University of Ibadan Journals
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Gender Bias in Property Letting: Discriminatory Rental Practices Against Women in Ibadan’s Real Estate Market
Full text linkGender discrimination in home rentals is a prevalent but little-study facet of housing discrepancy in Nigerian metropolises. This research examines inequitable rental practices in contradiction of women in the Ibadan real estate market with the main objective of detecting and evaluating the reasons that lead to unfairness in tenant choice. The research, which draws from investigation statistics from a limited metropolitan districts and qualitative discussions with female tenants, landlords, and estate agents,establishes how economic predispositions, a lack of strict implementation of laws, and severely rooted sociocultural stereotypes all work in contradiction of women who make available accommodation. Several researches have confirmed that the calibre of institutions significantly have influence on developmental outcomes, and our discoveries support the conception that in Nigeria's housing subdivision, biased practices are made worse by inadequate accountability. Also, the scrutiny highpoints by what means gender-sensitive rule design improves fairness and development results, in spite of the fact that these backgrounds keep on the edge in the authority of urban property. By contextualizing Ibadan's situation inside broader tête-à-têtes on institutional confidence and all-encompassing governance, biased tenant selection put in danger women's housing rights and social consistency. The paper concludes with strategy propositions that stress the need for robust anti-discrimination regulations, public consciousness campaigns, and gender-responsive urban housing guidelines. These observations contribute to the prevailing works on gendered admittance to real estate markets and strengthen current deliberations in Africa about impartiality, inclusive urban development, and institutional change
The Role of Artificial Intelligence (AI) in Circular Economy in a Bid to Ameliorate Global Waste Crises
Full text linkThe accelerating global waste crisis - driven by rising consumption, short product lifespans, and uneven waste management capacity - demands transformative solutions. This paper examines how advances in Artificial Intelligence (AI) can catalyze the transition from a linear “take-make-waste” model to a Circular Economy (CE) that designs out waste, keeps products and materials in use, and regenerates natural systems. The paper synthesises recent evidence and examples across waste prevention, product design, wastes sorting and recycling, resource recovery and policy enforcement. It further identifies technical, social and governance challenges; and proposes actionable policy and operational recommendations for governments, industry and research actors. The paper concludes that AI is an enabling technology for CE at scale, but real impact requires systemic integration, data-sharing architectures, human-centred governance and targeted investment
A Zero Trust Hybrid Machine Learning Algorithms for Threat Detection and Prevention with Explainable Threat Intelligence.
Full text linkThis study presents a dual-model intelligent cybersecurity framework integrating Malware Detection and SQL Injection Detection to enhance automated threat identification and prevention. For malware detection, a Random Forest classifier was employed to analyze users activities. The model achieved an accuracy of 99.13%, precision of 98.52%, and recall of 98.56%, demonstrating exceptional reliability in differentiating malicious from benign files. The ROC curve (AUC = 0.9994) and Precision–Recall curve confirmed the model’s high discriminative power, while LIME and Permutation Feature Importance analyses provided interpretability, revealing that features like MajorSubsystemVersion and SectionsMeanEntropy strongly influence classification outcomes. For SQL injection detection, a feedforward neural network (FFNN) with two dense layers (32 and 64 neurons) was implemented using three handcrafted features—query length, punctuation, and SQL keywords. The model achieved an accuracy of 99.73%, precision of 99.7%, recall of 99.95%, and F1-score of 99.8%, indicating near- perfect discrimination between malicious and benign queries. The ROC (AUC = 1.00) and Precision–Recall curves further confirmed its robustness. LIME explanations provided local interpretability by highlighting influential query attributes driving predictions. A real-time detection dashboard continuously validates every access attempt—file uploads or SQL queries—using both models in parallel. Malicious inputs are instantly flagged and blocked, ensuring proactive protection. Overall, the proposed framework combines high detection accuracy with explainable artificial intelligence (XAI) techniques, providing both transparency and reliability for modern cybersecurity defense systems
Sustainable Urban Growth: Assessing the Role of Environmental Management Policies in Ibadan, Nigeria
