Thesis

This thesis offers a comprehensive exploration of how big data analytics is revolutionizing customer engagement, personalization, and strategic decision-making in the digital economy. It delves into the integration of predictive analytics, behavioral segmentation, sentiment analysis, and AI-driven recommendation engines, demonstrating how organizations can anticipate customer preferences with precision and deliver hyper-targeted experiences. Drawing from a variety of case studies in e-commerce, retail, and service industries, the research illustrates how data-driven insights lead to measurable increases in customer retention, conversion rates, and long-term brand loyalty. It also highlights the competitive advantage gained by companies that successfully harness large-scale data for actionable business intelligence. The thesis adopts a mixed-method research design, combining quantitative modeling with qualitative stakeholder interviews to capture both the technical and human aspects of big data adoption. It presents a structured framework for integrating big data tools into existing CRM systems, from data collection and storage through to advanced analytics and visualization. Attention is given to the ethical, legal, and societal implications of large-scale data use, with discussions on algorithmic bias, data protection laws, and consumer trust. By bridging theory and application, the work positions big data not merely as a technological tool but as a strategic enabler for sustainable competitive growth in a customer-centric marketplace.

This study explores the deployment of Internet of Things (IoT) technologies within urban infrastructure, focusing on three key domains—smart traffic management, waste management, and energy efficiency. It examines how interconnected sensors, real-time analytics, and automated control systems enable cities to collect and process vast amounts of operational data, leading to faster decision-making and more efficient resource utilization. In smart traffic management, the research highlights IoT’s role in adaptive traffic signal control, congestion detection, and accident response. For waste management, it investigates sensor-based collection systems and predictive routing that optimize fleet usage and reduce environmental impact. In the area of energy efficiency, the study evaluates IoT-enabled monitoring and automation for street lighting, public building energy use, and renewable energy integration. Through case studies and performance metrics, the research assesses the tangible benefits and potential limitations of IoT adoption, offering insights into its role in building sustainable and responsive urban environments. As urban populations grow and cities face mounting sustainability and efficiency challenges, IoT technologies are emerging as critical enablers of smarter infrastructure systems. This thesis investigates how IoT-driven solutions transform municipal operations by embedding intelligent sensing and communication into everyday city functions. In smart traffic management, IoT enables dynamic traffic flow optimization, improves public transport scheduling, and enhances road safety through continuous monitoring. In waste management, it facilitates real-time bin level tracking, route optimization, and data-driven policy development to reduce operational costs and environmental strain. For energy efficiency, IoT supports automated lighting controls, energy usage analytics, and integration with decentralized renewable sources, thereby lowering carbon footprints and improving resilience. The work also addresses implementation challenges, including interoperability across heterogeneous systems, data privacy concerns, and infrastructure investment requirements. By evaluating both successes and limitations, the thesis provides a roadmap for urban planners, policymakers, and technology providers to leverage IoT for more sustainable, efficient, and livable cities.

This study investigates how edge computing can fundamentally improve real-time data processing in both manufacturing and autonomous vehicle industries by exploring both its key benefits and inherent challenges. It outlines how edge computing reduces latency, minimizes bandwidth demands, and enhances data privacy by enabling processing directly on-site or in-vehicle. The research examines use cases such as predictive maintenance in smart factories and rapid decisionmaking in autonomous navigation. At the same time, it addresses the technological and operational hurdles, including edge node scalability, system interoperability, network reliability, resource limitations, and security vulnerabilities. By combining real-world industry scenarios and comparative analysis, the study offers strategic insights into optimizing edge computing deployments to drive efficiency, reliability, and responsiveness in mission-critical environments. As manufacturing evolves toward smart, interconnected production and autonomous vehicles become more prevalent, the demand for immediate and reliable data handling has never been greater. This thesis explores the dual-sided dynamics of edge computing in these contexts— highlighting its potential to power real-time processing, system resilience, and privacy by conducting computations close to where data is generated. In manufacturing, it enables real-time monitoring and fault detection, reducing downtime and improving throughput. In autonomous vehicles, it supports split-second processing for safe navigation and environmental awareness. However, the adoption of edge computing also presents significant challenges: ensuring consistent interoperability across diverse hardware, managing limited computing and energy resources, maintaining low-latency communication, and safeguarding data integrity in distributed environments. Drawing upon both case studies and emerging deployment models, the work offers a framework for stakeholders—from engineers to strategists—to harness the promise of edge computing while navigating its complexities in high-stakes applications.

