Faculty of Natural Sciences

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1-Minute Rain Rate Distribution for Communication Link Design Based on Ground and Satellite Measurements in West Africa
(Begell House, Inc., 2020) Sanyaolu, Modupe
West Africa is in the tropical region and it is characterized by intense rainfall. Rain is a significant factor causing signal degradation on microwave links due to its variability; it causes scattering, absorption, and refraction of electromagnetic waves. Experimental studies have shown that rainfall intensities above 64 mm/h at 0.01% in this region results in noticeable digital television signal fading, squelching and complete outages. Hence the need for estimating rain rate distribution across West Africa. This paper analyzed the rain rate from six countries in West Africa, namely Benin, Cameroon, Cote d'Ivoire, Ghana, Nigeria, and Togo. Three locations were selected in each country. Rain data were obtained from the Tropical Rain Measuring Mission-Precipitation Radar (TRMM-PR) and the Global Precipitation Measurement (GPM) missions, and Tropospheric Data Acquisition Network (TRODAN) weather stations in Nigeria. This study used ITU-R and Moupfouma models for the conversion of the 5-minute rain rate to 1-minute integration time at a probability of exceedance ranging from 1% to 0.001%. The cumulative rain rate distribution from the measured rain rate is presented alongside the predictions of the models. ITU-R and Moupfouma predicted similar results at 0.1% probability of exceedance. ITU-R overestimates the rain rate above 0.01% probability of exceedance. On the other hand, the Moupfouma models prediction plots at 0.01% overlap for all locations, indicating that there will be a signal loss at 0.01% probability of exceedance across these locations. The result shows that the 5-minute conversion provides satisfactory performance and suitable for estimating the 1-minute rain rate statistics required for propagation planning over West Africa.
2-Aryl benzimidazoles: Synthesis, In vitro alpha-amylase inhibitory activity, and molecular docking study
(Elsevier, 2018-03-06) Akande, Akinsola
Despite of many diverse biological activities exhibited by benzimidazole scaffold, it is rarely explored for the -amylase inhibitory activity. For that purpose, 2-aryl benzimidazole derivatives 1-45 were synthesized and screened for in vitro -amylase inhibitory activity. Structures of all synthetic compounds were deduced by various spectroscopic techniques. All compounds revealed inhibition potential with IC50 values of 1.48 ± 0.38-2.99 ± 0.14 M, when compared to the standard acarbose (IC50 = 1.46 ± 0.26 M). Limited SAR suggested that the variation in the inhibitory activities of the compounds are the result of different substitutions on aryl ring. In order to rationalize the binding interactions of most active compounds with the active site of -amylase enzyme, in silico study was conducted.
A Comparative Analysis of the Performance of Parallel Ensemble and Sequential Ensemble Machine Learning Methods in the Detection of Diabetes Miletus
(Elsevier, ScienceDirect, 2025-05) Olorunfemi, Blessing O.
Diabetes Mellitus still forms a major cause of death rates soaring around the globe, heightening scares regarding shooting up diabetic population in the world; and hence straining health attendants to seek for rapid diagnostic tools specific to an incurable disease as described. Many models have been presented for machine learning as base learners, or else combined ensemble techniques. The performance of parallel and sequential ensemble machine learning approaches in the detection of diabetes mellitus: A comparative study, the parallel ensemble methods include Random Forest, J48, CART and Decision Stump (DS) classifiers and the sequential ensemble method includes XGBoost AdaBoostM1 Gradient Boosting. The data set was 70% training and 30 % testing using the dataset on UCI machine repository site. Python analysis using Jupyter Notebook of this model confirmed that sequential ensemble has a classification accuracy about 6% more than parallel method using the same dataset by applying the 5-fold Cross Validation (CV) technique. XGBoost was also 4% better than 10-fold CV. Sequential machine learning models perform better in predicting diabetes mellitus as per the results. Therefore, the study concludes that sequential ensemble approaches are robust and effective in enhancing early diagnosis of patients. Thus, these models can be employed to develop prospective diabetes mellitus detection systems which in turn contributes to better health outcomes and decreasing the load on healthcare.