Full text linkRapid urbanisation remains one of the defining features of developing economies, particularly in sub-Saharan Africa, where cities are expanding at unprecedented rates. Nigeria exemplifies this trend, with its urban population growing by an estimated 4.3% annually. Ibadan, one of Nigeria’s oldest and largest cities, has experienced significant spatial and demographic transformation, shifting from predominantly agrarian settlements to a rapidly expanding urban centre. However, this urban growth has largely been unplanned and poorly managed, resulting in severe environmental challenges, including flooding, improper waste disposal, deforestation, and unregulated land use. This study, therefore, examined the impact of environmental management policies on sustainable urban development in Ibadan, Nigeria, with a specific focus on the effectiveness of existing policies, governance mechanisms influencing policy implementation, and the challenges and opportunities affecting policy integration into urban planning. Anchored on the Sustainable Development Theory, the study adopted a qualitative research design utilizing semi-structured interviews and document review as primary and secondary data sources. Data were analysed thematically using Braun and Clarke’s (2021) six-phase framework, drawing insights from policymakers, urban planners, environmental officers, community leaders, and representatives of non-governmental organisations. Findings revealed that although Ibadan possesses a relatively comprehensive framework of environmental management policies, such as the Oyo State Environmental Policy and the Ibadan Urban Flood Management Project (IUFMP), their implementation remains weak due to inadequate funding, limited technical capacity, poor inter-agency coordination, and weak enforcement mechanisms (Aliyu & Adeyinka, 2024; Lawal et al., 2025). Institutional fragmentation and political interference further undermine policy continuity, while insufficient community engagement and accountability structures reduce compliance and legitimacy. Despite these challenges, the study identified opportunities for progress, including donor-funded partnerships, adoption of geospatial technologies, promotion of green infrastructure, and growing national-level policy reforms that could enhance local sustainability outcomes. The study concludes that Ibadan’s urban environmental governance reflects a paradox of strong policy intent but weak implementation. Sustainable urban growth can only be achieved when environmental management policies are effectively integrated into urban planning processes through coherent governance, stable institutional frameworks, adequate funding, and participatory inclusiveness. It recommends strengthening institutional capacity, establishing a central environmental coordination mechanism to harmonize agency roles, institutionalizing GIS and environmental assessment tools in planning, and enhancing citizen participation to ensure resilient, inclusive, and sustainable urban development
Enhancing Holistic Health through Healing Architecture: A Literature Review
Full text linkThe environment have long been recognized to usually have health effects on occupants. The aim of this study was to undergo a thematic analysis of existing literature towards providing a more coherent understanding of the concept of healing architecture. As humans are physically, mentally and emotionally connected to the built up environment through employment, retirement, education and play. This connection fosters a dynamic life in which individuals should flourish in all spheres and actively engage in fostering their relationship with the natural world. The research method entails literature search in Google scholar online engine using “healing architecture” and “holistic health” as themes. The study concludes that the major sub-themes of healing architecture are access to view of nature, use of soft landscape in the environment, improved sound and light levels, bioclimatic design and using the building envelope for environmental control
Development of a Hybrid Blockchain-Based File Sharing System in a Cloud Environment
Full text linkConventional file storage and sharing systems encounter issues with security vulnerabilities, transparency shortcomings, and ineffective distribution. This initiative introduces an innovative blockchain-based framework for secure file sharing, addressing these challenges through the utilization of blockchain, Interplanetary File System (IPFS), and Advanced Encryption System (AES) encryption. The system uses a blockchain network to record file metadata like ownership, timestamps, and access permissions, ensuring data transparency and integrity. AES encryption secures data confidentiality, while IPFS efficiently distributes file chunks across nodes, enhancing availability and reliability. This approach creates a secure, efficient, and transparent file sharing solution. Performance evaluations demonstrate that the developed model outperforms existing models in terms of response time and requests per second, highlighting its efficiency under load
Performance Analysis of a Hybrid Autoencoder-TCN Model for SQLi Detection: Accuracy, Efficiency and Generalizability
