DEPARTMENT OF COMPUTER SCIENCE

The increasing demand for flexible, affordable, and collaborative learning has created opportunities for digital platforms that support peer-to-peer skill exchange. This study focuses on the development of an online skill barter-based system for personalized competency matching using machine learning. The system enables users to offer and learn skills through direct exchange rather than monetary transactions. A K-Nearest Neighbor (KNN) algorithm was integrated with TF-IDF and Natural Language Processing (NLP) techniques to intelligently match users based on skill similarity, learning preferences, and availability. The platform was developed using Next.js and TailwindCSS for the frontend, with Supabase as the backend for authentication and data management. The system promotes inclusive and accessible learning by removing financial barriers while fostering collaboration and community growth. This research demonstrates that integrating machine learning into skill exchange platforms significantly enhances user experience, match accuracy, and trust in non-monetary, peer-driven learning environments.

In the 21st century, digital technology has become deeply embedded in nearly every aspect of daily life from communication and commerce to healthcare and education. While younger generations have adapted rapidly to this digital transformation, many elderly individuals remain on the margins of the digital world. This generational divide has created a significant barrier to inclusion, autonomy and access to essential services for older adults. The inability to navigate digital platforms not only limits their participation in modern society but also exacerbates feelings of isolation, dependence and vulnerability. This project investigates the multifaceted challenges that elderly populations face in adopting and using digital technologies. These challenges include physical limitations such as impaired vision and motor skills, cognitive barriers like memory decline and unfamiliarity with digital interfaces and socio-economic factors such as lack of access to devices or internet connectivity. The study also explores the psychological impacts of digital exclusion, including reduced self-esteem, social withdrawal and increased mental health risks. The research further examines how emerging technologies such as voice activated assistants, simplified user interfaces and wearable health devices can be tailored to meet the needs of older adults. It also considers the role of caregivers, family members and community organizations in supporting digital adoption among seniors. Ultimately, this project argues that closing the digital generational gap is not merely a technical challenge but a social imperative. By fostering inclusive design, empathetic education and supportive ecosystems, society can empower elderly individuals to engage meaningfully with technology. Through inclusive design and community driven support, this project envisions a future where older adults actively participate in the digital world, fostering equity and stronger bonds across generations.

The growing sophistication of phishing attacks continues to threaten digital security, causing financial losses, data compromise, and reduced trust in online platforms. Traditional blacklist and rule-based systems struggle to detect newly emerging phishing patterns, highlighting the need for adaptive, intelligent, and automated approaches. This study addresses this challenge by developing a machine learning-driven phishing vulnerability prediction model capable of accurately distinguishing phishing websites from legitimate ones. The objectives include dataset acquisition, feature preprocessing, model development, and performance evaluation. A labelled URL-based phishing dataset sourced from Kaggle was cleaned, encoded, normalized, and split using an 80:20 train-test ratio. Four supervised learning algorithms: Logistic Regression, Decision Tree, Random Forest, and Extreme Gradient Boosting (XGBoost), were implemented and assessed using Accuracy, Precision, Recall, and F1-score. Among all models, XGBoost achieved the best performance with an Accuracy of 0.96, Precision of 0.97, Recall of 0.96, and an F1-score of 0.96, demonstrating strong predictive ability and robustness. Random Forest also performed well but with slightly lower scores. The final XGBoost model was deployed in a Flask-based web application to provide realtime phishing detection and user awareness. The findings confirm that machine learning, particularly ensemble-based methods, provides a scalable and highly effective approach to phishing vulnerability prediction. However, limitations include reliance on static URL features, dataset-specific bias, and the absence of behavioural or real-time network indicators. Future work should incorporate hybrid features, adversarial robustness testing, and explainable AI techniques to improve transparency and adaptability to evolving phishing attacks.

