University of Ibadan Journals
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Implementation of the FAO Port State Measures Agreement in Combating IUU Fishing in West Africa: The Case of Nigeria
No full textIllegal, Unreported, and Unregulated (IUU) fishing threatens marine resources, food security,and governance in West Africa, where an estimated 37% of fish harvests are illicit.The FAO Port State Measures Agreement (PSMA), adopted in 2009, is the first binding internationaltreaty to combat IUU fishing by restricting port access, mandating inspections,and promoting information sharing. Nigeria’s accession in October 2022 was a milestonefor regional compliance, closing a critical enforcement gap within the Fisheries Committeefor the West Central Gulf of Guinea (FCWC). This study applies a governance implementationframework, grounded in institutional and compliance theory, to assess Nigeria’sprogress through legal alignment, institutional capacity, and operational readiness.Content analysis of legislation, policy documents, and institutional arrangements showsearly reforms, including a draft Fisheries Bill, designation of entry ports, and pilot inspections.However, significant challenges remain: outdated laws, limited inspectorateresources, fragmented interagency coordination, and weak integration with FAO’s GlobalInformation Exchange System (GIES). Nigeria is therefore positioned as a proactive butcapacity-limited PSMA Party. Sustained reforms, institutional investment, technologicalmodernization, and stronger regional cooperation are essential to operationalize commitments,strengthen fisheries governance, and enhanceWest Africa’s deterrence against IUUfishing
A Machine Learning-Based Fraud Prevention Model for Improving Customers’ Trust in E-Commerce
Full text linkThe growth of e-commerce has led to significant challenges regarding fraud, resulting in a decline in customer trust and confidence in online transactions. This research proposes a comprehensive Fraud Prevention Model aimed at enhancing customer trust and security within e-commerce platforms by integrating advanced machine learning (ML) techniques, an Address Verification System (AVS), and Two-Factor Authentication (2FA). The model leverages Convolutional Neural Network - Long Short-Term Memory Network (CNN-LSTM) and Random Forest techniques to capture the complexities and temporal dependencies of e-commerce transaction data. The AVS component of the system verifies transaction legitimacy by comparing billing addresses with credit card records, and the implementation of 2FA adds an extra layer of security. The system's effectiveness was evaluated through rigorous testing using a dataset of transaction records. The results indicate that the combined approach of machine learning, AVS, and 2FA significantly enhances the detection of fraudulent transactions and improves overall customer trust in e-commerce platforms
Assessment of the Initial Adoption and Implementation of an Electronic Medical Records (EMR) System in a Nigerian Teaching Hospital
Full text linkAll medical information related to patients can be displayed in a digital system known as the Electronic Medical Record System. The system is an attempt by many medical facilities to design and implement a means of collecting, storing, and presenting patients’ information at the point of care. EMRs are implemented worldwide because they are recognized as a potential means of improving medical services' quality, safety, and efficiency. In Nigeria, the Federal Ministry of Health has acknowledged the significance of EMRs for quality enhancement in healthcare delivery and has directed all teaching hospitals to computerize their clinical processes. Based on this directive, some teaching and national hospitals are now computerized. This paper aims to assess the initial adoption and implementation of the EMR system in a Nigerian teaching hospital. The research design adopted in this paper is a qualitative research design. Primary data were collected via participant observation and face-to-face interviews with medical staff and information technology personnel who deployed the system. Also, the researchers reviewed existing literature on adoption of electronic medical records systems and the pros and cons of adopting an electronic medical records system. The tools used in developing the EMR system were JavaScript, C#, MSSQL server, Web server (IIS), and Web browser is the program the user uses to view the web pages. The system has many modules which can only be accessed by authorized members of staff based on the role they perform. The system comprises of patient information module, Billing and payment module, Nurses module, Doctors module/clerking module, Customizable Reporting/Quality reporting, clinical coding, NHIS module, Diagnoses, Diagnostic imaging system (Radiology), E-prescription, and Laboratory information system. The system can still be improved upon through the inclusion of more relevant modules for better clinical and patient experience
Evaluation of Machine Learning-Based Algorithm to Predicting Loan Default in Nigeria
