FACULTY OF PHYSICAL SCIENCE

**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.

Due to industrialization, population growth and urbanization in Yenagoa and environs, anthropogenic activities are increasing daily and could introduce contaminants into soil and water resources.This study evaluated the causes, distribution, and environmental effects of heavy metal pollution in groundwater, surface water, and soil in Yenagoa and environs, Niger Delta, Southern Nigeria. The study sought to compare concentrations with control values for soil, surface and ground water, and regulatory standards (WHO and NSDWQ) for groundwater, as well as assess the degree and variability of heavy metal pollution across various anthropogenic sites, including waste disposal sites, mechanic shops, cassava mills, and areas affected by crude oil spills. A total of one hundred and twelve (112) samples were randomly collected for each season, including 77 soil samples depth 30cm and 35 surface and ground water samples. These samples were then evaluated using standard laboratory technique, Atomic Absorption Spectrophotometer (AAS) during both the wet and dry seasons. Soil contamination levels were measured using pollution indicators such the Geo-accumulation Index (Igeo), Enrichment Factor (EF), Contamination Factor (CF), and Pollution Load Index (PLI), and groundwater quality was assessed in relation to allowable limits. Results showed that all site types exhibited elevated concentrations of key heavy metals including iron (Fe), manganese (Mn), zinc (Zn), copper (Cu), lead (Pb), cadmium (Cd), chromium (Cr), and vanadium (V). Crude oil spill sites recorded the highest soil contamination, with lead reaching 15.354 mg/kg and

Due to industrialization, population growth and urbanization in Yenagoa and environs, anthropogenic activities are increasing daily and could introduce contaminants into soil and water resources.This study evaluated the causes, distribution, and environmental effects of heavy metal pollution in groundwater, surface water, and soil in Yenagoa and environs, Niger Delta, Southern Nigeria. The study sought to compare concentrations with control values for soil, surface and ground water, and regulatory standards (WHO and NSDWQ) for groundwater, as well as assess the degree and variability of heavy metal pollution across various anthropogenic sites, including waste disposal sites, mechanic shops, cassava mills, and areas affected by crude oil spills. A total of one hundred and twelve (112) samples were randomly collected for each season, including 77 soil samples depth 30cm and 35 surface and ground water samples. These samples were then evaluated using standard laboratory technique, Atomic Absorption Spectrophotometer (AAS) during both the wet and dry seasons. Soil contamination levels were measured using pollution indicators such the Geo-accumulation Index (Igeo), Enrichment Factor (EF), Contamination Factor (CF), and Pollution Load Index (PLI), and groundwater quality was assessed in relation to allowable limits. Results showed that all site types exhibited elevated concentrations of key heavy metals including iron (Fe), manganese (Mn), zinc (Zn), copper (Cu), lead (Pb), cadmium (Cd), chromium (Cr), and vanadium (V). Crude oil spill sites recorded the highest soil contamination, with lead reaching 15.354 mg/kg and

In the current fast-changing agricultural environment, farmers need to have access to timely and relevant information to make well-informed choices. This initiative seeks to create and implement a comprehensive Information System specifically designed for farmers, especially in rural and semi-urban areas. The system will utilize modern technology to deliver vital information regarding weather forecasts, crop management, pest control, market prices, and optimal agricultural practices. By integrating mobile and web platforms, the system guarantees easy access and real-time updates. The main goals include enhancing the knowledge base of farmers, boosting crop yields, minimizing losses, and promoting sustainable agricultural methods. The project will entail a thorough needs assessment, system design, development, and intensive field testing in partnership with local farming communities. Additionally, the system will incorporate user-friendly interfaces, multilingual support, and offline functionalities to accommodate diverse user requirements. In the end, this Information System aims to equip farmers with the knowledge and resources needed to enhance productivity and profitability, thus aiding in food security and economic development in the region.

Cardiovascular Diseases (CVDs) remain the paramount global health challenge, responsible for approximately 17.9 million deaths annually, with cardiac arrest representing a particularly critical and sudden event. Despite advancements in machine learning, accurate prediction is often hindered by highdimensional clinical data ("the curse of dimensionality") and the performance limitations of standalone classifiers. Consequently, this study aimed to develop a framework that integrates a novel Hybrid Recursive Feature Elimination-Firefly (RFE-Firefly) feature selection technique with a stacked ensemble classifier to enhance predictive accuracy. The study utilized a structured heart disease dataset from the Kaggle repository, subjecting it to rigorous pre-processing, including normalization and class balancing via the Synthetic Minority Over-sampling Technique (SMOTE). The proposed Hybrid RFEFirefly algorithm was implemented to optimize the feature space, successfully reducing the data to a parsimonious subset of seven critical features. For classification, a stacking ensemble model was constructed using five diverse base learners which are Decision Tree, Support Vector Machine, Logistic Regression, Random Forest, and XGBoost and unified by Random Forest as meta-learner to synthesize their predictive strengths. Experimental results demonstrated that the proposed Hybrid RFE-FA Stack Ensemble achieved a superior accuracy of 90.57% and an F1-score of 0.7619. This performance significantly outperformed individual base classifiers as well as ensembles using standalone feature selection methods. The study concludes that integrating hybrid feature selection strategy with an advance stack ensemble classifier effectively mitigates overfitting and provides a robust, highperformance tool for clinical decision-making in cardiac arrest detection.

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.

This study investigates the effectiveness of Winsorization, a statistical technique used to handle outliers in data analysis. Outliers can significantly distort statistical measures such as the mean, variance, and standard deviation, leading to inaccurate conclusions and decision-making. The research applied Winsorization to three different datasets representing employee income, monthly product sales, and customer complaints to assess its impact on mitigating the effects of outliers. In each case, extreme values were adjusted to a more reasonable range, resulting in more reliable statistical results and better representation of the underlying data distribution. The analysis demonstrated that Winsorization effectively reduced the influence of outliers on central tendency and variability measures, making the data more representative of the majority. The study highlights the importance of data cleaning techniques like Winsorization in business contexts, ensuring more accurate analysis and informed decision-making. The findings also suggest that further research is needed to explore optimal levels of Winsorization and its application in larger, more complex datasets. This research contributes to the understanding of how data preprocessing can improve the integrity of statistical analyses in various business environments.

In this project, I explored the spectral properties of the Kondo lattice model using MATLAB’s symbolic computation tools. By constructing and diagonalising a 9x9 Hamiltonian matrix, I analysed the interaction between conduction electrons and a magnetic impurity, a hallmark of the Kondo effect. The eigenvalue analysis revealed a non-degenerate ground state at (λ = 0) , representing a fully screened impurity spin, while higher energy levels indicated magnetic excitations within the system. Through this work, I developed a deeper understanding of how symbolic and numerical methods can be used to study strongly correlated electron systems. This project not only strengthened my grasp of quantum many-body theory but also enhanced my computational and analytical skills for future research in condensed matter physics.

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.

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.