Engineering Commons^™

Unveiling The Hidden Threat: How Wireless Networks Fuel Serious Cyber Attacks, Ibtesam Jomaa Hawi

Al-Esraa University College Journal for Engineering Sciences

The spread of wireless networks has led to an increase in serious cyber attacks due to their weak architecture. This article focuses on reevaluating cybersecurity in wireless network technology by integrating statistical information detection methods and artificial intelligence (AI) algorithms. To construct a wireless networking scenario that accurately reflects real-life conditions, we created a data fabrication that included four pre-existing anomalies as well as four newly introduced anomalies. The synthetic dataset created from these generation processes contains 20 thousand distinguishable values, which are later divided into training and validation sets. Using the strategy described before, we began to analyze the …

Post-Processing National Water Model Long-Range Forecasts With Random Forest Regression In The Cloud To Improve Forecast Accuracy For Decision-Makers And Water Managers, Jacob Matthew Anderson

Theses and Dissertations

Post-processing bias correction of streamflow forecasts can be useful in the hydrologic modeling workflow to fine-tune forecasts for operations, water management, and decision-making. Hydrologic model runoff simulations include errors, uncertainties, and biases, leading to less accuracy and precision for applications in real-world scenarios. We used random forest regression to correct biases and errors in streamflow predictions from the U.S. National Water Model (NWM) long-range streamflow forecasts, considering U.S. Geological Survey (USGS) gauge station measurements as a proxy for true streamflow. We used other features in model training, including watershed characteristics, time fraction of year, and lagged streamflow values, to help …

Enhancing Bedside Nursing Care: An Artificial Neural Network Approach To Predicting Cardiac Arrest In Hospitalized Adults, Katharine Czech, Alec Pannunzio, Maddie Anderson, Numair Khan, Jacob Lacanienta, Jonghyeok Lee, Aneesh Poddutur, Emily Rastovski, Kira Voelker, Julie Wasyliw, Sei Zou

The Journal of Purdue Undergraduate Research

No abstract provided.

Predictive Maintenance Analysis Of Turbofan Engine Sensor Data, Stanley A. Melkumian

The Journal of Purdue Undergraduate Research

Predictive maintenance in aviation and aerospace applications is among the most explored problems in machine learning (ML) and artificial intelligence (AI), and datasets such as NASA’s C-MAPPS turbofan engine degradation simulation data have proven invaluable, helping researchers explore numerous questions on engine performance, maintenance, and failure. The purpose of this study was to extend the current research on predicting the remaining useful life (RUL) of engines and their risk classification. Starting with simple yet under-investigated nonlinear survival and random forest models, the analysis implemented eXtreme Gradient Boosting (XGBoost) and long short-term memory (LSTM) from TensorFlow’s Keras library. For both regression …

Optimization Of Tps Films Using An Adaptive Design Of Experiments Approach In A Bayesian Optimization Framework, Theresa Marks, Gracie White, Scott Lohman, Mayank Malhotra

The Journal of Purdue Undergraduate Research

Plastic pollution, amounting to 12 million tons annually, necessitates sustainable alternatives to single-use plastics. Compostable thermoplastic starch (TPS) films show promise but lack strength and durability compared to traditional plastics. This study employs an adaptive design of experiments (DoE) approach to enhance TPS films by optimizing testing points. The research focuses on varying concentrations of plasticizers (acetic acid and glycerol) in a water and potato starch mixture, aiming to identify the optimal ratio maximizing tensile strength and % elongation at break. Gaussian process regression (GPR) with uncertainty estimation and Bayesian optimization (BO) utilizing an acquisition function (AF) are employed. The …

Integrating Image Data Fusion And Resnet Method For Accurate Fish Freshness Classification, Yahya Layth Khaleel, Mustafa Abdulfattah Habeeb, Ghadeer Ghazi Shayea

Iraqi Journal for Computer Science and Mathematics

Fish freshness classification is critical for protecting public health and ensuring efficient economic, regulatory and environmental sustainability. Classifying accurately reduces the risk of foodborne illness, protects product quality, builds consumer trust and supports sustainable resource conservation through waste minimization. However, the traditional methods for determining fish freshness are variable, time consuming and subjective, precluding practical use. This research presents an improved framework that integrates image data fusion and a deep learning ResNet model to differentiate fresh and nonfresh fish. From multiple sources, a comprehensive dataset including 16,640 samples was curated, and data fusion was used to increase the diversity and …

