FACULTY OF PHYSCIAL SCIENCES

Machine learning has become an important technology used in many areas such as healthcare, banking, education, and mobile applications. However, traditional machine learning methods usually require large amounts of data to be collected and stored in one central location, which can create privacy and security concerns. This project focuses on the design and implementation of a Federated Learning system for privacy-preserving machine learning. Federated Learning is a modern approach that allows multiple users or organizations to train a machine learning model together without sharing their raw data. Instead of sending personal or sensitive information to a central server, each participant trains the model on their local device and only shares model updates. This helps to protect privacy while still allowing the model to learn effectively. In this project, a federated learning prototype was developed using Python and machine learning tools. The system was designed with features such as secure aggregation and differential privacy to improve data protection and reduce the risk of information leakage. The performance of the system was tested using distributed datasets to simulate real-world situations where data is stored in different locations. The results showed that the proposed system was able to maintain a good level of model accuracy while protecting user privacy. It also demonstrated that federated learning can be a practical solution for organizations that need to use machine learning without exposing sensitive data. The study concludes that federated learning has great potential for institutions such as the University of Benin and other organizations in Nigeria that require secure and privacy-aware machine learning solutions.

Rice remains one of Nigeria’s most important staple crops, serving as both a major source of food and a significant contributor to national agricultural output. However, inconsistencies in production statistics and yield estimates have made it difficult to accurately assess the country’s progress toward self-sufficiency. This study, therefore, focuses on developing a rigorous mathematical framework for estimating and analyzing rice yield in Nigeria from 1990 to 2022. The research integrates statistical modeling and mathematical reasoning to provide a more objective and quantifiable understanding of yield dynamics, while addressing uncertainties associated with data collection, reporting errors, and environmental variability. The study utilizes secondary data from the Food and Agriculture Organization’s FAOSTAT database, which provides national figures on rice production and harvested area. The mathematical model adopts the classical yield equation � = �/� where � denotes yield (t/ha), � represents production (tonnes), and � is harvested area (hectares). To estimate the reliability of calculated yields, the propagation of uncertainty formula �� = � �� � 2 + 𝛿 � 2 was applied, allowing error terms in production and area to be combined mathematically. Statistical regression models (linear, exponential, and polynomial) were used to evaluate long-term yield trends and to test the hypothesis of yield improvement over time. In addition, stochastic simulation techniques and correlation analyses were introduced to capture the variability and interdependence between production and land-use parameters. Findings indicate that Nigeria’s rice yield followed a fluctuating but generally upward trend, rising from an average of 1.5 �/ℎ� in the early 1990s to about 2.8 �/ℎ� by 2022. The regression analysis revealed a statistically significant positive trend, confirming gradual improvements in productivity over the years. However, the propagated error analysis showed that yield uncertainties ranged between 5 –10% depending on data completeness and measurement precision. This highlights persistent limitations in the reliability of agricultural data collection systems. The study concludes that mathematical modeling provides a robust foundation for understanding agricultural yield trends and recommends the integration of error analysis and predictive modeling into national data reporting frameworks. By combining quantitative rigor with empirical agricultural data, the research establishes a replicable approach for improving the precision of yield estimation in Nigeria and other developing economies.

Medicinal plants represent one of the oldest forms of treatment, having been utilized for thousands of years across different cultures in traditional healing practices. They serve as a vital source of bioactive compounds that can be developed into pharmaceutical drugs (Rasool, 2012; Ogbeide et al., 2018). Across the world, many plant species grow abundantly, yet remain underutilized despite their potential medicinal benefits. This has led to a renewed interest in plant-based medicine, increasing demand for drugs derived from natural sources.

This research explores the photon (γ) as the gauge boson responsible for mediating the electromagnetic force, framed within Yang–Mills gauge field theory. The work investigates how U(1) gauge symmetry naturally gives rise to the electromagnetic field and explains the masslessness, spin-1 nature, and non-self-interaction of the photon as direct consequences of unbroken gauge invariance.

Through comparative analysis between Abelian and non-Abelian gauge theories, the study demonstrates that while Yang–Mills fields (SU(2), SU(3)) yield self-interacting gauge bosons, the Abelian U(1) symmetry of electromagnetism produces a linear, non-self-interacting field—the classical Maxwell equations emerging as a special case.

