DEPARTMENT OF COMPUTER SCIENCE

With the consistent improvement in information Technology, especially artificial intelligence, many sectors have benefited from it. Education has an immense role to play in the quality of the individuals who will drive the world to the next stage in each generation. The implementation of AI in education aims to equip the student with a plethora of resources from which information and knowledge can be attained. Though it boasts about potential to revolutionize the educational sector, the danger of over reliance on it still exists, aside from that, role of a teacher isn't one that should be replaced by AI, because human interactions are a necessary part of being a human being as outlined by UNESCO. The report emphasizes the need to discourage students from using generative AI as a shortcut to completing tasks, assignments, and even projects. The aim of teaching students is not for them to just produce answers but to learn how to produce them by working hard and dedicating themselves to the pursuit of knowledge. This is the goal that the educational institutions hope to achieve, and creating a system that would increase the chances of achieving that goal should be embraced and utilized.

Maternal mortality continues to pose a significant public health challenge in Sub-Saharan Africa, with Nigeria ranking among the countries with the highest burden. The death of women during their reproductive years not only disrupts family structures and causes emotional distress but also places an increased strain on healthcare systems and impedes national economic and developmental progress. In alignment with the United Nations Sustainable Development Goals (SDGs), particularly the goal focused on reducing maternal mortality, this study investigates the application of Artificial Intelligence (AI) in maternal healthcare through the development of a predictive ensemble model for maternal mortality in Delta State, Nigeria. The objective was to accurately classify maternal health risks, enabling the early identification of high-risk pregnancies and facilitating timely clinical interventions that can reduce preventable maternal deaths. Maternal health data were collected from three healthcare centers across Delta State, Nigeria. Nine supervised machine learning algorithms were employed, including Linear Support Vector Machine (SVM), Gaussian Naïve Bayes, Multilayer Perceptron (MLP), Decision Tree, Random Forest, Gradient Boosting Decision Tree (GBDT), Extreme Gradient Boosting (XGBoost), Light Gradient Boosting Machine (LightGBM), and Categorical Boosting (CatBoost).

Access to healthcare is a fundamental human right, yet significant disparities persist in many regions, particularly in Nigeria and across Africa. These disparities are often influenced by race, ethnicity, and age, with underserved populations facing considerable barriers to receiving quality healthcare. This project focuses on geospatial analysis as a tool to assess and address healthcare access inequities. By leveraging geospatial technologies, this study seeks to map healthcare facilities, analyze spatial patterns of accessibility, and evaluate disparities in healthcare availability across diverse demographic groups. Using Geographic Information Systems (GIS) and publicly available data on healthcare infrastructure, population demographics, and socioeconomic indicators, the project identifies regions with inadequate healthcare coverage. Key variables, including proximity to healthcare facilities, density of healthcare providers, and transportation infrastructure, are analyzed in relation to demographic data such as race, ethnicity, and age distribution. Special attention is given to rural and peri-urban areas where healthcare infrastructure is typically sparse. This study also integrates statistical models to quantify disparities, providing actionable insights into how race and ethnicity intersect with geographic location to impact access to essential health services. In regions like Nigeria, where the healthcare system faces significant challenges, the project explores how these disparities disproportionately affect vulnerable populations such as ethnic minorities, elderly citizens, and children. The findings are expected to highlight areas of acute need, where targeted policy interventions could have the greatest impact. Moreover, the geospatial approach offers a data-driven framework for decision-makers, empowering them to allocate resources more effectively and design strategies to bridge healthcare gaps.

Small and Medium Enterprises (SMEs) often face challenges in managing the overwhelming number of security alerts generated by their IT systems. Traditional alert systems lack contextual intelligence, leading to alert fatigue, delayed responses, and missed critical incidents. This study presents a context-aware Alert Management System that enhances prioritization accuracy by incorporating operational factors such as alert frequency, entity type, business hours, and historical severity. The system was designed and implemented using a React-based simulation environment with 50 synthetic alerts representing realistic SME security events. Comparative evaluation between a baseline model ((Severity + Criticality)/2) and an enhanced model ((Severity + Criticality + Context Factor)/3) demonstrated a 42.42% reduction in alert fatigue and
complete elimination of false-positive high-priority alerts while maintaining 100% detection of genuine threats. The findings confirm that context-aware alert management significantly improves prioritization accuracy and analyst efficiency. The proposed framework provides SMEs with a cost-effective, transparent, and scalable solution for strengthening their cybersecurity posture and improving real-time incident response.

