SACM - United States of America
Permanent URI for this collectionhttps://drepo.sdl.edu.sa/handle/20.500.14154/9668
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Item Restricted DOES AI INTEGRATION MODERATE THE RELATIONSHIP BETWEEN FIRM GROWTH AND PERFORMANCE IN SMES: THE INFLUENCE OF DECISION-MAKING AND OPERATIONAL PERFORMANCE(University of South Alabama, 2025-05) AlQahtani, Dalal T; Butler, Frank C; Gillis, William E; Hair Jr, Joe F; Scott, Justin TToday’s dynamic business environment requires small and medium-sized enterprises (SMEs) to keep up with technological advancements in order to remain competitive. Business growth creates more challenges for SMEs since they possess fewer available resources than big organizations. Since the introduction of artificial intelligence (AI), several SMEs have been able to compete more effectively and deliver better performance. As part of this research, I examine the possibility that AI integration (AII) will moderate the relationship between firm growth and both decision-making and operational performance, ultimately affecting the performance of SMEs. The aim of this research is to provide practical implications for AI as a strategic resource for improving decision-making capabilities, performance and growth by utilizing the resource-based view (RBV) and information processing theory (IPT). A partial least squares structural equation model (PLS-SEM) was used to analyze data from 338 SME business strategy decision-makers in the United States. In order to verify the measurement model’s reliability and validity, a Confirmatory Composite Analysis (CCA) was performed, followed by the evaluation of the structural model in order to test the hypotheses. In contrast to initial hypotheses, this study found that firm growth is positively related to both decision-making and operational performance. Nevertheless, the study results support the original hypothesis that both decision-making performance (DMP) and operational performance (OPP) positively affect a firm’s performance. Furthermore, AII significantly moderated the relationship between FG and OPP, while it did not significantly moderate the relationship between FG and DMP. This indicates the complexity of the role AI integration plays in SMEs. The paper concludes with recommendations for future research, as well as guidance for practitioners regarding how SMEs can improve their decision-making capabilities and performance using AI.7 0Item Restricted Assessing the Regulation of Medical Artificial Intelligence in Clinical Settings: Considerations for Form, Process, Authority, and Timing(University of Pittsburgh, 2025) Alotaibi, Hazim; Crossley, MaryThe rapid integration of artificial intelligence (AI) into healthcare has introduced a transformative category of technologies known as Medical Artificial Intelligence (MAI). These tools, often approved by the U.S. Food and Drug Administration (FDA), are now used to assist in diagnosis, treatment planning, patient monitoring, and clinical decision-making. However, MAI poses novel regulatory challenges due to its dynamic, adaptive, and sometimes opaque nature. This dissertation critically examines the current regulatory frameworks governing MAI in the United States, focusing on tools used by health professionals in clinical settings. Drawing on legal theory, empirical data, and interdisciplinary analysis, the study explores how MAI fits—or fails to fit—within existing regulatory categories designed for conventional medical devices. It analyzes FDA approval trends, clearance pathways, medical specialties, and manufacturer profiles for over 1,000 FDA-approved AI/ML-enabled devices. The study also investigates gaps in post-market surveillance, algorithmic transparency, and the role of third-party evaluators. Chapters in this dissertation evaluate the scope and limits of current regulatory mechanisms, such as the FDA’s 510(k), De Novo, and PMA pathways, and discuss the involvement of other regulatory bodies including the Federal Trade Commission and Department of Health and Human Services. Particular attention is given to unresolved legal questions, such as liability in AI-induced errors, the classification of MAI as a “system” versus a “device,” and the role of evolving real-world performance in determining regulatory adequacy. Ultimately, this work proposes a tailored regulatory framework that is adaptive, risk-based, and harmonized across international borders. It advocates for collaborative governance involving public agencies, private innovators, and global partners to ensure that regulation keeps pace with technological advancement while protecting patients, supporting clinicians, and promoting innovation.18 0Item Restricted Design, analysis, and evaluation of highly secure smart city