NDSU Theses & Dissertations

Permanent URI for this communityhdl:10365/26050

Research performed to achieve a formal degree from NDSU. Includes theses, dissertations, master's papers, and videos. The Libraries are currently undertaking a scanning project to include all bound student theses, dissertations, and masters papers.

Browse

Now showing 1 - 20 of 411

Adapting Web Page Tables on Mobile Web Browsers: Results from Two Controlled Empirical Studies
(North Dakota State University, 2014) Annadi, Ramakanth Reddy
Displaying web page content in mobile screens is a challenging task and users often face difficulty retrieving the relevant data. It can force them to adopt a time-consuming hunt-and-peck strategy. Application of design principles can improve the view of the webpage data content and reduce the time consumption in viewing it. This is especially true with HTML tabular data content. This thesis discusses the background and applications of the gestalt design principle techniques to HTML tabular data content. An empirical study was performed to investigate the usability of two types of the adaptive styles namely, single and multi-layout. This thesis also compared the adaptive styles that use gestalt principles with simple HTML tabular data on mobile screens. A controlled study which involved university students was performed showed that the adaptive layout styles improved the efficiency of finding information in the website by gestalt principles usage and eliminating horizontal scroll.
Adaptive Differential Evolution and its Application to Machine Vision
(North Dakota State University, 2016) Dawar, Deepak
Over recent years, Evolutionary Algorithms (EA) have emerged as a practical approach for solving hard optimization problems ubiquitously presented in real life. The inherent advantage of EA over other types of numerical optimization methods lies in the fact that they require much less or no prior knowledge of the objective function. Differential Evolution (DE) has emerged as a highly competitive and powerful real parameter optimizer in the diverse community of evolutionary algorithms. The study of this dissertation is focused on two main approaches. The first approach focuses on studying and improving DE by creating its variants that aim at altering/adapting its control parameters and mutation strategies during the course of the search. The performance of DE depends largely upon the mutation strategy used, its control parameters namely the scale factor F, the crossover rate Cr, and the population size NP, and is quite sensitive to their appropriate settings. A simple and effective technique that alters F in stages, first through random perturbations and then through the application of an annealing schedule, is proposed. After that, the impact and efficacy of adapting mutation strategies with or without adapting the control parameters is investigated. The second approach is concerned with the application side of DE which is used as an optimizer either as the primary algorithm or as a surrogate to improve the performance of the overall system. The focus area is video based vehicle classification. A DE based vehicle classification system is proposed. The system in its essence, aims to classify a vehicle, based on the number of circles (axles) in an image using Hough Transform which is a popular parameter based feature detection method. Differential Evolution (DE) is coupled with Hough Transform to improve the overall accuracy of the classification system. DE is further employed as an optimizer in an extension of the previous vehicle detector and classifier. This system has a novel appearance based model utilizing pixel color information and is capable of classifying multi-lane moving vehicles into seven different classes. Five different variants of DE on varied videos are tested, and a performance profile of all the variants is provided.
Adaptive Mesh Refinment Applications for Adviction-Diffusion Problems Using Amrex
(North Dakota State University, 2020) Kanumuru, Venkata
In this paper we implemented an adaptive mesh refinement in high performance computing environment to study a wide range of problems in engineering. They are nonlinear Fisher-Kolmogorov equation, heat equation, advection equation and poisons equation using traditional message passing interface (MPI). We used adaptive mesh refinement library called AMReX for computation. AMReX is a numerical library containing the functionality to write massively parallel, block-structured adaptive mesh refinement (AMR) applications. Our study includes examples to solve poisons equation in traditional MPI approach and compared the performance between the two methods.