Thorough intensive effort have been made to design powerful and outstanding e-learning platforms or environment but little or no attention have been paid to creating also environment that will provide assessment to test the knowledge and skills which learners may acquire form
these platforms. Competency based testing can be applied to various instance including real life or e-learning programmes with a focus of skill based learning outcomes. This study however is concerned with creating a competency-based e-learning platform for the regular desktop that teaches web development to students or interested learners. Web development here is a larger learning goal and is however divided or broken down into smaller component courses called competencies. Students or users of the platform must however go through each phase and must be classified Advanced, Competent or Master in order to advance to a next level as advancement to a next level component or competency would depend on the students tested competency level.
Included also in this study is a model for testing student’s competency level in relation with the 5-level scale which is used to classify student according to their competency leve

In this era, the best problem solving method is needed in all field irrespective of the complexity or simplicity of the problem. Researchers and developers are doing their best to make software’s and machines more potent and intelligent. This is the advantage of artificial intelligent in developing solutions to searching algorithms that are potent and optimal. The most potent highly developed investigate method in Artificial Intelligence is the genetic algorithm. Genetic algorithm was developed to get best result to a known difficulty premised on inheritance, collection, crossover, mutation and further method. It has been proven that genetic algorithm is the most potent, impartial optimization method for analyzing a solution with large space. this research have been able to define what is genetic algorithm, how it differs from other existing traditional search optimization method, review of ten (10) traditional techniques of finding the best route in a given network. Also the design of genetic algorithm, it’s implementation on finding the best route within 20 cities (point) which is invariably the travelling salesman problem (TSP), and areas of application of application of genetic algorithms. The best route is invariably the shortest path.

The web-based sectional result computation system abstracts the complexities of calculating and generating academic results in a University or educational institution. It provides a user-friendly interface that simplifies data management, computation algorithms, result generation, security, access control, and integration with other systems. By abstracting these components, the system streamlines the result computation process and ensures accuracy, reliability, and timeliness. It simplifies data storage, retrieval, and organization, automates grade calculations and performance indicators, and generates result sheets and reports in customizable formats. The system also abstracts security measures, ensuring authorized access to student results, and integrates with other institutional systems for seamless data synchronization. This abstraction allows users, such as faculty, staff, and administrators, to focus on result analysis, interpretation, and decision-making, rather than getting overwhelmed by technical details. The web-based nature of the system enables easy access from anywhere with an internet connection, enhancing accessibility and convenience. Overall, the result computation system's abstraction simplifies and automates result processing, improves efficiency, reduces errors, and provides standardized and reliable frameworks for handling academic outcomes. It empowers users with accurate and timely student results, facilitating informed decision-making and enhancing the overall academic experience within the institution.

This research focuses on the design and implementation of an intelligent chatbot course adviser system for the Department of Computer Science at the University of Benin. The study addresses the limitations of traditional manual course advisor methods by leveraging artificial intelligence, machine learning, and natural language processing technologies to create an automated, efficient, and user-friendly academic guidance system. The research employed a mixed-method approach, combining qualitative and quantitative data collection techniques to ensure comprehensive system development. The study included surveys of students and academic staff, analysis of existing course adviser processes, and systematic evaluation of technological requirements. The implementation phase involved developing an intelligent chatbot system with advanced features including 24/7 availability, personalized course recommendations, real-time prerequisite verification, and automated academic progress tracking. Results demonstrate significant improvements in academic adviser services, with the chatbot system providing immediate, accurate, and consistent course guidance. The system successfully reduced administrative workload, minimized advisory errors, and enhanced student access to academic support. User acceptance testing showed high satisfaction rates among students and staff, validating the system's effectiveness in addressing traditional advisory challenges. The research contributes to the growing body of knowledge in educational technology and provides a practical framework for implementing AI-driven academic support systems. The findings suggest that intelligent chatbot systems can significantly enhance academic adviser services, offering potential applications across various educational institutions. Recommendations for future development and system optimization are provided based on the research
outcomes.

This project, Product Expiry Alert Management System helps to improve the work efficiency of supermarket by providing daily, weekly or monthly expiry alerts of products. It also provides the basic information maintenance function of employees, memberships and products so that managers can through the function to add, delete, and modify the basic information of employees and the employees can through it to add, modify and delete the basic information of memberships and goods. It also tend to solve the problem of expired goods whereby notifying/alert supermarket managers when product is about to expire. Products expiry management system is very convenient for manage, input, output, and find the data so as to make the messy supermarket data to specific, visualizations, rationalization.

This research project provides a solution which would help enable the ease of navigating through a new campus environment as every year there are new students in every university. In this study, I highlighted the importance of the campus navigation system using University Of Benin, ugbowo, as a case study. Agile methodology was adopted for this project, and a few data collection method was adopted. This study evolved into a feasible navigation system, which would help ease the confusion that comes with entering a new university campus