A Comparative Study of Two Convolutional Neural Network Models for Detecting Rice Plant Diseases Using Online and Local Image Data
(LAUTECH Journal of Computing and Informatics (LAUJCI), 2024-03) Toluwase A. Olowookere
Rice is one of the most widely staple foods around the globe, however, rice fields are severely affected by diseases, which can disrupt global food security. Early and accurate detection of rice diseases is essential for the recovery of such rice plants. Manually identifying rice plant diseases can be tedious and error prone. Artificial intelligence (AI) driven models, such as Convolutional Neural Networks (CNN) have proven very successful in the identification or detection of various crop diseases. This study, therefore, presents a comparative study of the effectiveness of two popular CNN architectures; ResNet and AlexNet for the detection of rice plant disease. The data used to train the models include a combination of rice leaf images that were gathered locally from a rice field/farm in Ede, Osun State, Nigeria, and from an online repository. The dataset consists of 5200 images classified into four classes: Bacterial leaf blight, Brown spot, Blast, and Healthy, each containing 1300 images. The effectiveness of the two trained models was measured using classification performance metrics including Accuracy, Precision, Recall, and F1-Score. The finding from the study showed that The ResNet has a test accuracy of 95.25% as against 92.91% for the AlexNet. The ResNet had 0.93 precision, while AlexNet recorded a precision of 0.24. For recall, the ResNet model had 0.98 while the AlexNet model had 0.23 and for the f1-score, the ResNet model had 0.95 while the AlexNet model had 0.24. Generally, the ResNet model outperformed the AlexNet model in detecting rice plant diseases, most significantly, brown spot disease.
A Decision Tree Algorithm Based System for Predicting Crime in the University
(Machine Learning Research, 2017) OGUNDE, ADEWALE OPEOLUWA
CRIME is one of the major problems encountered in any society and universities together with other higher institutions of learning are not exceptions. Thus, there is an urgent need for security agents and agencies to battle and eradicate crime. The Directorate of Students and Services Development (DSSD) are responsible for investigating and detecting criminals of any crime committed within the Redeemer’s University. DSSD faces major challenges when it comes to detecting the real perpetrators of several crimes. An improvement in their strategy can produce positive results and high success rates, which is the basic objective of this project. Several methods have been applied to solve similar problems in the literature but none was tailored to solving the problem in Redeemer’s University and other universities. This work therefore applied classification rule mining method to develop a system for detecting crimes in universities. Past data for both crimes and criminals were collected from DSSD. In order to develop and test the proposed model, the data was pre-processed to get clean and accurate data. The Iterative Dichotomiser 3 (ID3) decision tree algorithm obtained from WEKA mining software was used to analyze and train the data. The model obtained was then used to develop a system that showed the hidden relationships between the crime-related data, in form of decision trees. This result was then used as a knowledge base for the development of the crime prediction system. The developed system could effectively predict a list of possible suspects by simply analyzing data retrieved from the crime scene with already existing data in the database. This system has all the potentials of helping the students’ affairs department and security apparatus of any university and other institutions to quickly detect either the real or possible perpetrators of crimes in the system.
A K-Nearest Neighbour Algorithm-Based Recommender System for the Dynamic Selection of Elective Undergraduate Courses
(Science Publishing Group, 2019) OGUNDE, ADEWALE OPEOLUWA
The task of selecting a few elective courses from a variety of available elective courses has been a difficult one for many students over the years. In many higher institutions, guidance and counsellors or level advisers are usually employed to assist the students in picking the right choice of courses. In reality, these counsellors and advisers are most times overloaded with too many students to attend to, and sometimes they do not have enough time for the students. Most times, the academic strength of the student based on past results are not considered in the new choice of electives. Recommender systems implement advanced data analysis techniques to help users find the items of their interest by producing a predicted likeliness score or a list of top recommended items for a given active user. Therefore, in this work, a collaborative filtering-based recommender system that will dynamically recommend elective courses to undergraduate students based on their past grades in related courses was developed. This approach employed the use of the k-nearest Neighbour algorithm to discover hidden relationships between the related courses passed by students in the past and the currently available elective courses. Real-life students’ results dataset was used to build and test the recommendation model. The new model was found to outperform existing results in the literature. The developed system will not only improve the academic performance of students; it will also help reduce the workload on the level advisers and school counsellors.
