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Articles 271 - 300 of 10107
Full-Text Articles in Entire DC Network
Exploring The Adoption Of Quality 4.0: A Qualitative Study Of Industry Practices And Barriers, Tama Rani Sarker
Exploring The Adoption Of Quality 4.0: A Qualitative Study Of Industry Practices And Barriers, Tama Rani Sarker
Theses
The emergence of Industry 4.0 has revolutionized quality management, introducing Quality 4.0 as a framework that integrates digital technologies with established quality principles. Despite its potential to enhance operational efficiency and decision-making, many organizations face challenges in adopting Quality 4.0, including skill gaps, resource constraints, and cultural resistance. This qualitative study explores industry practices, barriers, and enablers of Quality 4.0 implementation through surveys and interviews with quality professionals in southern Illinois manufacturing companies. Using a phenomenological approach, five major themes emerged: employee skillset, training, technology integration, quality tools, and supply chain management. Findings reveal that successful adoption depends on workforce …
Analyzing The Hidden Impact Of Work Stress On Mental Health, Shaikha Aljabry
Analyzing The Hidden Impact Of Work Stress On Mental Health, Shaikha Aljabry
Theses
Occupational stress and deteriorating mental health constitute an increasing issue in the global community, and the problem of how job demands, personal resources, and cognitive processes interrelate to determine employee well-being is hardly known. This dissertation will study these dynamics by incorporating theory-driven Structural Equation Modeling (SEM) with data-driven Machine Learning (ML) methods that offer explanatory and predictive information. The study examines the influences of job demands, personal resources, effort–reward imbalance (ERI), emotional labor, cognitive appraisal, and social support on the stress and mental health outcomes of a sample of 5,000 employees working in various industries and regions of the …
Trust Calibration In Human-Ai Teaming: Within-Session Dynamics, Transparency, And Performance Effects, Mohamed Ahmed Aljaziri
Trust Calibration In Human-Ai Teaming: Within-Session Dynamics, Transparency, And Performance Effects, Mohamed Ahmed Aljaziri
Theses
Trust plays a decisive role in the effectiveness of human-AI teams, particularly in tasks that depend on coordinated decision-making under uncertainty. While prior research acknowledges that trust in automation is dynamic, current work provides limited insight into how trust evolves within an interaction, what causes it to become miscalibrated, and how transparency affects these processes. This thesis examines trust calibration in a controlled 2-D grid-world search-and-rescue environment, where 54 participants collaborated with an AI teammate presented through four communication modes based on the Ability, Benevolence, and Integrity (ABI) framework. The study uses secondary analysis of experimental data to observe: (1) …
Practice?, Pei Chen
Practice?, Pei Chen
Theses
3D animation is a combination of art and technique. Art serves to convey your perspective, express beauty, tell stories, and more. However, to accomplish these goals, we need solid technical support. Only by paying attention to both, can we achieve excellent results. The title of this film is “Practice?”, which is a deliberate pun. On one hand, "practice" refers to the martial arts training sequences of the character, during which his master provides guidance, intervenes in his movements, and progressively increases the difficulty. On the other hand, "practice" also symbolizes my own artistic and technical experimentation—exploring and refining skills in …
Exploring The Perception Of Dynamic Electrotactile Patterns On The Fingertips, Vedika Nimbalkar
Exploring The Perception Of Dynamic Electrotactile Patterns On The Fingertips, Vedika Nimbalkar
Theses
The goal of this study is to evaluate the effectiveness of electrotactile feedback on the fingertips by examining how accurately different dynamic patterns can be perceived. We designed three pattern types (Single Line, Double Line, and Box) and programmed each to move in eight distinct directions: bottom to top, top to bottom, left to right, right to left, bottom-left to top-right, top-right to bottom-left, bottom-right to top- left, and top-left to bottom-right. A study was conducted to assess these patterns’ perceived accuracy and response time on fingertips. The study results revealed that the Single Line and Double Line patterns had …