infrastructures and services(University of Arizona, 2025) Almazyad, Ibrahim; Hariri, SalimCritical infrastructure resources and services, such as energy networks, water treatment facilities, and 5G telecommunications, form the backbone of national security and public welfare. However, many of these infrastructures rely on outdated technologies, rendering them increasingly vulnerable to evolving cyber threats. As these infrastructures become increasingly digitized and integrated under Industry 4.0 - integrating cloud computing, Artificial Intelligence (AI), and the Industrial Internet of Things (IIoT) - they simultaneously introduce a broader attack surface susceptible to threats such as sensor spoofing, Denial-of-Service (DoS), and man-in-the-middle attacks. Existing critical infrastructure testbeds are isolated and limited in their ability to replicate cross-domain dependencies and security vulnerabilities inherent in modern smart cities. To address this gap, this dissertation developed a Federated Cybersecurity Testbed as a Service (FCTaaS) environment, an innovative approach that integrates geographically dispersed critical infrastructure testbeds to enable the development and experimentation of effective algorithms to secure the normal operations of critical infrastructures against a wide range of cyberattacks. The research specifically focused on two critical infrastructures: The Water Treatment Facility Testbed (WTFT) and the 5G Telecommunication Testbed (5GTT). It begins with a comprehensive threat modeling across Industrial Control Systems (ICS) and 5G architecture to identify vulnerabilities, followed by designing and implementing security detection and mitigation algorithms. Specifically, we have developed an edge-deployed anomaly detection algorithm that is based on an autoencoder that achieved 98.3% accuracy in detecting cyberattacks against water treatment infrastructure. We have also demonstrated the effectiveness of our security defense algorithms in detecting cyberattacks against 5G networks with an accuracy of 98.9% against various cellular network attacks. This dissertation developed a unified and scalable cybersecurity research environment that significantly facilitates the development of realistic critical infrastructure experimentations and AI-driven security algorithms to secure and protect their normal operations against any type of cyberattacks known or unknown, regardless of their origins, insider or outsider.12 0Item Restricted Human Activity Monitoring for Telemedicine Using an Intelligent Millimeter-Wave System(University of Dayton, 2025) Alhazmi, Abdullah; Chodavarapu, VamsyThe growing aging population requires innovative solutions in the healthcare industry. Telemedicine is one such innovation that can improve healthcare access and delivery to diverse and aging populations. It uses various sensors to facilitate remote monitoring of physiological measures of people, such as heart rate, oxygen saturation, blood glucose, and blood pressure. Similarly, it is capable of monitoring critical events, such as falls. The key challenges in telemonitoring are ensuring accurate remote monitoring of physical activity or falls by preserving privacy and avoiding excessive reliance on expensive and/or obtrusive devices. Our approach initially addressed the need for secure, portable, and low-cost solutions specifically for fall detection. Our proposed system integrates a low-power millimeter-wave (mmWave) sensor with a NVIDIA Jetson Nano system and uses machine learning to accurately and remotely detect falls. Our initial work focused on processing the mmWave sensor's output by using neural network models, mainly employing Doppler signatures and a Long Short-Term Memory (LSTM) architecture. The proposed system achieved 79% accuracy in detecting three classes of human activities. In addition to reasonable accuracy, the system protected privacy by not recording camera images, ensuring real-time fall detection and Human Activity Recognition (HAR) for both single and multiple individuals at the same time. Building on this foundation, we developed an advanced system to enhance accuracy and robustness in continuous monitoring of human activities. This enhanced system also utilized a mmWave radar sensor (IWR6843ISK-ODS) connected to a NVIDIA Jetson Nano board, and focused on improving the accuracy and robustness of the monitoring process. This integration facilitated effective data processing and inference at the edge, making it suitable for telemedicine systems in both residential and institutional settings. By developing a PointNet neural network for real-time human activity monitoring, we achieved an inference accuracy of 99.5% when recognizing five types of activities: standing, walking, sitting, lying, and