This study examines the transformative applications of quantum computing in cryptography, material science, and machine learning, focusing on how this emerging technology addresses complex problems beyond the reach of classical systems. In cryptography, it explores advancements such as quantum key distribution, which leverages the principles of quantum mechanics to create secure communication channels resistant to eavesdropping and future quantum-based threats. Within material science, the research highlights quantum computing’s capability to simulate molecular and material properties with unprecedented accuracy, enabling faster discovery of new compounds, high-performance battery materials, and efficient catalysts. In the realm of machine learning, the study investigates quantum-enhanced algorithms—ranging from purely quantum to hybrid quantum-classical models—that offer significant speedups in data processing, image recognition, and natural language understanding, while improving model scalability and efficiency. In an era where classical computing faces physical and performance limits, this thesis explores how quantum computing is opening new frontiers in secure communication, advanced material discovery, and intelligent data analysis. It examines cryptographic innovations like quantum key distribution, which utilizes phenomena such as entanglement and quantum uncertainty to enable provably secure key exchange. The study also delves into quantum simulation techniques that allow scientists to model complex molecules and materials with extreme precision, accelerating breakthroughs in energy storage, pharmaceuticals, and industrial manufacturing. Furthermore, it analyzes quantum machine learning approaches that integrate quantum algorithms into model training and inference, promising exponential improvements in processing speed, scalability, and robustness across diverse AI tasks. By investigating these targeted applications, the research provides a forward-looking perspective on how quantum computing can be strategically leveraged to drive advancements in multiple high-impact fields.

This research investigates the interconnected economic and environmental impacts of electric vehicle (EV) adoption in the United States. Using a mixed-methods approach that integrates market data analysis, policy review, and environmental modeling, it examines how factors such as purchase price differentials, operating cost savings, tax incentives, and infrastructure investments influence EV market penetration. The study evaluates environmental outcomes including reductions in greenhouse gas emissions, improvements in urban air quality, and potential strains on the electricity grid during peak demand periods. It further analyzes how legislative measures— such as the Inflation Reduction Act—affect both consumer adoption and industry growth. By comparing EV performance and lifecycle impacts to conventional internal combustion engine vehicles, the research identifies key drivers for sustainable adoption and offers policy recommendations to align economic incentives with environmental benefits. As the U.S. seeks solutions to address climate change while maintaining economic growth, electric vehicles have emerged as a pivotal technology with dual potential for environmental improvement and economic transformation. This thesis explores how EV adoption influences national and regional economies through job creation, supply chain shifts, manufacturing investments, and infrastructure demands. It also examines critical environmental considerations, including reduced air pollution, lower carbon emissions, sustainable battery sourcing, and the development of recycling systems to address the environmental footprint of lithium, cobalt, and nickel extraction. The analysis considers challenges in integrating EVs into existing energy grids, highlighting the need for smart charging infrastructure and renewable energy expansion. Ultimately, the work provides a comprehensive assessment of the barriers and opportunities in the EV transition, offering a roadmap for policymakers, industry leaders, and communities to foster a cleaner, more resilient, and economically viable transportation future in the United States.

This study investigates how technological innovations can bridge the digital divide in e‑business and overcome the complex barriers facing cross‑border e‑commerce. Employing a mixed‑methods approach—combining both qualitative and quantitative analysis—it examines challenges such as cybersecurity threats, varying regulatory environments, inadequate physical infrastructure, financial constraints, cultural divides, and customer engagement issues. The research explores innovations across cybersecurity, compliance, infrastructure, fintech solutions, and user engagement, and recommends actionable strategies to address digital marketplace challenges and enhance e‑business development In today’s globalized digital economy, cross-border e-commerce has become a vital driver of trade and economic integration, yet businesses still face significant operational and strategic hurdles. This thesis examines how emerging technologies can mitigate these barriers and create opportunities for growth. It focuses on five critical domains: improving cybersecurity to protect transactions and enhance trust, implementing automated compliance systems to navigate complex regulatory landscapes, enhancing infrastructure for faster and more reliable logistics, deploying fintech innovations to streamline international payments, and designing culturally adaptive customer engagement strategies to meet diverse market needs. Drawing on interviews, surveys, and market data, the research highlights both the obstacles and the enabling opportunities that technological innovation presents. Ultimately, it provides a roadmap for businesses, policymakers, and technology providers to overcome challenges, seize competitive advantages, and expand the reach of e-commerce across international borders.

