University of Ibadan Journals
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Approximation Algorithm for Travelling Salesman Problem
Full text linkDesigning approximation algorithms often involves, among other things, relaxing the integrality constraint and obtaining a convex relaxation of the problem. The most well-known relaxation schemes are the Linear Programming (LP) relaxation and Semi-Definite Programing (SDP) relaxation. While LP relaxation has been widely used for solving TSP, SDP has been rarely employed. The primary goal of this paper therefore is to employ SDP and develop approximation algorithm for metric TSP. The SDP relaxation of the TSP is first obtained, and the approximation algorithm is thereafter developed. When compared to optimal results of some standard TSP instances, implementation result showed a relatively fair performance
Spam Detection In Email Communication Using Ensemble Learning
Full text linkSpam detection remains a critical challenge in cybersecurity due to the increasing sophistication of unsolicited and malicious communications. These messages, often containing phishing links, fraudulent offers, and malware, pose significant risks to users and information systems. This project addresses the challenge by implementing a robust spam detection system using ensemble learning techniques to enhance the security of email and SMS communications. Utilizing diverse datasets such as the UCI ML Corpus, Spam Assassin Dataset, Ling Phishing Dataset, Nigerian Fraud Dataset, and Enron Phishing Dataset, the study implemented rigorous data preprocessing and feature extraction, transforming raw text data into numerical vectors using Term Frequency Inverse Document Frequency (TFIDF) vectorization. Various Machine Learning algorithms in cluding Support Vector Machine, Logistic Regression, Naïve Bayes, Decision Trees, KNN, Extra Trees. Also, a range of ensemble learning algorithms, including Random Forest, AdaBoost, Gradient Boosting, and X GBoost, were implemented with their performance recorded. The project focuses on combining the efforts of some of these algorithms hereby comparing two primary ensemble models; the Stacking and Voting Classifiers, with the Voting Classifier emerging as the more effective. By aggregating the strengths of multiple models, the Voting Classifier demonstrated superior accuracy and reliability combining models like SVC, RF, ETC, and NB, to report accuracy and precision scores of around 98% and 99% for datasets 1 and 2, 97% and 97% for dataset 3 and 99% and 99% for dataset 5 respectively. This project underscores the potential of ensemble methods in enhancing spam detection systems and sets the stage for future research exploring the integration of deep learning models and real-time detection systems to secure digital communications further
Leveraging Machine Learning for Predicting Climate Change Impacts on Agricultural Productivity in Bayelsa State, Nigeria: A Pathway to Sustainable Solutions
Full text linkinsecurity. Limited access to localized climate data further complicates agricultural decision-making. This study applies machine learning to predict climate change effects on agricultural productivity, offering strategies for resilience and sustainable farming. Historical climate and agricultural data from sources like the Nigerian Meteorological Agency (NiMET) were analyzed. A stacking ensemble machine learning model was developed to predict crop yields, using a Random Forest Regressor and XGBoost Regressor as base models, with a Linear Regressor as the meta-learner. The model was optimized using 5-fold cross-validation to enhance predictive accuracy. Model validation using Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE) demonstrated high accuracy, with an RMSE of 9,861.6786, an R² of 0.9866, and an MAE of 3,716.7995 hg/ha. These results indicate minimal deviation from actual crop yields, demonstrating a significant improvement over earlier models and confirming its reliability in predicting agricultural productivity. Findings highlight the potential of machine learning for informed decision-making among policymakers, farmers, and stakeholders. By leveraging AI-driven solutions, this study promotes agricultural resilience, sustainable development, and long term food security in Bayelsa State
Improved Stock Price Prediction Model in the Nigeria Bank Sector Using Ensemble Machine Learning Models
Full text linkStock market prediction remains a critical challenge in emerging economies, particularly within volatile
financial landscapes like Nigeria. Despite significant technological advancements, existing research
predominantly relies on single-model approaches that inadequately capture the complex, non-linear dynamics of
financial markets. This study addresses the methodological gap by developing an ensemble machine learning
model for predicting stock prices in the Nigerian banking sector. The research utilized historical stock price data
from Guaranty Trust Bank and First Bank (2018-2023), integrating advanced preprocessing techniques,
employing rigorous data transformation, feature standardization, and cross-validation strategies, the study
transforms raw financial data into a robust predictive framework. Empirical results reveal distinct performance
metrics across ensemble models: Among the models, Gradient Boosting achieved an MAE of 0.1547, MSE of
0.0918, and RMSE of 0.999, while the Stacking Regressor yielded an MAE of 0.1912, MSE of 0.1396, and
RMSE of 0.9989, highlighting their accuracy and reliability in volatile market conditions. The ensemble
methodology demonstrates superior performance in capturing intricate market dynamics, offering significant
improvements over traditional forecasting techniques by integrating macroeconomic indicators and advanced
machine learning algorithms. The findings underscore the potential of ensemble machine learning in decoding
complex financial patterns, providing valuable insights for investors, financial analysts, and policymakers
A Machine Learning-Based Predictive Model for the Classification of Academic Performance of Students
Full text linkPredicting student academic performance is critical for enhancing personalized learning and improving educational
outcomes. Traditional assessment methods, while useful, often fail to capture the complex factors influencing
performance, such as socio-economic background and engagement metrics. This study explores the development of a
predictive model using an ensemble of machine learning algorithms to classify students' academic performance in
higher institutions. By leveraging data collected from Department of Computer Science, Tai Solarin University of
Education records, relevant features were selected using the mutual information method. The ensemble model was
formulated and simulated using multiple machine learning algorithms such as Naïve Bayes (NB), Support Vector
Machines (SVM) and Decision Trees (DT) in the Google CoLaboratory environment. The model’s predictive accuracy
was evaluated based on key performance metrics, including accuracy, precision, and F-measure. Results indicate that
the ensemble approach outperforms single-model methods by enhancing prediction robustness and reducing variance.
