PhD, University of Cincinnati, 2024, Education, Criminal Justice, and Human Services: Information Technology

Over the past ten years, cyberbullying has become a prevalent issue across various levels of education and society globally. This dissertation delves into the complex landscape of cyberbullying text detection. Through a thorough parametric analysis, it explores the intricacies of cyberbullying text detection research, presenting insights into potential solutions and strategies. A case study is conducted to investigate cultural variations and perceptions of offensiveness, particularly within Ghanaian culture, contributing to a deeper understanding of cyberbullying dynamics. The dissertation also explores strategies for prevention and fostering a safer online environment, along with examining cultural interpretations of technology features. Furthermore, this dissertation focuses on detecting cyberbullying in adversarial text content within social networking site, with a specific emphasis on identifying hate speech. Utilizing a deep learning-based approach with a correction algorithm, this dissertation yielded significant results. An LSTM model with a fixed epoch of 100 demonstrated remarkable performance, achieving high accuracy, precision, recall, F1-score, and AUC-ROC scores of 87.57%, 88.73%, 87.57%, 88.17%, and 91% respectively. The LSTM model's performance surpassed that of previous studies when compared. Additionally, the dissertation offers recommendations for defense strategies against adversarial attacks on AI-based models, providing valuable insights for future research endeavors.

MS, University of Cincinnati, 2024, Education, Criminal Justice, and Human Services: Information Technology

There has been a gross expansion of social media platforms and other platforms that generate lots of information that is very useful in the digital space called open-source intelligence (OSINT). A growing space where this information is of immense importance is criminal investigations and this research is to understand how we can apply open-source intelligence to help with criminal investigation. This research seeks to answer the question that is how the utilization of open-source intelligence has impacted the effectiveness of criminal investigations across various geographical locations and how software functionalities and capabilities for leveraging (OSINT) have evolved to support criminal investigations over the last couple of years. A multivocal literature review was adopted to help answer the research questions. This was adopted because there are not so many peer-reviewed articles that address our questions and wanted to include grey literature that captures the importance of open-source intelligence in criminal investigation. The research study revealed that most open-source intelligence applications are geared toward threat intelligence and darknet intelligence and have automation at the heart of their operations which helps the inclusion of intelligence from multiple sources. This research study embodies a comprehensive overview of the applications of OSINT to criminal investigations and establishes the various jurisprudence of OSINT application.

MS, University of Cincinnati, 2024, Education, Criminal Justice, and Human Services: Information Technology

The prevalence of wireless technology coupled to growing attacks of wireless networks has necessitated research into how to safeguard wireless infrastructure from attacks. There have been protocols like WEP and WPA2 and WPA3 implemented to safeguard devices working on a wireless network. However, these protocols have their own problems in terms of protecting the wireless network. FMS attack exposed the security flaw in WEP in the year 2001. WPA2 was also found to be vulnerable through Key reinstallation attack (KRACK). The problem with WPA3 has to do with its universality. Which means WPA3 cannot be used in every device. Even though IDS, IPS, SIEM and SOAR tools had been used in certain areas of network surveillance individually or in isolation of each other, the team is developing a framework to integrate these tools to efficiently protect wireless networks from incoming attacks. This will increase the protection surface for the wireless network to effectively detect and prevent attacks. The Authors would look at the security challenges in wireless networks, investigate and analyze the role of IDS/IPS/SIEM and SOAR in safeguarding cybersecurity attacks in a wireless network and then based on this investigation develop a framework to integrate IDS/IPS/SIEM and SOAR to detect and safeguard cybersecurity attacks in a wireless network. This integration will help reduce the MTTD, reduce MTTR, reduce false positives, reduce false negatives, increase true positives, increase true negatives, high automation rate, high resolution rate and increase efficiency. It also improves visibility into security functions complementing each other. The new ASMOAR system integrates the functionalities of IDS/IPS/SIEM and SOAR.

