University of Ibadan Journal of Science and Logics in ICT Research

Design and Construction of a Wireless Automatic Water Monitoring System

C. V. Nwufoh — 2025-12-23

Water 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 full

Behavioral Analysis Model for Enhancing Attendees Experiences in Events Through K-Clustering Technique

Babajide Samson Adegbite — 2025-12-23

Events management landscape plays an important role in delivering an exceptional attendee experience, from planning to implementation and attendee’s engagement which serves as a critical success element for event organizers. Despite the increasing use of technology in event management, there remains a limited understanding of attendees' behavioural engagement in events either during or after for enhanced attendees’ experiences. This study seeks to bridge the gap and examine attendees’ behavioural segmentation using K-clustering technique for the identification of attendees’ engagement during and after events. This research aims to develop a behavioural analysis model using K-clustering techniques to identify attendees' engagement in events for improved attendees’ experiences. The quantitative research method was used for this research. The designed and model implementation was developed using Python Programming language. The results showed that the attendees’ engagement was clustered into four namely the minimally, multidimensional, highly cognitive and quietly, highly affective and socially engaged. Also, there was no string engagement in terms of the observed age or gender. The elbow performance metrics shows that the four behavioural engagement patterns best represent the data without complexities with the within-cluster sum of squares value of 171820.15 as the inflection point. The silhouette score of 0.37 indicates a decent but not perfect clustering and good enough for the earl-stage attendee segmentation and need further tuning for decision making. The study concludes that event attendees participate more in highly affective and multidimensional segments and that K-clustering technique serve as an important method for understanding both low- and high-involvement of attendees in events

Igbo Text Named Identity Recognition (NER) System using Natural Language Processing Algorithms: A Review

Jacinta Chioma Odirichukwu — 2025-12-23

This is a review paper, which is concerned with the recent nature of Named Entity Recognition (NER) for the Igbo language. It is a low-resource language spoken in the Southeastern part of Nigeria. Irrespective of the numerous advancements in NER for high-resource languages, Igbo NER so far remains underrepresented. This is for its unique linguistic challenges, which includes morphological richness and dialect variations. In recent times, frank efforts have been put forward by MasakhaNER and WAZOBIA NER projects to develop NER datasets and models for the Igbo language. The existing datasets are limited in size and domain coverage. For this reason there are needs for high-quality, large-scale, manually annotated NER datasets for real-world deployment. This paper reviews the existing literature works on Igbo NER, highlighting the challenges, creating opportunities and looking into the potential applications of NER in developing Igbo digital assistants, intelligent search, and machine translation. This work aims to contribute to the growth and development of low-resource African NLP with the provision of future research in indigenous language NER

Integration of NaijaCaptcha System to an Intelligent PDF Reader with Translator

O. T Olanrewaju — 2025-12-23

Abstract The intelligent PDF reader with integrated translation capabilities is a software solution designed to enhance the accessibility and usability of PDF documents. This project addresses the challenges faced by users who need to interact with multilingual and scanned PDF files, providing a seamless experience for viewing, extracting, translating, and annotating text. The primary objectives of this project are to develop an application that allows users to upload and view PDFs, extract text using Optical Character Recognition (OCR), translate extracted text into various languages, annotate documents and integration of CAPTCHA system called NAIJACAPTCHA. These features are designed to improve efficiency, accessibility and security for users who need to manage and understand content in different languages and formats. Key features of the application include a PDF viewer for navigating documents, OCR for converting scanned images into editable text, translation of text into multiple languages, and annotation tools for highlighting and commenting on PDFs. The application also includes robust user authentication and authorization mechanisms to protect user data and maintain privacy. The system architecture consists of a user-friendly interface built with React, a backend developed using Flask, OCR capabilities provided by Tesseract, and translation services integrated via the Google Translate API. PostgreSQL is used for data storage, ensuring secure and efficient management of user data and application configurations. This study demonstrates the effective integration of various technologies to create a powerful tool that simplifies the management and interaction with complex PDF documents. The intelligent PDF reader with integrated translation capabilities contributes to a more inclusive and efficient digital environment, addressing the needs of users dealing with multilingual and scanned PDF documents.