The process of estimating property prices in Nigeria remains largely manual, subjective, and
inconsistent, as real estate valuation often depends on personal experience, incomplete records,
or unstructured web data. These limitations create inaccuracies that affect buyers, sellers,
investors, and policymakers. To address these challenges, this study aims to develop a more
reliable and data-driven method for house price prediction using machine learning techniques.
The objectives include collecting and preprocessing a multi-state Nigerian dataset, engineering
relevant features, developing multiple predictive models, and evaluating their performance to
identify the most accurate approach.
A dataset consisting of 28,915 property records scraped from PropertyPro was cleaned,
normalized, and transformed through techniques such as one-hot encoding and Mutual
Information Regression. Four machine learning models, which are Linear Regression, Decision
Tree, Random Forest, and XGBoost, were implemented and evaluated using MAE, MSE, and R².
Quantitatively, the models achieved R² scores of 0.475 (Linear Regression), 0.433 (Decision
Tree), 0.517 (XGBoost), and 0.520 (Random Forest). Qualitatively, Random Forest
demonstrated superior ability to capture nonlinear patterns, handle noisy data, and generalize
across diverse property locations. The best-performing model was deployed through a Streamlit
web application to enable real-time house price prediction for end users.
Despite its success, the study is limited by the absence of macroeconomic variables such as
inflation and interest rates, which play a substantial role in housing market dynamics.
Additionally, challenges in obtaining comprehensive and well-structured real estate data across
all Nigerian states constrained the breadth of the analysis. Future research should integrate
geospatial and macroeconomic datasets to further enhance prediction accuracy and model
robustness.

This project title is written to help hospitals especially in the areas they encounter problems in keeping their attendance scheme for patient and the solution given to tackle problem such as transforming the existing manual attendance scheme for patients system in which the existing problems involved at the time was laziness of the Doctors to work, misplacement of files, excessive loitering around of patient for their files and loitering of paper in the office. This software reports on our pilot evaluation of AN ELECTRONIC PATIENT MANAGEMENT SYSTEM and their Doctors. The aim is to improve the quality of care to patient and the information about them, as indicated by an improvement in the effectiveness and efficiency of care and in an increase in patient’s satisfaction. This study makes clear that a thorough exploration of users needs before building the system, using qualitative research methods may be crucial because it can prevent data mismatch and maximize the chance that the eventual management system meets its most important aim: to enhance patient’s empowerment and improve the quality of care services. In order to handle this, I decided to introduce a new Electronic Patient Management System for patients. The project dwells more on Computer duty schedule. This is implemented with Visual Basic programming 6.0 language and Microsoft Access for effective information keeping.

Child abuse remains a critical social issue that requires efficient data management and intervention. Traditional paper-based or fragmented digital systems used in child abuse case management often lead to delays, data loss, security risks, and poor interagency collaboration. This project focuses on the design and implementation of a computerized child abuse database management system to enhance the efficiency of case handling, improve data security, and facilitate real-time reporting. The system is designed to store, retrieve, and track child abuse cases, ensuring that relevant authorities—such as law enforcement, child protection agencies, social workers, and medical professionals—have secure and timely access to case information. Key features include a centralized database, role-based access control, automated case tracking, and real-time reporting tools. The system is developed using modern web technologies, including React.js for the frontend, Node.js for backend processing, PostgreSQL for data storage, and AWS for cloud deployment. Security measures such as data encryption and multi-factor authentication ensure the confidentiality of sensitive case details. By automating case management, offender tracking, and interagency communication, this system aims to streamline the reporting and investigation processes, leading to faster response times and better child protection outcomes. Additionally, the database provides statistical insights for policymakers to identify trends and develop preventive strategies. This project demonstrates how technology can be leveraged to improve child protection efforts, ensuring that cases are efficiently managed and victims receive timely intervention.

Electronic mail (email) remains a primary communication channel globally, making it a prime target for malicious actors. The continuous rise in spam emails poses severe threats, including network congestion, financial losses, and security vulnerabilities like phishing and malware distribution. Traditional rule-based spam filters struggle to keep pace with the evolving tactics of pammers, creating a critical need for more adaptive and robust solutions. This project presents the Design and Implementation of a Spam Email etection System Using Artificial Intelligence.The system leverages Natural anguage Processing (NLP) and Machine Learning techniques to accurately classify mails as "Spam" or "Ham" (legitimate). The methodology involves data collection using a standardized public dataset, followed by intensive text preprocessing including tokenization, stop-word removal, and stemming. Text data is converted into numerical vectors using Term Frequency-Inverse Document requency (TF-IDF). Multiple AI algorithms, including Naïve Bayes, Support Vector Machines (SVM), and Random Forest, are implemented and evaluated to determine the most effective classifier.The system was developed using Python, utilizing libraries such as Scikit-Learn and NLTK, and deployed with a user-friendly graphical interface. Experimental results demonstrate that the AI-powered system achieves high performance, with the top-performing model attaining an accuracy rate exceeding [Insert Accuracy, e.g., 98.2%] and a low false-positive rate. This research concludes that integrating artificial intelligence significantly enhances email security by providing dynamic, real-time protection against malicious messages, thereby ensuring a safer digital communication ecosystem.