Adaptive Regression Testing Strategies for Cost-Effective Regression Testing
(North Dakota State University, 2013) Schwartz, Amanda Jo
Regression testing is an important but expensive part of the software development life-cycle. Many different techniques have been proposed for reducing the cost of regression testing. To date, much research has been performed comparing regression testing techniques, but very little research has been performed to aid practitioners and researchers in choosing the most cost-effective technique for a particular regression testing session. One recent study investigated this problem and proposed Adaptive Regression Testing (ART) strategies to aid practitioners in choosing the most cost-effective technique for a specific version of a software system. The results of this study showed that the techniques chosen by the ART strategy were more cost-effective than techniques that did not consider system lifetime and testing processes. This work has several limitations, however. First, it only considers one ART strategy. There are many other strategies which could be developed and studied that could be more cost-effective. Second, the ART strategy used the Analytical Hierarchy Process (AHP). The AHP method is subjective to the weights made by the decision maker. Also, the AHP method is very time consuming because it requires many pairwise comparisons. Pairwise comparisons also limit the scalability of the approach and are often found to be inconsistent. This work proposes three new ART strategies to address these limitations. One strategy utilizing the fuzzy AHP method is proposed to address imprecision in the judgment made by the decision maker. A second strategy utilizing a fuzzy expert system is proposed to reduce the time required by the decision maker, eliminate inconsistencies due to pairwise comparisons, and increase scalability. A third strategy utilizing the Weighted Sum Model is proposed to study the performance of a simple, low cost strategy. Then, a series of empirical studies are performed to evaluate the new strategies. The results of the studies show that the strategies proposed in this work are more cost-effective than the strategy presented in the previous study.
Adaptive Regression Testing Strategy: An Empirical Study
(North Dakota State University, 2012) Arafeen, Md. Junaid
When software systems evolve, different amounts of code modifications can be involved in different versions. These factors can affect the costs and benefits of regression testing techniques, and thus, there may be no single regression testing technique that is the most cost-effective technique to use on every version. To date, many regression testing techniques have been proposed, but no research has been done on the problem of helping practitioners systematically choose appropriate techniques on new versions as systems evolve. To address this problem, we propose adaptive regression testing (ART) strategies that attempt to identify the regression testing techniques that will be the most cost-effective for each regression testing session considering organization’s situations and testing environment. To assess our approach, we conducted an experiment focusing on test case prioritization techniques. Our results show that prioritization techniques selected by our approach can be more cost-effective than those used by the control approaches.
Addressing Challenges in Data Privacy and Security: Various Approaches to Secure Data
(North Dakota State University, 2021) Pattanayak, Sayantica
Emerging neural networks based machine learning techniques such as deep learning and its variants have shown tremendous potential in many application domains. However, the neural network models raise serious privacy concerns due to the risk of leakage of highly privacy-sensitive data. In this dissertation, we propose various techniques to hide the sensitive information and also evaluate the performance and efficacy of our proposed models. In our first research work we propose a model, which can both encrypt and decrypt a ciphertext. Our model is based on symmetric key encryption and back propagation neural network. Our model takes the decimal values and converts them to ciphertext and then again to decimal values. In our second research work, we propose a remote password authentication scheme using neural network. In this model, we have shown how an user can communicate securely with more than one server. A user registers himself / herself with a trusted authority and gets a user id and a password. The user uses the password and the user id to login to one or multiple servers. The servers can validate the legitimacy of the user. Our experiments use different classifiers to evaluate the accuracy and the efficiency of our proposed model. In our third research paper, we develop a technique to securely send patient information to different organizations. Our technique used different fuzzy membership functions to hide the sensitive information about patients. In our fourth research paper, we introduced an approach to substitute the sensitive attributes with the non-sensitive attributes. We divide the data set into three different subsets: desired, sensitive and non-sensitive subsets. The output of the denoising autoencoder will only be the desired and non-sensitive subsets. The sensitive subsets are hidden by the non-sensitive subsets. We evaluate the efficacy of our predictive model using three different flavors of autoencoders. We measure the F1-score of our model against each of the three autoencoders. As our predictive model is based on privacy, we have also used a Generative Adversarial Neural Network (GAN), which is used to show to what extend our model is secure.
Addressing Off-Nominal Behaviors in Requirements for Embedded Systems
(North Dakota State University, 2015) Aceituna, Daniel
System requirements are typically specified on the assumption that the system's operating environment will behave in what is considered to be an expected and nominal manner. When gathering requirements, one concern is whether the requirements are too ambiguous to account for every possible, unintended, Off-Nominal Behavior (ONB) that the operating environment can create, which results in an undesired system state. In this dissertation, we present two automated approaches which can expose, within a set of embedded requirements, whether an ONB can result in an undesired system state. Both approaches employ a modeling technique developed as part of this dissertation called the Causal Component Model (CCM). The first approach described, uses model checking as the means of property checking requirements using temporal logic properties specifically created to oppose ONBs. To facilitate the use of model checking by requirements engineers and non-technical stakeholders who are the system domain experts, a framework for the model checker interface was developed using the CCM. The CCM serves as both a cognitive friendly input and output to the model checker. The second approach extends the CCM into a dedicated ONB property checker, which overcomes the limitations of the model checker, by not only exposing ONBs but also facilitating the correction of those ONBs. We demonstrate how both approaches can expose and help correct potential Off-Nominal Behavior problems using requirements that represent real-world products. Our case studies show that both approaches can expose a system’s susceptibility to ONBs and provide enough information to correct the potential problems that can be caused by those ONBs.