falling. Furthermore, the proposed system provided activity data reports, tracking maps, and fall alerts and significantly enhanced telemedicine applcations by enabling more timely and targeted interventions based on objective data. The final proposed system facilitates the ability to detect falls and monitor physical activity at both home and institutional settings, demonstrating the potential of Artificial Intelligence (AI) algorithms and mmWave sensors for HAR. In conclusion, our system enhances therapeutic adherence and optimizes healthcare resources by enabling patients to receive physical therapy services remotely. Furthermore, it could reduce the need for hospital visits and improve in-home nursing care, thus saving time and money and improving patient outcomes.13 0Item Restricted APPLYING MACHINE LEARNING (THE K-MEANS ALGORITHM) TO CLUSTERING AND ANALYZING SYNOVIAL FLUID CONTENTS AMONG DIFFERENT AGES AND GENDERS IN HEALTHY AND OSTEOARTHRITIS PATIENTS(Oakland University, 2024) Alabkary, Bader Eid; Zohdy, Mohamed AMachine learning, a subset of AI, has made a significant impact on the medical field by improving the speed and accuracy of test results. Among the many discrete ML tools, k-means is a type of data clustering that uses unsupervised ML to divide unclassified data into different groups with similar variances. This dissertation applied the k-means clustering algorithm to analyze synovial fluid compositions of healthy people and osteoarthritis (OA) patients, focusing on four components: hyaluronic acid (HA), chondroitin sulfate (C6S, C4S), and the C6S ratio. The main objective was to identify distinct patterns and clusters within these datasets based on age and gender. Data was extracted from two previously published research studies. The first dataset comprised 187 healthy participants, with ages ranging from 10 to 90 years. The second dataset consisted of 133 OA participants with ages ranging from 55 to 90 years. Applying ML algorithms, specifically k-means clustering, the MATLAB program was used for data analysis. The findings showed the k-means clustering successfully highlighted age- and gender-related synovial fluid concentration patterns. In addition, for both healthy and OA groups, younger people had higher levels of synovial fluid components, which decreased with age. In healthy people, HA levels were high among younger people but decreased with age. In the OA group, HA levels increased in older patients. These findings confirmed the potential of synovial fluid concentration in diagnosing joint health. These findings also asserted the utility of ML techniques, such as k-means clustering, in medical data analysis.10 0Item Restricted AI-GENERATED TEXT DETECTOR FOR ARABIC LANGUAGE(University of Bridgeport, 2024-08) Alshammari, Hamed; Elleithy, KhaledThe rise of AI-generated texts (AIGTs), particularly with the arrival of advanced language models like ChatGPT, has spurred a growing need for effective detection methods. While these models offer various beneficial applications, their potential for misuse, such as facilitating plagiarism and the generation of fake textual content, raises significant ethical concerns. These concerns have sparked extensive academic research into detecting AIGTs. Efforts to mitigate potential misuse include commercial platforms like Turnitin, GPTZero, and more. Notably, most evaluations conducted on the current AI detection thus far have predominantly focused on English or languages rooted in Latin-driven scripts. However, the effectiveness of existing AI detectors is notably hampered when processing Arabic texts due to the unique challenges posed by the language's diacritics, which are small marks placed above or below letters to indicate pronunciation. These diacritics can cause human-written texts (HWTs) to be misclassified as AIGTs. Recognizing the limitations of current detectors, this research first established a baseline performance assessment using a newly developed benchmark dataset of Arabic texts that contain HWTs and AIGTs against the existing detection systems such as OpenAI Text Classifier and GPTZero. This evaluation highlighted critical weaknesses in existing detectors' ability to handle diacritics and differentiate between HWTs and AIGTs, particularly in essay-length texts. This research introduces a novel AI text detector designed explicitly for Arabic to address these limitations, leveraging transformer-based pre-trained models trained on several novel datasets. Our resulting detector significantly outperforms the existing detection models in accurately identifying both HWTs and AIGTs in Arabic. Although the research focus was on Arabic due to its unique writing challenges, our detector architecture is adaptable to other languages.179 0Item Restricted