Decision Making And Performance Optimization In Distributed Cyber Physical Networks, Md Sahabul Hossain

Electrical and Computer Engineering ETDs

Cyber-Physical Systems (CPSs) integrate computation, networking, and physical processes, while Cyber-Physical Networks (CPNs) enable communication and coordination among components. With advances in IoT and smart devices, CPSs are increasingly vital for applications such as smart grids, industrial automation, and autonomous vehicles. This dissertation explores decision making and performance optimization in Distributed Cyber-Physical Networks (DCPNs), focusing on efficiency, reliability, decentralization, and security. Using network economic theories (e.g., game theory, contract theory) and machine learning (e.g., Reinforcement Learning, Federated Learning), it addresses key problems: 1) a cost-effective positioning system with Reconfigurable Intelligent Surfaces (RISs) and RL, 2) energy-efficient task offloading in Multi-access …

Predicting Chaotic Systems With Quantum Echo-State Networks, Erik Connerty, Ethan N. Evans, Gerasimos Angelatos, Vignesh Narayanan

Publications

Recent advancements in artificial neural networks have enabled impressive tasks on classical computers, but they demand significant computational resources. While quantum computing offers potential beyond classical systems, the advantages of quantum neural networks (QNNs) remain largely unexplored. In this work, we present and examine a quantum circuit (QC) that implements and aims to improve upon the classical echo-state network (ESN), a type of reservoir-based recurrent neural networks (RNNs), using quantum computers. Typically, ESNs consist of an extremely large reservoir that learns high-dimensional embeddings, enabling prediction of complex system trajectories. Quantum echo-state networks (QESNs) aim to reduce this need for prohibitively …

Field Canals Improvement Projects Duration Prediction: A Comparative Analysis Of Machine Learning Models, Hania Ghouse, Ukaegbu Chinonso Ishmael, Edgar Dario Obando-Paredes, Hashem Shafik Shakir, Ali Al-Bayaty

AUIQ Technical Engineering Science

There are several essential elements in project construction management to be studied appropriately, and priority to these elements, such as cost and duration, is predominantly interesting to be investigated. In this research, the duration of field canal improvement projects (D_FCIP) was predicted using two relatively new machine learning (ML) models - the Multivariate Adaptive Regression Spline (MARS) and Extreme Learning Machine (ELM). The targeted D_FCIP was calculated using other dependent parameters, such as the length of the pipe, years of construction, the geographical zone of the network, the supplied area with water, and finally the actual cost …

Causal Discovery In Time Series Data Using Deep Learning Techniques, Saima Zahin Farhana Absar

Graduate Theses and Dissertations

Causal structure learning from observational data has been an active field of research over the past decades. In the literature, different algorithms and models have been proposed, such as constrained-based methods and score-based methods including the emerging deep learning-based methods. However, most of the approaches apply to static and non-dynamic data only. In many applications, the data is temporal. For example, monitoring systems, weather surveillance systems, and stock data, to name but a few. Incorporating temporal information is an important extension of the causal discovery field. With the growth of observational data these days, the discovery of causal relationships from …

Deformation Mechanism In Gold Nanoparticles Under Compressive Loading: Insights From Atomistic Modelling And Unsupervised Machine Learning, Tanuj Gupta

All Dissertations

Gold nanoparticles (AuNPs) offer exciting possibilities due to their inertness, malleability, and tunable structures, making them valuable for applications ranging from nanomedicine to electronics. Their optical, mechanical, and other properties can be tailored by modifying shape and structure, underscoring the importance of understanding their deformation behaviour at the nanoscale. This study used classical molecular dynamics simulations with LAMMPS to investigate the deformation mechanisms of gold nanospheres (AuNS) under uniaxial compression. Employing the embedded atom method (EAM) potential to model atomic interactions, AuNS with 20 nm in diameter were compressed along the z-direction using planar indenters moving at a constant …

Mechanical Stress In Solar Cells: Beyond The Sun's Light, Md Motinu Rahman

Engineering Graduate Student 3MT

The average lifespan of a solar panel is typically 20-25 years, often limited by thermal mechanical stress caused by daily and seasonal temperature changes. These stresses create microcracks, leading to reduced efficiency and eventual failure. To address this, integrating stress and temperature sensors within solar panels can monitor and manage stress through a water-cooling system. The sensors are connected to the cloud through ThingSpeak, where data analysis is performed, and the entire system is integrated with a DC microgrid. A MATLAB algorithm, developed using reinforcement learning, manages the load and solar panel operation. This approach can potentially double the lifespan …

