IMPLEMENTATION

**Abstract** The manual calculation of Grade Point Average (GPA) and Cumulative Grade Point Average (CGPA) is often time-consuming, prone to computational errors, and may lead to inaccurate academic records. This project, titled **"Design and Implementation of a CGPA/GPA Calculator,"** was undertaken to develop a computerized system that automates the computation of students' academic performance with improved accuracy, speed, and reliability. The system was designed to calculate semester GPA and cumulative CGPA based on approved grading policies, taking into account course credit units, grades obtained, quality points, and cumulative academic records. The development of the application followed the Software Development Life Cycle (SDLC), which included requirements analysis, system design, implementation, testing, and evaluation. The system was developed using appropriate programming tools and technologies, with a user-friendly interface that allows users to input course details, credit units, and grades, after which the application automatically computes the corresponding GPA and CGPA. The calculator also provides features for validating user input, minimizing calculation errors, and displaying results in an easily understandable format. System testing was carried out using various datasets to evaluate the accuracy, efficiency, and reliability of the application. The results showed that the developed system accurately computed GPA and CGPA values within a short processing time while eliminating the errors commonly associated with manual calculations. Furthermore, the application enhanced record management, improved user convenience, and provided a dependable tool for students and academic institutions to monitor academic performance. The study concludes that the implementation of a CGPA/GPA Calculator significantly improves the efficiency and accuracy of academic result computation. It is therefore recommended that educational institutions adopt automated GPA/CGPA calculation systems to reduce computational errors, improve transparency, and facilitate effective academic performance monitoring.

Vehicle identification and verification play a critical role in ensuring the security of controlled environments such as university campuses. At the University of Benin’s Ugbowo Campus, the existing manual vehicle verification process suffers from several limitations, including delays, human error, poor record keeping, and the absence of real-time validation. This study addresses these challenges by designing and implementing an Automated Car Plate Number Recognition (ANPR) system capable of detecting, recognizing, and verifying vehicle license plates using computer vision and optical character recognition (OCR) techniques. The system was developed using the Flask web framework, OpenCV for image preprocessing and license plate localization, and the Tesseract OCR engine for extracting alphanumeric characters. A MySQL database was integrated for storing records of registered vehicles and maintaining logs of recognition events. The system allows security personnel to upload vehicle images through a web interface, after which the application automatically processes the image, recognizes the plate number, checks its validity against the stored records, and provides immediate feedback. Testing of the system using images captured under different conditions such as clear daytime lighting, low light, skewed angles, and motion blur revealed that the system performs accurately under favourable conditions and provides partial or usable information even under challenging scenarios. Although recognition performance decreases with poor image quality, the system still offers considerable improvements over manual verification by reducing reliance on human judgment and enhancing the accuracy, speed, and reliability of vehicle monitoring.

This study proposes the design and implementation of an intelligent chatbot system for the Department of Computer Science, University of Benin, to improve communication between students and departmental administration. Currently, information such as registration deadlines, exam timetables, and announcements is shared through traditional means like notice boards and emails, which often cause delays and inefficiencies. The proposed chatbot, built using Google Dialogflow (NLP) and a PHP-based backend, will provide real-time, accurate responses to student queries through a responsive web interface. System development will include requirement gathering, chatbot design, and integration with a structured knowledge base. Evaluation will focus on response accuracy, usability, and user satisfaction. Expected outcomes include reduced administrative workload, faster and more consistent information dissemination, and improved student experience. The study also aims to offer a scalable framework for applying AI-powered communication tools in other university departments across Nigeria.

The increasing complexity of modern programming environments has created a growing need for intelligent, real-time support systems capable of assisting students and developers in understanding, writing, and debugging code. This project presents the design and implementation of an AI-powered chatbot web application that leverages Large Language Model (LLM)- based Natural Language Processing (NLP) and Remote Code Execution APIs to address programming queries interactively. The system integrates Google’s Gemini API for natural language understanding and JDoodle API for live code execution within a secure, microservicebased architecture. The chatbot provides conversational assistance, real-time code validation, debugging support, and algorithmic explanations through a responsive web interface built with React, TypeScript, Tailwind CSS, and Vite. The backend is implemented using Node.js, Express.js, and MongoDB, with modular microservices for authentication, chatbot intelligence, and code execution, all orchestrated through an API Gateway. This architecture ensures scalability, maintainability, and independent service deployment, while also improving accessibility for users in resource-constrained environments. The system demonstrates how combining LLMs with execution APIs can create a more reliable and contextaware programming assistant than traditional static or rule-based systems. The outcome of this project is a robust, user-friendly, and intelligent chatbot that enhances learning efficiency, developer productivity, and accessibility to programming support, particularly for students in developing regions. It contributes to ongoing research in AI-driven education, NLP, and intelligent tutoring systems, offering a sustainable model for future AIintegrated learning platforms.