PhD, University of Cincinnati, 2024, Education, Criminal Justice, and Human Services: Information Technology

In the field of software development, Static Analysis Tools (SAT) have become increasingly popular for identifying and fixing potential issues in code. These tools help improve code quality and reduce the occurrence of bugs and vulnerabilities. However, evaluating the effectiveness of SAT can be challenging due to the subjective nature of user reviews. To address this challenge, we selected four popular SATs as case studies and they include SonarQube, FindBugs, Checkstyle, and PMD. The first part of this research involves conducting an empirical study for evaluating the performance of SATs. We compared the performance of the four SATs in detecting software defects in diverse open-source Java projects. The study results show that SonarQube performs considerably better than all other tools in defect detection. The second part of this research focuses on the user perspective by evaluating the performance of the SATs through sentiment analysis of user reviews. The study found that user sentiment is a valuable indicator of a tool's effectiveness and reliability. Positive user feedback typically corresponds to higher performance ratings, reflecting greater user satisfaction and tool efficiency. Conversely, negative sentiments often point to performance issues and user dissatisfaction. Thus, incorporating sentiment analysis can provide meaningful insights into the perceived quality and performance of SAT. In the third study, we applied topic modeling techniques to user reviews of SATs. Our analysis highlights the key aspects that users find beneficial and areas where improvements are needed. The findings provide valuable insights into user concerns and preferences, informing the development of more user-friendly and effective SAT. In our fourth study, we propose a theoretical framework that integrates sentiment analysis, composite sentiment score, topic modeling, and emotion detection to extract meaningful insights from user feedback. By quantifying polarity and subjectiv (open full item for complete abstract)

PhD, University of Cincinnati, 2024, Education, Criminal Justice, and Human Services: Information Technology

The transition to a fully remote or hybrid work model, expedited by the COVID-19 pandemic, marks a significant shift in the traditional organizational work model, ushering in new vulnerabilities and reshaping the cyber threat landscape. This shift has necessitated organizations worldwide to rethink their Cybersecurity strategies. Notably, Zero Trust Security model emerging as a more secure alternative to the traditional perimeter-based security approach due to its array of benefits. Despite the promising benefits of Zero Trust Security Model, its adoption is often met with hesitation, partly due to the lack of a unified implementation framework and comprehensive data-driven research on the cost-benefits of adopting the model. To address these gaps, the first part of this research focuses on identifying core components required to implement Zero Trust security effectively and to advance its global adoption through a comprehensive novel implementation framework. The second part of this research presents a novel secure and cost-effective approach that integrates open-source technology with cloud-based agent and non-agent tools to centrally monitor, detect, respond to, and prevent diverse attacks capable of breaching the security of an enterprise network. In our third study, we validated the effectiveness of our framework proposed in this research through a simulation deployed on a virtual environment to test the effectiveness of Zero Trust security in preventing and minimizing the risk of data breaches. The findings and contributions of this research are poised to significantly advance Cybersecurity by providing a practical and data-driven approach for implementing Zero Trust security in small, medium and large-scale organizations. Insights from this study are intended to benefit researchers working in the Zero Trust Security domain, as well as industry practitioners looking to transition to the Zero Trust security paradigm.

MS, University of Cincinnati, 2024, Education, Criminal Justice, and Human Services: Information Technology

Open-source software projects have become prevalent in everyday applications, and it is imperative to continue supporting these projects while empowering the people behind them. The state of diversity and inclusion efforts within the leadership of open-source communities is of utmost importance and help shape effective strategies for positive change. The purpose of this research is to gain insight into the challenges, experiences, and perspectives of individuals involved in open-source community leadership. Using participatory design methodology, this research explores what framework and design characteristics will help to develop a tool for the selection of open-source community leaders. Eleven people from open-source software communities were recruited to participate in a participatory design activity to inform the selection and design of a tool to support informed decision-making by voting bodies associated with a particular open-source community. The results show that profile and demographics of individuals, map view of group location, leadership experience of individuals and trends over time were found to be the most useful to open-source community members when learning more about candidates running for open-source boards. The discussion describes how these types of features can be integrated into tools used by these communities to make selections and how tools such as this one could be extended to other use cases.

