University of Ibadan Journals
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Evaluation of Pond Management Regimes of Fish Farmers: the Case Study in Tolon Fisheries Zone, Ghana
No full textDuring the last decade, capture fisheries production from capture fisheries has been on the decline. Fish farming has been preferred as an alternative to meet fish requirements. This study was therefore conducted to evaluate the effect of different pond management regimes on water quality parameters and the resultant effect on the production and profitability of fish farming in the Northern Region, Ghana. A total of 10 fish farmers were selected randomly for the study of pond management regimes and 5 fish ponds were chosen purposively for the water quality and profitability analyses. The study revealed that the highest concentration of fish farmers (50%) was in the Tamale Metropolis. The dominant type of fish holding facility was tank (70%) whiles pond constitutes 30%. The study revealed that 56% of the fish farmers use pelleted feed whiles 22% use both pelleted and powdered feed. The results also revealed that 60% use pipe-borne water for fish farming while 40% of the fish farmers had a dam as a source of water. The study indicated a positive relationship between feeding frequency and the concentration of dissolved oxygen
Integrating Zero-Trust Architecture with Deep Learning Algorithm to Prevent Structured Query Language Injection Attack in Cloud Database
Full text linkThe increasing reliance on cloud databases has made them a prime target for cyber attacks, with Structured
Query Language (SQL) injection being a particularly devastating threat. SQL injection attacks pose significant
threats to database security, compromising sensitive information. Deep learning algorithms have emerged as
effective solutions to detect and prevent SQL injection attacks. This study proposes a novel approach to
detecting SQL injection attack by integrating deep learning-based detection with zero-trust architectute. The
proposed system utilizes a Feed-Forward Neural Network (FNN)to analyze database queries and detect potential
SQL injection attacks. The FNN model is trained on a dataset of labelled queries, allowing it to learn patterns
and anomalies indictive of SQL injection attacks. The output of the FNN model is then integrated with zero-
trust architecture, which enforces strict access controls and authentication mechanisms based on the results
generated by the FNN model. The model exhibits a precision score approximating 100% accuracy in the
classification of queries deemed normal, while achieving a 94% rate of correct classification for queries
indicative of SQL injection attacks. By leveraging advanced machine learning techniques, our approach aims to
identify and block malicious queries in real-time, ensuring the integrity and security of cloud-based data.
Through a comprehensive evaluation, we demonstrate the effectiveness of our deep learning-based solution with
zero-trust architecture in detecting SQL injection attacks with high accuracy and low false positives
Education Systems Interoperability: Implications for Privacy and Security in Educational Management Information Systems
Full text linkThe importance of robust Educational Management Information System (EMIS) becomes very essential in addressing the complexities of data management, particularly as Nigerian educational systems is progressively leveraging the interrelated platforms in order to enhance operational efficiency and data sharing. However, educational sector is faced with several related challenges like: fragmented data, management systems, data privacy concerns, inadequate technology infrastructure, interoperability that is poor between different platforms, and lack of standardized protocols. These challenges made most institutions of learning to compromise the security and integrity of sensitive information. This study presents conceptual model that exemplifies the synergistic interactions among the major components of EMIS, by stressing the main roles of interoperability, security and privacy. Interoperability is central and surrounded by Data Governance, Technology Infrastructure, Privacy Measures, Regulatory Compliance, Security Measures, and Stakeholder Engagement. Each of the components is interconnected to illustrate how technology infrastructure enable effective data exchange while preserving sensitive information. The integration of these components in the proposed model offers qualitative understanding for educational institutions to strive well in enhancing EMIS while securing stakeholder’s privacy. This strategy addresses the present limitations of EMIS in Nigeria, opening way for a more efficient and safe educational data management system. It is recommended that educational institutions should be encouraged in adopting standardized procedures for seamless integration of EMIS in order to facilitate overall functionality and efficient exchange of data. Also, institutions should develop clear data governance policies that prioritize privacy and security, ensuring regulatory compliance and promoting responsible data use through regular training and awareness programs
Implementation of a Smart Farming Automation System
