Computer Sciences Commons^™

Rfid Key Fobs In Vehicles: Unmasking Vulnerabilities & Strengthening Security, Devon Magda, Bryson R. Payne

KSU Proceedings on Cybersecurity Education, Research and Practice

No abstract provided.

The Impact Of Individual Techno-Characteristics On Information Privacy Concerns In The Diffusion Of Mobile Contact Tracing, Jiesen Lin, Dapeng Liu, Lemuria Carter

KSU Proceedings on Cybersecurity Education, Research and Practice

In the wake of the global health crisis, mobile contact tracing applications have emerged as important tools in managing disease spread. However, their effectiveness heavily relies on mass adoption, significantly influenced by the public's information privacy concerns. To date, systematic examination of how these privacy concerns relate to the innovation adopter categories in mobile contact tracing remains sparse. Furthermore, the influence of individual techno-characteristics on these concerns is to be explored. This research seeks to fill these gaps. Drawing on the diffusion of innovation theory, we examine the impact of the key techno-characteristics—adopter category, propensity for identification misrepresentation, and exposure …

Exploring Information Privacy Concerns During The Covid-19 Pandemic: A Juxtaposition Of Three Models, Dapeng Liu, Lemuria Carter, Jiesen Lin

KSU Proceedings on Cybersecurity Education, Research and Practice

Government agencies across the globe utilize mobile applications to interact with constituents. In response to the global pandemic, several nations have employed contact tracing services to manage the spread of COVID-19. Extent literature includes various models that explore information privacy. Several researchers have highlighted the need to compare the effectiveness of diverse information privacy models. To fill this gap, we explore the impact of information privacy concerns on citizens’ willingness to download a federal contact tracing app. In particular, we compare three types of prevalent information privacy concerns: global information privacy concerns (GIPC), concern for information privacy (CFIP), and internet …

How Increased Ransomware Attacks Have Impacted Hospitals In The United States, Mackenzie Dotson

Theses, Dissertations and Capstones

Introduction: The healthcare industry, particularly hospitals, have fallen prey to the alarming rise of ransomware attacks. In recent years, highly sophisticated cybergroups, armed with substantial funds and advanced technology, have intensified their focus on hospitals. Despite the advice against it, most hospitals have paid the ransom in order to regain access to their electronic systems and patient data, underlining the severity of these attacks.

Purpose of the Study: The purpose of this research was to evaluate the effects of ransomware attacks on hospitals in the US to determine if the patients were at risk due to hackers withholding patient information …

The Measure Of Efficiency And Effectiveness When Using Artificial Intelligence (Ai) In Radiology, Jordan Watts

Theses, Dissertations and Capstones

Introduction: The use of artificial intelligence in radiology has helped radiologists identify patterns and abnormalities in medical images to diagnose and treat patients. Deep learning and machine learning algorithms have been used to assist physicians in detecting features that are not noticeable to the human eye. The FDA has approved almost 400 AI algorithms for radiology and estimated that the market for AI in medical imaging would grow from $21.48 billion in 2018 to $264.85 billion in 2028.

Purpose of the Study: The purpose of this research was to evaluate the use of artificial intelligence in radiology to determine its …

Machine Intelligence With Associative Memory And Event-Driven Transaction History, Rao Mikkilineni, W. Patrick Kelly

Barowsky School of Business | Faculty Scholarship

Digital machine intelligence has evolved from its inception in the form of computation of numbers to AI, which is centered around performing cognitive tasks that humans can perform, such as predictive reasoning or complex calculations. The state of the art includes tasks that are easily described by a list of formal, mathematical rules or a sequence of event-driven actions such as modeling, simulation, business workflows, interaction with devices, etc., and also tasks that are easy to do “intuitively”, but are hard to describe formally or as a sequence of event-driven actions such as recognizing spoken words or faces. While these …

Prediction Of Carbonation Capacity Of Scms Using Ensemble Learning Method, Kangyi Cai, Jian Liu, Edward Mwanza, Mahelet G. Fikru, Hongyan Ma, Donald C. Wunsch

Economics Faculty Research & Creative Works

The utilization of supplementary cementitious materials (SCMs) subjected to carbonation processing represents a viable strategy to mitigate anthropogenic CO₂ emissions associated with concrete production, potentially contributing to the achievement of carbon neutrality. However, existing studies have limitations in effectively predicting the varying carbonation capacities of different SCMs, a gap that this research aims to address. Recent research efforts focused on the carbonation of waste-material-sourced SCMs are reviewed, along with a comparative discussion on diverse carbonation methods. A detailed data set encapsulating the properties of SCMs, and carbonation configurations was compiled. At the same time, six ensemble learning models were …

