FACULTY OF PHYSICAL SCIENCES

The supervision of undergraduate final year projects constitutes a critical component of higher education, as it directly influences the quality of the student's capstone experience. So far the traditional methods adopted, such as physical meetings, email chains, and paper-based tracking, have proven to be inadequate, resulting in communication gaps, missed deadlines, and inefficient progress tracking. This project emphasizes the need to adopt a centralized, mobile-first solution over fragmented tools like general-purpose chat apps or complex Learning Management Systems (LMS). Taking into consideration the specific constraints and context of the University of Benin, Department of Computer Science, the method proposed by this project is a Mobile-Based Project Supervision System. However, this project focuses on only the design and high-fidelity prototyping of this automated system, providing the "output on mobile" as a "demo model." The design details a system where students can view their progress on a dashboard, track submission statuses, receive feedback, and communicate directly with their supervisor. This project makes use of a User-Centered Design (UCD) and Object-Oriented approach to system analysis and design; use case, class, and state machine diagrams were used to represent designs for the system architecture. These designs serve as the framework for a system that can handle the whole process of project supervision, from initial tracking to final feedback.

This study investigates the susceptibility of students at the University of Benin (UNIBEN) to phishing attacks and examines how to promote sustainable, safe email usage within the academic environment. Employing a mixed-methods research design, the study collected quantitative data through structured surveys and qualitative insights from interviews, focus group discussions, and document analysis. The findings reveal that while students have a basic awareness of phishing, their understanding is superficial and they often rely on visual cues, which increases their vulnerability. Additionally, the current email security measures at UNIBEN—such as basic spam filters and password-based authentication—prove insufficient against sophisticated phishing tactics. The qualitative data further indicate that a lack of practical, hands-on cybersecurity training contributes significantly to the risk, with many students calling for interactive training and simulated phishing exercises. Based on these insights, the study concludes that enhancing both technical security measures and cybersecurity education is essential to reduce phishing susceptibility. Recommendations include implementing comprehensive, practical training programs, upgrading security protocols with multi-factor authentication, establishing formal incident reporting systems, and fostering a proactive cybersecurity culture. These steps are expected to strengthen the overall security of UNIBEN’s email system and promote a resilient academic environment.

This study investigates the application of Exponential, Weibull, and Gamma distributions in modeling lifetime data. The primary objective is to compare these distributions in real world survival analysis and reliability modeling. The study utilizes secondary data from published research, including survival times of head and neck cancer patients and waiting times of bank customers .
Maximum Likelihood Estimation (MLE) was employed to estimate distribution parameters, and model comparisons were performed using Akaike Information Criterion (AIC) and Bayesian Information Criterion (BIC) to determine the best fitting distribution.

Findings reveal that while the Exponential distribution provides a simple model for constant failure rates, the Weibull distribution offers greater flexibility in modeling varying failure rates. The Gamma distribution demonstrates robust applicability in complex survival data. Results indicate that the Weibull and Gamma distributions provide superior fits in most real world cases.
This study contributes to the field of survival analysis and reliability engineering by providing insights into selecting appropriate lifetime distributions for different applications. The findings have practical implications for fields such as healthcare, engineering, and risk assessment, where accurate lifetime modeling is crucial for decision making.

As society develops and human motives and relationships change, so does the mission to control
crime and undermine law and order. A perfect society is governed by laws and rules that are all
agreed upon and have quantifiable consequences for any member of the community found to have
floated any specific legal infrastructure unit. Any society's legal framework is complemented by
security tools that carry out the law in an effort to preserve law and order. The legal infrastructure
is the phrase that describes how crucial it is for law enforcement officials to act or defend the
shared interests of people in society. Additionally, it outlines the human rights and civil obligations
of society's members. It is believed that members of the public will notify the appropriate civil and military
defense tools of any incidence involving a breakdown of the constitution and order. In order to arrive at a more
accurate and rational judgment, reported issues are also thought to be carefully collected,
examined, and observed. All of the evidence is gathered, safeguarded, and examined prior to the
accused being charged in court and facing legal action. Without fear or favor, investigations are
carried out. The legislation states that the due prevention procedure used during a criminal
investigation is a crime in and of itself.

