FACULTY OF PHYSICAL SCIENCES

Attendance tracking in classes is a very important activity in any institution, and taking attendance of students using facial recognition is a more efficient and accurate method than the traditional methods which includes paper- based and roll call methods. The facial recognition system is an application of computer vision that can perform two major tasks of identifying and verifying a person from a given database. Facial recognition proves to be more effective in taking attendance than the traditional method, which is inaccurate, time- consuming and vulnerable in most cases of large class environments. This system is designed with a login page for authentication, it also provides a mailing platform where the attendance will be sent to the school authority for record keeping. This study develops a deep-learning-based facial recognition system used to detect the face of students in a class environment for the sole purpose of taking their attendance records.

Web applications have become a mainstay of modern life, powering significant services such as e-commerce, online banking, and health systems. As a result of their pervasiveness and sophistication, however, they are enticing targets for cyber-attacks. Flaws in web applications, such as compromised access control, injection flaws, and insecure design, can lead to significant consequences, such as data breaches, service disruptions, and unauthorized data access. The Open Web Application Security Project (OWASP) Top 10 is a comprehensive list of the most critical web application vulnerabilities, which can be utilized as a foundation for comprehension and prevention of these threats. This study explores the evolving web application vulnerability environment, such as current trends including API vulnerabilities, single-page application (SPA) vulnerabilities, and cloud-native application threats. It discusses established techniques of vulnerability assessment, i.e., black-box, white-box, and gray-box testing, and industry best practices and standards, e.g., the Penetration Testing Execution Standard (PTES) and NIST SP 800-115. In addition, the study is comparable to familiar tools and platforms applied in vulnerability analysis, including Metasploit, Burp Suite, and OWASP ZAP, and establishes gaps in past research, including the need for increased automation, AI implementation, and specialty tools accommodating emerging technologies like IoT and serverless architecture. Through a review of these topics, the current research identifies the necessity for proactive management of vulnerabilities and ongoing development of techniques for examining vulnerabilities. The findings strive to offer insights that assist in the creation of more secure and agile security products that can aid organizations in protecting their web applications more effectively against emerging cyber threats.

Air and water pollutants are often treated with activated carbon. This project inspects this sorbent for heavy metal or inorganic compound removal. Production of activated carbon. Physical and chemical activation and raw material pyrolysis are discussed. Biochar has a surface areaof226.380m2/g and a modified Biochar of 248.650m2/g, pore volume of 0.134cm3/gand0.150cm3/g, pore size of 2.08nm and 2.118nm distribution, and chemical surface structure. Biochar and modified biochar FTIR spectra were analyzed for functional groups. Spectrum intensity enhanced with acid alteration. Peaks at 3200-3550 cm−1 indicate O-H stretching vibrations, as well as C=C stretching vibrations of alkane functional group at 1600-1660cm−1and -=CH3 group at 1430-1470 cm�� Modified biochar improved wastewater treatment and soil remediation by increasing specific surface area, pore volume, and pore size. Scanning electron microscopy revealed a rough modified biochar structure, increasing adsorption sites. Modification increased FTIR intensity, indicating functional group changes. Adsorption testing indicated that modified biochar eliminates Pb and Cd from water. Adsorption efficiency depended on initial concentration, pH, dose, and contact time. The Langmuir adsorption isotherm model fit the data better than the Freundlich model, implying monolayer adsorption onto a homogeneous surface. These studies demonstrate that acid-modified biochar can remove heavy metals from water.

This study was undertaken to determine the quality of surface, harvested and ground water of IGBANKE in Orihionmwan area of Edo State, southern Nigeria. Five water samples were collected from harvested water (well), four from surface water (river), and one from bore hole. The result of the analysis showed that appearance (clear), Calcium (0.185-0.445), Manganese (<0.05), Magnesium (0.106-0.124), Nitrate (0.004-0.011), Iron (0.008-0.109), Phosphate (0.000-0.006), Zinc (0.006-0.0015), Cu (<0.05), Cr (0<0.05) , Ammonium-N (0.003-0.008), Sulphate (0.006-0.026), Electric conduction (28-88) and pH (5.42-5.87). The results gotten were compared with the World Health Organization standard (W.H.O) 1995, to determine quality of the water. From the result, all the parameter falls within the permissible limit for drinking water set by World Health Organization (W.H.O.)