A Knowledge Sharing Architectural Model for Inter State e-Governement in Nigeria
(Nigeria Computer Society, 2010) OGUNDE, ADEWALE OPEOLUWA
A Lotka-Volterra Non-linear Differential Equation Model for Evaluating Tick Parasitism in Canine Populations
(Mathematical Modelling of Engineering Problems, 2023-08-30) Adesina, Olumide Sunday
This research employs a modified version of the Lotka-Volterra non-linear first-order ordinary differential equations to model and analyze the parasitic impact of ticks on dogs. The analysis reveals that fluctuations in pesticide effects significantly influence tick populations and the size of the canine host. The study also uncovers that alterations in the size of the interacting species can lead to both stable and unstable states. Interestingly, in a pesticide-free environment, a decline in the inter-competition coefficient catalyzes an increase in the sizes of both interacting species. This increase, although marginal for the tick population, contributes to overall system stability. The findings underscore the utility of the Lotka-Volterra non-linear first-order ordinary differential equations in modeling the parasitic effect of ticks on dogs. To protect pets, particularly dogs, from the harmful effects of tick infestation, this study recommends the appropriate and regular application of disinfectants.
A modified generalized class of exponential ratio type estimators in ranked set sampling
(Scientific African, 2023-03-22) Adesina, Olumide Sunday
Background: Researchers consider ranked set sampling (RSS) as an alternative to simple random sampling (SRS) for data collection because studies have shown that it is more efficient and less biased. Also, introducing population parameters to estimators increases the efficiency of such estimators. Aim: This study derived a modified generalized class of exponential ratio estimator in RSS by introducing available population parameters and compared the results with an existing version in SRS. Methodology: The biases and mean square errors (MSE) of the proposed estimators were derived up to the terms of first-order approximation using Taylor’s series expansion. Efficiency was used as the mode of comparison between the proposed and existing estimators. Results: Life data sets and simulated data supported the numerical illustration to corroborate the theoretical results. Conclusion: The MSEs of the modified generalized class of estimators under RSS were found to be smaller than those of the existing generalized class of estimators under SRS; hence they are more efficient estimators.
A modified generalized class of exponential ratio type estimators in ranked set sampling
(scientific African, 2022-11-22) Ayobami Fadilat Akintola
Background: Researchers consider ranked set sampling (RSS) as an alternative to simple random sampling (SRS) for data collection because studies have shown that it is more efficient and less biased. Also, introducing population parameters to estimators increases the efficiency of such estimators. Aim: This study derived a modified generalized class of exponential ratio estimator in RSS by introducing available population parameters and compared the results with an existing version in SRS. Methodology: The biases and mean square errors (MSE) of the proposed estimators were derived up to the terms of first-order approximation using Taylor’s series expansion. Effi- ciency was used as the mode of comparison between the proposed and existing estimators. Results: Life data sets and simulated data supported the numerical illustration to corrobo- rate the theoretical results. Conclusion: The MSEs of the modified generalized class of estimators under RSS were found to be smaller than those of the existing generalized class of estimators under SRS; hence they are more efficient estimators.
A MODIFIED SPECTRAL CONJUGATE GRADIENT METHOD FOR SOLVING UNCONSTRAINED MINIMIZATION PROBLEMS
(Nigerian Mathematical Society, 2020) Onanaye, Adeniyi Samson
The development a modified spectral conjugate gradient method for solving unconstrained minimization prob lems is considered in this paper. A new Conjugate (update) parameter isobtained by the idea of Dai-Kou’s technique for gen erating conjugate parameters. A new spectral parameter is also presented based on quasi-Newton direction and quasi-Newton condition. Under the strong Wolfe line search, the proposed method (DOO) is proved to be globally convergent. Numerical results showed that the algorithm takes lesser number of itera tions to obtain the minimum of a given function.
