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Outlier Detection Using the Relative Range Distribution
No full textA Master of Science thesis in Mathematics by Dania Dallah entitled, “Outlier Detection Using the Relative Range Distribution”, submitted in July 2024. Thesis advisor is Dr. Hana Sulieman and thesis co-advisor is Dr. Ayman Alzaatreh. Soft copy is available (Thesis, Completion Certificate, Approval Signatures, and AUS Archives Consent Form).Outlier detection plays a crucial role in data analysis. Outlier detection is a challenging task due to the subjective nature of defining what constitutes an outlier. By identifying and appropriately handling outliers, analysts can gain a deeper understanding of the data, improve the quality of analyses, and make more informed decisions. In this thesis, we propose a new measure for detecting outliers in univariate data. The new measure, called relative range, is defined as the range statistic divided by the interquartile range (IQR). Since the range provides a simple yet effective measure of data dispersion, analyzing the range distribution will help identify potential outliers that fall outside the expected range of values. The probability distribution of the relative range is estimated for both symmetrical and skewed data distributions using Monte Carlo simulations. Based on the estimated empirical distribution of the relative range, a threshold is determined and used to detect potential outliers. The thesis also proposes a sequential approach for outlier detection based on the relative range. In general, the relative range has shown to be a more robust statistic at detecting outliers in both sequential and non-sequential outlier detection.College of Arts and SciencesDepartment of Mathematics and StatisticsMaster of Science in Mathematics (MSMTH
Shear Behavior of FRP Spiral Reinforced Concrete Beams
No full textA Master of Science thesis in Civil Engineering by Firas Marwan Safadi entitled, “Shear Behavior of FRP Spiral Reinforced Concrete Beams”, submitted in May 2024. Thesis advisor is Dr. Rami Hawileh and thesis co-advisor is Dr. Jamal El-Din Abdalla. Soft copy is available (Thesis, Completion Certificate, Approval Signatures, and AUS Archives Consent Form).College of EngineeringDepartment of Civil EngineeringMaster of Science in Civil Engineering (MSCE
Mental Stress Assessment in the Workplace: A Review
No full textWorkers with demanding jobs are at risk of experiencing mental stress, leading to decreased performance, mental illness, and disrupted sleep. To detect elevated stress levels in the workplace, studies have explored stress measurement from physiological, psychological, and behavioral perspectives. This paper reviews the assessment methods and strategies for mitigating mental stress in the workplace and provides recommendations for early detection and mitigation of mental stress. Among the modalities, Electroencephalography (EEG), Electrocardiography (ECG) and Galvanic Skin Response (GSR) were found to be the most used in assessing mental stress in the workplace. Nevertheless, these modalities are sensitive to motion artifacts and are difficult to be integrated into real work environments. To further improve stress level assessment in the workplace, multimodality integration with a reduced number of sensors such as EEG, GSR and Functional near infrared spectroscopy (fNIRS) can be utilized. This would lead to developing strategies for stress management in real-time. Furthermore, combining EEG with fNIRS would improve source localization of mental stress. To mitigate stress, we recommend developing a closed loop system that incorporates brain data acquisition systems and machine learning with physical stimulations such as audio Binaural Beats Stimulation and/or Transcranial Electric Stimulation.American University of SharjahCollege of EngineeringDepartment of Electrical Engineerin
Nano-clay based flexible and implantable bioelectrodes for human body neurostimulation: fabrication and characterization
No full textA Master of Science thesis in Biomedical Engineering by Zaid Osama Alani entitled, “Nano-clay based flexible and implantable bioelectrodes for human body neurostimulation: fabrication and characterization”, submitted in May 2024. Thesis advisor is Dr. Amani Al-Othman and thesis co-advisor is Dr. Hasan Al-Nashash. Soft copy is available (Thesis, Completion Certificate, Approval Signatures, and AUS Archives Consent Form).This thesis explores the development and characterization of nano-clay based flexible and implantable bioelectrodes for human body neurostimulation, focusing on optimizing their biocompatibility, stability, and electrochemical attributes. Key materials such as silicone, nano-clay, glycerol, polyethylene glycol (PEG), and isoalcohol were employed to create a variety of composite samples. The primary objective is to ascertain how different material combinations influenced the electrodes' properties, aligning them with requirements for successful application