DEPARTMENT OF COMPUTER SCIENCE

This project focused on the design and implementation of SmartLMS, an Artificial Intelligence assisted Learning Management System (LMS) developed to enhance digital teaching and learning in the University of Benin. The aim of the study was to create a flexible and user friendly platform that integrates AI features to support both lecturers and students in managing learning materials, assessments and performance analytics. The system was designed using Next.Js for the Frontend, Node.Js with the Express.Js framework for the Backend and MongoDB as the database, with JWT authentication to regulate signup and secured login to the platform, Cloudinary to handle and store uploaded materials and OpenAI and Gemini AI for AI-driven functionalities. The AI modules were implemented to help students with course materials summarization, self-practice test generation and keep track of students’ analytics. The system followed a modular design and was implemented to be responsive, reliable and accessible on both mobile and desktop devices. Results showed that SmartLMS improved teaching efficiency, student engagement and learning personalization while maintaining lecturer control over assessments, grading and students’ academic performance. The project concluded that SmartLMS provides a practical and scalable solution for integrating AI into education, offering a more interactive, intelligent and accessible learning experience that can be integrated into
different learning environments

Phishing attacks are one of the most common and dangerous cybersecurity threat today, with attackers using techniques that are getting sophisticated daily to deceive users and get access to their sensitive information. This study shows the development and implementation of a machine learning based phishing URL detection tool designed to identify malicious URLs and do so with high accuracy. This research addresses the growing challenge of detecting phishing websites by analyzing URL characteristics and patterns that distinguish legitimate sites from fraudulent ones. Utilizing a comprehensive dataset of over 10,000 URLs (comprising both phishing and legitimate websites), this study implements multiple machine learning algorithms including Random Forest, Support Vector Machines (SVM), and Gradient Boosting to classify URLs. The system extracts 30 distinct features from URLs, including lexical properties, domain-based characteristics, and third-party service indicators. Feature engineering techniques were applied to optimize model performance, with priority given to handling imbalanced datasets through Synthetic Minority Over-sampling Technique(SMOTE ). The results shows that the Random Forest classifier achieved the highest accuracy of 96.8%, with precision and recall scores of 95.2% and 97.1% respectively. The Gradient Boosting model closely followed with 95.9% accuracy, while the SVM model achieved 92.4% accuracy. Cross-validation techniques were used to make sure the model is robust and prevent overfitting. Feature importance analysis revealed that URL length, presence of suspicious keywords, domain age, and SSL certificate status were among the most significant predictors of phishing attempts. To validate practical applicability, a web-based detection tool was developed using Flask framework, enabling real-time URL scanning and classification. The system incorporates a user-friendly interface that provides instant feedback on URL legitimacy, along with detailed risk analysis and security recommendations. Performance testing also verified an average response time below 200 milliseconds per analysis for URL, making the tool practical for real-world deployment. This research contributes to the study of cybersecurity with the presentation of an efficient, automated phishing detection system that can be employed with web browsers, email clients, or independently

This study explores the implementation of Large Language Models (LLMs) for analyzing open-ended survey responses in software engineering. Traditional surveys often focus on structured, multiple-choice questions, which provide quantitative insights but overlook the depth of qualitative developer feedback. To address this limitation, the project designed and implemented an LLM-powered system capable of summarizing responses, detecting sentiments, identifying recurring themes, and revealing open research problems from unstructured text. The system architecture was built using a three-tier design comprising a frontend interface, backend server, and LLM integration layer. A pre-trained model such as GPT was connected via API to process textual data. The study followed a design and implementation-oriented methodology, including data collection from developer surveys, system development, testing, and evaluation. Performance was assessed using both quantitative and qualitative metrics such as accuracy, coherence, and user feedback. Evaluation results demonstrated that the system effectively automated key qualitative analysis tasks with high accuracy and interpretability. However, challenges such as occasional hallucinations, dependency on third-party APIs, and limited dataset scope were noted. Overall, the findings confirm that LLMs can significantly enhance qualitative research in software engineering by providing faster, more consistent, and context-aware insights. The study concludes that integrating LLMs with human oversight presents a promising approach for future software engineering research anddecision-making

Waste management in Edo State suffers from poor communication, irregular service delivery, and weak accountability due to manual record-keeping and fragmented reporting systems. This study developed a web-based Customer Relationship Management (CRM) platform to improve communication, transparency, and citizen participation in waste management. Using the Waterfall methodology, data were collected through interviews and literature review, guiding the design and implementation of the system with ASP.NET Core MVC, C#, Razor Pages, Tailwind CSS, and Microsoft SQL Server. The platform includes a customer interface for registration, request tracking, real-time chat, and announcements, and a waste manager interface for zone management, complaint resolution, and information sharing. Security measures such as Argon2 password hashing and role-based access control were implemented. Testing confirmed full functionality and reliability. The system enhances service delivery bycentralizing communication, standardizing complaint tracking, and promoting citizenengagement, offering a scalable model for technology-drivenwaste management across Edo State.

