FACULTY OF PHYSICAL SCIENCE

Face detection and recognition are computer vision techniques used to identify and locate human faces within digital images or video frames, and to subsequently analyze and verify the identity of individuals based on their unique facial features. Some of the algorithms used for implementing face detection and recognition are Local Binary Pattern, Histogram of oriented Gradient, Linear discriminant analysis, and convolutional neural networks with classifier such as Support Vector machine, e.t.c. Face detection and recognition system has become relevant in security and access control, automated Attendance tracking, user authentication, Biometric identification, Human Computer Interaction and many other areas. This project is implemented using python programming language, because python programming language allows programmers flexibility, therefore is of no threat to write- ability, readability and reliability, it has it libraries for the implementation of the project. And the test run of the project result is contained in Appendix D

Globally, health care sector is the pivot and integral part of human lives. Thus, any error committed in the clinical services might leads to defect or termination of life. Recently, information and Communication has been used extensively to improve the various operations and services in the field of the health care service. Patient appointment scheduling with the Doctor is one of the clinical services that have been automated. In developing counties like Nigeria, the clinical system is faced with plethora of issues. These include: long waiting of patient, queues, congestion of patient over a long period of time without been attended to. This paper focuses on developing a system to improve upon the efficiency and quality of delivering. The proposed algorithm eliminates the manual system of registration and its drawbacks, by implementing a simple round robin (RR) architecture in real time system which introduce a concept of assigning different time quantum to different funds of RR scheduling algorithm. functionalities of registration of patient data in the database and scheduling of patient. The system is designed to enable a more efficient patient clinic section with an improved organization setting by maximizing the throughput. Minimize the time between a patient and doctor, the time spent in the waiting queue will be minimized. minimizing response to a patient, increasing the number of patients attended to and also Improve work efficiency.

The Dynamics of infectious diseases are vital in the disease control in populations. The mathematical methods that describe these diseases models often are insoluble hence, the need for numerical approximations. Stage two Runge-Kutta methods are used to integrate the system of differential equations that evolves in the model formulation of the infectious diseases being studied.
The stability analysis of Runge-Kutta method is done using boundary bars plot. The solution and plots are carried out using MATHEMATICA program.

The Niger Delta Basin is one of the most productive hydrocarbon regions globally, yet its complex depositional history, structural variations, and diagenetic processes present challenges 9 for reservoircharacterization and hydrocarbon exploration. This study integrates lithofacies analysis, mineralogical evaluation using X-ray diffraction (XRD), and petrophysical assessment to enhance the understanding of reservoir quality and hydrocarbon potential in the EstyWil-1 Well, located in the Northern Delta Depobelt. Lithofacies analysis indicates a transition from fluvial-deltaic to deep marine depositional environments, characterized by alternating layers of sandstone, shaly sandstone, sandy shale, and thick shale. Sandstone-rich intervals, particularly within distributary channels and delta-front facies, exhibit high porosity (25-32%) and permeability (500-1500 mD), making them favorable for hydrocarbon accumulation. In contrast, shaly interbeds and deep marine shale sequences serve as barriers that influencefluid flow and hydrocarbon entrapment. Mineralogical analysis reveals a predominance of quartz, along with kaolinite, illite, chlorite, and feldspar, all of which impact reservoir quality. High quartz content enhances porosity, whereas clay minerals, particularly illite and chlorite, contribute to permeability reduction. The presence of pyrite and carbonate minerals in deeper sections suggests reducing conditions, which favor organic matter preservation and potential hydrocarbon generation. Petrophysical analysis, incorporating gamma-ray, resistivity, neutron-density, and sonic logs, confirms the presence of hydrocarbon-bearing zones with low water saturation (Sw <40%) in productive intervals. Structural interpretations highlight the role of growth faults and rollover anticlines as primary trapping mechanisms that enhance hydrocarbon accumulation. By integrating sedimentological, petrophysical, and mineralogical data, this study provides a more comprehensive approach to reservoir characterization. The findings contribute to improved exploration strategies, optimized reservoir management, and enhanced oil recovery (EOR) techniques within the Niger Delta Basin.

This study examined the use of Electrical Resistivity Tomography (ERT) to investigate the subsurface lithology in Ugbogiobo, Ovia North East Local Government Area of Edo State. The research aimed to determine the variation in subsurface materials and identify geological structures that may influence groundwater potential, engineering suitability, and environmental conditions in the study area. The study adopted a geophysical survey approach using the ERT method, where resistivity measurements were taken along selected profiles to generate two-dimensional subsurface images. Data obtained were processed and interpreted using standard inversion software to produce resistivity models that reveal variations in lithological units. The results showed distinct subsurface layers characterized by varying resistivity values, indicating differences in soil composition, moisture content, and degree of weathering. The near-surface layer was generally composed of lateritic and sandy materials with relatively high resistivity values, while deeper zones exhibited lower resistivity indicative of clayey formations and possible water-bearing zones. The study also identified potential fracture zones and areas of structural weakness, which are important for groundwater accumulation and civil engineering planning. The study concludes that Electrical Resistivity Tomography is an effective non-invasive geophysical tool for subsurface investigation in Ugbogiobo. It provides reliable information on lithological variations and groundwater potential. The research recommends the integration of ERT surveys in site investigation studies before construction and borehole drilling to improve decision-making, reduce failure rates, and enhance sustainable groundwater development in the area.