This study demonstrates the effectiveness of machine learning techniques in identifying at-risk students early with NB
and SVM having 100% accuracy respectively, allowing for timely interventions and improved resource allocation.
Moreover, it contributes to evidence-based decision-making in educational institutions, helping to optimize learning
experiences and boost student retention rates
A Comparative Analysis of Ensemble Machine Learning Algorithms for Bank Customer Churn Prediction
Full text linkCustomers churn became a serious issues to banks manager because customers have numerous options where to
save their money. This justify why many researchers are attracted to this area. This study developed a bank
customers churn predictive model. The study used dataset from kaggle.com repository. It consists of 10127
instances and 20 parameters. One Hot Encoder was used as data preprocessing on the dataset. The data was divided
into 80% for training and 20% for testing. The predictive model was created using Long Short-Term Memory
(LSTM), Ensemble LSTM, and Random Forest (RF). The results of the model revealed LSTM with F1 score of
0.94, accuracy of 0.9235, specificity of 0. 6635 sensitivity of 0.97, AUC of 0.95 and loss value of 0.1663. Ensemble
LSTM with F1 score of 0.94, accuracy of 0.9057, specificity of 0.554, sensitivity of 0.98, AUC of 0.92 and loss
value of 0.238. RF with F1 score of 0.97, accuracy of 0.95, specificity of 0. 774, sensitivity of 0.99, AUC of 0.99
and loss value of 0.15. The study concluded that RF outperformed both LSTM and Ensemble LSTM. Also pointed
out that customer’s gender, marital status, customer income category and age against attrition are determining factor
for customer churn prediction. The model is recommended for banking sector to assist in decision making. Future
work can be done using more ensembles techniques and perform more data expositor
Leveraging Artificial Intelligence for Detecting Insider Threats in Corporate Networks
Full text linkIn the modern corporate environment, insider threats pose a significant risk to data integrity, financial stability, and overall cybersecurity. Unlike external attacks, insider threats originate from individuals within an organization like employees, contractors, or partners who possess legitimate access to critical systems. Traditional security measures often fail to identify these threats due to the complexity of distinguishing malicious behaviour from regular activities. Artificial Intelligence (AI) based systems, with their ability to analyse large datasets, detect subtle patterns, and adapt to evolving threat landscapes, offer a powerful approach to insider threat detection. This research involves the application of machine learning algorithms to identify deviations from normal users’ activities in corporate networks. The methodology involves analysing user behaviours and access patterns, development and training a machine learning model for classifying user behaviours into normal or abnormal activity. The system helps to identify abnormal user activities and flags suspicious activities in real time, providing an early warning sign for potential breaches. The results demonstrate the effectiveness of machine learning in enhancing threat detection, reducing insider threats, and improving overall cybersecurity in corporate networks
Optimising Malaria Prediction from Cell Images Using Forward Selection and Support Vector Machine Classifier
Full text linkMalaria is a significant health concern, primarily affecting tropical and subtropical regions. Traditional diagnostic methods for malaria detection, such as microscopic blood smear analysis of cell images, are timeconsuming, dependent on trained specialists, and prone to variability. Timely and accurate malaria detection is crucial for prompt treatment and preventing severe complications. Therefore, this study developed a machine learning (ML)-based model that could accurately predict malaria by analysing microscopic cell images, enabling efficient and reliable diagnosis to support timely treatment decisions. Using the Kaggle malaria dataset comprising 26,159 blood smear images, this study uniquely integrates forward feature selection and Support Vector Machines (SVM) to enhance malaria prediction accuracy. Unlike existing works, it addresses gaps in transparency and reproducibility in feature selection methods used for high-dimensional medical image datasets. Forward selection was employed to optimise and select relevant features for the model, reducing computational complexity and enhancing its performance. The SVM model achieved an accuracy of 97.1%, recall of 97.4%, precision of 96.8%, F1-score of 96.9%, and an AUC score of 97.4%. These findings highlight the potential of ML in automating malaria detection and demonstrate the practical advantage of combining feature selection with high-performing classifier to optimise diagnostic workflows, especially in resourcelimited settings
PHYTOCHEMICAL SCREENING, ANTIOXIDANT PROPERTIES AND ANTIMICROBIAL ACTIVITIES OF GINGER AND SWEET ORANGE PEELS ESSENTIAL OILS.