Full text linkThis study presents the design and implementation of a smart farming system that automates irrigation on farmland using an Arduino microcontroller, a Wi-Fi module, and various sensors. The system detects the soil moisture level and determines the optimal time to irrigate crops. It also monitors water levels to prevent overwatering, which can damage root systems. The main objectives of this project are: to develop a robust embedded system for real-time data collection from sensors deployed in agricultural fields, to design a user friendly interface for farmers to remotely monitor and control farming processes, and to implement intelligent systems that automate irrigation based on sensor data. Traditional agricultural practices rely heavily on manual labor and often lack real-time monitoring capabilities, resulting in inefficiencies, resource wastage, and suboptimal yields. Furthermore, unpredictable weather and the demand for precise resource management pose significant challenges. Addressing these issues requires a technologically advanced and integrated approach. The methodology adopted follows Rapid Prototyping and Iterative Model and this involves quickly developing an initial prototype, testing its functionality, gathering feedback, and then iteratively improving the design until the final implementation is achieved. The system was developed and tested to ensure functionality aligned with design specifications. The prototype successfully demonstrated autonomous control of irrigation based on soil moisture readings. In conclusion, smart farming—also known as precision agriculture—leverages technologies such as embedded systems, artificial intelligence (AI), and big data analytics. Through the integration of sensors, GPS, and automated machinery, it enables efficient crop and livestock management while promoting sustainability by reducing waste and conserving water
Development of Comparative Fake Transactions Alert Detection Models Using Machine Learning
Full text linkFraudulent payment evidences are the current techniques criminals employ to defraud businesses and small scale enterprises. Researchers have processed transactions textual data however, there is still the challenge in the ability to differentiate between legitimate and fake transaction alerts. In Nigeria, fake transaction alerts pose significant challenges for financial institutions and individuals losing on their hard earned assets, citizens are sceptical on electronic transactions and several Point of Sale (PoS) businesses have fallen victims. Hence, this study was aimed at the development of a better fake transaction alert detection model to distinguish fake transaction alerts. Artificial Commercial Data for Fraudulence Discovery was collected from Kaggle website. The collected data was pre-processed. Data imbalances were handled. Support Vector Machine (SVM) and Random Forest (RF) algorithms were ensembled to simulate fake transactions alert detection models using MATLAB programming. They were trained and tested with 70% training and 30% testing datasets, respectively. Performance evaluation was done on RF and SVM classifiers using exactness, precision, recollection, F-measure as benchmarks. The data record employed for this study had 1,048,575 transactions alerts. At performance evaluation, RF model had exactness, precision, recollection and F-measure values, 97.6, 97.48, 97.54 and 97.51%, respectively. Its RMSE was 0.02376. Moreover, SVM model had exactness, precision, recollection and F-measure values of 96.1, 96.88, 98.38, and 97.63%, respectively, it has RMSE of 0.03911. Random Forest algorithm was more suitable for the development of the fake transactions alert detection because it had higher performance. This model could be adopted by financial related institutions
Public Transport Operation and Compliance with Covid-19 Preventive Measures in the Cities of Southwestern Nigeria
Full text linkThis paper examines public transport operations and compliance with COVID-19 preventive measures in southwestern Nigeria. The concept of public transport governance and the theory of citizens' participation are used as an anchor for the study, while a cross-sectional survey research design was adopted. Primary and secondary data were sourced. A convenient sampling technique was used in selecting 123 respondents across the southwest geopolitical zone in Nigeria, while a Google Survey structured questionnaire was used in gathering data, and a ridge regression for hypothesis testing. The study revealed that despite movement restriction order majority (65%) use public transport to visit family-friends, commercial and worship centres; non-compliance with physical distancing (58%); commuters’ level of safety in contacting Corona-virus is low (33%); commuters affirmed the use of soap and water (27%), alcohol-based sanitizer (10%), nose mask (48%), hand gloves (1%); preventive measures were put in place by park managers (22%). Ridge Regression shows that education does not affect compliance (p>0.05), while restriction of movements, the presence of law enforcement, and their effectiveness do (p<0.05). It was suggested that more awareness be created, commuters and park managers should be educated, engaged, and comply with COVID-19 measures while sanctions and fines are levied on violators.
 
Effect of Chilled-frozen Storage on the Physico-chemical and Microbiological Quality of Silver Catfish (Chrysichthys Nigrodigitatus) from Asejire Lake, Ibadan, Nigeria
No full textFishes are highly perishable and are often discarded or sold at low prices over time due to deterioration. Freezing is a widely accepted preservation technique but fishers often lack freezing equipment on board and at the landing sites. Chilling or icing of fish immediately after the catch has been reported to reduce microbial and enzymatic spoilage before freezing or processing. This study, therefore, investigated microbial and physicochemical changes of frozen and prior chilled Chrysichthys nigrodigitatus obtained from Asejire reservoir, Nigeria. A total of 60 freshly caught samples of Chrysichthys nigrodigitatus (75-230g) were collected from Asejire dam. The fish were gutted and divided into two equal numbers. The first 30 pieces were frozen immediately after gutting (FRZ) while the remaining were chilled in ice for 24 hours before freezing (CBF). Samples of FRZ and CBF were subjected to sensory, proximate and microbial analysis following standard procedures. Crude protein reduced from 15.43±0.35 in FRZ while it increased from 17.13±0.31 to 17.70±0.25 after 24hrs and 12 weeks, respectively. This could be attributed to the higher moisture content in CBF. The initial and final microbial count increased in both FRZ and CBF 3.777x105±0.35 - 7.5x106±0.30 CFU/g and 17.13±0.31% - 17.70±0.25% respectively. FRZ is fit for consumption and had longer shelf life however CBF had higher protein and fat content at the end of the experiment
Achieving Food Security through Sustainable Aquaculture: An Evidence from Osun State, Nigeria.