Attendance tracking in classes is a very important activity in any institution, and taking attendance of students using facial recognition is a more efficient and accurate method than the traditional methods which includes paper- based and roll call methods. The facial recognition system is an application of computer vision that can perform two major tasks of identifying and verifying a person from a given database. Facial recognition proves to be more effective in taking attendance than the traditional method, which is inaccurate, time- consuming and vulnerable in most cases of large class environments. This system is designed with a login page for authentication, it also provides a mailing platform where the attendance will be sent to the school authority for record keeping. This study develops a deep-learning-based facial recognition system used to detect the face of students in a class environment for the sole purpose of taking their attendance records.

The web based brochure system for tailoring is a web based system application designed for the tailor’s shop. This project is aimed to automate the tailor’s shop which is manually maintained. After the automation this will mean, better services and good keeping of records, data integrity, data security, quick search and also paperless environment. The project has mainly tackled management of information for the customers and in decision making. Every user of the system will have to log into the system using username and password so that security and authentication will be ensured. Once logged in, a customer can make and order. The system administrator is able to manage customer information and also update records.

Color detection is a task that humans perform effortlessly; however, enabling computers to accurately identify colors remains a challenging problem. In many industries, traditional color recognition systems rely heavily on manual processes and paid labor for color-coding items or datasets, which are often time-consuming, repetitive, and proneto human error. To address these limitations, this project presents a deep learning–based color detection program capable of recognizing multiple colors in real time. The system is implemented using Python, a high-level general-purpose programming language, in conjunction with the Open Source Computer Vision Library (OpenCV). By leveraging deep learning techniques, the proposed solution enhances accuracy and efficiency in automated color recognition tasks. The developed system enables computer devices to detect and classify multiple colors in real time, making it suitable for applications across various industries, including pharmaceutical manufacturing, autonomous vehicle development, and robotics. The adoption of this system can significantly reduce production time, minimize reliance on manual labor, and lower operational costs while improving overall productivity

This study titled “A Study on the Role of Assistive Technology in Enhancing Digital Skills Acquisition Among Physically Challenged Persons” investigates how assistive technologies influence digital learning, productivity, and inclusion among physically challenged Persons in the Ugbowo–Egor axis of Benin City, Edo State. The study employed a descriptive survey design, with data collected through structured questionnaires administered to 100 Persons, of which 90 valid responses were analyzed using descriptive and inferential statistics (SPSS v26). Findings revealed a moderate level of awareness of assistive technologies but low accessibility due to cost, inadequate awareness, and poor infrastructural support. Despite these challenges, results showed that the use of assistive tools such as voice commands, adaptive keyboards, and screen readers significantly improved Persons’ digital learning outcomes, confidence, and productivity. Correlation analysis (r = 0.684, p < 0.05) confirmed a strong positive relationship between assistive technology use and digital skill acquisition. The study concludes that assistive technologies are critical enablers of digital inclusion and economic empowerment for persons with disabilities. It recommends targeted government funding, awareness campaigns, inclusive ICT training, and the development of affordable, locally adaptable assistive tools to bridge the accessibility gap and advance sustainable digital participation for physically challenged Persons.

The rapid evolution of digital technologies has transformed e-commerce design standards, leaving many early websites outdated and ineffective. This study focuses on the user- centered redesign of a legacy e-commerce interface, using Arngren.net as a case study. The objective was to evaluate the usability and visual experience of the site and to propose a redesign framework that aligns with modern user experience (UX) and interface design (UI) principles. The research adopted a qualitative case study approach, emphasizing heuristic evaluation and comparative analysis. Using Nielsen’s (2020) ten usability heuristics, Arngren.net was assessed for issues relating to layout consistency, navigation flow, accessibility, and visual hierarchy. Findings revealed significant usability flaws, including poor visual organization, low mobile responsiveness, and non-intuitive navigation. These weaknesses were compared with modern e-commerce platforms such as Amazon, eBay, and Shopify-based stores, which prioritize responsive layouts, accessibility compliance, and streamlined user journeys. Based on these insights, a user-centered redesign framework was proposed, integrating simplicity, responsive design, and user trust as key pillars. The redesigned interface emphasizes clear visual hierarchy, improved navigation menus, accessible content structure, and consistency across devices.

Online Auction management system is a web-based application which will help users to buy or sell item; they can trade anything they want by posting adverts. This application developed will allow students of the University of Benin to post their products for auction; bidder can register and can bid for any available product. The system was developed using HTML, CSS, JavaScript, and PHP for the program. The system provides some basic features, a friendly GUI, easy registration and bidding processes, etc. With the new system, students can easily auction items and sell to the highest bidder, this will bridge the gap between students that have items to sell and companies looking for used but in good condition items to buy