FACULTY OF SCIENCE
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Item "Evaluation of soil thermal diffusivity algorithms at two equatorial sites in West Africa"(2021) Otunla, T.A.; Oladiran, E. O.This study presents comparisons between six algorithms used in the calculation of apparent thermal diffusivity (Kh ) of the topsoil during measurement campaigns conducted at two equatorial sites. It further investigates the effects of transient and seasonal variations in soil moisture content (i) on the estimation of Kh. The data used comprise soil temperatures (T) measured at depths of 0.05 m and 0.10 m, and i within the period of transition from the dry season to the wet season at Ile Ife (7.55° N, 4.55° E), and for the peak of the wet season at Ibadan (7.44° N, 3.90° E). The thermal diffusivity, Kh, was calculated from six algorithms, of: harmonic, arctangent, logarithmic, amplitude, phase, and conduction-convection. The reliability of these algorithms was tested using their values to model T at a depth of 0.10 m, where direct measurements were available. The algorithms were further evaluated with statistical indices, including the empirical probability distribution function of the differences between the measured and modeled temperatures (DT). The maximum absolute values of DT for the six algorithms investigated were: 0.5°C, 0.5°C, 0.5°C, 1°C, 1°C and 1°C, respectively. Kh showed an increasing trend as i increased from the dry season to the peak of the wet season, with R2 = 0.70 for the harmonic algorithm. The accuracy of all of the algorithms in modeling T reduced with transient variations of i. The harmonic, arctangent and logarithmic algorithms were the most appropriate for calculating Kh for the region of study. The empirical relation between i and Kh and the values of Kh obtained in this study can be used to improve the accuracy of meteorological and hydrological models.Item Estimation of daily solar radiation at equatorial region of West Africa using a more generalized Angström‑based broadband hybrid model(2020) Otunla, T.A.A well-calibrated simple and economical viable Ȧngström–Prescott model has long been accepted to be more accurate than other surface meteorological data-based models. The major limitation is that it is site dependent. This study exploited the appropriateness of a more generalized Ȧngström-based broadband hybrid model in the estimation of solar radiation at seven stations in equatorial region of West Africa. This model features parametric equations that explicitly and accurately account for clear-sky damping processes in the atmosphere. It empirically estimates cloudy sky radiation extinctions using relative sunshine duration. A new cloud transmittance calibration curve that followed the cloud cover patterns of the region of study was also tried. The result indicated that the new cloud transmittance could be unique to equatorial region of West Africa. The performance of the hybrid model, after modification using the new cloud transmittance equation, was tested using mean bias error and root mean squared error. The performance was found to be comparable to the site-dependent, locally calibrated, Ȧngström–Prescott model at the calibration stations, and even better at validation stations. The same performance test comparisons with the original version of the hybrid model, and four other site-independent models: globally calibrated, FAO-recommended Ȧngström–Prescott models, Hay and Gopinathan models indicated the modified version of the hybrid model as betterItem "Assessment of Wind Speed Distributions and Turbine Characteristics in Equatorial West Africa"(2026-03) Otunla, T.A.Wind nullity, low wind, and bi- or multi-modality are common characteristics at high temporal resolution, especially in Equatorial regions. The traditional two-parameter Weibull (Weibull) distribution function (DF) is not designed to capture such peculiarities. Hourly mean wind speed data for eight locations that cut across different climate zones in an Equatorial region of West Africa have been analyzed using Weibull and Maximum Entropy Principle-based (MEP) distribution functions (DFs). Wind characteristics, such as power density, null wind speed, and modal distributions, together with turbine efficiency, capacity, and availability factors, were also assessed at a wind turbine hub height of 73 m using standard statistical tools. The results indicated that null wind speed and/or bimodality were present in the wind distributions at Abuja, Akure, Akungba, Nsukka, Makurdi, and Yola. The results of the assessments of the two DFs show that the MEP DF generated much better results across all time scales (R2: 0.83 - 0.98; RMSE: 0.0037 - 0.0109 m/s2) than the Weibull DF (R2: 0.47 - 0.98; RMSE: 0.0038 - 0.0191 m/s2), especially for locations where null wind speed and bimodality were prominent in the wind data distribution. MEP DF results further indicated that annual and rainy season periods were better modeled than the dry season in all the locations. The overall effect of all the turbine characteristics on annual and seasonal scales is that sufficient winds were available (Availability factor: 0.733 - 0.97; Capacity factor: 0.350 - 0.778) at the rated power for energy production in all the climate zones.Item Assessment of Air Quality Conditions in an Area in the Gulf of Guinea, Ibadan, Using Low-Cost Sensors(2025) Otunla, T.A.Air quality monitoring is essential for the determination of the potentialnegative impacts of air pollution on humans and the environment. This study investigated the contribution of particulate matter (PM 2.5 and 10) to air quality in an area in the Gulf of Guinea, far south of Sahara. The study used the hourly data of PM 2.5 and 10 concentrations and other auxiliary data in 2021 from the PurpleAir sensors. These PM concentration data were first converted into Air Quality Index (AQI) using appropriate method of aggregation. Subsequently, the AQI was used to categorize the ambient air into six classes that range between “Good" to "Severe" conditions. Results indicated higher prevalence of "Good “to “Satisfactory" AQI conditions during the peak of the rainy season (June, July and August), characterized by low PM concentrations, whereas the harmattan season (December, January and February) exhibited a higher prevalence of "Very Poor" to "Severe" conditions, characterized by high PM concentrations. High AQI and PM concentrations were attributable to organic PM Saharan dust in the harmattan season, while low AQI and PM concentrations in the rainy season were associated with localized anthropogenic sources. Thus, the low-cost sensor PurpleAir was able to capture the expected seasonal patterns peculiar to the study region.Item Policy brief on educationally-less-developed-states as a criterion for selecting undergraduate admission candidates into Nigerian federal universities(The Postgraduate College, University of Ibadan, Ibadan, Nigeria, 2025) Olayinka, A. I.Interrogating data published by the Joint Admissions and Matriculation Board (JAMB) over the period 2017 to 2023 we present quantitative, verifiable and objective assessment of addressing the question of equity and justice in the adoption of Educationally-Less-Developed-States (ELDS) as a criterion for the selection of candidates for admission into undergraduate programmes in Nigerian Federal Universities. Some of the 23 states on the current list of ELDS, in particular Kwara and Kogi, have posted excellent results and are now in the First Quartile justifying that they ought to be taken off the list. The same applies to Nasarawa which is in the Second Quartile. On the other hand, Lagos and the Federal Capital Territory are in the Fourth Quartile and if the ELDS is to be retained both should be included in the list. The National Council on Education should take a closer look at this recommendation with a view to implementing same.Item Leveraging user session for personalized e- commerce recommendation(2024-07) Onibonoje, S.; Ojo, A.The advent of the internet has propelled many shopping activities online, leading to the rapid growth of e- commerce. This shift has revolutionized the shopping experience, offering unparalleled convenience with anytime, anywhere access via computers and internet connectivity. Moreover, the vast array of easily accessible choices empowers buyers to make well-informed decisions. Numerous websites have emerged to provide e-commerce services, catering either as a complement to physical stores or as standalone businesses. However, the abundance of offerings often leads to information overload for buyers, making product searches time-consuming and frustrating. Personalized e-commerce recommendations alleviate this challenge by guiding users to relevant products swiftly, enhancing the overall shopping experience and ultimately boosting product sales. The study focuses on creating a session-based recommendation system for e-commerce websites, leveraging Recurrent Neural Networks with LSTM architectures to analyze sequential user behavior and browsing context for personalized product recommendations. The research methodology encompasses data collection and preprocessing, where data was splitted into training, testing and validation set. The model was efficiency was evaluated using precision, recall and mean reciprocal rank with the result showing considerable promise for recommendation. This research makes a substantial contribution by suggesting tailored options, users are more likely to find suitable products, leading to increased satisfaction and repeat purchases, thereby benefiting e-commerce platforms.Item Forecasting Nigerian equity stock returns using long short-term memory technique(2024) Ojo, A. K.; Okafor, I. J.Investors and stock market analysts face major challenges in predicting stock returns and making wise investment decisions. The predictability of equity stock returns can boost investor confidence, but it remains a difficult task. To address this issue, a study was conducted using a Long Short-term Memory (LSTM) model to predict future stock market movements. The study used a historical dataset from the Nigerian Stock Exchange (NSE), which was cleaned and normalized to design the LSTM model. The model was evaluated using performance