The convergence of Artificial Intelligence (AI) with Digital One Health systems, which integrate human, animal, and environmental health data into unified platforms, has introduced unprecedented cybersecurity challenges that conventional frameworks fail to sufficiently address. This project proposes the design, implementation, and evaluation of an adaptive AI- driven security framework tailored specifically to safeguard sensitive cross-sector health data within complex hybrid cloud and on-premise infrastructures. The framework leverages foundational cybersecurity principles, such as Zero Trust Architecture and layered defensein-depth, combined with advanced AI-powered anomaly detection models including Long Short-Term Memory (LSTM) Autoencoders and Isolation Forest algorithms to enable realtime identification and mitigation of emerging threats. Employing open-source technologies integral to the system architecture; OPNsense for perimeter firewall and VPN, WireGuard for encrypted communication, Suricata IDS/IPS for intrusion detection and prevention, Wazuh Security Information and Event Management (SIEM) for centralized log aggregation, Keycloak for Multi-Factor Authentication (MFA), and Debezium for Database Activity Monitoring (DAM), the framework demonstrates a holistic approach to securing Digital One Health ecosystems. Central to this effort is the development of a robust AI-powered security dashboard based on the ELK stack (Elasticsearch, Logstash, Kibana), which provides unified visualization, real-time alerting, and compliance monitoring. The research adopts a design science approach, systematically assessing security vulnerabilities unique to hybrid Digital One Health infrastructures and implementing layered controls to address gaps in cross-sector interoperability and AI-specific risks such as adversarial attacks and data poisoning. Validation is performed using comprehensive threat simulations that mimic real-world attack vectors including SQL injection, lateral movement, privilege escalation, and Distributed Denial-of-Service (DDoS) scenarios. Quantitative performance metrics reveal a high detection rate (approximately 97%), significant reductions in both mean time to detect (MTTD) threats by 70% and false positive alerts by 58%, thereby illustrating improved operational efficiency and accuracy compared to traditional static security measures. Despite promising results, challenges remain around optimizing AI model training data quality, managing performance overhead in resource-constrained settings, and balancing stringent security controls with healthcare delivery imperatives. The study advocates for future enhancements including federated learning models to preserve data privacy, lightweight AI implementations for broader accessibility, automated compliance frameworks
to navigate complex regulatory environments, and expanded user training programs to address human-centric security gaps. This project contributes a scalable, ethically aligned cybersecurity framework specifically engineered for AI-powered Digital One Health systems, addressing the multifaceted demands of securing sensitive, interconnected health data across diverse environments. By merging advanced AI capabilities with proven cybersecurity practices and a centralized management platform, it advances the resilience and trustworthiness of modern healthcare ecosystems, fostering innovation while safeguarding critical digital health infrastructure globally.

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.

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.

The growing interconnection between human, animal, and environmental health has emphasized the importance of integrated surveillance systems for early detection and coordinated response to zoonotic and environmentally linked diseases. However, in many low-resource settings such as Nigeria, disease monitoring remains fragmented across sectors, resulting in delayed reporting and weak cross-sectoral collaboration. This project presents the design and implementation of a Digital One Health Surveillance (DOHS) Dashboard, a web-based system developed to unify real-time disease surveillance across the three One Health domains—human, animal, and environmental health. The system was built using a React-based frontend and a RESTful backend API, integrating modules for authentication, case management, data analytics, and geospatial visualization. HTTP polling mechanisms were implemented to achieve real-time data synchronization without requiring WebSocket infrastructure, ensuring compatibility with constrained networks. The dashboard provides role-based access control, sector-specific reporting interfaces, Excel-based data export, and an interactive GIS map powered by React-Leaflet for spatial trend analysis. Data are securely transmitted via token-authenticated API calls, while the modular architecture allows future expansion and integration with existing national systems such as SORMAS or DHIS2. Deployment was designed for both cloud-based Virtual Private Servers (VPS) and onpremise servers within health facilities, supporting continuous operation even in areas with limited internet access. The system demonstrated efficient cross-sectoral data integration, realtime monitoring of case reports, and improved accessibility for public health officers, veterinary staff, and environmental personnel. The DOHS dashboard contributes to the operationalization of the One Health approach in Nigeria by providing a scalable, adaptable, and user-friendly digital infrastructure for integrated disease surveillance. It lays the foundation for data-driven decision-making and offers a replicable model for similar low-resource environments across Africa.