This thesis investigates the pivotal role played by local government institutions in Bangladesh’s socio-economic development, community welfare, and public policy execution. It examines how decentralized governance structures contribute to service delivery, resource allocation, and democratic participation at the grassroots level. Using a wide lens, the study evaluates both the historical evolution of local governance and its current challenges, including political interference, bureaucratic inefficiencies, and fiscal limitations. By drawing on examples of community-led development projects, it showcases the transformative impact of empowered local institutions on poverty reduction, education, healthcare, and infrastructure. The research employs a multidisciplinary approach, combining political science, development studies, and public administration theory with empirical evidence from field surveys and policy document analysis. Comparative case studies from other developing countries provide benchmarks and alternative models for reform. The thesis proposes a roadmap for strengthening Bangladesh’s local governance through capacity-building initiatives, adoption of digital governance tools, and legal reforms that increase transparency and accountability. It positions local government not only as a delivery mechanism for national policy but as an active agent of grassroots democracy and sustainable development.

This thesis offers a detailed legal, historical, and humanitarian assessment of the persecution of the Rohingya minority in Myanmar. It systematically evaluates whether the acts committed against them meet the international definitions of mass killing, ethnic cleansing, or genocide, as articulated in conventions and customary international law. The study draws upon eyewitness testimonies, NGO and UN reports, and photographic and satellite evidence to construct a factual narrative of the events. It also addresses the international community’s varied responses, including diplomatic actions, sanctions, and humanitarian interventions, and how these have shaped the evolving situation on the ground. The research is grounded in a rigorous legal framework, referencing the 1948 Genocide Convention, the Rome Statute of the International Criminal Court, and United Nations resolutions. It combines doctrinal legal analysis with political risk assessment, considering the influence of regional geopolitics and ASEAN’s non-interference principle. The thesis further examines the role of media framing, public advocacy, and civil society in bringing international attention to the crisis. Ultimately, the work offers a nuanced understanding of the legal classification of the atrocities and their implications for international justice, refugee protection, and human rights enforcement.

This thesis provides an in-depth financial performance evaluation of Bank Asia Limited, using a comprehensive set of indicators to assess profitability, liquidity, solvency, and operational efficiency. It examines historical financial statements, industry benchmarks, and market trends to determine the bank’s strengths, vulnerabilities, and future growth potential. The study offers a critical look at how external factors such as economic cycles, regulatory changes, and competition influence the bank’s strategic direction. Additionally, it highlights how internal governance and management decisions affect both short-term results and long-term sustainability. The research applies a blend of ratio analysis, comparative benchmarking, and trend analysis, supported by econometric modeling to forecast future performance scenarios. It also integrates a qualitative review of corporate governance practices, customer relationship strategies, and technological adoption within the bank. By correlating financial health indicators with market positioning, the thesis provides a roadmap for improving resilience, operational efficiency, and shareholder value. Its findings are designed to inform not only the management of Bank Asia Limited but also policy regulators, investors, and scholars of banking and finance.

This thesis examines the design, engineering, and construction planning of a multi-storey steelframed building intended for both office and educational use. It addresses the dual challenge of creating a structure that is both functionally versatile and structurally sound. The study explores load-bearing calculations, seismic resistance, wind load considerations, and the optimization of steel as a primary construction material. It also discusses how architectural design can be harmonized with engineering requirements to create an environment that supports productivity, learning, and safety Employing advanced structural analysis software, the research simulates various load scenarios to ensure compliance with national and international building codes. It evaluates cost-benefit tradeoffs between different design choices, materials, and construction methods, emphasizing sustainability and lifecycle performance. The thesis also considers fire safety engineering, accessibility standards, and the integration of flexible interior layouts that allow for future adaptation. By combining technical rigor with practical application, it provides a blueprint for multifunctional building design in densely populated urban areas.