A non-parametric analysis of the effect of Covid-19 pandemic on Nigerians’ well-being based on geopolitical zones
(JP Journal of Biostatistics, 2024-02-12) Adesina, Olumide Sunday
The COVID-19 pandemic has crippled the economic activities of so many nations across the globe since its outbreak in 2019. This study is focused on the consequential effect of the COVID-19 pandemic in terms of standard of living, and perception of economic and security situation of Nigerians. A non-parametric approach was adopted on a primary data obtained through the administration of questionnaires online by NoiPolls during the COVID-19 period. The result obtained from this study depicts that there is a significant relationship between the security situation and perception of the country’s economic situation. Kruskal-Wallis test was used to check if there is a significant difference in economic perception, security situation and standard of living where it was observed that there exists a significant difference based on the geopolitical zone. We, therefore, recommend that efforts should be made by the government towards improving the economic state of affairs especially in the southern part of the country as this will in the long run lead to sustainable cities and communities across the geopolitical zones which is one of the goals of SDGs. Moreover, efforts should also be made by the government towards improving the security situation in the north and southeast as improvement in the country’s economic situation has a direct influence on the security position of the country.
A partition enhanced mining algorithm for distributed association rule mining systems
(Elsevier., 2015) OGUNDE, ADEWALE OPEOLUWA
The extraction of patterns and rules from large distributed databases through existing Distributed Association Rule Mining (DARM) systems is still faced with enormous challenges such as high response times, high communication costs and inability to adapt to the constantly changing databases. In this work, a Partition Enhanced Mining Algorithm (PEMA) is presented to address these problems. In PEMA, the Association Rule Mining Coordinating Agent receives a request and decides the appropriate data sites, partitioning strategy and mining agents to use. The mining process is divided into two stages. In the first stage, the data agents horizontally segment the databases with small average transaction length into relatively smaller partitions based on the number of available sites and the available memory. On the other hand, databases with relatively large average transaction length were vertically partitioned. After this, Mobile Agent-Based Association Rule Mining-Agents, which are the mining agents, carry out the discovery of the local frequent itemsets. At the second stage, the local frequent itemsets were incrementally integrated by the from one data site to another to get the global frequent itemsets. This reduced the response time and communication cost in the system. Results from experiments conducted on real datasets showed that the average response time of PEMA showed an improvement over existing algorithms. Similarly, PEMA incurred lower communication costs with average size of messages exchanged lower when compared with benchmark DARM systems. This result showed that PEMA could be efficiently deployed for efficient discovery of valuable knowledge in distributed databases.
A Practical Guide to Data Analysis Using SPSS.
(GODAD Publishers Nigeria Limited, Ketu, Lagos., 2018-02-17) Alayande SA
A Prototype System for Mining Frequent Citizens’ Demand Patterns from E-Government Databases
(Computing and Information Systems Journal, 2017) OGUNDE, ADEWALE OPEOLUWA
Purpose: The objective of this study is to propose a prototype system for collecting, analyzing, understanding and mining citizens pressing needs and demands from e-government databases. Design/Methodology/Approach: Common citizens demands were obtained from published literature and used to populate a database which was mined with the Apriori algorithm to obtain the most frequent citizens demands. Findings: Mining a collection of citizens’ pressing demands data from e-government databases is very feasible. Research limitations/implications: The developed system is a prototype designed and tested with a randomized synthetic dataset. The prototype can greatly assist the government to make informed decisions regarding the needs of the citizens. Practical implications: This will serve as a framework to the government and interested organizations for deploying such an important system that will provide a reliable link between the government and the citizens. The results should provide improved governance and also enhance citizens' satisfaction. Originality/value: For any government to succeed, citizens’ relationship management and satisfaction must be given a top priority. The developed prototype is practicable, achievable and promises great benefits to any interested government, researchers and system developers.