Advanced Computational Ratings for College Football Teams
(North Dakota State University, 2010) Hensley, Joel Michael
This paper explores the subject of rating systems applied to the world of college football. Current rating system methodologies are examined, and four rating systems are developed and evaluated in a program. The Hensley Rating system is introduced as a new method. The details of each of these systems are discussed, and the results are analyzed and evaluated using data from the college football seasons of 2000 - 2009.
Agent Based Modeling for Simulation of Microbial Community
(North Dakota State University, 2018) Tekeste, Noah Daniel
Agent based modeling uses interacting agents and a governing rule to understand a complex phenomenon. It is an important mode of inquiry in the field of life sciences. For this paper a Haploid Evolutionary Constructor (HEC) tool was used for modeling and simulation of two sample models. The model samples were analyzed under the same and varying level of specific and non-specific substrates. In the first part of each experiment, the survival rate of the models was examined based on the model’s inputs and outputs. However, since close association of microbes enhances the probability of Horizontal Gene Transfer (HGT) between organisms, HGT behavior was introduced within the populations in the second sets of experiments. This enabled them to adapt to dwindling resources in their environment and creation of new populations that are better suited for the ecosystem. Based on the obtained results, the behavior of the dominant population(s) is assessed.
Agent Communication and Negotiation in a Supply Chain
(North Dakota State University, 2013) Upadhyay, Rajat
Supply Chain Management is an essential management paradigm for almost every organization. Effective implementation of Supply Chain Management (SCM) can significantly increase an organization’s profit. In this paper, a simple SCM is implemented using multi-agent modeling. A typical SCM consists of the supplier, manufacturer, inventory, seller, and customer. A multi-agent system provides a natural solution for SCM because various entities involved in SCM can be represented as intelligent agents. In the present approach various agents representing various entities of SCM, negotiate with each other in order to achieve their goals. Apart from the entities associated with typical SCM, additional entities have been added to the proposed system. This system consists of six agents representing the SCM entities: Customer Agent, Seller Agent, Coordinator Agent, Inventory Agent, Manufacturer Agent, and Supplier Agent. The system also consists of other agents to initialize the system and the database.
Agent-Based Modeling to Simulate the Movement of a Flock of Birds
(North Dakota State University, 2013) Byrisetty, Naga Chaitanya
The most beautiful, mysterious movements of bird flocks have always amused the human and led his thinking towards what makes these complex movements possible. This paper discusses the simulation of such bird behavior based on Craig W. Reynolds’s work on bird flocking, satisfying three main objectives and trying to mimic the natural behavioral characteristics of the flock in various circumstances using Netlogo5.1.  Objective One: To develop working software that simulates movement for a flock of birds using agent-based modeling in a two-dimensional world.  Objective Two: To direct the flock towards forage grounds while sustaining the wind and obstacles affecting the flock’s propagation.  Objective Three: To introduce a predator into the world, study, and simulate the flock’s escape behavior.
AI approaches in personalized meal planning for a multi criteria problem
(North Dakota State University, 2024) Amiri, Maryam
Food is one of the necessities of life. The food we consume every day provides us with the nutrition we need to have energy. However, food plays a more significant role in life. There is a relationship between food, culture, family, and society [1]. Since ancient civilization, people have realized the correlation between food and healthiness. Earlier, Physicians were treating people by prescribing special recipes. Last century, assorted studies investigated the impact meals have on human nutritional intake and the different diseases connected to it. There have been numerous other studies that focused on the required nutritional intake to ensure a good amount of energy for well-being in humans. A person who advises individuals on their food and nutrition is known as a dietarian and nutritionist. Nowadays nutritionists are experts in the use of food and nutrition to promote health and manage disease. They suggest several diet rules and food recommendations to assist people in living a healthy life. Due to technological advancements, previous time-consuming issues that required human attention are now being solved by utilizing automated procedures machines. Meal planning is one of the attractive domains that recently has received great notation by researchers who are using machine learning techniques in it. In general, those studies were performed to use extracted nutrition knowledge and food information for designing an automated meal planning system. However, in the majority of published research, the user’s preferences were an ignored feature. In this research, my journey through developing automated meal planning systems unfolds across distinct projects, each building upon the insights and advancements of its predecessors. Starting with a focus on incorporating user preferences, the exploration evolved through successive iterations, seeking to mirror the complexities of real-world decision-making more accurately. This progression led to the integration of advanced methodologies spanning artificial intelligence, optimization, multi-criteria decision making, and fuzzy logic. The ultimate aim was to refine and enhance the systems to not only align with users’ dietary restrictions and preferences but also to adapt to user feedback, thereby continually improving their efficacy and personalization. Through this comprehensive approach, the research endeavors to contribute novel solutions to the nuanced challenges of personalized meal planning.