in neurostimulation devices. Electrochemical Impedance Spectroscopy (EIS) and Cyclic Voltammetry (CV) provides comprehensive insights into the electrodes' performance. The EIS results indicated that varying the glycerol and PEG content affected the electrodes' bulk impedance, conductivity, and charge storage capacity. For instance, a sample with a 50% silicone, 20% glycerol, and 30% nano-clay composition showed a bulk impedance of 5.47 kΩ and conductivity of 2.33×10⁻⁵ S/cm, significantly outperforming a similar sample with PEG, which exhibited a higher bulk impedance of 38 kΩ and lower conductivity of 3.35×10⁻⁶ S/cm. These findings underscore the role of glycerol in enhancing electrochemical properties conducive to effective neural interface operations. Mechanical testing highlighted that the incorporation of nano-clay generally increased stiffness, whereas glycerol and PEG improved flexibility and conductivity. The optimal formulations displayed mechanical properties that were well-matched to the compliance required for integration with soft tissues, enhancing the potential for chronic implantation without adverse tissue reactions. Long-term immersion tests further demonstrated the electrodes' robustness, showing minimal degradation of electrochemical properties over extended periods, thus confirming their suitability for long-term neurostimulation and other clinical applications. The study successfully demonstrated that nano-clay based bioelectrodes could achieve excellent electrochemical performance and mechanical compliance, suggesting their potential for advanced biomedical applications. These findings pave the way for further research aimed at refining the bioelectrode technology for enhanced therapeutic outcomes in neurostimulation and other medical interventions.College of EngineeringMultidisciplinary ProgramsMaster of Science in Biomedical Engineering (MSBME
AI-based remaining useful life prediction and modelling of seawater desalination membranes
No full textA Master of Science thesis in Engineering Systems Management by Fajer Al Ali entitled, “AI-based remaining useful life prediction and modelling of seawater desalination membranes”, submitted in November 2024. Thesis advisor is Dr. Hussam Alshraideh. Soft copy is available (Thesis, Completion Certificate, Approval Signatures, and AUS Archives Consent Form).The increasing demand for freshwater has heightened the reliance on desalination plants as a vital resource, particularly in the context of the Sharjah Electricity, Water and Gas Authority (SEWA) in the United Arab Emirates (UAE), which integrates different water desalination plants, including reverse osmosis (RO) to produce water. This thesis focuses on the development of an artificial intelligence-based predictive model for estimating the remaining useful life (RUL) of RO membranes. It addresses the critical operational challenge of membrane fouling caused by particle accumulation, which can lead to significant efficiency losses and system damage. The concept of RUL is defined as the anticipated time until the RO membrane reaches a specified performance threshold, guiding maintenance actions to enhance system longevity and efficiency. The predictive model developed in this study utilizes data from SEWA's operational database and laboratory records to forecast the RUL. R Software was employed as the primary tool for building and testing the predictive models, including Linear Regression, Decision Tree, Random Forest, and XGBoost. The Random Forest algorithm demonstrated the best performance, achieving an R² coefficient of 0.984, an RMSE of 0.136, and an MAE of 0.0997. These results highlight the exceptional accuracy and reliability of the model in predicting the RUL. Additionally, the findings from the variable importance analysis revealed that the most significant features influencing the RUL were SDI, water temperature, pump speed, and the age of the membrane. Understanding these key variables can provide valuable insights into optimizing operational conditions, thus extending membrane lifespan. By accurately predicting RUL, the model aims to reduce pressure changes that contribute to fouling and mitigate potential membrane damage. The implementation of this AI-driven model is expected to optimize clean-in-place (CIP) scheduling, ultimately maximizing the longevity and performance of RO membranes. The findings of this research will not only enhance understanding of the necessary operating conditions for effective seawater treatment but will also contribute to the broader literature on predictive maintenance in desalination, supporting more efficient and sustainable water production practices.College of EngineeringDepartment of Industrial EngineeringMaster of Science in Engineering Systems Management (MSESM
Android Malware Detection Using Machine Learning
