University of Ibadan Journals
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EXPLORATION OF GROWTH AND SOME HEALTH STATUS INDICES OF Trichopodus trichopterus FED AT VARIED FEEDING REGIME
No full textThe health status of fish in captivity has been shown to be an important indicator to thewellbeing and life span of fishes, especially in the ornamental fish industry. Feed, feedingtechnique and water quality plays a pivotal role in optimizing the overall well-being ofthe fish. This study assessed the effect of the feeding regime on health status indicatorsin Trichopodus Trichopterus. Juveniles (3.6±0.2g) were stocked at 5 fish per tank in 12aquarium tanks. They were fed to satiation at different feeding regime (twice daily) (T1),once daily (T2), once in two days (T3), or once in three days (T4) for 12 weeks. At theend of the study, body weight, survival, cortisol, glucose and antioxidant activities weremeasured following standard procedures. The result showed that T2 had the highest feedintake (0.78±2.18 g and 0.72±2.01g) in the second and third months, respectively. Thehighest final weight of 10.7±0.1g was recorded in T1 while survival was best in T2(93.3%)at 12 weeks. Glutathione activity was highest in T4 (27.12±1.18u/mg) while the least is inT2. Cortisol has the highest activity in T4 (4.7±.03) while the least was in T1 (4.02±0.12).Malondialdehyde showed a high activity level in T4 but was not statistically significant. Itshows that an improper feeding regime exacts some level of stress on the fish which maylead to the loss of this precious pet over time hence pet fish keepers should put measuresin place that ensures the fish feeds at least once dail
Optimization of Paddle Wheel Aerator Parameters for Enhanced Aquaculture Water Quality Using Response Surface Methodology Techniques
No full textWater quality plays a pivotal role in the efficient management of aquaculture systems, particularlythrough the regulation of dissolved oxygen (DO) levels. This study focuses on themodification and performance evaluation of a locally fabricated mechanical paddle wheelaerator for optimizing fish pond agitation and oxygenation. Key operational parametersinvestigated include rotational speed (300–600 rpm), paddle submersion depth (0.1–0.2m), and inclination angle (15°–45°). The aerator was tested in a 2,000 L concrete pond(1×2×1 m) with a stocking density of 300 fish/m³ using unsteady-state aeration tests. Resultswere statistically analyzed using Design Expert Software (2022) with ANOVA at p<0.05. Maximum Standard Aeration Efficiency (SAE) of 5.856 kg O/kWh was achieved at450 rpm, 0.2 m depth, and 45° inclination, while the highest Standard Oxygen TransferRate (SOTR) was 5.656 kg O/h at 600 rpm. The findings demonstrate that optimized paddlewheel design and configuration significantly improve aeration performance, offering acost-effective solution for enhancing water quality in pond-based aquaculture systems
Influence of Knowledge Sharing and Collaboration on Estate Surveying and Valuation Firms' Performance in Abuja, Nigeria
Full text linkThis study examined the influence of knowledge sharing and collaboration on the performance of estate surveying and valuation firms in Abuja, Nigeria. It identified the various knowledge-sharing and collaboration mechanisms used by these firms, analysed their effectiveness, and the impact on the performance outcomes of the firms. To achieve these objectives, eighty-two (82) copies of the questionnaire were distributed to estate surveying and valuation firms in the study area via Google Forms, with a response rate of 85.36%. The data collected were analysed using frequency distribution tables, weighted mean scores, and correlation analysis. Results showed that direct person-to-person knowledge sharing and formal databases were the most common and effective mechanisms for knowledge sharing. IT-based tools, such as document management systems and video conferencing tools, were widely adopted; however, collaborative platforms were underutilised. The study also found that direct person-to-person sharing and formalised knowledge management significantly improve performance outcomes (r =0.284, p = 0.017) and (r =0.500, p < 0.001). Brainstorming, collaborative problem-solving, and digital communication methods also have positive effects, but to a lesser degree. The use of project reviews and Scrum meetings exhibited a complex relationship with performance (r = ?0.987, p = 0.002), being less effective in isolation but beneficial in structured contexts. The study recommends that estate surveying and valuation firms should leverage direct person-to-person knowledge sharing by organising regular face-to-face meetings, mentorship programmes, workshops, and brainstorming sessions to boost their performance
Predicting Student Academic Performance Using a Scalable Regression Based Data Mining Approach
Full text linkPredicting student academic performance is a key tool for supporting academic planning and identifying those who may need extra help. This study develops a regression-based model aimed at forecasting academic outcomes among students at the University of Ibadan, Nigeria. Data were collected from 92 departments over a three-year period, covering both academic records and non-academic factors. After data preparation—which involved cleaning, feature selection, and encoding—three regression techniques were applied: Stochastic Gradient Descent (SGD), Gradient Boosting Machine (GBM), and Extra Trees Regressor (ETR). Among these, the ETR model gave the most accurate predictions, based on performance metrics like Mean Squared Error (MSE), Root Mean Squared Error (RMSE), and R-squared (R²). The use of loss functions such as Huber further improved the model’s ability to handle outliers. The findings show that this model can help pinpoint students at risk of poor academic performance and support better decisions in academic advising, resource planning, and policy implementation
Performance Evaluation of MANET Protocols against Sleep Deprivation Attacks
Full text linkMobile Ad-Hoc Networks (MANETs) are critical in many modern applications due to their flexible and decentralized nature. However, they face significant security challenges, notably Sleep Deprivation Attacks (SDAs), which can severely degrade network performance. This study evaluates the performance of three MANET routing protocols—Adaptive On-demand Distance Vector (AODV), Low Energy Adaptive Clustered Hierarchical (LEACH), and Ensemble On-demand Low Energy Adaptive Clustered Hierarchical (EO LEACH)—under SDA conditions before and after the application of clustering techniques. Using MATLAB for simulation, the study compares protocol performance based on Packet Delivery Ratio (PDR), Average Energy Consumption, and End-to-End Delay. The findings highlight the importance of clustering in enhancing protocol resilience and efficiency, providing valuable insights for the design of more secure and robust MANETs
Development of an Android-Based MedicalBot to Diagnose and Suggest Remedies for Tuberculosis