Full text linkStructured Query Language Injection (SQLi) attacks remain a critical cybersecurity threat, exploiting vulnerabilities in web applications to compromise database integrity and confidentiality. Traditional detection methods, such as rule-based systems and conventional machine learning models, face limitations in generalizing to novel attack patterns and preserving sequential query context. This study proposes a novel hybrid deep learning architecture integrating autoencoders, tokenization, and Temporal Convolutional Networks (TCNs) to address these challenges. The framework employs SQL-aware tokenization to parse queries into syntactic units, an autoencoder to learn latent representations of benign query patterns, and a TCN to model temporal dependencies in token sequences. By combining anomaly detection (via reconstruction error) with temporal analysis, the model identifies both known and zero-day SQLi attacks with high precision. Evaluated on a labeled dataset of 10,000 SQL queries (1,200 malicious, 8,800 benign), the proposed approach achieves 95.5% accuracy, 94.0% F1-score, and 95.5% recall, outperforming baseline models such as CNNs, LSTMs, and standalone autoencoders. The TCN’s parallel processing capability reduces inference latency by 32% compared to recurrent architectures, making it suitable for real-time deployment. Furthermore, tokenization enables interpretability by localizing malicious query segments, aligning with regulatory demands for explainable AI in cybersecurity. This work advances SQLi detection by bridging gaps in temporal modeling, computational efficiency, and generalization, offering a scalable solution for securing web applications against evolving injection threats
Improved Sentimental Response for Classifying Emergency Incidence through Hybridized Mining Technique
Full text linkThis research addresses the classification of emergency incidents arising from both natural and human-induced events, emphasizing the necessity for timely intervention and strategic mitigation. It introduces a hybrid data mining approach that integrates Natural Language Processing (NLP) with Bayesian Belief Learning (BBL) to enhance sentiment analysis during crisis scenarios. Real-time data is extracted from Facebook through the Graph API using Python’s requests library. The collected data undergoes preprocessing and is stored in a MySQL database, while the system interface utilizes XML and PHP to display sentiment outcomes. The integration of supervised learning into the NLP process resulted in a signal precision exceeding 92.8%, surpassing the accuracy of existing approaches. A confusion matrix is employed to assess the model’s performance, confirming its high level of predictive precision. The system demonstrates strong capabilities for improving proactive emergency detection and management
Design and Construction of a Wireless Automatic Water Monitoring System
Full text linkWater management involves the planning, development, distribution, and control of the optimal use of water resources in an environment—sourced from boreholes, wells, and other means. Ensuring the sustainability of available water resources has become a critical concern globally, as water remains an essential element for human survival. Radio Frequency (RF) refers to the oscillation rate of electromagnetic radiation or radio waves. In this study, a Wireless Automatic Water Monitoring and Pump Control System was proposed, designed, and implemented to wirelessly monitor water levels in a tank using RF technology and to automatically control the pump operation. Sensors were placed at various levels in the tank to detect water levels at any given time. An embedded system, centred around the PIC16F877A microcontroller, was used to process input signals received via RF from the transmitter module. These inputs were processed through an inverter, and the resulting outputs determined whether the pump was activated or deactivated depending on the tank's water level. The system was tested and evaluated. Results showed that it accurately detected water levels and effectively managed the pump, switching it ON when water was low and OFF when the tank was ful
Practical Applications of Network Management Tools in Emerging Technologies
Full text linkThe rapid evolution of emerging technologies—such as the Internet of Things (IoT), edge computing, 5G network slicing, and artificial intelligence (AI)—has significantly reshaped network management practices. As networks become increasingly complex, large-scale, and diverse, traditional approaches relying on manual oversight and static, rule-based systems are no longer sufficient. To address these growing demands, modern network management is shifting toward intelligent, automated solutions capable of real-time analysis, dynamic resource allocation, and improved security. This article examines the practical applications of advanced network management tools, with a particular emphasis on AI-driven monitoring, anomaly detection, automation, and the move toward standardizing network intelligence. Based on recent technological developments from 2019 to 2025, it evaluates how these tools contribute to building more resilient, adaptive, and secure networks. The discussion highlights key advantages, including predictive maintenance, faster fault detection, optimized traffic handling, and proactive threat response. However, it also addresses limitations such as privacy risks, potential algorithmic bias, and integration challenges with legacy systems. Emerging trends such as self-healing networks, federated learning, and intent-based networking are explored as future directions for scalable and intelligent infrastructure. By addressing both the benefits and challenges, this article emphasizes the essential role of AI enhanced network management in enabling next-generation connectivity