The findings confirm that the photon’s existence and properties are not empirical accidents but logical necessities of gauge principles. Ultimately, this project underscores the profound unity between electromagnetism and the broader framework of modern gauge theory, situating the photon as a fundamental manifestation of symmetry in nature.

Malaria remains a global health crisis, particularly in low-resource regions, where traditional diagnostic methods face challenges such as human error, resource constraints, and delayed detection. This project addresses these limitations by leveraging supervised machine learning (ML) to enhance malaria diagnosis and outbreak prediction. The motivation stems from the urgent need for scalable, accurate, and cost-effective solutions to reduce the disease’s burden, which claims over 600,000 lives annually. The objective is to develop robust ML models capable of automating malaria diagnosis using blood smear images and patient metadata while improving outbreak forecasting through environmental and epidemiological data analysis. Methodologically, the study employs supervised learning algorithms, including convolutional neural networks (CNNs) for image- based detection and random forests for tabular data. Datasets were preprocessed to handle class imbalance and missing values, followed by hyperparameter tuning and cross-validation to optimize performance. Results demonstrated that CNNs achieved 96% accuracy in classifying infected blood cells, outperforming traditional methods like microscopy. Random Forest models yielded 92% recall and 89% precision in predicting malaria risk from clinical data, highlighting their utility in early diagnosis. Additionally, stratified k-fold cross-validation ensured model generalizability across diverse datasets. This work underscores the transformative potential of supervised ML in malaria control, offering tools that enhance diagnostic speed, accuracy, and accessibility. By bridging technological innovation with public health needs, the project contributes to global efforts toward malaria eradication, particularly in endemic regions

Heterocyclic compounds are very widely distributed in nature and very abundant in plant andanimalproducts. They are included in many biochemical materials essential for life like nucleicacids(nucleotides), sugars and their derivatives, vitamin C and also, most members of vitaminBgroup(vitamin B6- pyridoxine). They are also found in application of diverse field such as agriculture,pharmaceutical and manufacturing industries. Researches have shown that heterocyclicnuclei givehigh chemotherapeutic values such as anti-malaria, anti-diabetics, anticancer and also act as aremedyfor the development of novel drugs. Imidazole containing moiety occupied a unique position in organic compounds. It is a five-memberednitrogenous heterocyclic moiety that has three carbons, two nitrogens, four hydrogen atoms, andtwodouble bonds having general molecular formula of C3H4N2. It is also known as 1,3-diazolebecauseof the nitrogen atoms present at the first and third positions (non–adjacent position) of thering, onenitrogen bear a hydrogen atom as the pyridine structure, and the other is called pyrrole typenitrogenand position four and five are equivalent. It formed the basis of many therapeutic natural productssuch as histamine, purine, histidine among others The research work focuses on the synthesis, characterization and antimicrobial activitiesofthederivatives of some new imidazole derivatives. Wallach synthesis was used to synthesize 1-methyl-5-chloro-imidazole which was nitrated to give 1-methyl-4-nitro-5-chloroimidazole. H2Sgas wasbubbleinto the solution 1-methyl-4-nitro-5-chloroimidazole in the presence of sodiumethoxide whichgave62% yield of 1-methyl-4-nitroimidazole-5-thiol (compound 4). Compound 4 was coupledwithbenzoylchloride and phenylethylbromide to produce compound 4B and 4A respectively. 1-methyl-4-nitroimidazole-5-thiol underwent oxidative chlorination to yield an unstable intermediateof1-methyl-4-nitroimidazole-5–sulfonyl chloride. All attempt to stabilized 1-methyl-4-nitroimidazole-5–sulfonyl chloride failed. With the sulfonyl chloride at position 5 on imidazole ring, it wasthencoupled with proline, proline methylester, biphenylhydroxide, 2-aminophenol and N- ethylanilinewhich yielded 1-methyl-4-nitroimidazole-5- lphonylproline,1-methyl-4-nitroimidazole-5-sulphonylprolinemethylester, 1-methyl-4-nitroimidazole-5-sulphonyl(2-hydroxylbiphenyl), 1-methyl-4-nitroimidazole-5-sulphonyl(2-aminophenol),1-methyl-4-nitroimidazole-5-sulphonyl(N- ethylaniline). The 4-nitro-2-methyl-1-H-ethanolyimidazole was chlorosulfonated with thionyl chloride, andthencoupled with aniline and 2,4 dichloroaniline to give 1-chloroethane-2-[thioacylaniline-4-nitroimidazole and 1-chloroethane-2-[thioacyl-(2,4-dichloroaniline)]-4-nitroimidazole respectively.The synthesized compounds were purified and confirmed using thin layer chromatography(TLC) and column chromatography, FT-IR, 1HNMR and 13CNMR spectral and were evaluatedforAnti-bacterial activity against Staphylococcus aureus, Pseudomonas aeruginosa, Escherichiacoli,Klebsiella pneumoniae and Bacillus subtilis and Anti-fungal activity against CandidaalbicansandAspergillus niger