Full text linkTuberculosis (TB) is one of the top ten causes of death worldwide and the leading cause of death from an infectious disease. TB is an airborne bacterial infection caused by Mycobacterium tuberculosis, which mainly attacks the lungs. People who have Tuberculosis will have to go to the hospital and in many cases, the availability of the medical specialist cannot be guaranteed. In most cases, when the medical specialist is available, the patient will not be able to afford the charges of obtaining the hospital form and test conducted. The research work focused on the three stages of Tuberculosis which are Exposure, Latent and Active stages Tuberculosis. Agile methodology method was used to carry out the methodology and the programming tools used in achieving this are HTML JAVASCRIPT, CSS PHP and SQL as the database. These tools were used to develop a medical bot page where users can interact with the system; learn more page where user can get more information about Tuberculosis and Patient Data form page where user can register after diagnosing. The system is made flexible, versatile and user-friendly. The application has been tested by various students using Android devices operating system and successful result was confirmed
Socio-spatial Pattern of Crime Prevalence in Akure, Nigeria
Full text linkCrime is a human security problem affecting humanity around the world. In Nigeria, the upsurge in crime is a serious concern. This study examines the socio-spatial patterns of crime in Akure to provide physical planning measures that will aid policy formulation in the study area. Crime prevalence was analysed using statistical tools to examine the spatial patterns of crime, types and nature of crime committed, factors affecting crime and the impact of crime in the study area. Akure was zoned into high, medium, and low-density zones, and questionnaires were administered to 170 residential buildings using simple random sampling within the street of each zone. Findings showed that 70% of the types of crime examined in the three density zones were categorized as very high within the high-density area of Akure. The study highlighted unemployment, parent conflict, and dysfunctional families with uncaring behaviours as the major factors influencing crime across the board. In addition, confusion, fatigue, sadness, loss of property, and helplessness adversely affect criminal activities in the vicinity of Akure. Analysis of variance and Turkey’s honestly significant difference (HSD) test revealed significant differences in the prevalence of crime across different density zones in Akure. Therefore, the study encourages planning professionals to integrate crime prevention through environmental design (CPTED) to design neighbourhoods and commercial areas, promote natural surveillance, discourage illicit activities, and enhance community cohesion across the board.
 
A Systematic Review of Computational Approach to Pipeline Leakage Detection in a Water Distribution Network
Full text linkThe detection and localization of leakages in water distribution networks is crucial for both the conservation ofresources and the efficient operation. The process network has proved to be a difficult task over years,considering the complexities inherent in water distribution networks. The enormous interconnected pipelinesmake the leakage detection and location process burdensome. Computational techniques play a significant role inthis domain by offering advanced tools and techniques for leakage detection. This study, therefore, performed asystematic review of published articles on computational leakage detection and localization in a waterdistribution network. Findings show the number of recent quality studies on the computational approach to waterdistribution network leakage research is beginning to dwindle, considering the journal's impact factor. In therecent studies, a deep learning algorithm is beginning to trend as the most significant computational technique,as it accounts for 13.21 % (n=7) of the pipeline leakage research output. The univariable predicated studiesaccount for 83.33%of the research output disseminated in the past five years. The invention of various efficientlearning methods and network structures in deep learning algorithms makes it suitable for the realization ofmulti-disciplinary studies, as the multi-variable concept will reduce false positives and negatives, enhancing theoverall reliability of leak detection and localization models in future studies
A Multiclass Model for Adversary Domain Name Classification using Tree Based AI Classifiers
Full text linkThe rising prevalence of AI-generated adversary (malicious) domain names has escalated the challenge of combating cybercrime, particularly as spamming, phishing, and malware activities become increasingly common online. Traditional approaches, such as blacklisting, binary detection systems, and basic lexical analysis of domain names, prove insufficient for real-time identification of malicious domains across various cyber threat landscapes. This study presents a comprehensive strategy for the multiclass detection of malicious domain names (MDNs) utilizing data mining techniques. It investigates feature engineering processes, including dimensionality reduction and variance inflation factor analysis, to identify and select domain name features that enhance the performance of advanced AI and machine learning classifiers in classifying MDNs. We employed a train/test split ratio and cross-validation methods on the CIC-Bell-DNS2021 public dataset for training some cutting-edge AL/ML classifiers. The findings reveal that tree-based machine learning algorithms, particularly the Extreme Gradient Boosting (XGBoost) algorithm achieved outstanding results, with a mean accuracy score of 0.9998 (100%). Additionally, regarding execution time, XGBoost displayed a notable advantage, requiring less time to build models, which could significantly influence real-time detection capabilities when implemented as a cybersecurity tool for detecting malicious domain names
A Review of Fixed Input Size Limitation in Convolutional Neural Networks Models and Proposed Solutions
Full text linkConvolutional Neural Networks (CNNs) are incredibly powerful deep learning techniques that have been applied to computer vision applications to yield innovative results. CNNs are ideal for applications like object identification, image segmentation, and image classification because they can automatically extract pertinent information from the images without human supervision. While CNNs can attain state-of-the-art performance in many applications and domains, most CNNs currently have limitations in training and prediction due to their sensitivity to image size. As a result, image recognition datasets are typically downsized to the input size specification of the CNN models. This study's objective is to examine CNN models and suggest possible solutions to tackle the fixed input size problem that exists in CNN models