Towards Automated Security and Privacy Policies Specification and Analysis(Colorado State University, 2024-07-03) Alqurashi, Saja; Ray, IndrakshiSecurity and privacy policies, vital for information systems, are typically expressed in natural language documents. Security policy is represented by Access Control Policies (ACPs) within security requirements, initially drafted in natural language and subsequently translated into enforceable policy. The unstructured and ambiguous nature of the natural language documents makes the manual translation process tedious, expensive, labor-intensive, and prone to errors. On the other hand, Privacy policy, with its length and complexity, presents unique challenges. The dense lan- guage and extensive content of the privacy policies can be overwhelming, hindering both novice users and experts from fully understanding the practices related to data collection and sharing. The disclosure of these data practices to users, as mandated by privacy regulations such as the General Data Protection Regulation (GDPR) and the California Consumer Privacy Act (CCPA), is of utmost importance. To address these challenges, we have turned to Natural Language Processing (NLP) to automate extracting critical information from natural language documents and analyze those security and privacy policies. Thus, this dissertation aims to address two primary research questions: Question 1: How can we automate the translation of Access Control Policies (ACPs) from natural language expressions to the formal model of Next Generation Access Control (NGAC) and subsequently analyze the generated model? Question 2: How can we automate the extraction and analysis of data practices from privacy policies to ensure alignment with privacy regulations (GDPR and CCPA)? Addressing these research questions necessitates the development of a comprehensive framework comprising two key components. The first component, SR2ACM, focuses on translating natural language ACPs into the NGAC model. This component introduces a series of innovative contributions to the analysis of security policies. At the core of our contributions is an automated approach to constructing ACPs within the NGAC specification directly from natural language documents. Our approach integrates machine learning with software testing, a novel methodology to ensure the quality of the extracted access control model. The second component, Privacy2Practice, is designed to automate the extraction and analysis of the data practices from privacy policies written in natural language. We have developed an automated method to extract data practices mandated by privacy regulations and to analyze the disclosure of these data practices within the privacy policies. The novelty of this research lies in creating a comprehensive framework that identifies the critical elements within security and privacy policies. Thus, this innovative framework enables automated extraction and analysis of both types of policies directly from natural language documents.29 0Item Restricted A Human-Centered Approach to Improving Adolescent Real-Time Online Risk Detection Algorithms(Vanderbilt University, 2024-05-15) Alsoubai, Ashwaq; Wisniewski, PamelaComputational risk detection holds promise for shielding particularly vulnerable groups from online harm. A thorough literature review on real-time computational risk detection methods revealed that most research defined 'real-time' as approaches that analyze content retrospectively as early as possible or as preventive approaches to prevent risks from reaching online environments. This review provided a research agenda to advance the field, highlighting key areas: employing ecologically valid datasets, basing models and features on human understanding, developing responsive models, and evaluating model performance through detection timing and human assessment. This dissertation embraces human-centric methods for both gaining empirical insights into young people's risk experiences online and developing a real-time risk detection system using a dataset of youth social media. By analyzing adolescent posts on an online peer support mental health forum through a mixed-methods approach, it was discovered that online risks faced by youth could be laden by other factors, like mental health issues, suggesting a multidimensional nature of these risks. Leveraging these insights, a statistical model was used to create profiles of youth based on their reported online and offline risks, which were then mapped with their actual online discussions. This empirical study uncovered that approximately 20% of youth fall into the highest risk category, necessitating immediate intervention. Building on this critical finding, the third study of this dissertation introduced a novel