University of Ibadan Journals
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Effects of Indigenous spices on Microbial Quality and Sensory Attributes of Smoked Clarias gariepinus
No full textThis study investigated the influence of various indigenous spices on microbial load andsensory qualities of smoked African catfish (Clarias gariepinus). Twelve live specimens(1 kg) were processed and smoked using sterile protocols. Smoked samples preservedwith individual spices were stored in sealed containers for two weeks. Microbiologicalanalysis was performed biweekly on 1 g muscle tissue. Onion-spiced samples recordedthe highest moisture content (39.230.46%), while garlic-spiced samples exhibited elevatedtotal coliform counts (77.61021.16 cfu/g). Salmonella-related strains (includingShigella) were detected across treatments, alongside increased E. coli and S. aureus inweek two. Organoleptic assessment revealed highest acceptability for mixed spice-treatedfish (1.500.60). Indigenous spices improved sensory characteristics while exhibiting antimicrobialpotential
A Review of Automated Text Summarization Models on Diverse Datasets: An Evaluation Perspective
Full text linkThis paper reviews Automatic Text Summarization which is one of the tasks in Natural Language Processing (NLP). It is driven by speedy increase in textual data across domains. The reviews systematically examined the recent advancements in Extractive, Abstractive and hybrid automatic text Summarization Models between 2019 and 2025 using Preferred Reporting Items for Reviews and Meta-Analysis (PRISMA). Selected and relevant related papers were taken from Elsevier, Google scholar, IEEE Xplorer, ACM digital library, and Springer. After removing duplicates (n=96), 174 irrelevant records were removed to meet the inclusion criteria covering models like BERT (Bidirectional Encoder Representations from Transformers), BART (Bidirectional and Auto Regressive Transformers), T5 (Text-To-Text Transformer), TextRank, LSA (Latent Semantic Analaysis), and PEGASUS (Pre-training with Extracted Gap-sentences for Abstractive Summarization Sequence-to-to-Sequence Models) across Diverse datasets including news, scholarly and technical corpora. Extractive approaches depicted strong lexical accuracy and computational efficiency, whereas transformer-based Abstractive models showed superior semantic coherence but needed higher computational costs. This review paper also highlighted persistent gaps including dataset bias, long-document Summarization, hallucination in generative models, and over reliance on traditional metrics such as ROUGE.The results show the need for cross-domain evaluation, hybrid model integration, and adoption of advanced semantic metrics like BERTScore and MoverScore. Future directions should take into priority cross-domain benchmarks, standardized multi-metric evaluation, hybrid approach exploration and testing for long and multilingual documents. In furtherance, Reproducible Reporting of Computational cost such as GPU-hours and failure modes such as hallucinations will support more practical comparisons
Sustainable Urban Renewal and Green Housing Innovations: A Valuer’s Perspective on Emerging Trends in Nigerian Cities
Full text linkThis study investigates professionals’ perspectives on the valuation of green housing features in the context of urban renewal in Benin City, Nigeria. The objective was to assess levels of awareness, adoption, willingness to pay (WTP) and perceived valuation impacts of sustainable features, as well as to identify barriers to mainstream adoption. A mixed-methods approach was employed, involving survey data from 97 professionals, which are, estate surveyors and valuers, developers, planners and policymakers, and 15 follow-up interviews. Descriptive statistics, correlation, regression and ANOVA were applied. Results indicate moderate awareness, with energy efficiency (M= 3.42) and solar systems (M = 3.05) most widely recognized. WTP analysis revealed that 59.8% of respondents believed buyers would pay premiums averaging 8.5%. Regression results showed that energy efficiency, solar systems, water management and indoor environmental quality significantly predicted valuation premiums (Adjusted R² = 0.53). ANOVA indicated significant differences across professional groups (F = 3.20, p = 0.024), with developers projecting higher premiums. Qualitative findings emphasized barriers including lack of valuation guidelines, high costs and limited policy incentives. Triangulation confirmed that energy efficiency and solar systems are regarded as the most value-enhancing features. The study concludes that green housing has measurable potential to enhance property values andsupport sustainable urban renewal in Benin City. It recommends standardized valuation guidelines, fiscal incentives, consumer sensitization and professional training to mainstream adoption and strengthen sustainability integration in the housing market
A Machine Learning Approach to Flood Prediction
