Explainable ensemble deep learning model for predicting diabetic retinopathy based on APTOS 2019 eye pack dataset

Detection of diabetic retinopathy (DR) as early as possible is vital in mitigating the complicated issues associated with the disease. Recent advances in artificial intelligence (AI), particularly deep learning (DL) techniques, have led to appreciable increase in the accuracy of predicting various disease classes. However, the challenge of AI models is the difficulty in providing insights into how and why a model arrives in attaining decision-making to facilitate trust and adoption in clinical settings. Therefore, this study aimed to enhance the detection rate of DR and explain the significant regions on the image for the model's overall performance. This study utilised Convolutional Neural Networks (CNN), Long Short-Term Memory (LSTM) networks, Simple Recurrent Neural Networks (SRNN), and XGBoost in an ensemble model (EM). Specifically, Shapley Additive exPlanations (SHAP), a popular Explainable Artificial Intelligence (XAI) technique was utilised to identify and provide insights to which parts of the images features that contribute to the model's overall performance. After a series of experiments using the APTOS 2019 eye pack dataset collected from the Kaggle repository to evaluate the performance of CNN, LSTM, SRNN, and XGBoost. The EM outperformed all the other models with 95.63% accuracy, 97.79% precision, 93.64% recall rate, 98.79% F1-score and 97.75% AUC score. Also, SHAP analysis revealed significant regions on the image that influenced predictions, thus showing how important interpretability was for the model. The results imply that the ensemble DL, particularly with XGBoost, enhances the detection of DR, thereby improving the efficiency of screening tests and supporting personalised treatment plans in clinical practice through integrating these advanced models with XAI tools, creating trust towards automated diagnostic systems

Utilization of local gum Arabic as an adhesive for the production of particle board from maize cobs and coconut shells

The increase in the cost of building and furniture materials like particle board, ceiling and roofing sheets in Nigeria worsens by the day. The development of an alternative to curb this menace using agricultural waste such as maize cobs and coconut shells are required. In this study, maize cob, coconut shell and gum Arabic as adhesive were used in the development of particle board. The particle board was produced by mixing maize cob and coconut shell in different proportion. The physical and mechanical properties of the particle board such as density, water absorption, impact strength. compressive and tensile tests  were investigated. The density ranged from 518.6 -843.3 Kg/m3, water absorption was between 11.05 and 70.2% by varying time of immersion at 30min, 1hr and 2hr. The compressive strength, tensile strength and impact strength fell within the range of 15.9 - 20.15MPa, 18 - 62KPa and 175 - 274.06KJ/m2 respectively. The results showed that the maize cob, coconut shell and gum Arabic are good candidates for building and furniture applications

Evaluation of Pedestrian Safety on Highway Infrastructure in Ibadan

Pedestrian safety is becoming increasingly important in metropolitan settings, especially in developing nations like Nigeria. This study examines the pedestrian safety state in Ibadan, Nigeria, highlighting the main difficulties and hazards pedestrians confront. A mixed-methods approach was used, combining quantitative and qualitative data gathering and analysis techniques. Five hundred people shared their experiences. The study revealed that 48% of respondents rated the roads as fair, while 27% described them as poor and 6% as very poor, citing concerns such as poor road design, inadequate pedestrian facilities, and careless driver behaviour as major safety issues for walkers. Pedestrians are most worried about poor traffic control, a lack of pedestrian bridges, and inadequate lighting. The study identified particular Ibadan streets that desperately require improvements for pedestrian safety. The findings of this research emphasize the need for a comprehensive plan to tackle the challenging issues regarding pedestrian safety in Ibadan. Enhancing pedestrian safety should be regarded as a primary concern; this may include appropriate pedestrian infrastructure, better road design, and more effective traffic management, for policymakers, urban planners and other interested parties. The study contributes to the information already available on pedestrian safety in Nigeria and provides practical analysis for improving pedestrian safet

Renewable energy potential of bio-Oil from pyrolysis of gmelina arborea seeds cultivated in Nigeria

The increasing demand for renewable energy sources has intensified the search for sustainable alternatives to fossil fuels. Gmelina arborea seeds, an underutilized biomass abundant in Nigeria, hold potential for bio-oil production. This study aims to explore the feasibility of utilizing Gmelina arborea seeds for energy generation through pyrolysis, contributing to cleaner energy production and environmental conservation. Seeds were collected from LAUTECH, Ogbomoso, and subjected to air and oven drying to reduce moisture content before being ground into powder. Pyrolysis was performed in a bench-scale screw reactor at temperatures ranging from 485 to 596 °C. The bio-oil produced was characterized using standard ASTM methods, including proximate and ultimate analyses, Fourier Transform Infrared Spectroscopy (FTIR), and Gas Chromatography-Mass Spectrometry (GC-MS), to determine its chemical composition and energy potential. The bio-oil yield ranged from 21.3 to 25.3 wt.%, with the highest yield of 25.3 wt.% achieved at 596 °C. Characterization revealed favorable energy properties, including a Higher Heating Value (HHV) of 40.13 MJ/kg, kinematic viscosity, density, and flash point within practical application ranges. FTIR analysis identified functional groups such as alkenes, carboxylic acids, alcohols, ethers, and ketones, while GC-MS detected hydrocarbons like alkanes, alkenes, phenols, and naphthalene. The low nitrogen content (2.58-2.80 wt.%) indicates minimal environmental impact. This study highlights the viability of Gmelina arborea seeds as a renewable bioenergy feedstock, offering a cleaner, sustainable alternative to conventional fuels

