FACULTY OF PHYSICAL SCIENCE

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.

Water, the essence of life and the most abundant substance on Earth, is a molecule composed of two hydrogen atoms bonded to one oxygen atom. It's a vital resource that shapes our planet's geography, climate, and biological diversity. Water's unique properties, such as its ability to dissolve substances, transport nutrients, and regulate temperature, make it indispensable for all known forms of life. Its cycle through evaporation, condensation, precipitation, and runoff supports ecosystems and human civilizations alike. The study of water encompasses its history, from the ancient seas where life originated, to its presence in the outer reaches of space. Science delves into its molecular mysteries and conservation efforts focus on sustaining this irreplaceable resource for future generation.

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 research investigates the impact of smoking, level of exercise, and weight on health outcomes. The research employs a cross-sectional research design to investigate the associations between smoking, level of exercise, weight, and health outcomes among adults residing in Uyo Local Government Area, Akwa Ibom State, Nigeria. The study population comprises 360 adults, with a sample size of 124 determined using Taro Yamane’s sample size determination formula. Data collection involves a self-administered questionnaire covering demographic information, smoking habits, physical activity levels, weight, height, and health status. Convenience sampling is utilized at various locations within each of the four clans in Uyo LGA, facilitated by trained research assistants. Statistical Package for Social Sciences (SPSS) is employed for data analysis, including regression analysis to determine the relationships between independent variables (smoking, lack of regular exercise, weight) and the dependent variable (health outcome).The analysis reveals significant associations between these lifestyle factors and health outcomes, highlighting the need for targeted interventions to mitigate their adverse effects. Findings indicate a moderate positive correlation between smoking and health outcomes, emphasizing the importance of smoking cessation interventions. Similarly, a significant positive relationship is observed between lack of regular exercise and health outcomes, underscoring the importance of promoting physical activity. Additionally, the analysis reveals a moderate positive correlation between weight and health-related quality of life, emphasizing the need for interventions to promote healthy weight management.

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

The "Design and Implementation of Duty Scheduler for Doctors and Nurses" project introduces a sophisticated system tailored to the specific scheduling challenges faced by healthcare institutions. This innovative system employs advanced algorithms and a user-friendly interface to automate and optimize the intricate task of creating duty rosters for medical staff. Taking into account staff preferences, legal compliance, and workload distribution, the system generates fair and efficient schedules. It offers customization options, real-time updates, and compliance management features to ensure seamless scheduling while improving staff job satisfaction and work-life balance. By reducing administrative burdens and enhancing resource allocation, this project aims to elevate the efficiency and effectiveness of healthcare operations, ultimately enhancing the quality of patient
care

Maternal healthcare remains a critical index of global public health, yet many healthcare facilities in developing regions still rely heavily on paper-based record systems. These traditional methods are prone to data fragmentation, physical damage, and inefficiency, often leading to delayed clinical decisions during critical periods of gestation. This project addresses these challenges through the design and implementation of a digital, cloud-based Pregnancy Wellness Record Management System. The platform is engineered to streamline antenatal data collection, improve communication between expectant mothers and healthcare providers, and ensure seamless continuity of care. The system was developed utilizing a three-tier software architecture. The frontend user interface was built using responsive frameworks to ensure cross-platform accessibility on both mobile devices and desktops, while the backend logic was implemented using a robust server-side framework integrated with a secure relational database. The application features specialized portals tailored for two distinct user roles: expectant mothers and clinical personnel. For patients, the system offers tools for tracking daily gestational vitals (such as blood pressure, weight, and blood sugar levels), a gestational age calculator, medication reminders, and automated appointment scheduling. For healthcare providers, the system delivers a comprehensive clinician dashboard featuring real-time vitals visualization, antenatal visit logging, immunization tracking, and automated alert flags that trigger when a patient's logged vitals deviate into high-risk clinical thresholds. To safeguard sensitive protected health information (PHI), robust security measures—including role-based access control (RBAC), end-to-end data encryption using HTTPS/TLS protocols, and secure database hashing—were integrated into the system's design. Evaluation of the implemented prototype demonstrated a significant reduction in administrative retrieval times for patient files and enhanced user engagement in maternal self-monitoring. The developed system offers a scalable, secure, and highly efficient digital framework capable of modernizing antenatal care delivery, reducing clinical workloads, and ultimately contributing to improved maternal and neonatal health outcomes.

Phishing has become a major cybercrime threat due to the growth of online transactions and digital communication. This project addresses the challenge by developing a web-based phishing detection system that uses machine learning and explainable AI (XAI) to analyze lexical, content-based, and host-based URL features for real-time classification. Built with Python, Flask, and an XGBoost model, the system achieved approximately 94.79% accuracy, demonstrating strong reliability and effectiveness. Its modular architecture includes a web interface for user input, a backend API for processing, a feature extraction module, and the XGBoost classifier. The XAI component provides clear explanations of predictions, improving user trust, awareness, and decision-making. The system was tested in both local and simulated cloud environments to verify scalability and stability. Overall, it offers a practical, transparent cybersecurity tool suitable for individuals and institutions, contributes to research on intelligent phishing detection, and provides a foundat

This was an empirical study to determine the usage and impact of social media on academic learning and performance of undergraduates of University of Benin students. To achieve this general objective, four research questions was raised and four null hypotheses formulated, which guided the study. A survey research design was used for the research. Questionnaire was used to generate primary data from a representative random sample of 150 students (100 representing 100 percent was correctly filled and returned). The students who participated in the survey were drawn from various faculties in the University. Descriptive statistics of frequency counts and percentages were used in analysing demographic variables and research questions while Chi-Square (X2) were used to test the stated hypotheses at a 0.05 level of significance. The results and contributions to knowledge include that the most common reason for social media usage to keep up with trending issues on news and sports as well as to access an incredible amount of valuable information on a variety of topic, also that there is a significant relationship between social media usage and student academic performance; a significant difference between male and female students usage of social media; and that age has a significant relationship with social media usage. It was recommended that parents and guardians should help to ensure that students utilize the benefits of social media and that scholars and opinion leaders should make effort to educate undergraduates to minimize time wastage on frivolities (chatting, cyber stalking, etc.) and other irrelevant engagements that are not of major importance in their academic lives.