Full text linkClimate change, driven by both natural processes and human activities, has significantly disrupted living conditions across many countries. Among its most devastating effects is flooding, which impacts millions of people globally. Predicting the timing and severity of future floods remains a major challenge. This study adopts a data-driven methodology, employing machine learning techniques to forecast both the location and magnitude of floods based on historical flood data from Africa. We also investigate the most appropriate probability distribution models for recorded precipitation levels. Our findings indicate that, although Africa is a geographically distinct region that has received limited attention in the literature, its rainfall patterns can be effectively modeled using well-established probability distributions. Additionally, we identify the weeks with the highest and lowest rainfall as significant risk factors among various predictors of flooding. Our analysis further demonstrates that the accuracy of flood predictions is highly dependent on the choice of machine learning algorithm; with the optimal model, we achieve a prediction accuracy of approximately 85% for flood occurrence in targeted areas. These findings suggest that while certain flood predictors in Africa align with those commonly observed in other regions, region-specific factors must still be considere
An Hyperelliptic with Cellular Automata Encryption-based Data Protection Technique for Cloud Systems
Full text linkThe difficulty of data protection and privacy substantially inhibits the adoption of cloud technology, despite its popularity for efficiently providing access to data and other computing resources over the internet. Existing cloud data protection techniques have been found of failing to achieve lower computational costs, effective management of lengthy keys, efficient parallel computation, and the maintenance of balance between reliability of data protection and overhead. This research therefore, developed an improved encryption technique (HeCA) for achieving a high level of security with minimal computational cost and complexity in cloud-based systems. The HeCA was developed by combining a CA-based encryption/decryption, and a hyper-elliptic based signcryption/unsigncryption algorithms, that were formulated for the encryption/decryption of text and image data, and signcription/unsincryption of the secret keys respectively. The performance of the developed technique in comparison to existing BCAL and CA-RSA encryption techniques, was evaluated in the MATLAB R2020a environment using key length, processing time, and throughput as metrics. The results show that the developed HeCA technique outperforms the SBCAL and CA-RSA schemes in terms of key length, processing time and throughput. It is therefore concluded that the development of the HeCA has offered a superior data protection technique for cloud-based systems
Artificial Intelligence in Cybersecurity: A Comparative Review of Its Role across the Cyber Kill Chain
Full text linkThe adoption of Artificial Intelligence (AI) in diverse fields and the proliferation of interconnected devices have led to the emergence of highly sophisticated cyberattacks today. This new reality has compelled organisations to align their security policies by adopting cybersecurity frameworks. These frameworks provide organisations with models and methods for effectively managing digital security risks by promptly detecting and mitigating cyberattacks. The Cyber Kill Chain (CKC) decomposes cyberattacks into 7 phases, which cyber defenders can rely on when developing threat-informed strategies to mitigate cyberattacks. This paper presents a comprehensive overview of the CKC, highlighting the role Artificial Intelligence plays across each phase in terms of offensive and defensive cybersecurity operations. A comparative analysis of 3 cybersecurity frameworks, with justifications for each, was also examined. Drawing on real-world case studies and recent literature, this study further highlights current challenges with the fusion of AI into cybersecurity operations, ranging from data privacy, adversarial attacks, and AI explainability. The review concludes by advocating for the adaptation of dynamic, AI-driven modelling frameworks that better align with the rapidly evolving cyber threat landscape
Enhanced Malaria Detection Model using Deep Convolutional Neural Network with Comprehensive Data Augmentation and Grad-CAM Explainability for Clinical Trustworthiness
Full text linkMalaria remains a major global health challenge, particularly in sub-Saharan Africa and parts of Asia, where accurate and timely diagnosis is essential for effective treatment and control. Traditional microscopic examination, while widely used, is labor-intensive, subjective, and prone to misdiagnosis. To address these limitations, this study proposes deep learning-based approaches for automated malaria parasite detection from thin blood smear images. An enhanced malaria detection model using deep convolutional neural network with comprehensive data augmentation and Grad-CAM was developed. Using the NIH Malaria Dataset comprising 27,514 validated images, the models were trained and tested with rigorous preprocessing, augmentation, and stratified sampling. Results show that the CNN model achieved 96.37% accuracy, 98.40% recall for parasitized cells, and an AUC of 0.9935, outperforming conventional methods and providing robust generalization for unseen data. This study highlights the potential of deep learning in advancing malaria diagnostics while also addressing critical deployment considerations, including error calibration and clinical applicability. This enhances clinical Trustworthiness