Practical And Lightweight Defense Against Website Fingerprinting, Colman Mcguan, Chansu Yu, Kyoungwon Suh

Electrical and Computer Engineering Faculty Publications

Website fingerprinting is a passive network traffic analysis technique that enables an adversary to identify the website visited by a user despite encryption and the use of privacy services such as Tor. Several website fingerprinting defenses built on top of Tor have been proposed to guarantee a user's privacy by concealing trace features that are important to classification. However, some of the best defenses incur a high bandwidth and/or latency overhead. To combat this, new defenses have sought to be both lightweight - i.e., introduce a small amount of bandwidth overhead - and zero-delay to real network traffic. This work …

An Efficient Computational Frameworks For Design And Analysis Of Metamaterials, Raj Pradip Khawale

All Dissertations

Advancement in additive manufacturing helps in building artificial lattice structures with unique properties that are not available in naturally occurring materials or in continuum structures. Specifically, beam-based lattices are well known for producing lightweight structures with very high strength, auxetic behavior, and energy absorption capabilities. In recent years, numerous research studies have been conducted on generating algorithms and frameworks to obtain unusual properties based on the variation in the cell geometry and material properties. However, the exploration of the full design space is hampered in practice primarily due to restrictions on cell tiling variation. Additionally, the lattices are very intricate, …

Data-Driven Techno-Economic Analysis, Optimization, And Uncertainty Quantification Of Integrated Energy Systems In Deregulated Electricity Markets, Jacob A. Bryan

All Graduate Theses and Dissertations, Fall 2023 to Present

Electricity is a ubiquitous energy source in daily life, powering everything from stovetops and cellphones to vehicles and industrial processes. While wind and solar power have become increasingly common sources of electricity, the majority of electricity is still produced by burning fossil fuels, releasing greenhouse gases and propelling climate change. Wind and solar power cannot economically replace these fossil fuel energy sources on their own because they do not produce consistent power; the wind must be blowing, and the sun must be shining for them to make electricity. Nuclear power is a reliable source of energy that does not generate …

Secure Blind Medical Image Watermarking Using Hybrid Feature Extraction Techniques, Sawsan D. Mahmood, Yassine Aribi, Fadoua Drira, Adel M. Alimi

Iraqi Journal for Computer Science and Mathematics

Watermarking offers great potential for medical images by embedding identifiable information that ensures secure and authenticated sharing of patient data while maintaining both integrity and diagnostic quality. In this paper, we present an innovative framework for blind medical image watermarking that harnesses advanced feature extraction techniques, including K-Means clustering, BRISK (Binary Robust Invariant Scalable Key-points), GFTT (Good Features to Track), and chaotic systems algorithms.

We conducted extensive experiments on the Ocular Disease Intelligent Recognition (ODIR) dataset, focusing specifically on Retinal Optical Coherence Tomography (OCT) images. The results highlight the framework's ability to preserve image quality and diagnostic utility, with minimal …

Explainable Machine Learning Approach Enables Computer-Aided Identification System For Children Autism Spectrum Disorder (C-Asd), Karrar Hameed Abdulkareem, Zainab Hussein Arif, Mazin Abed Mohammed

Iraqi Journal for Computer Science and Mathematics

Neurodevelopmental disorders like autism spectrum disorder (ASD) cause significant cognitive, linguistic, object identification, communication, and social skills deficits. Although there is currently no cure for autism spectrum disorder (ASD), early detection can aid in diagnosis and implementing effective preventative measures. Artificial intelligence (AI) tools allow for an earlier diagnosis of ASD than was previously possible. Furthermore, many clinical and not clinical attributes can be used for identification of ASD but select the most proper ones still challenge. Therefore, in this study we propose a Computer-Aided Identification System based on machine learning concept and feature selection methods to diagnosis Children Autism …

Automated Fake News Detection System, Saja A. Al-Obaidi, Tuba Çağlıkantar

Iraqi Journal for Computer Science and Mathematics

Online news has been the majority of people’s information source in recent decades. However, a lot of the information that is accessible online is fake and sometimes even designed to mislead. It might be difficult for individuals to distinguish between certain false newspaper items and the real ones since they are so similar. Deep learning (DL) and machine learning (ML) models, among other automated false news detection (FND) techniques, are quickly becoming essential. A comparative study was conducted to analyze the performance of five prominent deep learning models across four distinct datasets, namely ISOT, FakeNewsNet, Dataset1, and Dataset2. Results indicated …

Designing Customized Loss Functions For Training Deep Neural Networks, Ali Pourramezan Fard

Electronic Theses and Dissertations

This dissertation explores the critical role of loss functions in enhancing the predictive performance of deep machine learning models. Loss functions are an integral element of all the ongoing advances we witness daily in this domain. I design custom loss functions and their impacts on various machine learning tasks, particularly in computer vision.