Online Auction management system is a web-based application which will help users to buy or sell item; they can trade anything they want by posting adverts. This application developed will allow students of the University of Benin to post their products for auction; bidder can register and can bid for any available product. The system was developed using HTML, CSS, JavaScript, and PHP for the program. The system provides some basic features, a friendly GUI, easy registration and bidding processes, etc. With the new system, students can easily auction items and sell to the highest bidder, this will bridge the gap between students that have items to sell and companies looking for used but in good condition items to buy

The exponential rise in digital banking transactions has heightened the risk of fraudulent and anomalous bank transfers, necessitating intelligent and automated mechanisms for its detection. Traditional rule-based systems often fail to capture evolving fraud patterns, motivating the adoption of machine learning-based anomaly detection techniques. This study aims to develop and evaluate robust unsupervised learning models for detecting anomalies in bank transaction datasets. Specifically, the research applies three state-of-the-art algorithms; Isolation Forest, Local Outlier Factor (LOF), and One-Class Support Vector Machine (OCSVM) to identify irregular transaction behaviors indicative of potential fraud. The methodology involves comprehensive data preprocessing, scaling, encoding, and feature selection to improve model learning. Real-world bank transfer datasets from kaggle were utilized, for this training. Each model’s performance was assessed using standard evaluation metrics, including; Silhouette Score, Anomaly Ratio, and Average Decision Score. Results show that OCSVM performed best (Silhouette = 0.635, strong decision scores), reliably flagging about 5% of records as anomalies. Analysis of flagged transactions revealed consistent patterns high transaction amounts or balance-change ratios, very short or very long intervals between transactions, and activity at unusual hours making the alerts interpretable and practically useful.

This study statistically examined the impact of choice of study programme, gender, age at admission, and ethnic affiliation on students’ academic performance in the University of Benin. The main objective was to determine the extent to which these variables influence students’ academic outcomes. A quantitative research design was adopted, and data were collected from undergraduate students across various faculties using structured questionnaires. The data were analyzed using the Statistical Package for the Social Sciences (SPSS) through descriptive statistics, chi-square test of independence and multiple regression analysis.The results revealed that the first choice of study programme had a significant influence on students’ academic performance, indicating that students who were given their first choice performed better than those who were placed in programmes by external influence. However, the demographic variables—gender, age at admission, and ethnic affiliation—did not show statistically significant effects on academic performance. This implies that while demographic characteristics may influence students’ experiences, they do not independently determine academic achievement in the University of Benin based on this study. The study concludes that academic performance is more strongly influenced by students’ motivation and programme alignment as encapsulated in their first choice course of study than by demographic differences.

Several methods have been employed to calculate the mechanical properties of GaN with reliable results. In this work, we employed the energy strain method from first-principles calculation to review the structural and mechanical properties of GaN. From our results, we observed that there is agreement in some of the elastic constants when compared with literature review.

Laplace transforms involves the mathematical study of Laplaces, or using it to obtain solution of differential equations. The formation of differential equations is, of course, a common phenomenon that occurs whenever the demand to solve systems of differential equations. Principles regarding the various approaches to it, which may be made frequent in technology like the transfer system used. In this project work, we look at some different system of different equations and consider such procedure as how long it takes on contribution to technology to pass through the system of introduction to application. The kinds of method with which we are all familiar in mathematical field, mechanics. However, the result of Laplace theory have equal if not more important applications in computer such as Data transfer system, Data and signal impulse response and needing to join a Laplace system to be considered. Hence the need in technology is of another usefulness

This research delves into the elements that lead to the creation of gullies, with a particular emphasis on the soil attributes at the gully location of the University of Benin. Soil samples were extracted from three distinct points at the site. A series of examinations were performed on these samples to ascertain their soil properties, encompassing sieve analysis, specific gravity, Atterberg limits, compaction, and triaxial tests. The results of the various tests reveals that soil samples had a medium liquid limit and relatively low plasticity for the Atterberg limits. There is a specific gravity range of 2.61 to 2.64 for the soil samples classifying them as organic soils according to classification system proposed by Bowles (2012). The compaction test revealed that soil retains a significant amount of moisture even when compacted to its densest state. The triaxial test revealed that the soil exhibits a relatively low friction angle coupled with substantial cohesion. And finally the sieve analysis shed light on the particle size distribution in the soil samples, which exhibit a reddish-brown coloration and are located below the A-line on the Unified Soil Classification System (USCS) chart, classifying them as silts. Known for their moderate infiltration rates, these silts contribute to increased surface runoff, which is a key factor in gully development.