algorithmic framework aimed at the 'timely' identification of high-risk situations in youth online interactions. This framework prioritizes the riskiest interactions for high-risk evaluation, rather than uniformly assessing all youth discussions. A notable aspect of this study is the application of reinforcement learning for prioritizing conversations that need urgent attention. This innovative method uses decision-making processes to flag conversations as high or low priority. After training several deep learning models, the study identified Bi-Long Short-Term Memory (Bi-LSTM) networks as the most effective for categorizing conversation priority. The Bi-LSTM model's capability to retain information over long durations is crucial for ongoing online risk monitoring. This dissertation sheds light on crucial factors that enhance the capability to detect risks in real time within private conversations among youth.23 0Item Restricted Developing Novel Antiviral Agents: Targeting the N-Terminal Domain of SARS-CoV-2 Nucleocapsid Protein with Small Molecule Inhibitors(Virginia Commonwealth University, 2024-05-13) Alkhairi, Mona A.; Safo, Martin K.The COVID-19 pandemic, caused by SARS-CoV-2, persists globally with over 7 million deaths and 774 million infections. Urgent research is needed to understand virus behavior, especially considering the limited availability of approved medications. Despite vaccination efforts, the virus continues to pose a significant threat, highlighting the need for innovative approaches to combat it. The SARS-CoV-2 nucleocapsid protein (NP) emerges as a crucial target due to its role in viral replication and pathogenesis. The SARS-CoV-2 NP, essential for various stages of the viral life cycle, including genomic replication, virion assembly, and evasion of host immune defenses, comprises three critical domains: the N-terminal domain (NTD), C-terminal domain (CTD), and the central linker region (LKR). Notably, the NTD is characterized by a conserved electropositive pocket, which is crucial for viral RNA binding during packaging stages. This highlights the multifunctionality of the nucleocapsid protein and its potential as a therapeutic target due to its essential roles and conserved features across diverse pathogenic coronavirus species. Our collaborators previously initiated an intriguing drug repurposing screen, identifying certain β-lactam antibiotics as potential SARS-CoV-2 NP-NTD protein inhibitors in vitro. The current study employed ensemble of computational methodologies, biophysical, biochemical and X-ray crystallographic studies to discover novel chemotype hits against NP-NTD. Utilizing a combination of traditional molecular docking tools such as AutoDock Vina, alongside AI-enhanced techniques including Gnina and DiffDock for enhanced performance, eleven structurally diverse hit compounds predicted to target the SARS-CoV-2 NP-NTD were identified from the virtual screening (VS) studies. The hits include MY1, MY2, MY3, MY4, NP6, NP7, NP1, NP2, NP3, NP4 and NP5, which demonstrated favorable binding orientations and affinity scores. Additionally, one supplementary compound provided by Dr. Cen’s laboratory (denoted as CE) was assessed in parallel. These hits were further evaluated for their in vitro activity using various biophysical and biochemical techniques including differential scanning fluorimetry (DSF), microscale thermophoresis (MST), fluorescence polarization (FP), and electrophoretic mobility shift assay (EMSA). DSF revealed native NTD had a baseline thermal melting temperature (Tm) of 43.82°C. The compounds NP3, NP6 and NP7 notably increased the Tm by 2.55°C, 2.47°C and 2.93°C respectively, indicating strong thermal stabilization over the native protein. In contrast, NP4 and NP5 only achieved marginal Tm increases. MST studies showed NP1, NP3, and NP7 exhibited the strongest affinity with low micromolar dissociation constants (KD) of 0.32 μM, 0.57 μM, and 0.87 μM, respectively, significantly outperforming the control compounds PJ34 and Suramin, with dissociation constants of 8.35 μM and 5.24 μM, respectively. Although NP2, NP6, and CE showed relatively weaker affinity, these compounds still demonstrated better binding affinities with dissociation constants of 4.1 μM, 2.50 μM, and 1.81 μM, respectively than the control compounds PJ34 and Suramin. These results substantiate the potential of these scaffolds as modulators of NTD activity. In FP competition assays, NP1 and NP3 exhibited the lowest half-maximal inhibitory concentrations (IC50) of 5.18 μM and 5.66 μM, respectively, indicating the highest potency at disrupting the NTD-ssRNA complex among the compounds, outperforming the positive controls PJ34 and Suramin, with IC50 of 21.72 μM and 17.03 μM, respectively. The compounds