Evaluation of Quantum Computing for Enhancing Online Examination Security at Federal University, Gashua, Yobe State
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- NGN 5000
Background of the Study
Online examination systems at Federal University, Gashua, face challenges such as hacking, impersonation, and data manipulation. With the increasing shift toward digital assessment, robust security is essential to maintain academic integrity. Quantum computing offers novel encryption techniques and complex problem‐solving methods that can enhance security protocols (Adekunle, 2023; Ibrahim, 2024). Its ability to perform simultaneous computations could detect anomalies in real time and secure data channels, making online examinations more reliable. This study investigates how quantum algorithms and quantum-resistant encryption can be integrated to fortify online exam systems and reduce vulnerabilities.
Statement of the Problem
Current online examination platforms rely on classical encryption methods that may be insufficient against sophisticated cyberattacks. Vulnerabilities in authentication and data transmission can lead to exam fraud and breaches. The gap between emerging quantum capabilities and traditional security systems poses a risk to academic integrity and student data protection (Emeka, 2023). This study seeks to assess the potential of quantum computing to mitigate these vulnerabilities while addressing integration challenges and cost implications.
Objectives of the Study
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Evaluate the impact of quantum computing on online exam security.
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Identify current vulnerabilities in the system.
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Propose recommendations for integrating quantum-resistant measures.
Research Questions
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How does quantum computing enhance online examination security?
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What vulnerabilities exist in current systems?
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What framework best facilitates quantum integration?
Significance of the Study
This study is significant as it explores the transformative potential of quantum computing in securing online examinations. Its findings will provide policymakers and educational technologists with a strategic roadmap to mitigate cyber threats, protect student data, and ensure fair assessment. The enhanced security protocols will foster trust in digital education systems and promote innovation in academic integrity measures (Chinwe, 2024).
Scope and Limitations of the Study
The study is limited to the Federal University in Gashua, Yobe State, focusing on online examination security, selected objectives, and local data centers in sampled LGAs only.
Definitions of Terms
• Online Examination Security: Measures to protect digital assessments from fraud and cyberattacks.
• Quantum Computing: A computational paradigm using quantum phenomena to solve problems beyond classical limits.
• Encryption: The process of converting information into a secure format to prevent unauthorized access.
Enhancing Automated Healthcare Diagnosis Using Quantum AI at University of Ilorin Teaching Hospital, Kwara State
Background of the Study
Automated healthcare diagnosis systems at University of Ilorin Teaching Hospital are critical for timely treatment. However, classical AI systems sometimes struggle with complex diagnostic patterns. Quantum AI—combining quantum computing’s massive parallelism with adaptive machine learning—can process intricate medical data faster and more accurately (Ibrahim, 2024; Adekunle, 2023). This study explores how quantum-assisted models can improve diagnostic accuracy, support early detection, and reduce diagnostic errors by analyzing large volumes of clinical data in real time.
Statement of the Problem
Existing automated diagnosis systems are limited by classical algorithms that may not capture subtle correlations in high-dimensional medical data. This limitation can lead to misdiagnoses and delayed treatment. With increasing patient loads and diverse health conditions, there is a pressing need to enhance diagnostic accuracy. The lack of integration of quantum AI techniques restricts the potential for rapid, precise diagnosis (Emeka, 2023), thereby impacting overall patient care.
Objectives of the Study
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Investigate quantum AI’s role in improving diagnosis accuracy.
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Assess integration challenges with current systems.
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Develop a framework for deploying quantum-assisted diagnosis.
Research Questions
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How can quantum AI enhance automated diagnosis?
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What limitations exist in current diagnostic models?
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What strategies enable successful quantum integration?
Significance of the Study
This study is significant as it pioneers the integration of quantum AI into automated healthcare diagnosis. Improved diagnostic precision will enhance patient outcomes and optimize hospital operations. The research provides actionable insights for integrating advanced technologies into clinical settings, supporting improved public health delivery (Chinwe, 2024).
Scope and Limitations of the Study
The study is confined to the University of Ilorin Teaching Hospital in Kwara State, focusing on diagnostic systems, selected objectives, and sampled hospital units in specific LGAs only.
Definitions of Terms
• Quantum AI: Integration of quantum computing with artificial intelligence for enhanced data processing.
• Automated Diagnosis: Computer-based systems for interpreting medical data to aid diagnosis.
• Machine Learning: A method of data analysis that automates analytical model building.
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