A Review Of Hybrid Cyber Threats Modelling And Detection Using Artificial Intelligence In Iiot, Yifan Liu, Shancang Li, Xinheng Wang, Li Xu

Information Technology & Decision Sciences Faculty Publications

The Industrial Internet of Things (IIoT) has brought numerous benefits, such as improved efficiency, smart analytics, and increased automation. However, it also exposes connected devices, users, applications, and data generated to cyber security threats that need to be addressed. This work investigates hybrid cyber threats (HCTs), which are now working on an entirely new level with the increasingly adopted IIoT. This work focuses on emerging methods to model, detect, and defend against hybrid cyber attacks using machine learning (ML) techniques. Specifically, a novel ML-based HCT modelling and analysis framework was proposed, in which regularisation and Random Forest …

Sscm: A Secured Approach To Supply Chain Management Using Blowfish Optimization, Shitharth Selvarajan, Hariprasath Manoharan, Alaa O. Khadidos, Achyut Shankar, Adil O. Khadidos, Wattana Viriyasitavat, Li Da Xu

Information Technology & Decision Sciences Faculty Publications

This study examines the importance of enterprise information systems that link several corporate organisations to share information about diverse products under high security settings. The primary goal of the proposed strategy is to create a direct link between product demand and production to minimise the impact of rising costs. The research motive to make a connection cannot be resolved without suitable data that shows both quantity and quality in each organisation unit. The suggested method is designed to deliver accurate data to authorised end users while preventing any data exposure to unauthorised users. Security cryptographic keys are utilised to create …

Asem 4.0/5.0 - Evolving The Engineering Management Profession Through Industry 4.0/5.0 Collaborative Networks, T. Steven Cotter, Faisal Mahmud, Ziniya Zahedi

Engineering Management & Systems Engineering Faculty Publications

The American Society for Engineering Management was created and matured under Industry 3.0 automation. The emergence of Industry 4.0 and 5.0 are forcing all organizational sectors to rethink their long-term strategy with respect to emerging horizontal/vertical cyber-physical systems integration. This leaves open the question of the directions in which ASEM should evolve into the 21st century. This paper reports an initial mapping of Industry 4.0 and 5.0 technologies and initiatives as goal-oriented, long-term strategic collaborative networks. The research method began with the Boston Consulting Group nine technologies of Industry 4.0 (2015) and the Industry 5.0 technologies within its human-centric, sustainability, …

A Comprehensive Study Of Patent Litigation In The Pharmaceutical Sector: Employing Network Theories, Graph Neural Networks, Agent Based Modeling, Bayesian Network Autocorrelation Models, Sreehas Gopinathan

Information Systems & Operations Management Dissertations - Archive

Understanding the dynamics and predictors of patent litigation is crucial in intellectual property management, especially given the competitive edge patents offer companies. Also, patents serve as both legal tools and repositories of innovation. This research delves into the complex world of patent litigation within the pharmaceutical industry, focusing on creating and applying advanced computational models to study litigation propensities. Techniques such as Graph Neural Networks (GNN), Agent-Based Modeling (ABM), and Bayesian Analysis of Network Autocorrelation Models (BANAM) are employed to explore the litigation phenomenon

Expanding Australia's Defence Capabilities For Technological Asymmetric Advantage In Information, Cyber And Space In The Context Of Accelerating Regional Military Modernisation: A Systemic Design Approach, Pi-Shen Seet, Anton Klarin, Janice Jones, Michael N. Johnstone, Violetta Wilk, Stephanie Meek, Summer O'Brien

Research outputs 2022 to 2026

Introduction. The aim of the project was to conduct a systemic design study to evaluate Australia'sopportunities and barriers for achieving a technological advantage in light of regional military technological advancement. It focussed on the three domains of (1) cybersecurity technology, (2) information technology, and (3) space technology.