Doctor of Philosophy (Ph.D.), University of Dayton, 2024, Electrical and Computer Engineering

Internet of things (IoT), an all pervasive technology, is expected to reach 41 billions by 2027. Such a revolutionary technology is utilized in plethora of applications such as health, and agriculture. IoT offers numerous advantages in terms of computing, and intelligence. Such a growth of IoT devices lead to the proliferation of wireless technologies to cater to the growing demands of users. Such proliferation of wireless technologies pose multiple challenges such as higher interference, limited spectrum resources, compatibility issues between different standards, and higher power consumption. The existing approaches as well as their limitations are surveyed in addition to including end-to-end deep learning based frameworks to alleviate the challenges described above. The proposed framework is validated, and evaluated on open-source and real-time data respectively.

Doctor of Organization Development & Change (D.O.D.C.), Bowling Green State University, 2024, Organization Development

This dissertation explores how state and local governments incorporate Generative Artificial Intelligence (GenAI) into their operations. As these technologies evolve, they offer opportunities to improve efficiency and decision-making but also bring challenges that require updated governance frameworks. The study uses content analysis to examine how well current governance frameworks manage the integration of GenAI, focusing on strategic alignment, risk management, data governance, and ethical considerations. The findings show that governments are at different stages of incorporating GenAI, emphasizing the need for improved governance frameworks to handle these new technologies effectively. This research helps understand how GenAI is changing public sector governance and suggests directions for future policies.

Master of Science in Computer Engineering (MSCE), Wright State University, 2024, Computer Engineering

Hardware components are becoming prone to threats with increasing technological advances. Malicious modifications to such components are increasing and are known as hardware Trojans. Traditional approaches rely on functional assessments and are not sufficient to detect such malicious actions of Trojans. Machine learning (ML) assisted techniques play a vital role in the overall detection and improvement of Trojan. Our novel approach using various ML models brings an improvement in hardware Trojan identification with power signal side channel analysis. This study brings a paradigm shift in the improvement of Trojan detection in integrated circuits (ICs). In addition to this, our further analysis towards hardware authentication extends towards PUFs (Physical Unclonable Functions). Arbiter PUFs were chosen for this purpose. These are also Vulnerable towards ML attacks. Advanced ML assisted techniques predict the responses and perform attacks which leads to the integrity of PUFs. Our study helps improve ML-assisted hardware authentication for ML attacks. In addition, our study also focused on the defense part with the addition of noise and applying the same attack ML-assisted model. Detection of Trojan in hardware components is achieved by implementing machine learning techniques. For this Purpose, several Machine learning models were chosen. Among them, Random Forest classifier (RFC) and Deep neural network shows the highest accuracy. This analysis plays a vital role in the security aspect of all hardware components and sets a benchmark for the overall security aspects of hardware. Feature extraction process plays major role for the improvement of accuracy and reliability of hardware Trojan classification. Overall, this study brings significant improvement in the field of overall hardware security. Our study shows that RFC performs best in hardware classification with an average of 98. 33% precision of all chips, and deep learning techniques give 93. 16% prec (open full item for complete abstract)

Master of Science (MS), Wright State University, 2024, Computer Science

Semiconductor microelectronics integrated circuits (ICs) are increasingly integrated into modern life-critical applications, from intelligent infrastructure and consumer electronics to the Internet of Things (IoT) and advanced military and medical systems. Unfortunately, these applications are vulnerable to new hardware security attacks, including microelectronics counterfeits and hardware modification attacks. Physical Unclonable Functions (PUFs) are state-of-the-art hardware security solutions that utilize process variations of integrated circuits for device authentication, secret key generation, and microelectronics counterfeit detection. The negative impact of aging on Static Random \linebreak Access Memory Physical Unclonable Functions (SRAM PUFs) has significant consequences for microelectronics authentication, security, and reliability. This research thoroughly \linebreak examines the effect of aging on the reliability of SRAM PUFs used for secure and trusted microelectronics integrated circuit applications. It initially provides an overview of SRAM PUFs, highlighting their significance and essential features while addressing encountered challenges. The study then covers mitigation techniques, including multi-modal PUFs, that already exist to boost the resilience of SRAM PUFs against aging impacts, highlighting their advantages and the gap in the research addressed in this research. This work proposes a novel AI-enabled security for reliable SRAM PUFs. The proposed approach aims to study and countermeasure the impact of aging on SRAM PUF by analyzing data samples, including Bias Temperature Instability (BTI), Bit Flips, Accelerated aging, and Hot Carrier Injection (HCI) and to study their effects on SRAM PUF cell properties and output. Accelerated aging is a direct result of a change in the environmental temperature and voltage for a few hours. We aim to mitigate the impact of accelerated aging on the reliability authentication and encryption keys of (open full item for complete abstract)