This study examined the adoption of Information and Communication Technology (ICT) in early childhood learning in Nigeria through the development and evaluation of an interactive educational system designed for kindergarten children. The study was motivated by the challenges associated with traditional teaching methods, including limited learner engagement, inadequate educational resources, and the need for innovative approaches to enhance literacy development among young learners. The primary objective was to develop an interactive educational database that supports alphabet learning and promotes literacy skills through engaging, child-friendly activities. A survey research design was adopted, and data were x collected using structured questionnaires administered to teachers in selected public and private schools in Edo State, Nigeria. A total of 25 valid responses were analyzed using descriptive statistics. Findings revealed that teachers strongly support the integration of interactive educational technologies into kindergarten education, with all respondents agreeing that such systems can enhance learner engagement and learning outcomes. The study also identified major challenges facing kindergarten education, including inadequate funding, insufficient infrastructure, and lack of technical support. Based on the findings, an interactive educational system was designed and implemented using the MERN stack (MongoDB, Express.js, React.js, and Node.js). The system incorporates interactive games, colorful graphics, personalized learning features, and curriculum-aligned educational content. The study concludes that ICT- driven educational systems have significant potential to improve early childhood learning in Nigeria by making learning more engaging, accessible, and effective. It recommends increased investment in educational technology infrastructure, teacher training, parental involvement, and the development of culturally relevant digital learning resources to support the successful adoption of ICT in early childhood education..

This study examined the role of social engineering in campus-based cybercrimes, focusing on Nigerian tertiary institutions with the University of Benin as case study. Social engineering continues to thrive in academic environments due to trust-based communication systems and increasing reliance on digital platforms for academic, financial, and social interactions. A mixed-method research design was adopted, combining structured questionnaires administered to 400 respondents and semi- structured interviews with key ICT personnel. Findings revealed phishing, impersonation, and fake scholarship scams as the most prevalent social engineering tactics. Behavioral factors such as curiosity, peer influence, and financial vulnerability were identified as major contributors to student susceptibility, alongside institutional gaps including weak cyber awareness programs and poor reporting mechanisms. The study further established that although many students possess basic digital skills, deeper cybersecurity literacy remains insufficient. To address this, the research proposes targeted awareness interventions and the implementation of a Campus Cyber Complaint Reporting System to support early detection, incident reporting, and response coordination. The study concludes that proactive training and structured institutional response measures are essential in reducing the success of social engineering attacks within university environments

This project presents a detailed comparative study of six prominent AI-powered chatbots: DeepSeek, ChatGPT, Poe, DeepAI, Jasper, and Rasa. As organizations increasingly integrate chatbot technology to enhance customer service and user engagement, understanding the diverse capabilities of these tools becomes essential. This study aims to systematically evaluate the performance of each chatbot across multiple parameters, including functionality, accuracy, user satisfaction, task completion rates, and cost- effectiveness. To facilitate this comparison, we employ a weighted scoring model that assigns relative importance to each parameter based on organizational needs. This model allows for a nuanced analysis, accommodating the various strengths and weaknesses exhibited by each chatbot. For instance, while ChatGPT is recognized for its versatile conversational abilities and high user satisfaction, DeepSeek excels in specialized information retrieval. Similarly, Jasper focuses on content generation, whereas Rasa offers flexibility for developers in building contextual AI assistants. Data for this study is collected through user surveys, performance testing, and analysis of chatbot logs. The results are presented in a comprehensive format, providing both quantitative scores and qualitative insights. By the end of this project, we aim to deliver actionable recommendations for organizations, guiding them in selecting the most appropriate chatbot solution tailored to their specific requirements. Ultimately, this study contributes to the growing body of knowledge in AI chatbot technology, facilitating informed decision-making in an increasingly competitive digital landscape.