Full text linkAccurately predicting loan defaults is critical in the financial sector to minimize losses and optimize credit risk management. Traditional creditworthiness assessment methods often fail to capture the complex, dynamic interactions in financial data, leading to inaccurate predictions. This study harnesses advanced machine learning techniques to enhance the prediction of loan defaults, aiming to outperform traditional statistical models. A dataset containing 50,000 borrower records with diverse characteristics, including demographic, financial, and loan-specific features, was utilized. The data was split into training (70%) and test (30%) sets for model development and evaluation. Various machine learning algorithms were tested, including Logistic Regression, Decision Trees, Gradient Boosting Classifiers, Random Forest, and Gaussian Naive Bayes. The Gaussian Naive Bayes (GaussianNB) model demonstrated superior performance, achieving an accuracy of 78.8% on the test set. This model effectively captured complex patterns in the high-dimensional data, significantly reducing false positives and false negatives compared to other models. The findings suggest that machine learning models, particularly GaussianNB, offer substantial improvements in predictive accuracy for loan default risk assessments. This findings can enhance lenders' decision-making processes by improving risk stratification and resource allocation. Future research should explore integrating non-traditional data sources, such as behavioral and macroeconomic variables, and employing deep learning techniques to further refine predictive accuracy
A Robust Biometric Authentication Framework for Access Control
Full text linkUnauthorized access poses significant security concerns, jeopardizing the confidentiality, integrity, and availability of critical data and resources. Ensuring authorized access is essential for protecting sensitive systems across diverse fields, including smart buildings, military bases, hospitals, airports, and financial institutions. Biometric authentication has emerged as a reliable solution for access control, leveraging unique human traits for verification. However, traditional feature-based biometric systems are limited by environmental sensitivity, poor generalization, and vulnerability to spoofing, while deep learning-based systems face challenges such as high computational demands, reliance on large datasets, and lack of interpretability. To address these limitations, this research proposes a hybrid biometric authentication framework that combines the strengths of deep learning, specifically Residual Network (ResNet)-a Convolutional Neural Network (CNN), with the Local Binary Pattern (LBP) method. By integrating interpretable, computationally efficient features from LBP with ResNet’s ability to learn complex patterns, the framework improves robustness, reduces overfitting, and enhances scalability. This approach offers a balanced, efficient solution for secure biometric authentication, tailored for real-world and resource-constrained environments
Assessment of Problems Associated with the Management of Public Infrastructure in Ondo West LGA, Ondo State, Nigeria
Full text linkOne of the factors that militate against effective management of infrastructure is poor maintenance culture, as most infrastructural facilities deteriorate rapidly and are left to rot away within a very short number of years after their installation, thereby shortening their effective life span. This study examined the problems associated with the management of public infrastructures in Ondo West LGA with a view to proffering sustainable methods of managing infrastructures in Ondo State. Data for the study were obtained from both primary and secondary sources. A total of 230 households were sampled. The data collection process involved field surveys and personal observations of the existing infrastructure in the study area. Both descriptive and inferential statistical tools were used in data analysis. Data were obtained based on the objectives and the hypothesis of the study through the use of a structured questionnaire. The finding revealed that educational infrastructure is the most prominent, cited by 36.5% of respondents, followed by electricity at 28.3%, while other infrastructures are inconsistently distributed. The majority of infrastructures, accounting for 36.1%, are located within a distance of 6-10 km from the respondents. The study concluded that there is a significant relationship between the management agencies and the associated problem of attitude, indicating that the nonchalant attitudes of both the agencies and the community towards maintenance play a crucial role in the declining service quality. The study recommends that there is a need to foster a stronger maintenance culture among both the management agencies and the community. Also, community engagement initiatives should be introduced to involve residents in the care and monitoring of public facilities.
 
Bottom-Up Strategies for Creating Sustainable Urban Settlements through Sustainable Real Estate Development Practices: A Review
Full text linkThis study explores the concept of sustainable urban design and its significance in achieving Sustainable Development Goal-11 (SDG-11) through sustainable real estate developments, focusing on Lagos, Nigeria. It investigates effective strategies for creating urban settlements by integrating sustainable real estate (buildings) and developing Eco-cities such as Alaro City in Epe, Lagos. An exploratory research method was employed, and key findings highlight the importance of integrating sustainable building design, green spaces, efficient public transportation, renewable energy, efficient water management systems, etc., into real estate development at the planning stage. The study identifies government, architects, quantity surveyors, builders, and individual real estate developers as crucial stakeholders in promoting sustainable real estate developments through the bottom-up approach that will eventually result in sustainable urban settlements. Recommendations emphasize the need for government prioritization of public awareness campaigns and strategic planning, such as giving necessary encouragement and support to local building materials industries. It was equally recommended that certain specific built environment professionals, such as land surveyors, town planners, architects, and quantity surveyors who are the first contact to real estate developers should endeavour to always advise and convince their clients to embrace sustainable real estate design and construction.