Master of Science in Education (M.S.E.), University of Dayton, 2024, Early Childhood Education

In this qualitative study, we examined the effects of augmented reality (AR) and physical-digital interaction in educational applications on preschoolers' engagement, skill development, and collaborative learning in the classroom. Drawing on Creswell's (2018) systematic method, the research design included immersive participant observation and semi-structured interviews to document the lived experiences of young learners using the Osmo Genius Kit. The study, conducted across three varied preschool classrooms affiliated with a Midwestern university, included a representative sample of children aged 3 to 5 years, allowing for a thorough examination of AR's complex influence in early childhood education. This methodology allows us to address the research questions and gain a thorough grasp of how AR technology can be used to improve education in preschool settings.

Master of Computing and Information Systems, Youngstown State University, 0, Department of Computer Science and Information Systems

Bootstrap sampling serves as a cornerstone in statistical analysis, providing a robust method to evaluate the precision of sample-based estimators. As the landscape of data processing expands to accommodate big data, approximate query processing (AQP) emerges as a promising avenue, albeit accompanied by challenges inaccurate assessment. By leveraging bootstrap sampling, the errors of sample-based estimators in AQP can be effectively evaluated. However, the implementation of bootstrap sampling encounters obstacles, particularly in the computation-intensive resampling procedure. This thesis embarks on an exploration of various resampling methods, scrutinizing five distinct approaches: On Demand Materialization (ODM) Method, Conditional Binomial Method (CBM), Naive Method, Two-Step Poisson Random (TSPR), and Two-Step Poisson Adaptive (TSPA). Through rigorous evaluation and comparison of the execution time for each method, this thesis elucidates their relative efficiencies and contributions to AQP analyses within the realm of big data processing. Furthermore, this research contributes to the broader understanding of resampling techniques in statistical analysis, offering insights into their computational complexities and implications for big data analytics. By addressing the challenges posed by AQP in the context of bootstrap sampling, this thesis seeks to advance methodologies for accurate assessment in the era of big data processing.

Doctor of Philosophy, The Ohio State University, 2024, English

This dissertation explores the contrast between the Athenian and metic rhetorical paradigms through the lens of the hacker. Arguing the dominant Athenian rhetorical paradigm is marked by public, persuasive, often-disembodied rhetorics in pursuit of epistemic truths, I argue the metic paradigm focuses on stealthy, deceptive, embodied rhetoric in pursuit of advantages over adversaries. Noting how today's digital rhetorical situation is largely adversarial, this dissertation points to the hacker as an exemplar of metic rhetorics. Through three case studies, each focusing on a different type of computer hack, this dissertation explores how the hacker subjectivity is produced and describes its beneficial lines of flight, discusses the interplay of metaphor and physicality in digital activism and cyberwar, and shows how metic rhetorical practices can be leveraged to create a safer and more just world and thus improve personal and organizational cybersecurity.

MS, University of Cincinnati, 2024, Education, Criminal Justice, and Human Services: Information Technology

In today's increasingly urbanized world, the intersection of technology and agriculture presents opportunities to design innovative solutions to address food insecurity and promote community wellness. Within this context, urban farming emerges as a promising avenue, particularly for marginalized communities seeking to reclaim control over their food systems and to foster sustainability. This study lies at the intersection the technology, urban farming, and wellness. Through this study, we aim to co-design a technology solution with Black urban farmers in Cincinnati to meet the nutritional needs of Black pregnant individuals in the area. First, we conducted needs assessment activities with the farmers to identify key pain points. Then, we designed and developed a technology solution - Homegrown Dashboards. These interactive dashboards, with a web front-end, enables farmers to track crops and land usage, manage events and contracts, and share crops based on demand and supply within the network. Multiple iterations, from paper prototyping to implementation of the dashboards, we incorporated feedback from farmers to ensure usability and user experience. The interactive dashboards promote collaboration, streamline operations, and enhance efficiency, contributing to improved sustainability and resilience within the local food network. This study demonstrates a novel approach of co-designing technology solution with Black urban farmers to address their specific needs and challenges, highlighting the importance of participatory approaches in technology development for social impact.