Particle Swarm Optimization-Random Forest Weather-Based Crop Yield Prediction Model

O.A. Abisoye — 2025-12-23

Crop production is a vital source of food for humans, and improving crop yield requires a deep understanding of crop production processes. It has been proven that increasing crop yield reduces poverty, crop failure risk, increases productivity, and optimizes the value of agricultural land. Many factors affect the amount of crop harvested in a specific area and several studies, mainly in the agricultural context, have been conducted to estimate crop yield production with Machine learning (ML) techniques. This study explores five cereal crop yields: rice, maize, wheat, sorghum, and soybeans with Particle Swarm Optimization (PSO) and Random Forest prediction approaches. Performance metrics such as R2 score, Mean Absolute Error, and Root Mean Squared Error confirm the authenticity of the model. The result of the optimized Crop yield prediction has an R2 score of 97.13, MAE of 124.75, and RMSE of 1273.73. The model performed better than other existing approaches, such as Random Forest (RF) and Decision Tree (DT). This study will provide farmers with reliable crop yield predictions, enabling better planning based on weather conditions.

Enhanced Malaria Detection Model using Deep Convolutional Neural Network with Comprehensive Data Augmentation and Grad-CAM Explainability for Clinical Trustworthiness

E. C. M Obasi — 2025-12-23

Malaria 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.

Design and Implementation of Monopoly Game using Yoruba Language

G. A. Akinosho — 2025-12-23

Monopoly is a classic board game that simulates real estate trading and economic principles. Played by two to eight players, its objective is to accumulate wealth through strategic property acquisition and development, while bankrupting opponents. The game is known for its iconic board layout featuring properties named after real streets and locations, alongside utilities and railroads. Players roll dice to move around the board, purchasing properties they land on and collecting rent from opponents who land on their owned properties. Monopoly also incorporates chance and community chest cards that can either benefit or hinder players' financial positions but all the features use in playing the game was written in English language which is a common language in a large community. However, some non-educated people most especially people from western part of Nigeria always find interest in this game but their inability to read characters written in English has incapacitate them from playing the game. To resolve this problem a monopoly game using Yoruba Language that will allow larger audience and Yoruba like to participate in the game players and to monopolize property groups to increase rental income, making negotiations and trades essential components of game play was developed. The research methodology, flow chart and the board interface system are explored. The flow chart illustrates interactions between player, color representing each player, players turn, money deduction or addition when landed on each corner on the board. The new system, a monopoly game using Yoruba Language, is user-friendly interface and the players were able to play it conveniently with no stress.

Pipeline Leakage Detection and Monitoring Model using Enhanced Multiple Signal Classification Algorithm and Hybrid Acoustic Emission Techniques

O.A. Abisoye — 2025-12-23

The consequences of pipeline leakages pose great multifaceted hazards, including carcinogenicity and cytotoxicity in humans exposed to leaked toxic substance from pipelines. Pipeline leak also causes environmental contamination of soil resulting to environmental pollution, fire disaster and even loss of life. Therefore, pipeline leakage detection monitoring is a crucial concern in pipeline industry for ensuring the safe and efficient operations. Background noise and detection of single leak are significant limitations of the existing pipeline monitoring and leakage detection techniques. These undesired noises can arise from multiple sources, including environmental, proximity industries, pipe vibration, and electronic interferences. This study therefore optimizes the conventional Multiple Signal Classification (MUSIC) algorithm and Acoustic Emission (AE) technique with the aim to develop a novel technique to address the effect of the background noise. The proposed method combines the advantages of the MUSIC algorithm and AE techniques with real-time monitoring to promptly and accurately detect leakages in pipeline systems. The model achieved Accuracy of 95.5%, Sensitivity of 75%, Mean Detection Time of 1.02 seconds and Response Time of 1.06 seconds. These quantitative results demonstrate the effectiveness of our proposed Enhanced MUSIC algorithm and Hybrid AE technique (Enhanced-MUSICHAE) to detect and monitor pipeline leakage. This has the potential to improve pipeline safety, reduce economic losses, and minimize environmental damages.