No full textFish consumption accounts for over 40% of the protein sources consumed in Nigeria. This number is expected to increase with increasing population in the coming years as fish is a primary source of protein and essential nutrients. This increasing demand means that the current capacity of the aquaculture industry would be overstretched as catch from the wild is already dwindling as a result of stock depletion, overfishing and other global trends. Fish farmers, in their quest to meet up with this demand are shorthanded by increasing cost of production. This is likely to push-out fish farmers from business and would end up leaving consumers vulnerable as the production process might be compromised. The reason being that sustainable aquaculture practices would have been substituted for higher revenue. This research therefore sought to examine sustainability in ensuring food security. The study used the descriptive correlational survey design to ascertain the interplay between environmental, social, and economic sustainability on food security using fish farmers in Osun state, Nigeria as the respondents. The study used multistage sampling to generate a sample size of 150 fish farmers. The study made use of the SmartPLS 3.3.2 software to run the analysis. Results from the study showed that there is a positive significant effect between economic sustainability and food security (? = 0.195, p ? 0.1), as well as social sustainability and food security (? = -0.450, p < 0.01). However, environmental sustainability showed no effect on food security (? = -0.085, p > 0.1). Also, it was revealed that there was no significant association between socioeconomic characteristics of fish farmers and food security. Again, it was revealed that the major constraint of fish farmers was the high cost of feed and poor pricing by customers. It was concluded that for fish farmers to achieve food security, there is the need for an improved economic and social sustainability practices. The research recommends that government of Nigeria should empower extension agents to educate farmers on the need to improve on social and economic sustainability to safeguard food security while farmers themselves are encouraged to seek current and improved knowledge to improve their productivity
Growth Response and Nutrient Utilization of Heterobranchus longifilis Fingerlings Fed Diets Fortified with Lactobacillus Paracasei
No full textA 12-week feeding trial was conducted in 12 plastic tanks (50cm x 34cm x 27cm) to assess the performance of Heterobranchus longifilis fingerlings fed diets containing Lactobacillus paracasei at different inclusion levels. Four diets were formulated at 40% crude protein content containing: no L. paracasei (control), L. paracasei at 2.0 x 108 cfu/ml (LPA1), L. paracasei at 4.0 x 108 cfu/ml (LPA2), L. paracasei at 6.0 x 108 cfu/ml (LPA3). Each treatment was done in triplicate containing 20 fish (mean weight of 3.05±0.02g) each. Fish were fed to satiation. Heterobranchus longifilis fed LPA1 had significantly higher mean weight gain, specific growth rate, Protein efficiency ratio, nitrogen metabolism, and the lowest feed conversion ratio of 29.36±0.51, 1.22±0.01, 38.47±0.42, 15.39±0.17, 1.91±0.01, 817.41±11.56 and 1.31±0.01, respectively. The result from this study indicates that dietary L. paracasei at inclusion level of 2.0 x 108 cfu/ml could enhance the growth and nutrient utilization of Heterobranchus longifilis
Market Efficiency of Seafood Retailing in Some Selected Markets in Lagos State
No full textThis study investigates the market efficiency and profitability of seafood retailing in threemajor markets in Lagos State, Nigeria - Epe, Makoko, and Badagry - drawing on marketefficiency theory to examine income distribution, pricing structures, and operational performance.Employing a stratified random sampling method, this study collected primarydata from 150 seafood retailers and applied descriptive statistics, cost-return analysis, Ginicoefficient, and multiple regression modeling for analysis. The results reveal that seafoodmarketing is largely decentralized and competitive, yielding an average gross margin of62While previous studies focus primarily on wholesale channels or aggregate-level performance,this research offers a more detailed understanding of urban retail dynamics,highlighting market channel flexibility and spatial variations in profitability and institutionalsupport. Structural constraints; including price volatility, poor infrastructure, limitedcredit, and inadequate energy access - continue to impede efficiency and scalability. Thesefindings provide empirical grounding for policy and donor interventions aimed at strengtheningurban fish markets through targeted investments in infrastructure, inclusive financingschemes, cooperative development, and extension services. The study offers actionable insightsfor regional fisheries policy and development program, particularly in designinggender-sensitive, pro-poor market support systems. Limitations include the cross-sectionaldesign, seasonal price fluctuations, and reliance on self-reported data. Nevertheless, theresearch underscores the strategic potential of retail fish markets as engines of inclusiveeconomic growth and food system resilience