Whisper Wall: Transforming Global Superstitions Into A Unified, Sound-Reactive Immersive Experience, Hardika Nitin Patil
Whisper Wall: Transforming Global Superstitions Into A Unified, Sound-Reactive Immersive Experience, Hardika Nitin Patil
Theses
Superstitions, often dismissed as irrational in today’s data-driven world, remain deeply rooted in human culture and behavior, offering meaning and a sense of control amid uncertainty. Whisper Wall is an immersive, sound-reactive installation that invites audiences to explore these enduring beliefs through whispered audio narration and symbolic, shadow-inspired visual storytelling. This project re-engages audiences with rich, cross-cultural superstitions, encouraging reflection beyond skepticism to thoughtfully examine their origins and significance. Blending visual communication, motion design, sound, and interaction, Whisper Wall creates a multisensory experience where users activate intimate stories via interactive elements—whisper-like audio delivered through red paper cups coupled with minimalist, …
The Impact Of The 2008 Financial Crisis On Crime And Cybercrime In The United States, Rashed Adel Alsuwaidi
The Impact Of The 2008 Financial Crisis On Crime And Cybercrime In The United States, Rashed Adel Alsuwaidi
Theses
This thesis will examine how the traditional crime and cy- were affected by the 2008 financial crisis. United States are also experiencing a rise in crime between 2005 and 2012. Based on the FBI data at the national level. The Internet Crime Complaint Center (IC3), Uniform Crime Reports and important economic indicators. The study, which involves tors, including unemployment, GDP, rates of foreclosures, and mortgage rates, is a combination. correlationbased interpretation supported by exploratory descriptive trend analysis. model-fit checks. The results indicate that contrary to the conventional expectations, violent and property crime also maintained their long-term reduction during the period …
Detecting Fraud In Police Reports Using Machine Learning And Natural Language Processing, Maryam Almarar
Detecting Fraud In Police Reports Using Machine Learning And Natural Language Processing, Maryam Almarar
Theses
The paper explores how statistical analysis and machine learning can be used to identify the fraud patterns in the police reports. The study aims at establishing the most important predictive factors and indicators distinguishing fraudulent and valid cases with the use of structured data of police databases. The work was done in the background of the increase in financial fraud instances and the rising necessity of the introduction of automated detection systems in police departments. Police reports of the pastwere mined down to data and analyzed on SPSS 1, to carry out statistical operations. The sample was structured data which …
The Klik Chair - Designed To Move, Built To Last, Neeraj Sugumaran Menon
The Klik Chair - Designed To Move, Built To Last, Neeraj Sugumaran Menon
Theses
For the modern individual who moves housing frequently, buying furniture has become an increasingly tedious and frustrating process. Traditional furniture often presents challenges due to its bulk, weight, and complex assembly requirements, making it difficult to transport and reassemble when moving to a new living space. As a result, the true value of a piece of furniture is no longer determined solely by its price tag or aesthetic appeal but by its ability to adapt to a nomadic lifestyle. Additionally, furniture that holds long-term sentimental or functional value has become even more significant in an era where disposability is routine. …
Segment-Level Machine Learning For Detecting Partial Deepfake Audio: An Rnn–Svm Hybrid Approach For Real-World Adversarial Environments, Ahmad Alhelli
Segment-Level Machine Learning For Detecting Partial Deepfake Audio: An Rnn–Svm Hybrid Approach For Real-World Adversarial Environments, Ahmad Alhelli
Theses
This study investigates the detection of real, fully fake, and partially fake (PF) audio using classical machine-learning models as well as a segment-level analysis model. Unlike most existing research, which focuses solely on binary real-vs-fake classification, this work introduces a three-class detection framework and constructs realistic PF samples by inserting short synthetic speech segments into otherwise genuine audio recordings. Its method combines MFCC and spectral feature engineering,Wav2Vec2 embeddings, controlled PF synthesis and various models such as SVM, Random Forest, XGBoost and an attention based RNN. Segment level windowing allows fine-grained study of transition of time and breaks of manipulation. The …
