Computational Engineering Commons^™

A Capability Maturity Model For Artificial Intelligence Integration In Supply Chain Management, Lordt Becklines

Dissertations

Artificial Intelligence (AI) is transforming Supply Chain Management (SCM), yet many organizations struggle to assess their readiness for AI adoption and to understand how AI capabilities develop across maturity stages. This dissertation addresses this gap by developing a Capability Maturity Model (CMM) for AI integration in SCM, grounded in Organizational Information Processing Theory (OIPT), the Resource-Based View, and related capability frameworks. The model provides a structured approach for evaluating an organization's information-processing requirements, resource configurations, and alignment needed for effective AI-enabled supply chain operations.

Using a design science research approach, the AI-SCM CMM and its associated assessment instrument were derived …

Collision Dynamics Of Compound Droplets In Microchannels: A Combined Numerical And Data-Driven Study, S M Abdullah Al Mamun

Dissertations

Understanding and predicting the hydrodynamic interactions of micron-scale droplets is crucial in a wide range of industrial and real-life applications, including microfluidics, pharmaceutics, drug delivery, food science, and enhanced oil recovery. These multi-phase and multi-scale phenomena are further complicated by the presence of core droplets of an immiscible fluid within shell droplets, known as compound droplets. The collisions and interactions of droplets in emulsions are influenced by various physical and geometric parameters, leading to distinct rheological and dynamic responses. This research employs numerical methods for a systematic parametric study of both simple and compound droplet pair collisions under confined shear …

Medical Imaging Dataset Management Leveraging Deep Learning Frameworks In Breast Cancer Screening, Inchan Hwang

Dissertations

In the domain of Computer-Aided Diagnosis (CADx) for breast cancer diagnosis through mammography, prevailing models have traditionally been trained and validated using old film-based mammography. However, contemporary U.S. hospital practices involve the utilization of Full Field Digital Mammography (FFDM), offering more detailed images captured at various angles than old film-scanned mammography. Despite this shift, the existing body of research predominantly focuses on old-film based datasets, the implications of FFDM for CADx systems have not been understood. This dissertation addresses the issues emerged from FFDM such as data augmentation between old film-based set and new FFDM whether they are more effective …

Enhancing Cybersecurity In Wireless Sensor Networks: Machine Learning, Blockchain And Future Perspectives, Ilemona Solomon Atawodi

Dissertations

Security in the Industrial Internet of Things encounters various security issues but the main issues can be broken down into three core issues: Availability, Integrity, and Confidentiality. Security challenges generally tend to be caused by a failure of the system in one of these areas or cause a failure in one of these areas. Therefore researching scalable solutions to these security issues is prudent to explore methods that could be applied to large-scale industrial IIoT with tens to hundreds of devices as well as small-scale systems on a tiny factory floor comprising of just a few devices. In our research, …

Timeseries Forecasting Of U.S. Housing Price Index Using Machine Learning And Deep Learning Models, Krishna Chaitanya Nunna

Dissertations

Time series forecasting is a promising technique for various applications which predicts future values or patterns by taking historical data as base. Forecasting future trends is very beneficial for different industries to make valuable decisions and strategies. One such industry is housing market; it has biggest influence on U.S. economy. Housing price index (HPI) is a one of the crucial economic indices published by various government funded and private agency to benefit several industries and individuals for better analysis of future trends of housing market.

Several factors influence the HPI, economical, geographical, and demographic features. Development of traditional time series …

Intelpvt And Opt-Stvit: Advances In Vision Transformers For Object Detection, Classification And Video Recognition, Divya Nimma

Dissertations

Since the advent of Transformers, followed by Vision Transformers (ViTs), great success has been achieved by researchers in the field of computer vision and object detection. The difficult mechanism of splitting images into fixed patches posed a serious challenge in this arena and resulted in the loss of useful information at the time of object detection and classification. We propose an innovative Intelligent-based patching mechanism to overcome the challenges and integrate it seamlessly into the conventional Patch-based ViT framework. The proposed method enables the utilization of patches with flexible sizes to capture and retain essential semantic content from input images, …

Missing Value Imputation For Single Omics And Multi-Omics Data, Meng Song

Dissertations

The integration analyses of multi-omics data have the advantages of extending our understanding of biological system across multiple omics layers, unraveling the functional mechanism of complex disease development, and refining the discovery of novel drug targets. However, multi-omics studies often face challenges such as data heterogeneity, missing values problem, interpretability, and imbalance classes. Among these challenges, the missing values problem is a critical issue for large cohort studies as not all samples will get a complete measurement for all the omics layers. To address the problem of missing values in multi-omics data, I focused on the imputation of completely missing …

A Computational Model Of Trust Based On Dynamic Interaction In The Stack Overflow Community, Patrick O’Neill