No full textMalware, or malicious software, poses a significant threat to systems and networks. Malware attacks are becoming extremely sophisticated, and the ability to detect and prevent them is becoming more challenging. Detecting and preventing malware is crucial for several reasons, including the security of personal information, data loss and tampering, system disruptions, financial losses, and reputation damage. This paper presents a machine learning approach for Android malware detection. In this work, several machine learning algorithms were utilized, namely k-Nearest neighbor (KNN), Decision Trees (DT), Naive Bayes (NB), Support Vector Machine (SVM) and other ensemble classifiers including Extreme Gradient Boosting (XGBoost), Light Gradient Boosting Machine (LGBM) and CatBoost. It was found that SVM using radial basis function (RBF) kernel achieved the highest performance with an accuracy of 99.5%. This work proved the feasibility of using machine learning in detecting malware and improving the security of mobile devices. The results of this work can be used to build more robust systems to protect devices and networks from malicious attacks
Accelerating Blockchain Transaction Verification With Parallel Computing
No full textA Master of Science thesis in Computer Engineering by Huangjin Zhou entitled, “Accelerating Blockchain Transaction Verification with Parallel Computing”, submitted in May 2024. Thesis advisor is Dr. Gerassimos Barlas. Soft copy is available (Thesis, Completion Certificate, Approval Signatures, and AUS Archives Consent Form).Blockchain technology has emerged as a groundbreaking distributed ledger system, within this decentralized network, all historical transactions are recorded in blocks and synchronized across the entire network through block propagation among nodes. To maintain the network's security and data integrity, nodes undergo rigorous verification processes upon receiving new blocks. The most computationally demanding aspect of this process is the verification of each transaction's digital signature within the block. Despite the efficiency of elliptic curve digital signature algorithms used by prominent blockchain platforms like Bitcoin (e.g., Secp256k1), the process often underutilizes the parallel computing capabilities of contemporary GPUs and multi-core CPUs. In this study, we extract and store hash values, digital signatures, and public keys from massive volumes of Bitcoin block data. These signatures are then imported and verified using multi-core CPUs, GPUs, and clusters. Our experiments reveal that on a single-machine basis, including multiple-core CPU computing, CPU+GPU heterogenous computing and pure GPU computing, the speedup of multi-core parallel computing compared to single-core CPU performance can vary between 5 to 50 times. Given the distinct architectures of GPUs and CPUs, the speedup of GPUs is not inherently greater than that of CPUs. Nevertheless, the hybrid scheme, by fully leveraging the computational resources of a single machine, achieves a higher speedup compared to the CPU and GPU schemes individually. By transitioning to distributed computing and forming a cluster of five machines, we achieve a speedup up to 12 times higher than a single machine, incurring only up to 16% in communication and scheduling overhead.College of EngineeringDepartment of Computer Science and EngineeringMaster of Science in Computer Engineering (MSCoE
The Enablers of Modular Construction in the UAE
No full textA Master of Science thesis in Construction Management by Yara Abu Jbarah entitled, “The Enablers of Modular Construction in the UAE”, submitted in May 2024. Thesis advisor is Dr. Sameh El-Sayegh. Soft copy is available (Thesis, Completion Certificate, Approval Signatures, and AUS Archives Consent Form).As the demand, for construction grows it's important to raise awareness and gather knowledge about this modular construction to enhance sustainability and efficiency in the construction industry. Embracing modular construction is key to project success; project owners and managers can improve their chances of achieving their goals by utilizing this approach. A review of existing literature has highlighted the benefits, challenges and enablers that support modular construction in the industry. However, there is a lack of understanding about modular construction in the United Arab Emirates (UAE) and a need for a comprehensive list outlining its benefits, challenges, and enablers. The primary aim of this research is to prioritize and rank these aspects of construction. If embraced fully, modular construction could revolutionize the UAEs building sector by offering efficient and eco-friendly solutions while leveraging advanced technologies to overcome traditional hurdles. Additionally, successful implementation of modular architecture in the UAE will require collaboration between private sectors along, with the introduction of new funding strategies. The decision to adopt modular construction should involve a thorough assessment of its benefits, challenges, and enablers. Key benefits include faster completion, improved quality control, and reduced rework, while drawbacks like limited flexibility and regulatory issues must be considered. Future research should target modular construction specialists and incorporate interviews to enhance insights. Key research areas include sustainability, AI and robotics, smart technologies, economic and market analyses, policy frameworks, detailed case studies, supply chain optimization, and workforce development. These efforts will advance innovation and efficiency in modular construction.College of EngineeringMultidisciplinary ProgramsMaster of Science in Construction Management (MSCM