 
Growth Performance, Fatty Acid Profile, And Lipid Composition of Nile Tilapia (Oreochromis Niloticus) Fed Diets with Graded Levels of Groundnut Meal
No full textThis study evaluated the impact of substituting fish meal with groundnut meal on the growth perfor- mance, fatty acid composition, and lipid profile of Oreochromis niloticus fingerlings. Diets contain- ing varying levels of groundnut meal (0%, 20%, 40%, 60%, 80% and 100%), each replicated three times over 84 days, were evaluated. The highest mean weight gain (8.00 ± 1.68 g) was observed at 100% replacement, while the lowest (4.19 ± 0.89 g) occurred at 40% inclusion. Protein intake was highest in the 20% group (9.67 ± 0.08 g) and lowest in the 100% group (5.85 ± 0.05 g). Sixteen fatty acids were detected, notably high levels of palmitic acid, stearic acid, oleic acid, and arachidonic acid. Saturation indices peaked at 20–40% groundnut inclusion. The control and 20% treatments recorded the highest triglyceride levels, whereas cholesterol levels were highest at 100% inclusion. Overall, diets containing 20% groundnut meal appear optimal for tilapia growth while offering a promising alternative to fish meal
Structure, Conduct and Performance of Crayfish Market in Ibadan Metropolis, Oyo State, Nigeria
No full textThe study assessed the market structure, conduct and performance of Crayfish markets in Ibadan, Oyo State Nigeria.
Six (6) markets, where crayfish are predominantly sold were used in the survey. A multi-stage sampling procedure was used to select samples for data collection. A total of 50 crayfish sellers sampled were randomly selected from the six markets proportionate to the size and semi-structured questionnaires were used to collect data. Descriptive statistics, Gross margin analysis and Gini coefficient were deployed for data analysis. Analysis of the size and different composition of costs of marketing as well as margin revealed that purchase cost N25,000 (96%) the highest share of the total marketing cost. Aleshinloye market has the highest gross margin of N6,500. When the gross and profit margins were expressed as a percentage of total revenue, the average was found as 17.56 and 14.16% respectively. The crayfish marketing efficiency evaluation showed that they are highly efficient with an average efficiency ratio of 0.86. The Gini coefficient (0.77) showed that the market is an oligopoly. Problems encountered include lack of finance among others. It was however recommended that inefficiency may be improved by creating some measures that will improve competition such as the provision of sufficient market space and micro-credit facilities
Level of Awareness and Practice of Sustainable Project Management among Quantity Surveyors in Ibadan, Oyo State, Nigeria
Full text linkThe construction industry has a substantial influence on environmental sustainability, social well-being, and economic development. With the rapid urbanisation of cities like Ibadan, the capital of Oyo State, Nigeria, there is a growing urgency for the implementation of sustainable construction practices. Sustainable project management entails the incorporation ofenvironmental, social, and economic factors throughout all stages of construction projects in order to mitigate adverse effects and maximise long-term advantages. The purpose of this paper is to explore the level of Awareness and Practice of Sustainable Project Management among Quantity Surveyors in Ibadan, Oyo State. The study adopted a quantitative approach by distributing a questionnaire to 171 professional quantity surveyors in the state. However, a total of 62 were retrieved, equating to 36% of the total administered questionnaire. The study adopted descriptive statistics using the Mean Item Score (MIS) to rank the variables. The study concluded that majority of the respondents in the study scope are aware of the concept of sustainable project management. This finding highlights a gap in local or region-specific sustainability certifications and points to a reliance on international standards. Furthermore, the study identifies "High initial investment costs" as the most significant barrier to the adoption of sustainable practices, echoing similar findings in global studies. It was recommended that public awareness campaigns be conducted to emphasise the enduring advantages of sustainable construction, including its financial and environmental benefits