Enhancing Airport Operations With Ai For A Seamless Passenger Experience, Rashed Alsubousi
Enhancing Airport Operations With Ai For A Seamless Passenger Experience, Rashed Alsubousi
Theses
The paper is research exploring the importance of Artificial Intelligence (AI) and Data Analytics to optimize airport operations and the passenger experience in the environment of the expanding air traffic in the world and the smart city movement. With increasing tasks that airports currently experience, congestion, flight delays, mishandling of baggage, and limited capacity, nowadays AI-based technologies integration is crucial to the operational efficiency, sustainability, and customer satisfaction. This study discusses the use of predictive analytics, machine learning, and clustering models to enhance passenger flows, resource allocation, and performance in general at airports. The research is based on the working …
Machine Learning Approaches For Predicting And Preventing Vehicle Theft, Hamad Almutawa
Machine Learning Approaches For Predicting And Preventing Vehicle Theft, Hamad Almutawa
Theses
This study develops a data-driven framework for predicting vehicle theft using machine-learning techniques applied to a comprehensive crime dataset containing demographic, spatial ,and operational policing variables. The dataset, sourced from publicly available crime records, included 9,712 cases and 30 variables, with minimal missing data and balanced class distribution. After performing extensive preprocessing—including outlier analysis, missing-value imputation, categorical encoding, feature selection, and correlation testing—four supervised classification models were implemented: Logistic Regression, Random Forest, Support Vector Machine, and a Neural Network. Bivariate statistical tests, including Chi-square and Mann–Whitney U, revealed significant relationships between theft occurrence and factors such as crime area, luxury …
Advancements In Ml Via Efficient Generative Modeling, Robust Domain Adaptation, And Explainable Multimodal Retrieval, Prasanna Reddy Pulakurthi
Advancements In Ml Via Efficient Generative Modeling, Robust Domain Adaptation, And Explainable Multimodal Retrieval, Prasanna Reddy Pulakurthi
Theses
The rapid evolution of AI heightens the need for learning systems that are efficient, robust, and explainable. This dissertation advances these three pillars through innovations in classification, generative modeling, domain adaptation under data-scarce conditions, and multimodal retrieval. Collectively, the methods reduce dependence on large, labeled datasets, improve adaptability under distribution shifts, enable deployment on resource-constrained platforms, and enhance interpretability. For classification, the Iterative Maximum Likelihood Classifier (IMLC) recasts regularized maximum likelihood training as a fixed-point contraction with convergence guarantees, enabling faster and more stable optimization. Results on synthetic data and MNIST validate its efficiency. For generative models, we introduce Parametric …
Assessing The Impact Of Gis Data Inaccuracies On Urban Infrastructure, Majid Aldashti
Assessing The Impact Of Gis Data Inaccuracies On Urban Infrastructure, Majid Aldashti
Theses
Urban infrastructure delivery is a complex process influenced by project type, spatial distribution, stakeholder coordination, and data quality. This thesis examines the application of Microsoft Power BI, a no-code visual analytics tool, as a means to analyze performance patterns in urban projects and inform decision-making in smart city governance. The study uses a structured secondary dataset simulating over 600 infrastructure projects across various departments and neighborhoods. By transforming raw data into an interactive dashboard, the research explores relationships between delay rates, customer satisfaction, budget compliance, and technology adoption. The analysis revealed that project categories such as metal furniture and police …
Rise Of Social Media Hacking: Ai-Based Ip Tracking For Uae Law Enforcement, Mounikha Naarrayen Chakravarthula
Rise Of Social Media Hacking: Ai-Based Ip Tracking For Uae Law Enforcement, Mounikha Naarrayen Chakravarthula
Theses
