DEPARTMENT OF COMPUTER SCIENCE

The rapid evolution of digital technologies has transformed e-commerce design standards, leaving many early websites outdated and ineffective.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

The exponential rise in digital banking transactions has heightened the risk of fraudulent and anomalous bank transfers, necessitating intelligent and automated mechanisms for its detection. Traditional rule-based systems often fail to capture evolving fraud patterns, motivating the adoption of machine learning-based anomaly detection techniques. This study aims to develop and evaluate robust unsupervised learning models for detecting anomalies in bank transaction datasets. Specifically, the research applies three state-of-the-art algorithms; Isolation Forest, Local Outlier Factor (LOF), and One-Class Support Vector Machine (OCSVM) to identify irregular transaction behaviors indicative of potential fraud. The methodology involves comprehensive data preprocessing, scaling, encoding, and feature selection to improve model learning. Real-world bank transfer datasets from kaggle were utilized, for this training. Each model’s performance was assessed using standard evaluation metrics, including; Silhouette Score, Anomaly Ratio, and Average Decision Score. Results show that OCSVM performed best (Silhouette = 0.635, strong decision scores), reliably flagging about 5% of records as anomalies. Analysis of flagged transactions revealed consistent patterns high transaction amounts or balance-change ratios, very short or very long intervals between transactions, and activity at unusual hours making the alerts interpretable and practically useful.

The performance quality of web content is a critical determinant of success for e-commerce platforms, directly influencing user engagement and conversion rates. This is especially critical in Nigeria's mobile-first, bandwidth-constrained digital market, where many local websites struggle to meet global performance standards, leading to prolonged load times and a suboptimal user experience that risks customer loss. This study therefore aimed to investigate the performance of selected Nigerian e-commerce websites and diagnose major performance bottlenecks. To achieve this, the project employed a tool-based approach, conducting a performance investigation using Google PageSpeed Insights and GTmetrix to analyze key pages across three major platforms: Jumia, Konga, and Slot. The findings revealed significant performance disparities; Jumia demonstrated relatively optimized performance, Konga exhibited critical inefficiencies with excessively slow load times, and Slot showed inconsistent results, particularly on mobile. In conclusion, the study establishes that technical performance is an inseparable component of web content quality. It provides actionable recommendations, such as image compression and script optimization, underscoring the strategic need for Nigerian e-commerce platforms to prioritize technical excellence to meet up with global standards, enhance user trust, and ensure sustained growth and competitiveness in the evolving digital marketplace.

In a world where movement of people can’t be overemphasized, the need for navigation is crucial. This project seeks to bring a solution to some of the many problems of the existing navigation systems we have today by proposing and offline navigation system model. It also gives a theoretical background to the existing navigation systems already in place with an idea of their workings.

Emotions and views of people have become important factors in communication and decision-making in the age of computer science and information technology. Because web documents are machine-readable and easily accessible, there is a growing interest in leveraging these feelings in the context of consumer evaluations and weblogs. This increase is in line with developments in information retrieval (IR), machine learning (ML), and natural language processing (NLP), which have made accessible techniques for gathering and evaluating opinions. With an emphasis on web papers, this extensive article explores Opinion Mining and Sentiment Analysis (OMSA). It sheds light on the changing area of sentiment analysis and makes recommendations for future research directions by examining important ideas, technical developments, research obstacles, and current solutions. This study's scope is broad and includes anything from explaining basic terms to assessing the effects of technology, resolving issues, and suggesting new research directions. Its importance is extensive, as it advances knowledge in academia, real-world applications, technology, information retrieval, business and marketing tactics, and other areas. Technically speaking, this effort highlights the critical role that OMSA plays in algorithmic decision- making and computational science by providing a thorough computational exploration of the topic

This work intends to provide a computer based system for the maintenance of personnel records of employees in organization. The work addresses limitations identified with manual method of handling personnel records of employees in a firm by providing a better platform to eliminate fraud, corruption, file hiding and misplacement, records falsification, duplication, fragmentation, inconsistencies and
other vices attendant with manual method of handling personnel records. The Personnel Information System (PIS) software is a user friendly package that gives one the fit to accurately monitor employees' records effortless. With Personnel Information System (PIS), the personnel records of employees in a firm regardless of their place of assignment are simultaneously integrated and rationalized through
the creation of a single system that provides accurate information to all in a time and cost efficient manner. This software is designed for stand-alone windows environment, but has the ability to be networked. The interface is Visual Basic language with the structured query language (SQL). The implementation of the system will provide speedy retrieval of data as well as enhancing effective and efficient data.

The growing demand for energy conservation and sustainable technologies has highlighted the need for intelligent systems to optimize energy consumption in various domains, including lighting. This paper presents the design and implementation of a real-time energy-efficient smart lighting system that integrates advanced sensors, wireless communication, and adaptive control strategies. The system employs motion sensors, ambient light sensors, and time-based algorithms to dynamically adjust lighting levels based on environmental conditions, occupancy, and user preferences. The smart lighting system is capable of reducing energy wastage by automatically dimming or turning off lights in unoccupied spaces, while ensuring adequate illumination when needed. Additionally, the system incorporates real-time monitoring and data analytics to track energy consumption patterns, providing actionable insights for further optimization. Experimental results demonstrate the effectiveness of the proposed system in reducing energy consumption, enhancing user comfort, and contributing to sustainable building management practices. The system offers a scalable solution for residential, commercial, and industrial applications, addressing both environmental and economic goals of energy efficiency

The objective of this research is to create a web-based movie recommender system that effectively tackles the challenges of data sparsity and cold start. The system effectively combines explicit data, such as movie ratings and implicit data, including watch history, search queries, and interactions, in order to gain a comprehensive understanding of user preferences. Data cleaning and normalisation are essential preprocessing steps. Sophisticated recommendation algorithms are utilised to improve recommendations, incorporating collaborative filtering, sparsity reduction, and cold start mitigation techniques. The evaluation findings indicate better recommendation, underscoring the system's efficacy in addressing sparsity and cold start obstacles

Traditional cybersecurity awareness programs often struggle with engagement, scalability, and the ability to measure real behavioral change among users. To address these challenges, this project presents the development of a Phishing Simulation and Awareness Portal using Google Workspace tools. The system is designed to simulate real-world phishing attacks in a controlled environment and educate users through interactive, micro-based lessons. Built with Google Apps Script, Gmail, and Google Sheets, the platform automates the creation, deployment, and tracking of phishing campaigns, while maintaining real-time event logging and analytics. It also provides administrators with a simple, centralized interface for managing email templates, campaign configurations, recipient data, and awareness metrics. By integrating simulation and training within the familiar Google ecosystem, the system eliminates the need for external applications and ensures ease of deployment across organizations. Ultimately, this project aims to enhance user vigilance, reduce susceptibility to phishing threats, and promote a culture of proactive cybersecurity awareness through continuous education and data-driven feedback