This study presents an integrated approach to reservoir delineation and prospect identification in the Indraz Field, onshore Niger Delta, utilizing 3D seismic data and well logs. The primary objective was to construct a robust static reservoir model by synthesizing structural interpretation with quantitative petrophysical and seismic attribute analysis. The methodology encompassed a comprehensive workflow within the Schlumberger Petrel software environment, including seismic to-well tie, fault and horizon mapping, depth conversion, and the extraction of key amplitude-based attributes (RMS, Maximum, Average, and Mean Amplitude). Well log analysis identified a laterally continuous reservoir sand, with fluid diagnostics confirming hydrocarbon presence in specific wells. Structural interpretation revealed a complex system of listric growth faults forming a fault-bounded anticlinal closure. Crucially, the integration of depth structure maps with seismic attribute anomalies identified three distinct prospects (A, B, and C). The analysis demonstrates a classic case of hydrocarbon bypass, where the primary accumulation (Prospect A) is located at the structurally highest crest of the anticline, separate from the existing well control which encountered the marginal accumulations (Prospects B and C). The prospects were ranked based on the conformance of strong amplitude anomalies to structural closure, with Prospect A being the highest-priority target. This research concludes that the integrated application of seismic structural and attribute analysis is indispensable for accurate reservoir characterization and de-risking exploration targets in the structurally complex settings of the Niger Delta, providing a clear strategy for future field development

"The Niger Delta is a complex and dynamic sedimentary basin located offshore Nigeria. The Delta
Field within this region has been a significant hydrocarbon producer. Understanding the sequence stratigraphy of this area is crucial for exploration and production activities. This study aims to analyze the stratigraphic framework, depositional environments, and sequence architecture of the Delta Field using well logs, seismic data, and core samples. By integrating these data, we aim to identify the key stratigraphic units, depositional sequences, and their correlation across the field. This research will provide valuable insights into the geological history and hydrocarbon potential of the Niger Delta, aiding in future exploration and reservoir characterization efforts."

Five (05) clay samples were obtained from various locations in Uzebba and its environs with the aim of ascertaining its mineralogical and geochemical compositions, qualifying the clay samples and determining its economic potentials. The results obtained showed the predominance of SiO2 (62.23 to 77.48 wt.%), Al2O3 (13.50 to 18.88wt.%), Fe2O3 (0.83 to 13.83 wt.%), TiO2 (2.15 to 3.05 wt.%), CaO (0.20 to 1.05wt.%) with traces of MnO (0.02 to 0.03 wt.%), P2O5 (0.03 to 0.23 wt.%) and K2O(0.87-0.39wt.%), respectively. The mineralogical results showed that quartz was predominant in all the samples (51.00 to 64.00%) followed by Orthoclase (4.20 to 20.00%) and Kaolinite (11.00 to 16.80%). From the results obtained it shows that the Uzebba clay is kaolinitic in nature. The clay minerals found in the study area is suitable for ceramic, glass, paint, paper and refractory bricks production.

The result for proximate analysis on Ginger( Zingiber officinale) showed carbohydrates 59.97± 0.41, crude fibre 3.65± 0.05, Ash 6.23 ± 0.10, Crude fat 8.66±0.35, protein 11.90 ±0.15 and moisture 9.47±0.09. From the results, ginger has high carbohydrate content and contain low value of moisture indicating a longer shelf life.The Phytochemical screening of ginger showed the presence of alkaloid, flavonoid, phenolics, terpenoids, saponin, glycoside,steroids and the absence of tannin, phlobotanin and anthraquinone. Antimicrobial, anti-oxidant, anti-inflammatory property can be attributed to the presence of the phytochemicals present.