Research process. Employing a systemic design approach, the study first leveraged scientometric analysis, utilising informetric mapping software (VOSviewer) to evaluate emerging trends and their implications on defence capabilities. This approach facilitated a broader understanding of the interdisciplinary nature of defence technologies, identifying key areas for further exploration. The subsequent survey study, engaging 828 …

Unleashing The Power Of Internet Of Things And Blockchain: A Comprehensive Analysis And Future Directions, Abderahman Rejeb, Karim Rejeb, Andrea Appolloni, Sandeep Jagtap, Mohammad Iranmanesh, Salem Alghamdi, Yaser Alhasawi, Yasanur Kayikci

Research outputs 2022 to 2026

As the fusion of the Internet of Things (IoT) and blockchain technology advances, it is increasingly shaping diverse fields. The potential of this convergence to fortify security, enhance privacy, and streamline operations has ignited considerable academic interest, resulting in an impressive body of literature. However, there is a noticeable scarcity of studies employing Latent Dirichlet Allocation (LDA) to dissect and categorize this field. This review paper endeavours to bridge this gap by meticulously analysing a dataset of 4455 journal articles drawn solely from the Scopus database, cantered around IoT and blockchain applications. Utilizing LDA, we have extracted 14 distinct topics …

Trading Cloud Computing Stocks Using Sma, Xianrong Zheng, Lingyu Li

Information Technology & Decision Sciences Faculty Publications

As cloud computing adoption becomes mainstream, the cloud services market offers vast profits. Moreover, serverless computing, the next stage of cloud computing, comes with huge economic potential. To capitalize on this trend, investors are interested in trading cloud stocks. As high-growth technology stocks, investing in cloud stocks is both rewarding and challenging. The research question here is how a trading strategy will perform on cloud stocks. As a result, this paper employs an effective method—Simple Moving Average (SMA)—to trade cloud stocks. To evaluate its performance, we conducted extensive experiments with real market data that spans over 23 years. Results show …

Data Science In Finance: Challenges And Opportunities, Xianrong Zheng, Elizabeth Gildea, Sheng Chai, Tongxiao Zhang, Shuxi Wang

Information Technology & Decision Sciences Faculty Publications

Data science has become increasingly popular due to emerging technologies, including generative AI, big data, deep learning, etc. It can provide insights from data that are hard to determine from a human perspective. Data science in finance helps to provide more personal and safer experiences for customers and develop cutting-edge solutions for a company. This paper surveys the challenges and opportunities in applying data science to finance. It provides a state-of-the-art review of financial technologies, algorithmic trading, and fraud detection. Also, the paper identifies two research topics. One is how to use generative AI in algorithmic trading. The other is …

Exploring Hedonic And Utilitarian Aspects Through Perceived Warmth In Human-Designed Vs. Ai-Generated Fashion, Dooyoung Choi, Ha Kyung Lee

Educational Leadership & Workforce Development Faculty Publications

Among various ways in which artificial intelligence (AI) is used in the fashion industry, its utilization in design has sparked public discussion about the potential replacement of human designers by AI. Along with this critical question, it is imminent to examine how consumers would respond to designs by AI. The purpose of this study is to explore consumers’ perceptions toward a fashion product labeled as generated by an AI system, comparing it to the same product labeled as designed by a human designer. Specifically, drawing from existing literature, we examine if the design source affects consumers’ perceptions of a product …

Ai-Designed Clothing And Perceived Values: What Can Move Consumers' Minds With The Ai-Designed Clothing?, Dooyoung Choi, Ha Kyung Lee

STEMPS Faculty Publications

This study investigates the perceived values of AI-designed clothing (quality, emotion, ease) and their impact on willingness to pay (WTP) and word-of-mouth (WOM), with the moderating effect of gender differences. A total of 314 respondents completed the survey via MTurk. Participants watched a video clip demonstrating how an AI system creates various clothing designs by altering garment elements (e.g., style, size). After watching the video clip, they were asked to answer a series of questions about the AI-designed clothing and themselves. The collected data were analyzed using AMOS 26.0. Results showed that, for male and female consumers, the quality value …

Applications Of Ai/Ml In Maritime Cyber Supply Chains, Rafael Diaz, Ricardo Ungo, Katie Smith, Lida Haghnegahdar, Bikash Singh, Tran Phuong

School of Cybersecurity Faculty Publications

Digital transformation is a new trend that describes enterprise efforts in transitioning manual and likely outdated processes and activities to digital formats dominated by the extensive use of Industry 4.0 elements, including the pervasive use of cyber-physical systems to increase efficiency, reduce waste, and increase responsiveness. A new domain that intersects supply chain management and cybersecurity emerges as many processes as possible of the enterprise require the convergence and synchronizing of resources and information flows in data-driven environments to support planning and execution activities. Protecting the information becomes imperative as big data flows must be parsed and translated into actions …