A recommender system for selecting potential industrial training organizations
(John Wiley & Sons Ltd., 2019) OGUNDE, ADEWALE OPEOLUWA
The difficulty in securing students industrial work experience scheme (SIWES) placements has negatively affected the final grades of some undergraduates. A recommender system is hereby proposed to solve this problem. This systemwill use past SIWES data to recommend potential organizations to future SIWES students. Research data collection was through an online questionnaire, with 200 respondents. The data was divided into a training data set (70%) and test data set (30%). Collaborative filtering recommendation approach was employed using the C4.5 algorithm to classify the data and generate a decision tree model from the training data set. The model generated was used to predict the class label of the test data set. Results from the data analysis carried out revealed that Kappa statistics was 0.7839, mean absolute error = 0.0058, and root mean squared error = 0.0586. In addition, true positive rate = 0.788, recall = 0.788, precision = 0.749, F-measure = 0.755,MCC = 0.760, ROC = 0.985, and PRC = 0.813. The high values obtained indicates that the model was predicting with a high level of accuracy, ie, 78.84% correctly classified instances. The developed model was used as a knowledge base to develop a very beneficial front-end web application, tagged “RecommendIT” where students can enter their preferences and view company recommendations.
A Review of Some Issues and Challenges in Current Agent-Based Distributed Association Rule Mining
(Asian Journal of Information Technology, 2011) OGUNDE, ADEWALE OPEOLUWA
A review on the versatility of Carica papaya seed: an agrogenic waste for the removal of organic, inorganic and microbial contaminants in water
(John Wiley, 2023-05-21) Martins O. Omorogie
The advent of civilization, coupled with growing industrialization in many countries, is placing more demand on the available water sources. At the same time, the daily surge in wastes generated by man's anthropogenic activities has led to microbial, organic and inorganic contamination of water sources. Based on available evidence, signiﬁcant research efforts are being made into the use of low-cost agricultural materials such as Carica papaya seed (CPS) in the removal of these contaminants from water sources in a bid to provide clean water. In the present review, the organic, inorganic and microbial contaminants in waters were elucidated. Furthermore, the chemical composition of the CPS was illustrated. The adsorption capacity and efﬁciency of CPS and their composites in the remediation of the selected contaminants were discussed while identifying the various factors affecting the adsorption efﬁciency. Finally, the reusability of this agricultural material was discussed. Solution pH was identiﬁed as a major factor inﬂuencing the sorption process. The high removal efﬁciency reported in the studies that adopted CPS showed its vast potential in the elimination of contaminants from water sources. Also, the regenerative potential of the adsorbent over several cycles indicated its long-term use. The economic feasibility and environmental sustainability afforded by using CPS chart a path for further investigation into the use of other low-cost agricultural materials in the elimination of environmental contaminants.
A Super Learner Ensemble-based Intrusion Detection System to Mitigate Network Attacks
(IEEE, 2024) Toluwase Ayobami Olowookere
Governments and corporate institutions are now mostly reliant on integrated digital infrastructures. These digital infrastructures are usually targets of cyber threats such as intrusion, for which intrusion detection systems (IDS) have emerged. One of the key needs for a robust IDS includes reducing the rate of false positives and thus improving accuracy. In this study, three traditional machine learning (ML) algorithms, including K-Nearest Neighbor (KNN), Naive Bayes (NB), and Decision Tree (DT), and three ensemble Machine Learning (ML) algorithms, including Random Forest (RF), Light Gradient Boosting Machine (LGBM), and Extreme Gradient Boosting (XGBOOST), were used on the UNSW-NB15 dataset from the Australian Centre for Cyber Security's Cyber Range Lab, to train intrusion detection models. A super-learner ensemble model was then built using the best two ensemble models (XGBOOST and RF) along with the best traditional model (KNN) as its base learners. The super-learner ensemble model was able to reduce false positives and improve detection accuracy with 98% accuracy. The model was then deployed in an IDS application to mitigate network attacks effectively and efficiently.
A TOPIC-SPECIFIC EVALUATION OF STUDENTS’ ATTITUDES TOWARDS STATISTICS
(International Association of Statistics Education, 2022) Okewole, Dorcas Modupe
This study involves an evaluation of students’ attitudes towards various topics in statistics. The purpose of the study is to determine how students’ attitudes towards statistics vary across different topics and to determine possible changes in students’ attitudes from the beginning to the end of a course. The target population is that of students taking a statistics course for non-majors at a university in Nigeria. The study involved a pre-test (within the first week of the course) and a post-test (applied at the end of the course) focused on specific topics in the statistics course. Results indicated that students’ attitudes were moderately positive at the onset and remained the same at the end of the course for most topics. Implications for teaching statistics are discussed.

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