Social media has evolved into a critical channel for communication, expression, and public influence, but it has also become a prevalent avenue for cybercrime, particularly in digitally advanced nations such as the United Arab Emirates (UAE). The rising complexity of online offences, coupled with anonymisation tools and cross-border digital behaviour, has made the attribution of social-media-based cyber incidents increasingly challenging for law enforcement. In this context, artificial intelligence (AI) offers the potential to strengthen digital investigations by providing intelligent, scalable, and evidence-driven attribution capabilities. This research develops an AI-assisted Internet Protocol (IP) attribution framework tailored specifically for UAE law enforcement …
Predicting Mental Health Risk In Remote Workers: A Machine Learning Approach, Majed Al Mheiri
Predicting Mental Health Risk In Remote Workers: A Machine Learning Approach, Majed Al Mheiri
Theses
This dissertation explores the application of machine learning in mental health risk prediction of remote workers, which is increasingly becoming a significant issue, with the practise of flexible working reshaping organisational practises. It is based on the theoretical framework that has been used previously such as Job Demands Resources model and Stress-Strain model which emphasise the role of workload, support systems and personal resources in determining the well-being of employees. The context of the study indicates the growing rate of remote and hybrid employment, and the associated increase in the number of issues associated with stress and isolation, as well …
Predictive Ai Models For Phishing Attack Detection: A Data-Driven And Statistical Analysis Approach, Suhail Othman Alfalasi
Predictive Ai Models For Phishing Attack Detection: A Data-Driven And Statistical Analysis Approach, Suhail Othman Alfalasi
Theses
Phishing has remained a serious threat to cybersecurity, as this type of attack can easily bypass detection systems that are either rule-based or blacklist-based. The proposed thesis work will present a solution that will make use of both statistical analysis and machine learning to correctly identify a phishing website. The solution will make use of a hybrid approach comprising statistical-based preprocessing methodologies, such as PCA and decision tree-based feature selection, to filter the crucial URL features that a website may possess. A carefully balanced dataset has been utilized, as well as a non-parametric approach utilizing the Mann-Whitney U-test to validate …
The Role Of Ai And Predictive Policing In Crime Prevention, Saif Salem Mohammad Hassan Abdulla
The Role Of Ai And Predictive Policing In Crime Prevention, Saif Salem Mohammad Hassan Abdulla
Theses
The current thesis examines the application of Artificial Intelligence (AI) in the arena of predictive policing and crime forecasting using an integrated case study based on the empirical approach supported by a narrative review of literature. Due to the growing use of digital data by the law-enforcement agencies, AI techniques, including machine learning and spatio-temporal modelling, are implemented to detect patterns of crimes, predict high-risk areas, and assist law-enforcement decision-making. Though these technologies have the potential to make the processes of accuracy and resource allocation better, they also bring up the issue of the fairness, transparency, and disproportionate effects on …
Data-Driven Crime Prediction: Toward Smarter Reduction Strategies, Zayed Almarri
Data-Driven Crime Prediction: Toward Smarter Reduction Strategies, Zayed Almarri
Theses
This study explores how machine learning and weather data can be used for the prediction of crime more accurately in the city of Seattle. Predictive policing is a method in law enforcement that uses data and computer algorithms to forecast the locations that crimes are likely to happen. Although many studies focused on using past crime data alone, this research also includes weather conditions like temperature, rainfall, and humidity, which may influence when and where crimes occur. Several machine learning models, including Random Forest (RF) and Support Vector Machines (SVM), were used to classify areas of the city into high-risk …
An Interpretable Machine Learning Framework For Detecting Phishing Urls Based On Lexical Features, Khalid Alrokhaimi
An Interpretable Machine Learning Framework For Detecting Phishing Urls Based On Lexical Features, Khalid Alrokhaimi
Theses
Phishing attacks represent one of the most significant and persistent threats in the cybersecu- rity landscape, with attackers increasingly using sophisticated URL manipulation techniques to deceive users and steal sensitive information. Traditional detection methods, which rely primarily on blacklists and heuristic rules, struggle to identify zero-day phishing URLs that have not yet been catalogued in security databases. This research addresses this critical gap by developing an interpretable machine learning framework for detecting phishing URLs usingclexical, structural, content-based, and domain metadata features. The study employs a comprehensive dataset of 11,430 labeled URLs (5,715 legitimate and 5,715 phishing) with 87 extracted features, …