NP6, NP7, CE, and NP2 also showed significant IC50 values that ranged from 7.00 μM to 10.13 μM. EMSA studies confirmed the NTD-ssRNA complex disruptive abilities of the compounds, with NP1 and NP3 as the most potent with IC50 of 2.70 μM and 3.31 μM, respectively. These values compare to IC50 of 8.64 μM and 3.61 μM of the positive controls PJ34 and Suramin, respectively. NP7, CE, NP6, and NP2 also showed IC50 ranging from 4.31 μM to 7.61 μM. The use of full-length nucleocapsid protein also showed that NP1 and NP3 disrupted the NP-ssRNA binding with IC50 of 1.67 μM and 1.95 μM, which was better than Suramin with IC50 of 3.24 μM. These consistent results from both FP and EMSA highlight the superior effectiveness of NP1 and NP3 in disrupting nucleocapsid protein-ssRNA binding, showcasing their potential as particularly powerful antiviral agents. Extensive crystallization trials were conducted to elucidate the atomic structures of SARS-CoV-2 NP-NTD in complex with selected hit compounds, assessing over 8000 unique crystallization conditions. Ultimately, only a PJ34-bound structure could be determined, albeit with weak ligand density, likely due to tight crystal packing impeding binding site access. The crystal structure was determined to 2.2 Å by molecular replacement using the published apo NP-NTD (PDB 7CDZ) coordinates as a search model, and refined to R-factors of 0.193 (Rwork) and 0.234 (Rfree). The refined NP-NTD structure showed conserved intermolecular interactions with PJ34 at the RNA binding pocket as observed in the previously reported HCoV-OC43 NP-NTD-PJ34 complex (PDB 4KXJ). This multi-faceted drug discovery endeavor, combining computational screening and in vitro assays resulted in successful identification of novel compounds inhibiting the SARS-CoV-2 nucleocapsid N-terminal domain. Biophysical and biochemical studies established compounds NP1 and NP3 as superior hits with low micromolar binding affinities, as well as low micromolar potency superior to standard inhibitors at disrupting both isolated N-NTD-RNA and full-length nucleocapsid-RNA complex formation. Though crystallographic efforts encountered challenges, important validation was achieved through a resolved crystal structure of PJ34 in complex with NP-NTD. Future effort will be to obtain co-crystals of NP-NTD with our compounds to allow for targeted structure modification to improve on the potency of the compounds.7 0Item Restricted Adaptive Cyber Security for Smart Home Systems(Howard University, 2024-04-29) Alsabilah, Nasser; Rawat, Danda B.Throughout the recent decade, smart homes have made an enormous expansion around the world among residential customers; hence the most intimate place for people becomes connected to cyberspace. This environment attracts more hackers because of the amount and nature of data.Furthermore, most of the new technologies suffer from difficulties such as afford the proper level of security for their users.Therefore, the cybersecurity in smart homes is becoming increas- ingly a real concern for many reasons, and the conventional security methods are not effective in the smart home environment as well. The consequences of cyber attacks’ impact in this environment exceed direct users to society in some cases. Thus, from a historical perspective, many examples of cybersecurity breaches were reported within smart homes to either gain information from con- nected smart devices or exploit smart home devices within botnet networks to execute Distributed Denial of Service (DDoS) as well as others.Therefore, there is an insistent demand to detect these malicious attacks targeting smart homes to protect security and privacy.This dissertation presents a comprehensive approach to address these challenges, leveraging insights from energy consumption and network traffic analysis to enhance cybersecurity in smart home environments.The first objec- tive of this research focuses on estimating vulnerability indices of smart devices within smart home systems using energy consumption data. Through sophisticated methodology based on Kalman filter and Shapiro-Wilk test, this objective provides estimating for the vulnerability indices of smart devices in smart home system. Building upon the understanding that energy consumption is greatly affected by network traffic based on many empirical observations that have revealed alterations in the energy consumption and network behavior of compromised devices, the subsequent objectives as complementary endeavors to the first objective delve into the development of adaptive technique for cyber-attack detection and cyber-behavior prediction using Rough Set Theory combined with XGBoost. These objectives aim to detect and predict cyber threats, thus enhancing the overall security posture of smart home systems.14 0