Application of Machine Learning Algorithms in Predicting the Toxicity of Chemical Compounds for Safer Pharmaceuticals

Obasi E. C. M. Obasi E. C. M. — 2025-12-23

The development of safe pharmaceuticals requires accurate and efficient prediction of chemical toxicity to minimize adverse health risks and reduce reliance on costly and ethically challenging animal testing. This study investigates the application of three machine learning (ML) algorithms—Random Forest (RF), Support Vector Machine (SVM), and Linear Regression (LR)—for predicting the toxicity of aromatic chemical compounds. A dataset of 11,001 compounds was curated, preprocessed, and analyzed using molecular descriptors such as molecular weight, lipophilicity, and polar surface area. Model performance was evaluated using accuracy, precision, recall, F1-score, and specificity. Results showed that the Linear Regression model performed poorly, with accuracy around 52%, indicating limited suitability for toxicity classification. The SVM model achieved substantially better results, with an accuracy of 80%, demonstrating its effectiveness in capturing nonlinear structure–toxicity relationships. Notably, the Random Forest model outperformed both, achieving perfect classification accuracy (100%) across all metrics, with zero false positives and false negatives. Feature importance analysis revealed that descriptors such as Topological Polar Surface Area and Molecular Fractional Polar Surface Area were key contributors to toxicity prediction. The findings demonstrate that Random Forest is a robust and interpretable tool for early toxicity screening, offering both predictive accuracy and insight into molecular features driving toxicity. By integrating ML models into pharmaceutical research pipelines, drug discovery can be accelerated, costs reduced, and ethical imperatives met by minimizing animal testing. Future work should focus on external validation, hybrid model development, and explainable AI techniques to enhance generalizability and regulatory acceptance.

A Machine Learning Framework for Classifying Haemoglobin Levels in Sickle Cell Anaemia Patients

O. B. Olajide — 2025-12-23

Sickle Cell Anaemia (SCA) significantly impacts haemoglobin (HGB) levels, leading to severe health complications with high mortality rates. In Nigeria, about 2% of newborns, approximately 150,000 annually, are diagnosed with SCA. Accurate HGB monitoring is essential for effective disease management, yet traditional methods are labour-intensive and prone to errors. This necessitates automated and reliable diagnostic techniques like machine learning (ML) for improved SCA management. This study classifies HGB levels in SCA patients using clinical records and ML techniques. A dataset of 364 records (203 female population) was obtained from Kaggle; a public data repository containing eleven (11) features namely: age, sex, red blood cell (RBC) count, packed cell volume (PCV), mean corpuscular volume (MCV), mean corpuscular haemoglobin (MCH), mean corpuscular haemoglobin concentration (MCHC), red cell distribution width (RDW), total leukocyte count (TLC), platelets per cubic millimeter (PLT/mm³), and haemoglobin (HGB). Two ML models, Logistic Regression (LR) and Support Vector Machine (SVM), were used with two feature selection methods: all features and selected features. The latter identified age, RBC, PCV, MCV, and HGB as key predictors. Continuous HGB values were categorized into (1) low, (2) normal, and (3) high using standard medical metrics. SMOTE analysis was also carried out to mitigate class imbalance. SVM with a Radial Basis Function (RBF) kernel achieved 84.90% accuracy and AUC-ROC of 93.40%, while LR underperformed with 79.50% accuracy and AUC-ROC of 90.90%. Using all feature selection, SVM improved to 91.80% accuracy and AUC-ROC of 98.20%, with LR achieving accuracy of 93.20% and AUC-ROC of 98.90%. Both models demonstrated high accuracy, with LR excelling using all features, while SVM performed better with selected features. Future work will involve the use of primary datasets, additional feature selection techniques and ML algorithms, and incorporate the use of Haemoglobin variants to provide further insight into SCA progression and in turn offer personalized treatment.

Implementation of a Smart Farming Automation System

G. A. Akinosho — 2025-12-23

This 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.