The convergence of Artificial Intelligence (AI) with Digital One Health systems, which integrate human, animal, and environmental health data into unified platforms, has introduced unprecedented cybersecurity challenges that conventional frameworks fail to sufficiently address. This project proposes the design, implementation, and evaluation of an adaptive AI- driven security framework tailored specifically to safeguard sensitive cross-sector health data within complex hybrid cloud and on-premise infrastructures. The framework leverages foundational cybersecurity principles, such as Zero Trust Architecture and layered defensein-depth, combined with advanced AI-powered anomaly detection models including Long Short-Term Memory (LSTM) Autoencoders and Isolation Forest algorithms to enable realtime identification and mitigation of emerging threats. Employing open-source technologies integral to the system architecture; OPNsense for perimeter firewall and VPN, WireGuard for encrypted communication, Suricata IDS/IPS for intrusion detection and prevention, Wazuh Security Information and Event Management (SIEM) for centralized log aggregation, Keycloak for Multi-Factor Authentication (MFA), and Debezium for Database Activity Monitoring (DAM), the framework demonstrates a holistic approach to securing Digital One Health ecosystems. Central to this effort is the development of a robust AI-powered security dashboard based on the ELK stack (Elasticsearch, Logstash, Kibana), which provides unified visualization, real-time alerting, and compliance monitoring. The research adopts a design science approach, systematically assessing security vulnerabilities unique to hybrid Digital One Health infrastructures and implementing layered controls to address gaps in cross-sector interoperability and AI-specific risks such as adversarial attacks and data poisoning. Validation is performed using comprehensive threat simulations that mimic real-world attack vectors including SQL injection, lateral movement, privilege escalation, and Distributed Denial-of-Service (DDoS) scenarios. Quantitative performance metrics reveal a high detection rate (approximately 97%), significant reductions in both mean time to detect (MTTD) threats by 70% and false positive alerts by 58%, thereby illustrating improved operational efficiency and accuracy compared to traditional static security measures. Despite promising results, challenges remain around optimizing AI model training data quality, managing performance overhead in resource-constrained settings, and balancing stringent security controls with healthcare delivery imperatives. The study advocates for future enhancements including federated learning models to preserve data privacy, lightweight AI implementations for broader accessibility, automated compliance frameworks to navigate complex regulatory environments, and expanded user training programs to address human-centric security gaps. This project contributes a scalable, ethically aligned cybersecurity framework specifically engineered for AI-powered Digital One Health systems, addressing the multifaceted demands of securing sensitive, interconnected health data across diverse environments. By merging advanced AI capabilities with proven cybersecurity practices and a centralized management platform, it advances the resilience and trustworthiness of modern healthcare ecosystems, fostering innovation while safeguarding critical digital health infrastructure globally.

The growing interconnection between human, animal, and environmental health has emphasized the importance of integrated surveillance systems for early detection and coordinated response to zoonotic and environmentally linked diseases. However, in many low-resource settings such as Nigeria, disease monitoring remains fragmented across sectors, resulting in delayed reporting and weak cross-sectoral collaboration. This project presents the design and implementation of a Digital One Health Surveillance (DOHS) Dashboard, a web-based system developed to unify real-time disease surveillance across the three One Health domains—human, animal, and environmental health. The system was built using a React-based frontend and a RESTful backend API, integrating modules for authentication, case management, data analytics, and geospatial visualization. HTTP polling mechanisms were implemented to achieve real-time data synchronization without requiring WebSocket infrastructure, ensuring compatibility with constrained networks. The dashboard provides role-based access control, sector-specific reporting interfaces, Excel-based data export, and an interactive GIS map powered by React-Leaflet for spatial trend analysis. Data are securely transmitted via token-authenticated API calls, while the modular architecture allows future expansion and integration with existing national systems such as SORMAS or DHIS2. Deployment was designed for both cloud-based Virtual Private Servers (VPS) and onpremise servers within health facilities, supporting continuous operation even in areas with limited internet access. The system demonstrated efficient cross-sectoral data integration, realtime monitoring of case reports, and improved accessibility for public health officers, veterinary staff, and environmental personnel. The DOHS dashboard contributes to the operationalization of the One Health approach in Nigeria by providing a scalable, adaptable, and user-friendly digital infrastructure for integrated disease surveillance. It lays the foundation for data-driven decision-making and offers a replicable model for similar low-resource environments across Africa.