MS, University of Cincinnati, 2024, Education, Criminal Justice, and Human Services: Information Technology

We are witnessing a profound transformation within the automotive industry, propelled by the mass adoption of Electric Vehicles (EVs). As this transition unfolds at a rapid pace, it unveils security gaps within the emerging infrastructure, necessitating urgent attention and solutions. This thesis investigates the resilience to cybersecurity threats of a new standalone branch of EV charging application, which we propose terming "Fleet Charging Infrastructure" (FCI). We conducted a case study on one of the emerging leaders in EV fleet charging in the US market, Electrada. To start the study, we analyzed Electrada's operation, the structure of their charging infrastructure, and the importance of internal processes that guarantee a 99% uptime commitment to their customers. Our research questions focused on characterizing FCI, identifying cybersecurity threats specific to it, and devising strategies for enhancing its resilience against cyberattacks. The study utilized STRIDE and DREAD frameworks for threat modeling and prioritization, along with well-established industry frameworks like NIST IR8473 and CIS CSC for threat mitigation planning. As a result, we succeeded in outlining the key processes within FCI, enumerating major components and dataflows, and establishing distinctive features that later allowed us to identify 27 unique threats, propose mitigation actions for each threat, and develop a 3-step mitigation strategy plan for FCI operators based on their resource availability. Our findings highlight the distinctiveness of FCI as a standalone branch of EV charging applications, due to the uniqueness of internal processes, components, motivations, and goals compared to other iterations of EV Charging.

Master of Science (M.S.), University of Dayton, 2024, Aerospace Engineering

This research introduces an Origami-inspired dynamic spacecraft radiator, capable of adjusting heat rejection in response to orbital variations and extreme temperature fluctuations in lunar environments. The research centers around the square twist origami tessellation, an adaptable geometric structure with significant potential for revolutionizing radiative heat control in space. The investigative involves simulations of square twist origami tessellation panels using vector math and algebra. This study examines both a two-dimensional (2- D), infinitely thin tessellation, and a three-dimensional (3-D), rigidly-foldable tessellation, each characterized by an adjustable closure or actuation angle “φ”. Meticulously analyzed the heat loss characteristics of both the 2D and 3D radiators over a 180-degree range of actuation. Utilizing Monte Carlo Ray Tracing and the concept of “view factors”, the study quantifies radiative heat loss, exploring the interplay of emitted, interrupted, and escaped rays as the geometry adapts to various positions. This method allowed for an in-depth understanding of the changing radiative heat loss behavior as the tessellation actuates from fully closed to fully deployed. The findings reveal a significant divergence between the 2D and 3D square twist origami radiators. With an emissivity of 1, the 3D model demonstrated a slower decrease in the ratio of escaped to emitted rays (Ψ) as the closure/actuation angle increased, while the 2D model exhibited a more linear decline. This divergence underscores the superior radiative heat loss control capabilities of the 2D square twist origami geometry, offering a promising turndown ratio of 4.42, validating the model's efficiency and practicality for radiative heat loss control. Further exploration involved both non-rigidly and rigidly foldable radiator models. The non-rigidly foldable geometry, initially a theoretical concept, is realized through 3D modeling and physica (open full item for complete abstract)

PhD, University of Cincinnati, 2023, Education, Criminal Justice, and Human Services: Information Technology