Dissertations

A member’s reputation in an online community is a quantified representation of their trustworthiness within the community. Reputation is calculated using rules-based algorithms which are primarily tied to the upvotes or downvotes a member receives on posts. The main drawback of this form of reputation calculation is the inability to consider dynamic factors such as a member’s activity (or inactivity) within the community. The research involves the construction of dynamic mathematical models to calculate reputation and then determine to what extent these results compare with rules-based models. This research begins with exploratory research of the existing corpus of knowledge. Constructive …

Software Protection And Secure Authentication For Autonomous Vehicular Cloud Computing, Muhammad Hataba

Dissertations

Artificial Intelligence (AI) is changing every technology we deal with. Autonomy has been a sought-after goal in vehicles, and now more than ever we are very close to that goal. Vehicles before were dumb mechanical devices, now they are becoming smart, computerized, and connected coined as Autonomous Vehicles (AVs). Moreover, researchers found a way to make more use of these enormous capabilities and introduced Autonomous Vehicles Cloud Computing (AVCC). In these platforms, vehicles can lend their unused resources and sensory data to join AVCC.

In this dissertation, we investigate security and privacy issues in AVCC. As background, we built our …

Application Of Deep Learning For Medical Sciences And Epidemiology Data Analysis And Diagnostic Modeling, Somenath Chakraborty

Dissertations

Machine Learning and Artificial Intelligence have made significant progress concurrent with new advancements in hardware and software technologies. Deep learning methods heavily utilize parallel computing and Graphical Processing Units(GPU). It is already used in many applications ranging from image classification, object detection, segmentation, cyber security problems and others. Deep Learning is emerging as a viable choice in dealing with today’s real-time medical problems. We need new methods and technologies in the field of Medical Science and Epidemiology for detecting and diagnosing emerging threats from new viruses such as COVID-19. The use of Artificial Intelligence in these domains is becoming more …

On Resource-Efficiency And Performance Optimization In Big Data Computing And Networking Using Machine Learning, Wuji Liu

Dissertations

Due to the rapid transition from traditional experiment-based approaches to large-scale, computational intensive simulations, next-generation scientific applications typically involve complex numerical modeling and extreme-scale simulations. Such model-based simulations oftentimes generate colossal amounts of data, which must be transferred over high-performance network (HPN) infrastructures to remote sites and analyzed against experimental or observation data on high-performance computing (HPC) facility. Optimizing the performance of both data transfer in HPN and simulation-based model development on HPC is critical to enabling and accelerating knowledge discovery and scientific innovation. However, such processes generally involve an enormous set of attributes including domain-specific model parameters, network transport …

Ensemble Data Fitting For Bathymetric Models Informed By Nominal Data, Samantha Zambo

Dissertations

Due to the difficulty and expense of collecting bathymetric data, modeling is the primary tool to produce detailed maps of the ocean floor. Current modeling practices typically utilize only one interpolator; the industry standard is splines-in-tension.

In this dissertation we introduce a new nominal-informed ensemble interpolator designed to improve modeling accuracy in regions of sparse data. The method is guided by a priori domain knowledge provided by artificially intelligent classifiers. We recast such geomorphological classifications, such as ‘seamount’ or ‘ridge’, as nominal data which we utilize as foundational shapes in an expanded ordinary least squares regression-based algorithm. To our knowledge …

Software Quality Control Through Formal Method, Jialiang Chang

Dissertations

With the improvement of theories in the software industry, software quality is becoming the most significant part of the procedure of software development. Due to the implicit and explicit vulnerabilities inside the software, software quality control has caught more researchers and engineers’ attention and interest.

Current research on software quality control and verification are involving various manual and automated testing methods, which can be categorized into static analysis and dynamic analysis. However, both of them have their own disadvantages. With static analysis methods, inputs will not be taken into consideration because the software system isn’t executed so we do not …

Modeling And Evaluating Cost-Effectiveness Of Host-Microbiome Investigations, Renuka Panchagavi

Dissertations

Cost-effectiveness modeling accounts for how expenditures impact outcomes and is an appropriate step towards efficacy of the different methods used for modeling the dynamics of microbial communities. This will help to identify challenging aspects of microbiome studies and the associated costs, including the major differences in research designs (cross-sectional or time series-based) used for conducting such studies. The two major stages of our investigation were to first collect and model cost variable data for microbiome investigations, and then to evaluate how trade-offs related to sample size and expenditures impact investigational outcomes. We screened different potential sources of data for microbiome …

Face Recognition With Multi-Stage Matching Algorithms, Xianming Chen

Dissertations

For every face recognition method, the primary goal is to achieve higher recognition accuracy and spend less computational costs. However, as the gallery size increases, especially when one probe image corresponds to only one training image, face recognition becomes more and more challenging. First, a larger gallery size requires more computational costs and memory usage. Meanwhile, that the large gallery sizes degrade the recognition accuracy becomes an even more significant problem to be solved.

