Data Processing And Machine Learning In Solar Flare Prediction, Mohammadreza Eskandarinasab, Shah Muhammad Hamdi, Soukaina Filali Boubrahimi

Computer Science Student Research

Solar flare prediction is a central challenge in space weather forecasting, with direct implications for satellite operations, aviation safety, and power grid reliability. Machine learning has achieved state-of-the-art performance for this task, particularly when applied to photospheric magnetic field parameters. FlaPLeT is an open-source, full-stack web platform that supports end-to-end machine learning workflows for multivariate time-series–based solar flare prediction without requiring any coding expertise. Built with React, Django, Celery, and PostgreSQL, the system integrates dataset preprocessing, data augmentation, functional network (graph) construction, and machine learning model training into modular asynchronous tasks that generate downloadable datasets, trained models, and structured JSON …

Hybrid Data-Driven Cement-Stabilized Soil Design: An Integration Of Machine Learning, Multi-Objective Optimization, And Life Cycle Assessment, Chikezie Chimere Onyekwena, Yunli Li, Ikenna J. Okeke, Ubani Obinna Uzodimma, Monday Uchenna Okoronkwo, Wenping Wu

Chemical and Biochemical Engineering Faculty Research & Creative Works

Soil stabilization is crucial in geotechnical engineering, yet conventional methods are often time-consuming, resource-intensive, and environmentally unsustainable. Despite growing interest in Machine Learning (ML) and optimization tools for mix design, few studies integrate these methods with decision-making techniques and environmental assessment to support practical implementation. This study proposes a hybrid data-driven framework for predicting strength, optimizing mix compositions, and evaluating environmental impacts via life cycle assessment of cement-stabilized soft soils. Six ML models were evaluated, and the top-performing eXtreme Gradient Boosting (XGB) model was further improved using the Grey Wolf Optimizer (GWO). The optimized XGB-GWO model, integrated with a polynomial …

Obstructive Sleep Apnea Prediction: A Comprehensive Review And Comparative Study, Thi Khanh Chi Huynh, Amonae Dabbs-Brown, Anna Jurek-Loughrey, James Mulhall, Tuan Dung Pham, Ngoc Phu Doan, Viet Hung Tran, Zichi Zhang, Xuan Hoang Nguyen, Yimeng An, Peixin Li, Phi Hung Nguyen, Thi Linh Hoang, Xinming Shi, Hans Vandierendonck, Sebastien Bailly, Jean-Louis Pépin, Thai Son Mai

Research Collection School Of Computing and Information Systems

Obstructive Sleep Apnea (OSA) is a highly prevalent sleep disorder linked to considerable public health burdens and comorbidities. However, its heterogeneous presentation and the limited accessibility of traditional diagnostic tools such as polysomnography (PSG) lead to widespread underdiagnosis. As a result, artificial intelligence (AI) approaches, including machine learning (ML) and deep learning (DL) models, have attracted attention as an alternative pathway to detection. This paper first provides a comprehensive review of AI-driven OSA diagnosis, covering different diagnosis problems, input-data types, data biases, pre-processing techniques, and model performance. We then leverage the largest clinical dataset used in OSA prediction to date, …

Xgboost-Powered Predictive Analytics For Early Identification Of Thermal Runaway In Lithium-Ion Batteries, Isslam Alhasan, Mohd H.S. Alrashdan

All Works

Lithium-ion batteries are pivotal in powering modern technology, from electric vehicles to portable electronics. However, their safety is challenged by the risk of thermal runaway, a critical failure mode leading to catastrophic consequences such as fires and explosions. This study presents a machine learning framework for the early detection of thermal runaway events using sensor data from over 210 open-source battery tests. The framework utilizes voltage, temperature, and force measurements from experimental mechanical indentation tests, with force data providing additional predictive value beyond standard BMS sensors. Key features such as the rate of temperature change and voltage change were engineered …

Cellscout: Visual Analytics For Mining Biomarkers In Cell State Discovery, Rui Sheng, Zelin Zang, Jiachen Wang, Yan Luo, Zixin Chen, Yan Zhou, Shaolun Ruan, Huamin Qu

Research Collection School Of Computing and Information Systems

Cell state discovery is crucial for understanding biological systems and enhancing medical outcomes. A key aspect of this process is identifying distinct biomarkers that define specific cell states. However, difficulties arise from the co-discovery process of cell states and biomarkers: biologists often use dimensionality reduction to visualize cells in a two-dimensional space. Then they usually interpret visually clustered cells as distinct states, from which they seek to identify unique biomarkers. However, this assumption is often this assumption often fails to hold due to internal inconsistencies in a cluster, making the process trial-and-error and highly uncertain. Therefore, biologists urgently need effective …

Harmonizing Neuropsychological Test Data Across Prospective Studies, Rosita Shishegar, James D. Doecke, Yen Ying Lim, Pierrick Bourgeat, Vincent Dore, Bhargav Tallapragada, Simon M. Laws, Tenielle Porter, Samantha Burnham, Azadeh Feizpour, Ashley Gillman, Michael Weiner, Jason Hassenstab, Christopher C. Rowe, Victor L. Villemagne, Colin L. Masters, Jurgen Fripp, Hamid Sohrabi, Paul Maruff

