DEPARTMENT OF COMPUTER SCIENCE

The "Design and Implementation of Duty Scheduler for Doctors and Nurses" project introduces a sophisticated system tailored to the specific scheduling challenges faced by healthcare institutions. This innovative system employs advanced algorithms and a user-friendly interface to automate and optimize the intricate task of creating duty rosters for medical staff. Taking into account staff preferences, legal compliance, and workload distribution, the system generates fair and efficient schedules. It offers customization options, real-time updates, and compliance management features to ensure seamless scheduling while improving staff job satisfaction and work-life balance. By reducing administrative burdens and enhancing resource allocation, this project aims to elevate the efficiency and effectiveness of healthcare operations, ultimately enhancing the quality of patient
care

Maternal healthcare remains a critical index of global public health, yet many healthcare facilities in developing regions still rely heavily on paper-based record systems. These traditional methods are prone to data fragmentation, physical damage, and inefficiency, often leading to delayed clinical decisions during critical periods of gestation. This project addresses these challenges through the design and implementation of a digital, cloud-based Pregnancy Wellness Record Management System. The platform is engineered to streamline antenatal data collection, improve communication between expectant mothers and healthcare providers, and ensure seamless continuity of care. The system was developed utilizing a three-tier software architecture. The frontend user interface was built using responsive frameworks to ensure cross-platform accessibility on both mobile devices and desktops, while the backend logic was implemented using a robust server-side framework integrated with a secure relational database. The application features specialized portals tailored for two distinct user roles: expectant mothers and clinical personnel. For patients, the system offers tools for tracking daily gestational vitals (such as blood pressure, weight, and blood sugar levels), a gestational age calculator, medication reminders, and automated appointment scheduling. For healthcare providers, the system delivers a comprehensive clinician dashboard featuring real-time vitals visualization, antenatal visit logging, immunization tracking, and automated alert flags that trigger when a patient's logged vitals deviate into high-risk clinical thresholds. To safeguard sensitive protected health information (PHI), robust security measures—including role-based access control (RBAC), end-to-end data encryption using HTTPS/TLS protocols, and secure database hashing—were integrated into the system's design. Evaluation of the implemented prototype demonstrated a significant reduction in administrative retrieval times for patient files and enhanced user engagement in maternal self-monitoring. The developed system offers a scalable, secure, and highly efficient digital framework capable of modernizing antenatal care delivery, reducing clinical workloads, and ultimately contributing to improved maternal and neonatal health outcomes.

Phishing has become a major cybercrime threat due to the growth of online transactions and digital communication. This project addresses the challenge by developing a web-based phishing detection system that uses machine learning and explainable AI (XAI) to analyze lexical, content-based, and host-based URL features for real-time classification. Built with Python, Flask, and an XGBoost model, the system achieved approximately 94.79% accuracy, demonstrating strong reliability and effectiveness. Its modular architecture includes a web interface for user input, a backend API for processing, a feature extraction module, and the XGBoost classifier. The XAI component provides clear explanations of predictions, improving user trust, awareness, and decision-making. The system was tested in both local and simulated cloud environments to verify scalability and stability. Overall, it offers a practical, transparent cybersecurity tool suitable for individuals and institutions, contributes to research on intelligent phishing detection, and provides a foundat

The rapid evolution of the digital economy presents both opportunities and challenges for Small and Medium Enterprises (SMEs) in Nigeria. Despite the growing significance of Information and Communication Technology (ICT) in business operations, many SMEs struggle with inadequate digital infrastructure, limited ICT skills, security concerns, and restricted access to digital markets. This study develops an ICT-Readiness Framework tailored to the Nigerian SME landscape, addressing these challenges and providing a structured approach for digital adoption. The framework consists of six key components: Infrastructure Development, Digital Literacy, and Access to Digital Markets, Policy & Regulatory Support, Innovation & Sustainability, and Monitoring & Evaluation. To ensure its practicality, the framework was implemented as a C program, enabling SMEs to assess their ICT readiness, identify gaps, and receive actionable recommendations. The system was successfully executed, providing accurate evaluations based on SME-specific inputs. Findings from the implementation demonstrate that the framework can effectively guide SMEs in enhancing their ICT adoption by improving connectivity, promoting digital skills, expanding market reach, ensuring policy compliance, and fostering innovation. By adopting this structured approach, Nigerian SMEs can better position themselves for growth in the digital economy. This study contributes to the ongoing discourse on SME digital transformation by offering a practical, data-driven solution for ICT readiness. Future research can focus on expanding the framework to include artificial intelligence-driven assessments and cloud-based integration for broader accessibility.

Polycystic Ovary Syndrome (PCOS) is a chronic endocrine disorder requiring continuous monitoring to manage its complex and irregular symptoms. Current digital health applications often fail to address this need, providing generalized tracking, inadequate visualization, and insufficient privacy protection. This project presents the design and development of a specialized, Python-based symptom tracking and visualization system to empower women with PCOS. Built using an Object- Oriented Analysis and Design (OOAD) methodology, the system ensures a modular and user- centered architecture. It leverages Matplotlib for generating insightful visualizations of symptoms, mood, and lifestyle factors. A Flask-based web interface offers an intuitive dashboard for data entry and review, with a commitment to user privacy through local data storage. The resulting system demonstrates that open-source Python technologies can effectively bridge gaps in women's digital health by providing an affordable, transparent, and adaptable solution. It facilitates improved health literacy, data-driven self-management, and enhances patient-clinician communication, highlighting the significant potential of Python in advancing personalized chronic disease management.