Many elderly individuals face challenges in remembering to take their medications as prescribed, which often leads to health problems. This paper presents the design and implementation of a medical reminder system tailored specifically for the elderly. This system aims to address the challenges faced by the elderly population in adhering to complex medication regimens and timely appointments. The proposed system leverages a combination of hardware and software components to provide timely reminders, medication management features, and emergency contact capabilities. The hardware components include a user-friendly interface, a robust communication module, and sensors for monitoring vital signs. The software component encompasses a user-friendly interface, a comprehensive medication management system, and a secure communication platform. The system is designed to be easily customizable to accommodate individual needs and preferences, ensuring its adaptability to diverse elderly populations. The evaluation of the system demonstrates its effectiveness in improving medication adherence, reducing medication errors, and enhancing overall healthcare management for the elderly.

Electronic prescription (e-prescribing) systems, which promise improved patient safety, increased productivity, and a reduction in pharmaceutical errors, have revolutionized the healthcare industry by replacing traditional paper-based prescriptions. By allowing medical professionals to electronically communicate prescriptions directly to pharmacies, e-prescribing lowers the risk of human error and unreadable handwriting. Research has demonstrated that this digital strategy improves workflow and patient care in addition to streamlining the prescription procedure (Baruah et al. 2020; Samad et al. 2021)

Groundwater pollution from improper waste disposal is a growing environmental and publichealth concern in rapidly urbanizing regions such as Benin City, Nigeria. The widespread use of unlined, open dumpsites without leachate control has intensified the infiltration of decomposed organic and inorganic wastes into the subsurface, resulting in gradual deterioration of groundwater quality. This study investigates the influence of leachate from two major dumpsites on groundwater in the Saint Saviour area of Ikpoba-Okha LGA, where a dense population relies heavily on groundwater for domestic use. Groundwater samples were collected at radial distances of 100 m, 200 m, and 300 m from Dumpsite 1 and Dumpsite 2. Leachate samples were obtained directly from seepage zones within the waste cells. All samples were analyzed following APHA standard procedures. In situ measurements included temperature, pH, electrical conductivity (EC), total dissolved solids (TDS), and salinity. Nutrient parameters—nitrate, phosphate, and sulphate— were determined using UV-Vis spectrophotometry. Heavy metals (Pb, Cd, Cr, Ni, Zn, Fe, Cu, Mn, Ca, Mg, Na, K) were analyzed through acid digestion (leachate) and Atomic Absorption Spectrophotometry (AAS). Results were compared with WHO (2017; 2021) and SON (2015) drinking-water standards. Findings indicate a clear spatial gradient of contamination: pollutant concentrations were highest within the dumpsites and decreased with distance. Leachate exhibited extremely high EC (up to 6,888 µS/cm), elevated TDS (>3,300 mg/L), high salinity, and pH values ranging from slightly acidic to neutral (4.70–7.46). These patterns reflect the breakdown of organic refuse, plastics, detergents, metals, and other waste materials. The acidity promotes metal solubility, increasing the mobility of toxic elements. Nutrient concentrations were elevated near the dumpsites, with nitrate (0.481–0.585 mg/L), phosphate (0.429–1.584 mg/L), and sulphate (0.070–1.538 mg/L), indicating early nutrient enrichment from sewage, food waste, and detergents. Though below WHO limits, these values suggest a potential progression toward eutrophication and long-term ecological stress. Heavy metals provided the most significant evidence of contamination. Lead (Pb) reached 0.70 mg/L—over sixty times the WHO guideline of 0.01 mg/L. Cadmium (0.01 mg/L), chromium (0.10 mg/L), and nickel (0.06 mg/L) exceeded or approached recommended limits. Their sources include batteries, electronics, metal scraps, plastics, and paints commonly found in municipal waste. These metals are persistent, nonbiodegradable, and pose severe health risks such as neurological damage (Pb), renal dysfunction (Cd), carcinogenicity (Cr⁶⁺), and respiratory disorders (Ni). Iron levels (up to 2.50 mg/L) exceeded aesthetic limits, affecting taste and appearance of water. Overall, results show that the shallow aquifers of Saint Saviour, characterized by the permeable sandy soils of the Benin Formation, are highly vulnerable to leachate migration. The findings align with previous studies highlighting groundwater deterioration around unregulated dumpsites in developing urban centers. This study underscores the urgent need for improved waste-management policies, groundwater monitoring, and the prevention of prolonged human exposure to contaminated water sources.