Research outputs 2022 to 2026

Introduction: Alzheimer's disease (AD) research relies on large datasets and advanced statistical models. However, individual population studies often lack sufficient sample size for conclusive results. Harmonizing cognitive test data across studies can address this gap, despite differences in testing protocols. This study harmonizes cognitive data from three major AD cohorts to support robust clinical–pathological modelling. Methods: Information from the Alzheimer's Disease Neuroimaging Initiative (N = 1446); Australian Imaging, Biomarkers and Lifestyle (N = 1764); and Open Access Series of Imaging Studies-3 (N = 440) were integrated, including cognitive scores, demographics, genetics, and clinical and neuroimaging data. Neuropsychological tests relevant to …

Machine Learning In Peak Demand Forecasting: Foundations, Trends, And Insights, Shuang Dai, Fanlin Meng, Hongsheng Dai, Qian Wang, Xizhong Chen, Wenlei Bai, Peizhi Shi, Richard Allmendinger, Yuchen Zhang, Jian Liu

Electrical and Computer Engineering Faculty Research & Creative Works

Peak demand forecasting involves predicting the maximum electricity demand within a specific period, which plays a key role in maintaining the efficiency and stability of power systems. The rapid evolution of power systems, driven by advanced metering infrastructure, local energy applications such as electric vehicles, and the increasing adoption of intermittent renewable energy, has introduced greater randomness and reduced predictability in peak demand. Given the pressing need to address more diverse implementation requirements across different contexts, accurate and reliable peak demand forecasting has become increasingly important. To the best of our knowledge, this study is the first to provide a …

Ai-Driven Stratified Modeling For Early Liver Disease Detection: A Comparative Study Of Ensemble And Conventional Machine Learning Classifiers, Ghadeer Murtadha Ali, Ali Aqeel Hadi, Mustafa Abdulkareem Abbas, Abdullah Alkarar Mohammad, Ahmed Fadhil Abdulhussein

AUIQ Technical Engineering Science

Background: Early prediction of liver disease remains challenging in routine clinical diagnostics due to the multifactorial nature of hepatic dysfunction and the limited discriminative capacity of conventional laboratory-only assessments.

Objective: This study aims to develop and rigorously evaluate a robust machine learning framework for binary liver disease classification, emphasizing predictive stability, diagnostic balance (sensitivity–specificity), and statistical reproducibility across repeated experiments.

Methodology: A structured dataset of 1,700 records with 11 features representing demographic, behavioral, genetic, and clinical determinants was used to train and compare five supervised models: CatBoost, AdaBoost, Random Forest, Support Vector Machine (SVM), and Decision Tree. Performance was assessed …

A Systematic Review Of Cyber Risk Analysis Approaches For Wind Power Plants, Muhammad Arsal, Tamer Kamel, Hafizul Asad, Asiya Khan

School of Engineering, Computing and Mathematics

Wind power plants (WPPs), as large-scale cyber–physical systems (CPSs), have become essential to renewable energy generation but are increasingly exposed to cyber threats. Attacks on supervisory control and data acquisition (SCADA) networks can cause cascading physical and economic impacts. The systematic synthesis of cyber risk analysis methods specific to WPPs and cyber–physical energy systems (CPESs) is a need of the hour to identify research gaps and guide the development of resilient protection frameworks. This study employs a Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) protocol to review the state of the art in this area. Peer-reviewed studies published …

Ai-Driven Automatic Fault Detection Systems: Revolutionizing Modern Smart Grids, Aravind Sanikommu

Student Theses and Dissertations

The increasing complexity of current power systems, resulting from the integration of distributed generators and renewable energy sources, necessitates intelligent and adaptive fault detection schemes. Traditional protection using impedance and phasor analysis is usually weak when operating in nonlinear and transient operating conditions. Consequently, the tools of Data-driven fault classification and decision-making have gained strength under artificial intelligence (AI) and machine learning (ML) to improve grid reliability. This thesis is a proposal of an automatic fault detection and classification system based on AI applied to a smart mini-grid setting built in MATLAB/Simulink. A complete set of voltage and current data …

Bridging Physics And Data: Hybrid Modelling Of Nox And Soot Emissions In Alternative-Fuel Diesel Engine, Fatih Okumuş

Seatific Journal

In this study, a basic machine learning approach based on fuel type for estimating NOx and soot emissions in diesel engines was comparatively evaluated against a physics-based model incorporating physical indicators related to the combustion process. Experimental data were obtained from a single-cylinder, four-stroke diesel engine under single and dual fuel conditions using diesel, biodiesel, and ammonia. Quantities derived from the in-cylinder pressure signal, including IMEP, maximum pressure rise rate, maximum pressure, the crankshaft angle corresponding to maximum pressure, the CA10–CA90 combustion interval, ignition delay and total combustion duration, were integrated into the model as quantitative indicators representing the engine’s …