Computational Engineering Commons^™

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 …

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 …

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 …

A Deep Learning-Based Automatic Object Detection Method For Autonomous Driving Ships, Ojonoka Erika Atawodi

Master's Theses

An important feature of an Autonomous Surface Vehicles (ASV) is its capability of automatic object detection to avoid collisions, obstacles and navigate on their own.

Deep learning has made some significant headway in solving fundamental challenges associated with object detection and computer vision. With tremendous demand and advancement in the technologies associated with ASVs, a growing interest in applying deep learning techniques in handling challenges pertaining to autonomous ship driving has substantially increased over the years.

In this thesis, we study, design, and implement an object recognition framework that detects and recognizes objects found in the sea. We first curated …

A Machine Learning Approach To Network Intrusion Detection System Using K Nearest Neighbor And Random Forest, Ilemona S. Atawodi

Master's Theses

The evolving area of cybersecurity presents a dynamic battlefield for cyber criminals and security experts. Intrusions have now become a major concern in the cyberspace. Different methods are employed in tackling these threats, but there has been a need now more than ever to updating the traditional methods from rudimentary approaches such as manually updated blacklists and whitelists. Another method involves manually creating rules, this is usually one of the most common methods to date.

A lot of similar research that involves incorporating machine learning and artificial intelligence into both host and network-based intrusion systems recently. Doing this originally presented …

A Computational Analysis Of The Gradient Concentration Profile Of Deet And The Mosquito Behavioral Response, Brandon Carver

Master's Theses

DEET is a common active ingredient in most spatial repellents. DEET is also a volatile organic compound. DEET prevents mosquitoes from detecting and coming into contact with an human individual. Gas sensing technologies such as metal oxide semiconductor sensors can detect VOCs. The World Health Organization provides the majority of efficacy testing methods. This research adapts methods from the WHO and use of MOS sensors to further understand how and why DEET affects mosquitos. A custom developed system is used to measure DEET dissipation and observe mosquito behavioral response to the DEET. DEET dissipations and mosquito behavior is measured within …

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 …

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 …