This project addresses the inefficiencies of managing shuttle services in university campuses. The study aimed to develop operational efficiency and improve passenger experience by leveraging advanced technology. The solution was designed to provide users with accurate arrival times and route information through an intuitive web application, minimizing wait times and increasing satisfaction using a three tier client-server architecture and UML modeling to define clear interactions between Student, Drivers and Administrators. Implementations utilized PHP, XAMPP, and my SQL to deliver a responsive platform. Key functionalities of the system includes real-time tracking, automated scheduling, and effective communication channels for passengers and operators. Digital Shuttle Management System serves as a sustainable transportation solution, aimed at optimizing fuel consumption and reducing manual appointment scheduling system. By integrating these elements, the project facilitates a smarter, more responsive shuttle service tailored to meet the evolving demands of mobility

This study presents a comparative analysis of three major frontend frameworks—React, Vue, and Angular—with the goal of identifying their strengths, weaknesses, and suitability for different types of web development projects. The research adopted a mixed-method approach that combined experimental performance testing with a developer survey. The experimental analysis focused on key performance metrics such as rendering speed, memory usage, CPU utilization, and bundle size, while the survey gathered developers’ perceptions on usability, learning curve, community support, and development efficiency. Findings revealed that no single framework is superior in all aspects. React recorded the fastest initial load time and the highest overall performance score, making it ideal for applications that prioritize quick content delivery. Vue demonstrated the best runtime efficiency, smallest bundle size, and lowest resource consumption, proving effective for lightweight and performance- sensitive projects. Angular, although heavier in resource usage, stood out for its comprehensive structure, built-in tools, and scalability—features that make it highly suitable for large enterprise systems. Survey results showed strong developer satisfaction across all three frameworks, with most respondents acknowledging their active communities, extensive learning resources, and productivity benefits. The study concludes that framework selection should depend on project requirements and team expertise rather than popularity. It recommends React for performancefocused projects, Vue for resource-efficient and adaptable applications, and Angular for large- scale, structured enterprise solutions.

This study presents a comparative analysis of three major frontend frameworks—React, Vue, and Angular—with the goal of identifying their strengths, weaknesses, and suitability for different types of web development projects. The research adopted a mixed-method approach that combined experimental performance testing with a developer survey. The experimental analysis focused on key performance metrics such as rendering speed, memory usage, CPU utilization, and bundle size, while the survey gathered developers’ perceptions on usability, learning curve, community support, and development efficiency. Findings revealed that no single framework is superior in all aspects. React recorded the fastest initial load time and the highest overall performance score, making it ideal for applications that prioritize quick content delivery. Vue demonstrated the best runtime efficiency, smallest bundle size, and lowest resource consumption, proving effective for lightweight and performance- sensitive projects. Angular, although heavier in resource usage, stood out for its comprehensive structure, built-in tools, and scalability—features that make it highly suitable for large enterprise systems. Survey results showed strong developer satisfaction across all three frameworks, with most respondents acknowledging their active communities, extensive learning resources, and productivity benefits. The study concludes that framework selection should depend on project requirements and team expertise rather than popularity. It recommends React for performancefocused projects, Vue for resource-efficient and adaptable applications, and Angular for large- scale, structured enterprise solutions.

Credit risk assessment is a critical process in lending institutions aimed at evaluating the likelihood of loan default by applicants. Traditional manual assessment methods are often inconsistent and time-consuming, while many automated credit scoring systems lack transparency and explainability. This study presents the design and implementation of a web- based credit risk assessment system . The proposed system captures key applicant attributes and applies predefined decision rules to compute credit risk scores. Debt-to-income ratio and other affordability indicators are used to classify applicants into low, medium, or high risk categories. The system generates clear and interpretable credit decisions accompanied by explanatory reasons. A structured system analysis and design methodology was adopted to guide development. The system was implemented using web technologies and deployed as a browser- based prototype. Functional testing was conducted using representative test cases. Test results confirmed that the system produces accurate, consistent, and explainable credit decisions. The system enhances transparency, usability, and decision accountability. The study demonstrates that explainable rule-based models can effectively support automated credit risk assessment in small and medium-scale lending environments.

Attendance management is a key part of academic administration, yet many Nigerian universities still rely on manual methods prone to errors and impersonation. This study, titled “An Assessment of User Acceptance of Biometric Fingerprint Attendance Systems in Educational Institutions,” examined how students of the University of Benin (UNIBEN) perceive and are willing to adopt biometric fingerprint technology as a modern attendance tool. Using a descriptive survey design, data were gathered from 85 students across different faculties. Guided by the Technology Acceptance Model (TAM) and the Unified Theory of Acceptance and Use of Technology (UTAUT), the study analyzed factors such as awareness, ease of use, trust, and data security. Results showed high awareness and positive attitudes toward the technology, though concerns about privacy and technical support persisted. The study recommends improved infrastructure, training, and clear data protection measures to enhance user trust and successful adoption of biometric systems in universities.