A coarse parallel algorithm that equally divides training images and probe images into multiple processors is proposed to deal with the large computational costs and huge …

Novel Software Defined Radio Architecture With Graphics Processor Acceleration, Lalith Narasimhan

Dissertations

Wireless has become one of the most pervasive core technologies in the modern world. Demand for faster data rates, improved spectrum efficiency, higher system access capacity, seamless protocol integration, improved security and robustness under varying channel environments has led to the resurgence of programmable software defined radio (SDR) as an alternative to traditional ASIC based radios. Future SDR implementations will need support for multiple standards on platforms with multi-Gb/s connectivity, parallel processing and spectrum sensing capabilities. This dissertation implemented key technologies of importance in addressing these issues namely development of cost effective multi-mode reconfigurable SDR and providing a framework to …

On The Selection Of A Good Shape Parameter For Rbf Approximation And Its Application For Solving Pdes, Lei-Hsin Kuo

Dissertations

Meshless methods utilizing Radial Basis Functions~(RBFs) are a numerical method that require no mesh connections within the computational domain. They are useful for solving numerous real-world engineering problems. Over the past decades, after the 1970s, several RBFs have been developed and successfully applied to recover unknown functions and to solve Partial Differential Equations (PDEs).
However, some RBFs, such as Multiquadratic (MQ), Gaussian (GA), and Matern functions, contain a free variable, the shape parameter, c. Because c exerts a strong influence on the accuracy of numerical solutions, much effort has been devoted to developing methods for determining shape parameters which provide …

Secure And Reliable Routing Protocol For Transmission Data In Wireless Sensor Mesh Networks, Nooh Adel Bany Muhammad

Dissertations

Abstract

Sensor nodes collect data from the physical world then exchange it until it reaches the intended destination. This information can be sensitive, such as battlefield surveillance. Therefore, providing secure and continuous data transmissions among sensor nodes in wireless network environments is crucial. Wireless sensor networks (WSN) have limited resources, limited computation capabilities, and the exchange of data through the air and deployment in accessible areas makes the energy, security, and routing major concerns in WSN. In this research we are looking at security issues for the above reasons. WSN is susceptible to malicious activities such as hacking and physical …

Reducing Ambiguities In Customer Requirements Through Historical Rule-Based Knowledge In A Small Organization, Silvia Brum Preston

Dissertations

During the elicitation process the requirements for a software application are obtained from the customer. Customers often do not know how to clearly express the requirements of the application to be built, causing requirements to be ambiguous. Many studies have been found to cover different characteristics of the requirements elicitation process including methods for reducing ambiguities in requirements. The methods and findings of these studies were found to be too general when it comes to the specific domain of the requirements and knowledge about the requirements. In addition, some studies did not take into consideration the level of expertise of …

Intelligent Approaches For Modeling And Optimizing Hvac Systems, Iii Raymond Tesiero

Dissertations

Advanced energy management control systems (EMCS), or building automation systems (BAS), offer an excellent means of reducing energy consumption in heating, ventilating, and air conditioning (HVAC) systems while maintaining and improving indoor environmental conditions. This can be achieved through the use of computational intelligence and optimization. This research will evaluate model-based optimization processes (OP) for HVAC systems utilizing MATLAB, genetic algorithms and self-learning or self-tuning models (STM), which minimizes the error between measured and predicted performance data. The OP can be integrated into the EMCS to perform several intelligent functions achieving optimal system performance. The development of several self-learning HVAC …

Architecture--Performance Interrelationship Analysis In Single/Multiple Cpu/Gpu Computing Systems: Application To Composite Process Flow Modeling, Richard Harrison Haney

Dissertations

Current developments in computing have shown the advantage of using one or more Graphic Processing Units (GPU) to boost the performance of many computationally intensive applications but there are still limits to these GPU-enhanced systems. The major factors that contribute to the limitations of GPU(s) for High Performance Computing (HPC) can be categorized as hardware and software oriented in nature. Understanding how these factors affect performance is essential to develop efficient and robust applications codes that employ one or more GPU devices as powerful co-processors for HPC computational modeling. The present work analyzes and understands the intrinsic interrelationship of both …

Computational Material Modeling For Mechanical Properties Prediction And A Methodology For Mie Gruneisen Equation Of State Characterization Via Molecular/Nano Scale Cementitious Material Constituents, Ahmed Mohamed

Dissertations

Cementitious materials have complex hierarchical structures with random features that range from nanometer (nm) to millimeter (mm) scale. Processes occurring at the nanometer scale affect the performance at larger length scales. The present work employs molecular dynamics (MD) simulations as the computational modeling methodology to predict mechanical properties for both hydrated and unhydrated cementitious materials at the molecular/nano scale level. A detailed study on the effect of increasing MD simulation cell size, dynamics time duration on the predicted mechanical properties was performed. Further studies focused on understanding the effect of higher thermodynamic pressure states on predicted …