Sales forecasting and recommendation systems have become essential tools for businesses seeking to optimize inventory management, enhance customer experience, and maximize revenue. This project focuses on developing a machine learning-based sales forecasting and recommendation system to analyze historical sales data, predict future trends, and provide personalized product recommendations. The forecasting component leverages time series analysis and deep learning techniques such as Long Short-Term Memory (LSTM) networks and ARIMA models to predict future sales with high accuracy. The recommendation system utilizes collaborative and content-based filtering to suggest products tailored to customer preferences. The system is implemented using Python, with data preprocessing, feature engineering, and model training conducted using libraries such as TensorFlow, Scikit-Learn, and Pandas. The recommendation engine is integrated into an interactive user interface that enables businesses to gain insights into customer behavior and optimize their marketing strategies. Through extensive testing and evaluation, the system demonstrates improved forecasting accuracy and enhances the user experience by providing intelligent product recommendations. This project contributes to the field of e-commerce analytics by offering a data-driven solution to boost sales performance and customer engagement

This study looks at how AI-powered chatbots affect student academic performance. It focuses on how these systems impact learning efficiency, engagement, and overall outcomes. As artificial intelligence becomes more common in education, chatbots are increasingly used as virtual learning assistants. They provide instant feedback, personalized study support, and interactive experiences. The research uses both quantitative and qualitative methods to evaluate students’ perceptions, usage patterns, and performance data before and after interacting with chatbots. The findings show that AI chatbots significantly improve students’ understanding of course materials, encourage self-paced learning, and increase access to academic resources. However, the study also points out challenges, such as students becoming too reliant on automated responses and limited development of critical thinking skills. In conclusion, the research emphasizes that while AI-powered chatbots can be effective educational tools, their benefits are greatest when combined with traditional teaching methods and guided human interaction.

This study evaluated the physicochemical and microbiological quality of borehole water from five student-dominated areas in Ekosodin Village, Benin City, to assess compliance with World Health Organization (WHO) and Nigerian Industrial Standard (NIS)
guidelines. Standard analytical methods were employed to determine key physicochemical parameters, major ions, heavy metals, and microbial indicators Most physicochemical parameters, including turbidity (0.98 - 4.38 NTU), TDS (10.32 - 92.27 mg/L), Conductivity (16.13 - 144.10 µS/cm), hardness (0.15 - 2.90 mg/L), sulphates (3.0 - 4.61 mg/L), nitrates (2.87- 8.94 mg/L), and nitrites(0.01- 0.02 mg/L), were within permissible limits of 5NTU, 500mg/L, 1000µS/cm, 500mg/L, 250mg/L, <50mg/L and 3mg/l respectively. However, pH values at Edo Street (5.77) were below the recommended range of 6.5-8.5, and water temperatures exceeded the 25 °C guideline in most locations. Concentrations of cadmium (up to 0.03 mg/L) and lead (up to 0.03 mg/L) exceeded permissible limits (0.003 mg/L and 0.01 mg/L, respectively), suggesting potential toxicological risks. Microbiological assessment indicated elevated heterotrophic bacterial counts (3.00– 76 CFU/mL) and fecal contamination, making the water unsuitable for direct consumption. The findings from this study underscore the need for regular water quality monitoring, appropriate treatment measures, and improved borehole management to safeguard student health in Ekosodin Village.