Enhancing Symmetric Encryption Using Digital Signatures
Full text linkMaintaining the confidentiality and integrity of digital documents transmitted through electronic media is a critical security concern in the field of Information Security. To address this security concern, this paper proposes a system that uses a digital signature to ensure the authenticity, non-repudiation and integrity of the transmitted data and it also uses symmetric encryption to provide authentication and confidentiality of the transmitted data. The Rivest, Shamir & Adleman (RSA) algorithm was used to implement the Digital Signature while the Advanced Encryption Standard (AES) was used for symmetric encryption. The system involves encrypting a plaintext using AES, then a hash function (SHA-256) is used to create a hash value of the ciphertext and the private key of the RSA algorithm is used to encrypt the hash value to produce the digital signature. The ciphertext and the digital signature are attached and sent to the recipient. The digital signature is decrypted by the recipient to obtain the hash value of the ciphertext, then it verifies if it is a valid signature before proceeding to decrypt the ciphertext using the AES secret key. The proposed system was evaluated against the existing AES algorithm. The size of the test file was observed and analyzed before and after encryption, this showed that the size did not change. Different RSA key sizes were used to perform signature and verification processes to see how long it takes to perform the operations, this also showed that the smaller the key size the faster the signature and verification processes and the verification process is a much faster process than the signature process. The system was able to meet the cryptography objectives and will be useful to individuals and businesses in transmitting sensitive information over insecure communication mediums
Evaluation of the factors Governing Higher Institution Solid Waste Management: case of Obafemi Awolowo University, Ile-Ife, Nigeria
Full text linkThis study examines the factors militating against sustainable solid waste management in the institution of higher learning, using Obafemi Awolowo University, Ile-Ife, Nigeria, as the study area. The study utilised primary and secondary data. Primary data were obtained through personal observation, interviews, and the administration of a structured questionnaire in different activity areas of the university. The information requested is on solid waste sorting, storage, collection, transportation, and disposal, as well as the factors influencing solid waste management in the university. The questionnaire was administered to the stakeholders in the four major activity areas of the study area. These are the hostels, staff quarters, academic area, and the market. A total of 306 respondents selected through systematic random sampling of every 10th occupant were administered a questionnaire on the space users. The author's collaborative efforts with four assistants ensured a comprehensive and robust data collection process. The data collected were analysed through inferential statistical methods such as the relative importance index and factor analysis, a statistical technique used to elucidate and rank the variables that contribute significantly to the solid waste management activity. Factor analysis was specifically harnessed to identify and hierarchically order the salient factors responsible for the management of solid waste. The study concluded that the most important factors militating against effective solid waste management are inadequate human and material resources, ineffective institutional framework, the composition and quantity of solid waste generated, inadequate monitoring and evaluation of waste management practices, and lack of technical know-how of the waste collectors.
 
Some Methods of Processing Fish: Strengths,Weaknesses, Opportunities and Threats (SWOT) Analysis in Nigeria.
No full textFish processing and preservation play a crucial role in reducing post-harvest losses andenhancing food security in Nigeria. This paper presents a comprehensive SWOT analysisof key fish processing methods, including freezing, heat-based curing, canning, extrusioncooking, coated/fried products and fermentation, with emphasis on their applicationin the Nigerian context. It highlights the strengths of these technologies in extendingshelf-life, improving market value, and supporting aquaculture development. However,it also identifies significant weaknesses such as infrastructure deficits, high capital costs,and food safety concerns. Opportunities for innovation and market expansion, throughimproved technologies, value-added product development, and domestic industrializationare explored alongside threats including economic instability, environmental challenges,and shifting consumer preferences. By critically evaluating each method’s viability, thisstudy underscores the need for policy support, investment in infrastructure, and adoptionof modern techniques to ensure sustainable fish consumption and production in Nigeri