The Role Of Shopping Orientations And Intrinsic Experiential Value In Consumer's Willingness To Follow Embodied-Ai's Advice In Fashion Shoe Stores, Christina Soyoung Song, Ji Young Lee, Dooyoung Choi

STEMPS Faculty Publications

This study employs a synthesis of Intrinsic Motivation Theory with three shopping orientations, namely “adventure,” “idea,” and “personalized” shopping, in order to examine their potential influence on individuals' motivation towards shopping. We proposed that consumers’ experiential value of intrinsic enjoyment is an indispensable mediator that affects their willingness to follow EAI’s advice. The study offers novel insights into the way that consumers’ characteristics of influencing others’ clothing consumption affect their shopping motivations to find adventure and stimulation, keep up with new fashion trends and products information, and their preference to patronize stores and interact with store staff on a personal …

Digitalization Of Railway Transportation Through Ai-Powered Services: Digital Twin Trains, Salih Sarp, Murat Kuzlu, Vukica Jovanovic, Zekeriya Polat, Ozgur Guler

Engineering Technology Faculty Publications

Digitalization is a key concept that transformed the various industries through technologies like Internet of Things (IoT), Artificial Intelligence (AI), and Digital Twin (DT). Although innovations provided by the advancement of digitalization have paved the way for more efficient operations and products for transportation, the rail transportation sector struggles to keep up with the rest of the transportation industry, since trains are designed to last for decades, and the insufficient infrastructure investment leads to multiple railroad derailments across the globe. Therefore, the primary aim is to transform current railway systems into human-centric, adaptable, sustainable and future-proof networks, aligning with Industry …

Ai Fairness In Action: A Human-Computer Perspective On Ai Fairness In Organizations And Society, David De Cremer, Jack Mcguire, Jack Mcguire

Research Collection Lee Kong Chian School Of Business

Artificial intelligence (AI) systems are being increasingly adopted by society, governments, and organizations in various decision-making contexts. For example, organizations use AI systems to decide whether applicants can be considered for a job, whether bonuses and other rewards should be allocated, or whether promotions and further training need to be invested in. In fact, as AI is seen as an important catalyst of economic growth, organizations today seem to know no boundaries in their AI adoption efforts, making employees and society more dependent on and thus also more vulnerable to the decisions made by or in partnership with AI (De …

Towards Algorithmic Justice: Human Centered Approaches To Artificial Intelligence Design To Support Fairness And Mitigate Bias In The Financial Services Sector, Jihyun Kim

CMC Senior Theses

Artificial Intelligence (AI) has positively transformed the Financial services sector but also introduced AI biases against protected groups, amplifying existing prejudices against marginalized communities. The financial decisions made by biased algorithms could cause life-changing ramifications in applications such as lending and credit scoring. Human Centered AI (HCAI) is an emerging concept where AI systems seek to augment, not replace human abilities while preserving human control to ensure transparency, equity and privacy. The evolving field of HCAI shares a common ground with and can be enhanced by the Human Centered Design principles in that they both put humans, the user, at …

Graph-Partitioning Entity Resolution For Resolving Noisy Product Names In Ocr Scans Of Retail Receipts, Jose Ramon Ilagan, Joseph Benjamin R. Ilagan

Quantitative Methods and Information Technology Faculty Publications

In business intelligence for retail, it is critical to ensure consistent and unambiguous product dimension information. This is challenging, especially if an organization does not have full control over the source of either transaction or master data. Such lack of control is the case when brands rely on data provided directly by consumers through images of receipts. Product name strings obtained from the digitization of receipts often contain substitution, insertion, and deletion errors. These errors prevent product names from serving as a useful dimension for further analysis. This paper proposes a clustering-based approach to link error-laden product names to underlying …

Optimizing Sports Outcome Prediction Through Feature Engineering And Machine Learning, Vitor S. Freitas

Graduate Theses/Dissertations

The challenge of predicting the outcome of a team game lies in the high complexity and dynamics of the sports data. This thesis focuses on the aspect of using feature engineering and the genetic algorithm to predict the winner and the score of various sports events. Generally, it deals with how machine learning algorithms are combined with state-of-the-art feature engineering techniques in sports datasets derived from various sports disciplines. In this thesis, five different machine learning models have been applied, classification and regression trees (CART), random forest (RF), stochastic gradient boosting (SGB), eXtreme gradient boosting (XGBoost), and extreme learning machine …

Assessing Performance Optimization Strategies In Cloud-Native Environments Through Containerization And Orchestration Analysis, Daniel E. Ukene