2-D survey of a part of Ugbogiobo community and its environs has been carried out successfully and this research has helped in providing information about the subsurface of the study area. This information is of utmost importance as it gives the necessary constituents of the profile of the study area. The subsurface of a study area is related to various geological parameters such as mineral and fluid content, porosity and degree of water saturation in the rock. Major resistivity structures were delineated in both profile which is seen to be generally characterized by moderate resistivity values, at the top layers we can inferred from the low resistivity that is characterize by Clayey and Alluvium soil having a resistivity range between 200 Ωm– 800 Ωm profile 7 and 8 respectively. The major mineral occurrence in profile 7 and 8 are majorly compose of sedimentary Rocks ranging from Limestone, Shale and Sandstone with a resistivity range for Limestone between 2000 Ωm– 3000 Ωm, Shale with a resistivity range 3200 Ωm– 4000 Ωm Sandstone between resistivity range 4000 Ωm– 5000 Ωm, it will be noticed from the profiled line that all inferred mineral types fall between a depth of 2 m to 39 m. which form the lithological mineral occurrence of the both profiles. The development of two dimensional inversion resistivity algorithms has aided the processing and interpretation of complex data. Due to the inferred rock types and mineral occurences (Alluvium, Clayey soil, limestone, sandstone, and clay) gotten from the lithological interpretation of the 2-D data inversion, it can be concluded that the lithology of the study area is good for engineering purpose and construction work.

Hospital readmissions create challenges for healthcare systems, increasing costs and putting pressure on resources. This project introduces a machine learning-based system designed to predict patient readmissions, helping medical personnel and hospitals take early action to improve patient care and manage resources more effectively. By analyzing electronic health record (EHR) data, the model assesses a patient’s risk and provides explanations for key factors influencing the prediction. The system was trained on a dataset containing patient details such as age, medical history, lab results, and past hospital visits. It was developed using Python for machine learning, Express.js for the backend, and TypeScript with React for the frontend, ensuring smooth data processing and an easy-to-use interface. Strong security features like authentication, encryption, and error handling were added to protect patient information. The result shows that the model was able to achieve 63.87% accuracy, with recall scores of 72% and 55% in different areas. These results highlight the model’s ability to predict readmissions while also showing areas where improvements can be madethrough better data processing and tuning. By using predictive analytics, this system helps healthcare professionals make informed decisions, reduce avoidable readmissions, and improve hospital efficiency. This project demonstrates how AI-powered solutions can transform healthcare by enabling proactive patient management and better decision-making.

This research will undertake a comprehensive statistical analysis of Nigeria's
Exchange rate spanning a decade, with a focus on estimating Autoregressive (AR) models using a prominent statistical methods: the Yule-Walker method. The study aims to provide statistical insights into the underlying dynamics of Nigeria's economic performance during this period. The research will commence by delineating the statistical framework of AR models, which offer a statistical representation of a time series based on its past values. Subsequently, the Yule-Walker method will be introduced, a statistical technique leveraging autocorrelation functions to estimate AR model parameters. The statistical properties of Yule-Walker estimators will be elucidated in the context of Nigeria's Exchange rate data. In contrast, the Least Squares method will be presented as an alternative statistical approach, characterized by its objective to minimize the sum of squared prediction errors. A statistical framework for the least squares estimators will be outlined, providing insights into the statistical properties of parameter estimates and their significance in explaining variations in Nigeria's Exchange rate. The core of the research involves the statistical analysis of Nigeria's Exchange rate time series data over the forty-three year period. The Yule-Walker method will be applied to estimate AR models tailored to the Exchange rate data. The statistical comparison will be based on goodness-of-fit statistics, such as the Akaike Information Criterion (AIC), to evaluate the models' adequacy in capturing the statistical patterns within the Exchange rate dataset.

The internet has made the flow of scheduling appointments easy and more convenient across various platforms, including mobile phones and personal computers (PCs). Nowadays, businesses and service providers make use of appointment management systems to deliver services to customers efficiently. In most barbershops, the traditional system of queuing and waiting is still practiced; however, this approach has several limitations and lacks efficiency, as barbers may struggle to properly manage and satisfy clients. This project develops a mobile-based barbing appointment management system designed to simplify service delivery by addressing issues such as appointment delays, missed bookings, and customer no-shows, which often result in losses for barbers. The system was designed using models such as use case diagrams, sequence diagrams, logical models, and data flow diagrams. Flutter was used for the front-end development, while PHP and MySQL were used for the back-end. The project provides a platform where users can conveniently schedule appointments at their preferred time using mobile devices. A thorough evaluation of the system was conducted to ensure usability, functionality, and performance in line with its intended purpose. The adoption of this system by both barbers and customers would enhance time management, improve service efficiency, and provide a more convenient experience for all users.