metrics and compared with other deep learning models like Artificial and Convolutional Neural Networks (CNN). The experimental results showed that the LSTM model can predict future stock market prices and returns with over 90% accuracy when trained with a reliable dataset. The study concludes that LSTM models can be useful in predicting financial time-series-related problems if well-trained. Future studies should explore combining LSTM models with other deep learning techniques like CNN to create hybrid models that mitigate the risks associated with relying on a single model for future equity stock predictions.Item Improvement on emotional variance analysis technique (EVA) for sentiment analysis in healthcare service delivery(Foundation of Computer Science FCS, New York, USA, 2024-05) Agada, V. E.; Ojo, A. K.This research introduces an innovative approach to improving sentiment analysis in healthcare service delivery by integrating Emotion and Affect Recognition (EAR) techniques into Emotional Variance Analysis (EVA). Leveraging logistic regression, the modifications, including adjusting confidence thresholds and utilizing the Rectified Linear Unit (ReLU) function, aim to address high polarity and enable real-time analysis. The methodology outlines a systematic process for EAR integration, offering practical insights for healthcare practitioners. In this study, additional datasets, including the Healthcare Patient Satisfaction Data Collection, the 9 Popular Patient Portal App Reviews for November 2023, and the HCAHPS Hospital Ratings Survey, are incorporated to enhance the robustness and reliability of the approach. The results across three healthcare centers demonstrate the effectiveness of this augmented approach, with comparisons against existing models using performance metrics. While showcasing promising potential, further research is needed to explore scalability and generalizability.Item Weather forecasting using deep learning and seasonal autoregressive integrated moving average model(2024-04) Ojukotimi, O. O.; Ojo, A. K.A number of industries, including mining, agriculture, transportation, and disaster relief, rely heavily on weather forecasting. Time-series trends have been well-captured by conventional forecasting models like SARIMA (Seasonal Auto Regressive Integrated Moving Average). Deep learning methods have become effective instruments for identifying complex patterns and raising predicting precision in recent years. This paper suggests a method to improve the accuracy of weather forecasting by utilizing SARIMA models and deep learning. To capture spatial and temporal correlations in meteorological data, the integration of the SARIMA model and Long Short-Term Memory (LSTM) networks of deep learning architecture is investigated. The Nigeria Metrological Agency (NIMET) provided 20 years' worth of temperature, humidity, wind, and rainfall data for this study. According to the evaluation results, the LSTM had an RMSE of 41.00 for the features of the training dataset, whereas the proposed SARIMA had RMSEs of 0.59 for rainfall, 23.99 for temperature, 1.23 for wind, and 24.47 for relative humidity. This demonstrates unequivocally that SARIMA outperformed the LSTM model.Item SMS spam detection and classification to combat abuse in telephone networks using natural language processing(2023) Oyeyemi, D. A.; Ojo, A. K.In the modern era, mobile phones have become ubiquitous, and Short Message Service (SMS) has grown to become a multi-million-dollar service due to the widespread adoption of mobile devices and the millions of people who use SMS daily. However, SMS spam has also become a pervasive problem that endangers users' privacy and security through phishing and fraud. Despite numerous spam filtering techniques, there is still a need for a more effective solution to address this problem [1]. This research addresses the pervasive issue of SMS spam, which poses threats to users' privacy and security. Despite existing spam filtering techniques, the high false-positive rate persists as a challenge. The study introduces a novel approach utilizing Natural Language Processing (NLP) and machine learning models, particularly BERT (Bidirectional Encoder Representations from Transformers), for SMS spam detection and classification. Data preprocessing techniques, such as stop word removal and tokenization, are applied, along with feature extraction using BERT. Machine learning models, including SVM, Logistic Regression, Naive Bayes, Gradient Boosting, and Random Forest, are integrated with BERT for differentiating spam from ham messages. Evaluation results revealed that the Naïve Bayes classifier + BERT model achieves the highest accuracy at 97.31% with the fastest execution time of 0.3 seconds on the test dataset. This approach demonstrates a notable enhancement in spam detection efficiency and a low false-positive rate. The developed model presents a valuable solution to combat SMS spam, ensuring faster and more accurate detection. This model not only safeguards users' privacy but also assists network providers in effectively identifying and blocking SMS spam messages.