In the first stage of my research, I aim to improve the prediction performance of deep learning models by providing them with more precise feedback associated with task requirements. This led me to create the concept of assistive loss functions. My first proposed loss function, inspired by …

Applications Of Computer Vision In Biomechanics And Orthopaedics, William Stewart Burton Ii

Electronic Theses and Dissertations

Machine learning has emerged as a key technology for enabling advanced computer vision systems. These systems now permeate many industries, and have enhanced traditional processes through autonomous interpretation of visual data. In the field of orthopaedics, the increasing prevalence of imaging highlights a need for similar tools. In many cases, however, direct translation of available frameworks fails to resolve the complex problems currently facing this field. Stringent performance requirements, complex visual environments, data scarcity, and implications for patient safety represent domain-specific factors which pose unique challenges to proven techniques. Novel approaches are needed to realize the full benefits of visual …

Machine Learning-Driven Process Analysis And Optimization In Solid-State Welding And Fusion-Based Additive Manufacturing, Radif Uddin Ahmed

Master's Theses

In the modern era of advanced manufacturing, optimizing process parameters is pivotal in ensuring the quality and reliability of sophisticated component fabrication. This study presents a novel, data-driven approach to parameter optimization in two cutting-edge manufacturing techniques: Friction Stir Welding (FSW) and Laser Powder Bed Fusion (LPBF). By leveraging machine learning methodologies, this research addresses the critical challenge of efficiently determining optimal process parameters, a task traditionally relying on time-consuming and resource-intensive trial-and-error methods. This study will lead to a robust data-driven framework for process analysis of more advanced manufacturing techniques like the Additive Friction Stir Deposition (AFSD) process. Friction …

Enhancing Frp-Concrete Interface Bearing Capacity Prediction With Explainable Machine Learning: A Feature Engineering Approach And Shap Analysis, Yanping Zhu, Woubishet Zewdu Taffese, Genda Chen

Civil, Architectural and Environmental Engineering Faculty Research & Creative Works

This study introduces a novel approach to predict the shear bearing capacity of FRP-concrete interfaces using explainable machine learning. Eight algorithms are employed: three standalone models (Artificial Neural Network, Support Vector Regression, and Decision Tree) and five ensemble learning models (Bagging, Random Forest, Adaptive Boosting, Gradient Boosting, and Extreme Gradient Boosting). Four scenarios with varying input features, including engineered features inspired by mechanics-based bearing capacity equations, are examined. Notably, the inclusion of engineered features such as the stiffness of the FRP strip (Kf) significantly enhanced prediction accuracy and efficiency, although the width correction coefficient (bf/bc) did not yield significant benefits, …

Sustainable Mobility: Machine Learning-Driven Deployment Of Ev Charging Points In Dublin, Ruairí De Fréin, Alexander Mutiso Mutua Mr

Articles

Electric vehicle (EV) drivers in urban areas face range anxiety due to the fear of running out of charge without timely access to charging points (CPs). The lack of sufficient numbers of CPs has hindered EV adoption and negatively impacted the progress of sustainable mobility. We propose a CP distribution algorithm that is machine learning-based and leverages population density, points of interest (POIs), and the most used roads as input parameters to determine the best locations for deploying CPs. The objects of the following research are as follows: (1) to allocate weights to the three parameters in a $6$ km …

Impaired Speech Recognition Of Neurological Disorder Persons Using Machine Learning And Deep Learning Techniques, Vishnika Veni S

Theses and Dissertations

Speech Assistive Tools have emerged in recent years to support individuals with cognitive and neurological disorders in the field of assistive technology. People affected by neurological disorders such as autism, stroke, cerebral palsy, dysarthria, Parkinson’s disease, and brain injury often find it difficult to articulate desired sounds, resulting in impaired speech. As the population of impaired speakers continues to increase every year, there is a strong need to develop intelligent speech recognition systems for affected individuals. The primary objective of this research is to develop an Impaired Speech Recognition (ISR) system for the Tamil language. Word Recognition Accuracy (WRA) is …