Alexa for Health Practitioners
(North Dakota State University, 2020) Bhatt, Vidisha Nareshkumar
Many industries, including healthcare, are trying to take advantage of voice assistant systems by incorporating their technology into the industries’ environment. However, not many companies or researchers have successfully integrated this technology into the daily practice of healthcare practitioners. Doctors, nurses and other healthcare practitioners spend much of their interaction time with patients clicking on the Electronic Medical Record (EMR) screen trying to access and update data. An important contribution of this research is to analyze this healthcare need for this technology in the healthcare practitioner’s workflow. This research developed an Alexa chatbot skill, “Doctor’s Assistant,” as a generic application to help healthcare practitioners access and update EMR data via speech, while reducing data entry time and providing better patient care. The evaluation of this application illustrates that the “Doctor’s Assistant” skill is both effective and accurate.
Alexa, What Should I Eat? A Personalized Virtual Nutrition Coach for Native American Diabetes Patients Using Amazon's Smart Speaker Technology
(North Dakota State University, 2020) Maharjan, Bikesh
Among all other ethnic groups in the USA, native American’s have higher chances of developing diabetes. A lot of tools have been developed to address this issue and help them in managing diabetes. However, these tools fail to address two major issues, first, the focus on research and need of Native Americans, and second, the intuitive user interface to use the functions available in these tools without requiring a complex knowledge of technology. This project focuses on reducing these two gaps. The project uses the underlying knowledge base to provide personalized recommendations and leverages the benefit of smart speakers to deliver the service to the user’s which is highly intuitive and less demanding technologically. This project utilizes Ontology as a knowledge base and Amazon’s Alexa platform for the initial experiment to provide personalized recommendations to the users.
Algorithms for Coverage Improvement in a Sensor Network
(North Dakota State University, 2011) Maddi, Sunil Reddy
Sensors are devices which have the ability to receive and respond to a signal. These sensors, when used as a group, form a sensor network. Sensors in a sensor network can communicate and transmit data. In the early stages of research on sensor networks, only static sensors were used to form a sensor network. As research advanced, a combination of static and mobile sensors was used to form a wireless sensor network instead of just static sensors. The primary advantage of this type of sensor network over a sensor network with static sensors is the ability of mobile sensors to move to a new location in the network to increase the overall area covered by the sensors. Some concerns in a wireless sensor network are coverage area, energy consumption of the sensors, the ratio of static and mobile sensors to be used in sensor network, and the deployment of sensors in a network. Major research in sensor networks is focused on addressing the issue of coverage area. The objective of this paper is to design, implement and analyze Most Overlapped First and Highest Coverage Gain algorithms that address the issue of coverage area in a wireless sensor network. Local Spiral Search was used as the base to develop these two algorithms in combination with the Utility Function. Both algorithms were tested, and the results were analyzed with coverage area and change in overlap area as the metrics. Results showed a significant gain in coverage area using both the algorithms, and there was a consistent change in overlap area for a varying number of sensors.
Alternative Clustering Algorithms in Sensor Networks
(North Dakota State University, 2010) Gupta, Divya
A wireless sensor network is composed of a large number of tiny sensor nodes that can be deployed in a variety of environments like battle fields, water, large fields, and the like, and can transmit data to a Base station (BS). In a clusterbased network organization, sensor nodes are organized into clusters and one sensor node is selected as a sensor head (SH) in each cluster. Each SH denotes a facility and sends useful information to the Base Station (BS) through other SHs via the shortest path. In this paper, we study two clustering techniques, namely kmedian clustering and k-center clustering for a wireless sensor network. All the sensor nodes are static and homogeneous (having the same specifications) and SHs are assumed to be heterogeneous with respect to other sensor nodes in their respective clusters (but homogeneous to other SHs once they are located). The focus of this paper is to compare the k-median and k-center clustering techniques based on shortest path and total intra-cluster distance. We have implemented the two clustering techniques using the Java language and necessary experimental and statistical results are provided.