Persuasive Pathways In Digital Apologies: The Role Of Persuasion Routes In Engagement With Youtuber Apology Videos, Darrin Majocha
Persuasive Pathways In Digital Apologies: The Role Of Persuasion Routes In Engagement With Youtuber Apology Videos, Darrin Majocha
Theses
This study explores how viewers process YouTube apology videos and how those processing methods relate to their judgments of the creator and their willingness to continue engaging with the channel. Guided by the Elaboration Likelihood Model, a single-session online survey experiment randomly assigned 413 adult participants to watch one of five publicly available apology videos from well-known YouTubers. After viewing, participants reported the extent to which they engaged in central versus peripheral processing, rated the creator's credibility and the apology's sincerity, and indicated their intentions to engage. Central processing was positively associated with engagement intentions, whereas peripheral processing was weakly …
Achilles, Sreelekha Samala
Achilles, Sreelekha Samala
Theses
ACHILLES is a speculative design project and conceptual artifact. It is not intended to function as a medically certified or biomechanically operational prosthetic arm. Instead, it operates within the realm of design research, using visual realism and luxury aesthetics to explore how assistive technologies could be perceived differently in cultural, emotional, and commercial contexts. Using 3D software to experiment with color, pattern, and texture, I designed a realistic mockup to demonstrate how Achilles could appear in a high-end commercial setting. Instead of animating the model, I focused on camera motion to highlight the prosthetic from multiple angles, emphasizing its sculptural …
Predicting Employee Attrition With Machine Learning: Data-Driven Strategies For Enhancingworkforce Retention, Ali Almheiri
Predicting Employee Attrition With Machine Learning: Data-Driven Strategies For Enhancingworkforce Retention, Ali Almheiri
Theses
In this research, the dual prediction and prescription model is developed and validated so that this model can not only predict employee turnover risk, but it also proposes the appropriate retention interventions, which apply across industries. On the basis of the IBM HR Analytics Attrition dataset (n=1,470), we preprocessed demography, job and satisfaction variables and trained three machine-learning classifiers, Random Forest, Logistic Regression and XGBoost to predict voluntary turnover. XGBoost model recorded the best discrimination (AUC=0.87), sensitivity (0.76), and specificity (0.81), which signifies strong predictive power. Analysis of feature-importance was conclusive with time, rate of frequent business travel and compensation …
Event-Driven Traffic Management, Abdulla Humaid Alhosani
Event-Driven Traffic Management, Abdulla Humaid Alhosani
Theses
Traffic congestion during peak hours and large public events is a persistent challenge in urban areas, affecting mobility, economic productivity, and quality of life. While many cities are moving towards smart, data-driven traffic management, the practical effectiveness of predictive models for event-driven traffic control remains uncertain. This thesis presents an offline, data-driven feasibility study that investigates whether ma- chine learning and time-series models can predict traffic volume patterns under different conditions, including weather and the presence of events. Using a historical traffic dataset with derived trend variables, the study applies exploratory data analysis (EDA) and two predictive approaches: ARIMA for …
Ai-Powered Mobile Phone Activity Insights: Developing Predictive Models For Smarter Decision-Making, Maryam Al Ali
Ai-Powered Mobile Phone Activity Insights: Developing Predictive Models For Smarter Decision-Making, Maryam Al Ali
Theses
This study investigates how artificial intelligence can enhance telecom network management by forecasting internet usage, predicting congestion, and identifying user behavior patterns from mobile phone activity data. The study made use of anonymized logs for calls, SMS and internet, and put up a multi-model analytical pipeline, which was composed of time-series forecasting (ARIMA, LSTM), clustering (K-Means), and classification (XGBoost), to perform the analysis. Among the time-series methods, ARIMA ranked first in the forecast performance (RMSE=0.31) and gave LSTM a convincing defeat in the case of this particular short and stable dataset. Based on K-Means segmentation, users were sorted into five …