Firms Response to Misconduct: Evidence from Internal Control
No full textA Master of Science thesis in Accounting by Shatha Ali Sarhan entitled, “Firms Response to Misconduct: Evidence from Internal Control”, submitted in December 2024. Thesis advisor is Dr. Yumin Zhang Perry. Soft copy is available (Thesis, Approval Signatures, Completion Certificate, and AUS Archives Consent Form).In our research, we examine how firms respond to instances of misconduct, with a particular focus on the critical role of internal control systems in mitigating the negative consequences of unethical behavior. By exploring various forms of corporate wrongdoing, such as financial misreporting and ethical violations, we highlight the significant impact such misconduct can have on a company's reputation, stakeholder trust, and overall organizational integrity. Using a comprehensive, data-driven methodology, we provide a nuanced analysis of how firms can effectively navigate the challenges posed by misconduct. Our key findings reveal that robust internal controls not only enhance compliance with legal and ethical standards but also play a crucial role in restoring stakeholder confidence following incidents of wrongdoing. Our research offers valuable insights into corporate governance and risk management, providing actionable recommendations for organizations seeking to strengthen their ethical frameworks and resilience against misconduct. The implications of our research extend beyond academia, offering practical guidance for practitioners and policymakers in fostering a culture of accountability and ethical leadership within the corporate landscape.School of Business AdministrationDepartment of AccountingMaster of Science in Accounting (MSA
Structural Performance Of 3D Printed Concrete Load Bearing Walls
No full textA Master of Science thesis in Civil Engineering by Arafat Abdulrahman Mohammed entitled, “Structural Performance Of 3D Printed Concrete Load Bearing Walls”, submitted in June 2024. Thesis advisor is Dr. Adil Al-Tamimi. Soft copy is available (Thesis, Completion Certificate, Approval Signatures, and AUS Archives Consent Form).Modern construction techniques have evolved in recent years with the introduction of 3D Concrete Printing (3DCP) technology. This innovative digital construction approach offers rapid, cost-effective, and sustainability solutions. The 3DCP walls used as load-bearing walls are an essential component and the primary section of the 3D printed constructions. Therefore, this study investigates the behavior of large-scale 3DCP walls under axial loading to evaluate their load-bearing capacity. The used mix consists of optimized materials, including glass fibers (GF) for reinforcement, enhancing mechanical properties, and crack resistance. Rheological testing ensured the mix’s quality and efficiency, assessing properties like extrudability, flowability, and buildability. Mechanical tests confirmed the mix as high-strength concrete with an 85 MPa compressive strength. Uniform axial compression loading tests were conducted to analyze the structural behavior, deformation, and crack patterns of 3DCP load-bearing walls with different cross-section configurations. The walls were categorized into two types: gapped walls, which include GLBW-3T (three-truss gapped wall), GLBW-4T (four-truss gapped wall), and GLBW-5T (five-truss gapped wall), and a solid wall, SLBW, which is a fully solid section designed to achieve maximum load-bearing capacity. In this study, materials consumption was a key factor in identifying the optimal and economical structural efficiency. The SLBW exhibited the highest resistance to cracking with a crack load-to-volume ratio of 4.02 N/cm³. Conversely, GLBW-3T and GLBW-5T, both featuring gapped sections with varying truss configurations, showed similar efficiency with crack load-to-volume ratios of 3.12 N/cm³ and 3.04 N/cm³, respectively. GLBW-4T demonstrated the least efficient performance, with a crack load-to-volume ratio of 2.48 N/cm³. Moreover, the crack pattern intensity and structural stability were highest in the SLBW, followed by the GLBW-3T, while, the GLBW-5T performed the least favorably. This study underscores the superior structural performance and material efficiency of the GLBW-3T configuration, achieving an optimal balance of material use and structural integrity.College of EngineeringDepartment of Civil EngineeringMaster of Science in Civil Engineering (MSCE