Development Of Crop2cloud: An Iot-Integrated Platform For Automated Irrigation Scheduling Using Multi-Source Stress Indices, Bryan Nsoh

Department of Agricultural and Biological Systems Engineering: Dissertations, Theses, and Student Research

Efficient water management in agriculture is critical, as irrigation accounts for 70% of global freshwater withdrawals. While precision agriculture technologies offer potential solutions, integrating diverse data streams for real-time irrigation scheduling remains challenging, particularly in combining plant and soil-based stress indicators. This research addresses these challenges through three interconnected studies.

A systematic review first examined the current state and future potential of IoT-based automated irrigation management systems, analyzing how these technologies can enhance agricultural water use efficiency and crop productivity. The review identified critical gaps in data integration and real-time processing while highlighting opportunities for combining multiple stress indices for …

Combined 3d Fea And Machine Learning Design Of Inductive Polyphase Coils For Wireless Ev Charging, Lucas A. Gastineau, Donovin D. Lewis, Dan M. Ionel

Electrical and Computer Engineering Graduate Research

Wireless power transfer (WPT) technologies are currently researched and developed for charging the batteries of electric unmanned air and ground vehicles. This paper presents systems with special polyphase inductive coils, which generate rotating fields and achieve high power density and efficiency. The complex geometry is modeled and studied with 3D electromagnetic finite element analysis (FEA). In order to reduce the substantial computational effort, machine learning techniques are proposed for surrogate modeling. A deep learning algorithm is introduced to capture the physics-based relationships between geometry and electromagnetic properties in inductive coils for wireless charging. Parametric models are systematically generated and analyzed …

Sedimentary Environments, Mechanical Properties, And Rock Burst Risk Identification Of Overburden In Deep Mines, Qiao Wei, Cheng Xianggang, Dou Linming, He Hu, Meng Xiangsheng, Ren Yangyang, Xiao Chong, Cai Jin

Coal Geology & Exploration

Objective Disaster risk identification and prediction serve as a prerequisite for disaster control. An engineering geological environment is identified as the fundamental condition inducing dynamic disasters like rock bursts in mines. Exploring the sedimentary genetic mechanisms of engineering geological environments holds great significance for predicting rock burst risks. Methods With the Gaojiapu Coal Mine in the Binchang mining area, Shaanxi Province, as a case study, this study analyzed the engineering geological characteristics of rock masses under different sedimentary microfacies. Furthermore, it explored the deformation and failure characteristics of rocks in different sedimentary environments, along with the energy release patterns of …

Development Of Structures Of Intellectual Control Systems Of The Rectification Process, Shodievich Avazov Yusuf

Chemical Technology, Control and Management

Rectification processes, which are considered complex technological processes, and the development of structures of intelligent systems for controling the work of rectification devices and columns, which serve to organize them, were considered. The need to build intelligent control systems is based on the fact that the progress of the rectification process is affected by a large number of factors, including temperature, pressure, consumption, temperature difference, concentration of mixtures, properties of light and heavy volatile components. The structure of an intelligent control system based on the situational control method for rectification devices is proposed. The architecture of the proposed intelligent control …

Automation Of Medical Facilities, Automation Of Diagnostic Processes For Intelligent Primary Diagnosis Of Diseases Of The Oral Cavity., Otabek Ismailov, X.F. Temirova

Chemical Technology, Control and Management

A mathematical machine learning model for dental disease diagnosis plays an important role in medicine with high accuracy, speed, and personalization capabilities. This allows for increased efficiency not only for patients but also for medical personnel. For this reason, in this article, an improved mathematical model has been developed to effectively detect dental diseases.

Advancing Data-Driven Disaster Debris Characterization And Quantification, Jasmine Bekkaye

LSU Doctoral Dissertations

Natural hazards generate tremendous amounts of debris that negatively impact communities and overwhelm waste management infrastructure. The challenge in disaster debris planning and management stems from inconsistent and scarce post-disaster debris quantity data due to the chaotic nature of recovery operations. This leads to a limited understanding of key factors influencing disaster debris generation across hazards and regions, hindering the accuracy and efficiency of modeling. With the increasing availability of comprehensive post-disaster waste datasets, the capacity to understand debris generation is expanding. This dissertation aims to fill knowledge gaps on disaster debris generation as follows: (1) investigate existing technologies for …