Optimizing Human Resource Decisions: Predicting Promotions Using Data Analytics, Mohammad Khalid A Mohammad Abdulrahim
Optimizing Human Resource Decisions: Predicting Promotions Using Data Analytics, Mohammad Khalid A Mohammad Abdulrahim
Theses
Proper and objective selection of high potential employees to promote them is a major dilemma in the Human Resources (HR) department, more so in sensitive and hierarchal environments in the public sector where subjectivity is likely to take place. This paper is based on this ubiquitous issue, and it seeks to develop, experiment, and examine a clear and equitable machine learning model that can forecast the possibility of an employee to get a promotion according to organized past HR records. The technique was solid preprocessing, alleviation of extreme class imbalances on the basis of the Synthetic Minority Over-sampling Technique (SMOTE), …
Integrated Machine Learning For Smart Home Resource Optimization, Ahmed Almazrouei
Integrated Machine Learning For Smart Home Resource Optimization, Ahmed Almazrouei
Theses
This thesis explores howmachine learning can be used to support better energy management in smart homes. Many smart home systems today collect a large amount of data through sensors and smart meters, but they still depend on simple rules and do not make predictive or automatic decisions. In the academic field, energy forecasting and energy optimization are often studied separately, which creates a gap in understanding how the two components can work together in a real setting. Because of this, there is a need to test an integrated approach that uses both forecasting and optimization in one framework. In this …
Analyzing Airline Customer Experience Challenges And Their Impact On Dubai's Tourism Sector, Suhail Alfalasi
Analyzing Airline Customer Experience Challenges And Their Impact On Dubai's Tourism Sector, Suhail Alfalasi
Theses
The paper explores whether there is a connection between operational performance, customer sentiment, and digital administrative complexity among major regional Middle Eastern airlines, such as Flag Carriers (e.g., Emirates) and Low-Cost carriers (LCCs) (e.g., Air Arabia). With the use of a highly detailed dataset of customer review and operations data, the study proves that a large service paradox is present in which, despite the high Net Promoter Scores (NPS: 48.004) of the carriers, which are positively reinforced by delivering world-class soft products, the loyalty is constantly disrupted by low-frequency but high-severe operational delays (Delay_Minutes). Since it has been analyzed that …
Ai-Driven Cyber Threat Detection, Humaid Thani Almheiri
Ai-Driven Cyber Threat Detection, Humaid Thani Almheiri
Theses
Bycreating an AI-driven method using deep learning and statistical analysis tools, this study seeks to fill important security holes in conventional intrusion detection systems. Current signature-based systems miss new and complex cyberattacks, which have significant financial and operational consequences for companies. The suggested approach detects unusual network activity in real-time by combining statistical analysis with long short-term memory networks (LSTMs), convolutional neural networks (CNNs), and statistical analysis. This study will create and test hybrid models that can identify both known and zero-day threats while reducing false positives using publicly accessible datasets like UNSW-NB15, CIC-IDS2017, and NSL-KDD. Expected results are a …
Optimizing Delivery Time Predictions Using Machine Learning: A Data-Driven Approach To Last-Mile Logistics, Mohamed Burqaiba
Optimizing Delivery Time Predictions Using Machine Learning: A Data-Driven Approach To Last-Mile Logistics, Mohamed Burqaiba
Theses
This paper addresses how machine learning can be utilized to improve the prediction of delivery times during the last mile, specifically in Dubai urban logistics issues whereby the traffic congestion and weather circumstances usually contribute to unpredictable delivery times. The main goal was to evolve machine learning models that can predict properly delivery time depending on several parameters, i.e., speed of traffic, weather, length of delivery, and geography. The reason why three machine learning models were chosen [Random Forest, Gradient Boosting, and XGBoost] in this analysis is that they have the opportunity to work with non-linear relationships in the data. …