No full textEssential oils are highly concentrated substances obtained from vegetable raw materials and are complex mixtures whose composition may include hydrocarbons, alcohols, esters and aldehydes. These oils are often used for their flavour and their therapeutic or odiferous properties. The study was carried out to determine the phytochemical constituents, antioxidant properties and antimicrobial activities of essential oil from Ginger (Zingiber officinale) and sweet orange (Citrus sinenesis) peels. Fresh ginger rhizomes and sweet orange peels were hydro-distilled to get the essential oils which were screened for the presence of phytochemicals and their effect on 1,1-Diphenyl-2-picryl-hydrazyl radical (DPPH) was used to determine their free radical scavenging activity. Total alkaloids and total terpenoids were quantitatively estimated. The oils were also evaluated for antimicrobial activity against fish pathogenic bacteria by disc diffusion method. Antibacterial activity was determined against four di?erent fish pathogens Vibrio sp, Escherichia coli, Pseudomonas aeruginosa and Bacillus sp. Bacillus sp. Phytochemical screening of the ginger and sweet orange peel essential oils showed the presence of alkaloids, terpenoids, cardiac glycosides, coumarin, anthraquinones and vitamin C which are useful substances that have medicinal and physiological activities. They did not contain saponin, tannins, steroids, phenols and flavonoids. Concentrations of the essential oil required for 50% inhibition of the DPPH radical scavenging effect (IC50) were recorded as 200 µg/ml, 400 µg/ml, 600 µg/ml, 800 µg/ml and 1000µg/ml for ginger and sweet orange peel essential oil. Antioxidant screening of the ginger and sweet orange peels essential oils DPPH was positive indicating the presence of free radical scavenging molecules and antioxidant potency of the essential oils. The disc di?usion results indicated that essential oil of Zingiber officinale and Citrus sinensis peel significantly inhibited the growth of Vibrio sp, Escherichia coli, Pseudomonas aeruginosa, and Bacillus sp. The inhibition of the test isolates was dependent on the concentration of the solvent used. The phytochemical analysis of ginger and sweet orange peel essential oil revealed the presence of phytochemical constituents which conferred antimicrobial property on the oils
Infestation of Copepods Parasite in the Gills of Economically Important Mugilidae Species from Lake Nokoue (Republic of Benin) and Lagos Lagoon (Nigeria)
No full textParasitic copepods infect virtually all aquatic animal groups and show a staggeringdiversity in body form and life cycle strategies infestation from Caligid copepods arerelated to farmed fish populations in temperate zones, due to the economic costs theyrepresent and their higher incidence at high population densities such as thoseoccurring in aquaculture. However, only a few research examined copepod infestationson wild fish populations or assemblages. Although the occurrenceof Lepeophtheirus species has been widely recorded on different fish species, thecharacteristics and effects of infestation are poorly known hence this study. This studyaims to detect the infestation of parasitic copepod and analyze its relationshipsaccording to season, sex and maturity on two fish species of Mugilidae (LF: Lizafalcipinnis and MC: Mugil cephalus). A total number of 1139 pieces of LF werecollected from Lake Nokoue and 1135 pieces of MC were collected from Lagos lagoonbetween April 2019 and October 2021. They were examined for parasites in both dryand wet seasons, results were analyzed using Chi-squared or Fisher’s Exact test.The result obtained shows the highest total percentage of copepod prevalence wasfound in Ganvie (86.23%) and the least in Djdje (63.14%). There is a significantdifference (P<0.05) in the rate of infestation of L. falcipinnis at Djdje to the other twostations, there is a high prevalence of copepod parasites infestation in the Mugilidaestudied in the wet season than the dry season and sex did not impact the degree of LFand MC infestation by parasites in the Lake Nokoue and Lagos lagoon