In modern urban landscape, the safety and efficiency of both home and city environments are paramount. Action Recognition (AR) has emerged as a pivotal technology to enhance these domains, particularly in the realms of Smart Home and Smart City applications. This dissertation delves into the intricacies of AR, underscoring its transformative role in monitoring and ensuring safety across diverse contexts. Diving into the realm of deep learning, its transformative impact on action recognition over recent years becomes evident. Notwithstanding these advances, inherent challenges remain, particularly when addressing specific AR tasks that rely on limited datasets. To navigate these complexities, our research introduces a novel, resource-efficient framework combining transfer learning techniques with Conv2D LSTM layers for tasks such as Smart Baby Care. This initiative resulted in the creation of a benchmark dataset and an automated model tailored for recognizing and predicting baby activities, setting new standards in computational efficiency and performance. From the intimate confines of smart baby care within homes, we broadened our lens to encompass the bustling streets of urban landscapes. Complementing home safety, the safety of these urban environments became a pivotal focus of our research. Through an empirical analysis, we delved into the intricacies of accident detection. Identifying and analyzing prevailing techniques, taxonomies, and algorithms showcased the central role of AR in accident detection and autonomous transportation. Furthermore, by leveraging data from reputable sources like the NHTSA Crash Report Sampling System, we provided a holistic view of traffic accident trends, underlining the dire need for robust accident detection systems. Our seminal contribution is the introduction of the I3D-CONVLSTM2D model architecture, uniquely designed for accident detection in smart city with 87% mean average precision and 80% for detecting traf (open full item for complete abstract)

Master of Computing and Information Systems, Youngstown State University, 2023, Department of Computer Science and Information Systems

Additive Manufacturing (AM), particularly LASER Powder Bed Fusion (LPBF), has gained prominence for its flexibility and precision in handling complex metal structures. However, optimizing L-PBF for intricate designs involves analyzing over 130 process parameters, leading to prolonged duration and increased costs. This thesis proposes a novel approach by harnessing statistical and machine learning algorithms to predict manufacturability issues before the printing process. By performing a comparative analysis of the intended design with the machine produced result, the study introduces two machine learning and one artificial neural network (ANN) algorithm to forecast the printability of new designs accurately. This innovative method aims to reduce or eliminate the need for iterative printing, reducing productivity costs and optimizing the LPBF additive manufacturing process.

Doctor of Education , University of Dayton, 2023, Educational Administration

My Dissertation in Practice focuses on how collaboration has the potential to create a positive effect on the Minot Operations Division (MOD). Within the Minot Operations Division (MOD) exists various departments designed to help private organizations improve their organizational output within the continental United States and abroad. To ensure that MOD meets its vision and mission of improving leadership and organizational cohesion in other organizations the use of collaboration as a tool within the MOD is used to exchange ideas and improve on methods to assist other organizations to develop in a positive way.

Doctor of Business Administration (D.B.A.), Franklin University, 2023, Business Administration

This study will evaluate the organizational effectiveness of an artificial intelligence (AI)/machine learning (ML) recommender system at a higher education institution. It will determine the positive or negative net benefits (i.e., organizational effectiveness) of utilizing the D&M ISSM. Identifying the value and efficacy of information systems (IS) management actions and investments requires evaluating their success or effectiveness. A system's effectiveness is evaluated from the organizational perspective based on the degree to which it meets the goals of the organization. Although the pandemic has negatively impacted numerous lives and business activities, more leaders considered it an opportunity because it offered new opportunities for business innovation and entrepreneurship, despite it being viewed as the most significant crisis in the modern world. Considering the significant changes caused by the COVID-19 pandemic and the response to it, it is no longer simply considered an option to adopt and use AI/ML systems, but rather an obligation. There is a lack of understanding of the factors contributing to the success of recommendation systems, therefore, these systems are rarely used to their full potential. An analysis of the relationship between information quality, system quality, use/intention to use, and user satisfaction in recommender systems was conducted using a mixed-method approach based on the DeLone and McLean IS success model. Students enrolled in a two-year college who used a portal as part of their academic journey were the target population of this study, and a total of 8,559 participants were contacted to participate, and 305 of them completed the survey. The results of this study indicate that quality factors relate closely to the success of the recommender system as measured by organizational effectiveness. The results indicate that there are statistically significant relationships between the independent variables, Information Qu (open full item for complete abstract)