College of Graduate Studies: Theses & Dissertations

This thesis comprises three distinct, yet interconnected studies addressing critical aspects of web infrastructure management. We begin by studying containerization via Docker and its impact on web server performance, focusing on Apache and Nginx hosted on virtualized environments. Through meticulous load testing and analysis, we provide insights into the comparative performance of these servers, adding users of this technology know which webservers to leverage when hosting their webservice along alongside the infrastructure to host it on. Next, we expand our focus to examine the performance of caching systems, namely Redis and Memcached, across traditional VMs and Docker containers. By comparing …

Opportunities And Challenges Posed By Disruptive And Converging Information Technologies For Australia's Future Defence Capabilities: A Horizon Scan, Pi-Shen Seet, Anton Klarin, Janice Jones, Mike Johnstone, Helen Cripps, Jalleh Sharafizad, Violetta Wilk, David Suter, Tony Marceddo

Research outputs 2022 to 2026

Introduction: The research project's objective was to conduct a comprehensive horizon scan of Network Centric Warfare (NCW) technologies-specifically, Cyber, IoT/IoBT, AI, and Autonomous Systems. Recognised as pivotal force multipliers, these technologies are critical to reshaping the mission, design, structure, and operations of the Australian Defence Force (ADF), aligning with the Department of Defence (Defence)’s offset strategies and ensuring technological advantage, especially in the Indo-Pacific's competitive landscape.

Research process: Employing a two-pronged research approach, the study first leveraged scientometric analysis, utilising informetric mapping software (VOSviewer) to evaluate emerging trends and their implications on defence capabilities. This approach facilitated a broader understanding …

Why The Ethical Use Of Ai Matters For Your Career, Jack Mcguire, David De Cremer, Yorck Hesselbarth, Leander De Schutter

Research Collection Lee Kong Chian School Of Business

In the contemporary digital era, innovations such as artificial intelligence (AI) are profoundly transforming the business landscape (De Cremer, 2020). The buzz surrounding ChatGPT, coupled with recent assertions about the sentience of Google’s LaMDA, a large language model, underscore the prominence of chatbot technology in these advancements (Adamopoulou & Moussiades, 2020; Ryu & Lee, 2018; Tiku, 2022). Customer-oriented chatbots, an emergent application of this tech, offer unparalleled efficiency and cost-effectiveness, operating ceaselessly and responding to client inquiries in real time (Salesforce, Research, 2019). Yet, amidst these advantages lies an ethical conundrum. Customers cherish genuine human interaction and can become quickly …

Trust: The Feature That Vending Machines And Atms Share, But Simplygo Lacks, Sun Sun Lim

Research Collection College of Integrative Studies

The article discussed the intricacies of trust in the SimplyGo debacle and highlighted how the design of physical interfaces like vending machines and ATMs and digital interfaces from apps like Grab, Parking.sg and ShopBack have critical features to instil trust. People need to be reassured that their transactions have proceeded as they should, and thay have not been short-changed.

All In One Place: Ensuring Usable Access To Online Shopping Items For Blind Users, Yash Prakash, Akshay Kolgar Nayak, Mohan Sunkara, Sampath Jayarathna, Hae-Na Lee, Vikas Ashok

Computer Science Faculty Publications

Perusing web data items such as shopping products is a core online user activity. To prevent information overload, the content associated with data items is typically dispersed across multiple webpage sections over multiple web pages. However, such content distribution manifests an unintended side effect of significantly increasing the interaction burden for blind users, since navigating to-and-fro between different sections in different pages is tedious and cumbersome with their screen readers. While existing works have proposed methods for the context of a single webpage, solutions enabling usable access to content distributed across multiple webpages are few and far between. In this …

Archiving Digital Marketing: Examining Preservation Of Dynamic Content On The Web Through The Lens Of Online Advertisements, Christopher Rauch, Alex H. Poole, Travis Reid, Michele C. Weigle, Michael L. Nelson, Faryaneh Poursardar, Mat Kelly

Computer Science Faculty Publications

The transition to digital marketing has revolutionized advertising, reflecting and shaping societal norms and trends. The “Saving Ads” project addresses the challenges of preserving these ephemeral digital artifacts, essential for understanding the evolution of advertising and its socio-cultural impact. The initiative focuses on technical solutions for archiving dynamic online ads and enhancing access to these critical resources for future scholarship. By examining the preservation of online advertisements and suggesting improved approaches for archiving dynamic online content, this project contributes to the documentation of digital history.