Analyses of People’s Perceptions Toward Risks in Genetically Modified Organisms
(North Dakota State University, 2016) Dass, Pranav
This research aims to analyze people’s perceptions about the potential risks associated with the presence of genetically modified organisms (GMOs) in food products. We formulated research questions and hypotheses based on parameters, including age, gender, state of residence, and more to analyze these perceptions. We conducted an online nationwide survey across the United States and recruited participants from the general population to understand their perceptions about risks for GMOs and GM foods. We formulated a set of questions regarding the effects of GMOs on food products (including both the pre- and post-study questions) and investigated the changes in people’s perceptions after reading selected news releases about GMOs. The survey responses were collected and categorized according to the research parameters and statistical assessments were conducted to test the hypotheses. Additionally, we introduced a novel approach to analyze the responses by creating a mind-map framework for both the pre- and post-study responses. We found that people residing in the southern region of the United States responded more positively toward GMOs when compared to individuals residing in the northeast, west and mid-west regions. We also deduced that people’s perceptions about GMOs were not significantly different from each other whether they resided in states with Republican or Democrat/non-partisan party affiliations. Further, we observed that the male participants responded more negatively compared to the female participants across the nation. We compared the results obtained from respondents in the general population to those from a group of Computer Science students at North Dakota State University who completed the same survey. We found that students considered GMOs less risky when compared to the general population. A third research study compared participants from the general population to a group of participants who were recruited from the general population. The second group didn’t read the news releases that separated the survey’s pre- and post-study questions. We observed that the news releases impacted the participants from the first group and, eventually, changed the individuals’ perceptions about GMOs when compared to the participants from the second group who possessed no or fewer perception changes.
Analysis of Image Classification Deep Learning Algorithm
(North Dakota State University, 2023) Kanungo, Shivam
This study explores the use of TensorFlow 2 and Python for image classification problems. Image categorization is an important area in computer vision, with several real-world applications such as object identification/recognition, medical imaging, and autonomous driving. This work studies TensorFlow 2 and its image categorization capabilities. We also demonstrate how to construct an image classification model using Python and TensorFlow 2. This analysis of image classification neural network problems with the use of Convolutional Neural Network (CNN) on the German and the Chinese traffic sign datasets is an engineering task. Ultimately, this work provides step-by-step guidance for creating an image classification model using TensorFlow 2 and Python, while also showcasing its potential to tackle image classification issues across various domains.
Analysis of Java's Common Vulnerabilities and Exposures in GitHub's Open-Source Projects
(North Dakota State University, 2022) Akanmu, Semiu
Java developers rely on code reusability because of its time and effort reduction advantage. However, they are exposed to vulnerabilities in publicly available open-source software (OSS) projects. This study employed a multi-stage research approach to investigate the extent to which open-source Java projects are secured. The research process includes text analysis of Java’s Common Vulnerabilities and Exposures (CVE) descriptions and static code analysis using GitHub’s CodeQL. This study found (a) cross-site scripting, (b) buffer overflow (though analyzed as array index out of bounds), (c) data deserialization, (d) input non-validation for an untrusted object, and (e) validation method bypass as the prevalent Java’s vulnerabilities from the MITRE CVEs. The static code analysis of the compatible seven (7) Java projects out of the 100 top projects cloned from GitHub revealed a 71.4% presence of the array index out-of-bounds vulnerability.
Analysis of SDR to Detect Long Range RFID Badge Cloners
(North Dakota State University, 2022) Knecht, Brett
This thesis proposes a way of detecting when radio frequency identification (RFID) badge credentials are being captured through the use of software defined radio (SDR). A method for using SDR to detect when badge cloning technologies are in use on the premises is presented, tested, and analyzed. This thesis presents an overview of the problem with badge systems and a background literature review. Next, the proposed method of detection and its workings are presented. Then, the strategy for evaluating the methods performance. This is discussed by discussion and evaluation of the results. Finally, the thesis concludes with a discussion of the method’s potential benefits and proposed future work.

Browse

Browsing NDSU Theses & Dissertations by browse.metadata.department "Computer Science"