Comparative analysis of SVM and logistic regression for classifying diagnostic microRNA signatures in colorectal cancer

Abstract The selection and classification of genes are critical for determining which ones are linked to a particular illness, especially cancer. As a result, it's critical to use machine learning algorithms to analyze relevant statistical data to aid biomedical researchers and end-users in the work of selection and classification. Few researches have been done on the early diagnosis of CRC using machine learning techniques to detect biomarkers, which are very important in colorectal cancer disease diagnoses. We therefore conduct a comprehensive gene selection and classification functionality using SVM and Logistic Regression algorithms on high-dimensional datasets. The results show that under the receiver operating characteristic (ROC) curve, the SVM and Logistic Regression models' discriminative capacities for classification were 83.5% and 73.2 %, respectively. This study thus reveals that the SVM algorithm outperforms the Logistic Regression algorithm in classifying data in the detection of Colorectal Cancer.             Keywords: Algorithm, Biomarkers, Classification, Colorectal Cancer, Disease Diagnosi

Evaluation of the Environmental and Social Benefits of Conversion Process of Open Cycle to Combined Cycle Gas Power Plant

Worldwide concern on reducing global warming consequences and combating energy crisis has motivated the development of power generation technologies to move towards sustainable energy production with higher efficiency and low environmental impacts. This study evaluated the environmental and social benefits of converting open cycle to combined cycle gas power plants in electric power generating system in Nigeria. All the current operational open and combined cycle gas power plants were considered. Green House Gas (GHG) emission data were collected for both open and combined cycle plants. The results showed that after conversion from open cycle to combined cycle, society bears a lesser cost of generating electricity as there is a minimum difference of 3.78 N/kWh (Calabar NIPP), which is about 23.34% change in cost and a maximum of 4.00 N/kWh (Omotosho Pacific Energy plant), which is about 25.20% change in cost for a minimum range of emission cost (40USD/tCO2e). There is a minimum difference of 8.54 N/kWh (Calabar NIPP), which is about 28.57% change in cost and a maximum of 8.76 N/kWh (Omotosho Pacific Energy plant) which is about 29.64% change in cost for a maximum emission cost (100USD/tCO2e). The study concluded that it costs less to reduce GHG and air pollution damage during the process of conversion from open cycle to combined cycle gas. Also, it is more beneficial to generate electricity using combined gas turbine and the society bears less cost for a higher electricity generation by a combined cycle when compared with an open cycle

Design and simulation of a sustainable water distribution network in iseyin, southwestern nigeria, using epanet 2.0 hydraulic software : Design and simulation of a sustainable water distribution network in iseyin, southwestern nigeria, using epanet 2.0 hydraulic software

Efficient water distribution systems ensure water quality and a reliable supply. In Iseyin, the existing water distribution networks are non-functional, despite recent population growth. This research thus designs and simulates a sustainable water distribution network for Iseyin town Southwestern Nigeria, using the Environmental Protection Agency Network (EPANET) 2.0 software to enhance water resources management. Climatic data such as temperature, precipitation, humidity, rainfall days, sunlight, and evaporation rate values were obtained from the Nigerian Meteorological Agency between 2014 - 2024. Population data were sourced from the National Population Commission and projected for 50 years using the geometric mean approach. Water samples from Atoori and Ajumoda reservoirs were analyzed for physico-chemical and bacteriological parameters. Water demand and Water Quality Index (WQI) were estimated. The water distribution network was simulated using EPANET software to compute demand, pressure, velocity, and headloss at 1:00, 12:00, and 24:00 hours. Annual climatic conditions revealed that temperature ranged from 23.90°C to 28.40°C, precipitation from 7.00 mm to 188.00 mm, humidity from 47% to 85%, rainfall days from 1 to 19, sunlight hours from 3.30 to 8.90, and evaporation from 9.21 mm to 17.19 mm. The projected population of 1,491,036 by 2052 yields a total water demand of 213,005,142.86 Lpd. WQI indicates that Ajumoda was moderately polluted in the rainy season but excessively polluted in the dry season, with WQI of 73.03 and 303.89, respectively. In contrast, Atoori was excessively polluted in both rainy and dry seasons, with WQI of 261.74 and 498.29, respectively. The simulated network at different times indicates that Atoori has greater fluctuations in demand, headloss, and pressure. This study emphasizes optimizing reservoirs, pressure regulation, and responsive network design to address variations

Review of N-Bundled Conductors on Right-of-Way in Transmission Network Reinforcement in Electrical Systems

Transmission line expansion has become one of the critical network planning strategies to ensure the continuous evacuation of power. Right-of-way (ROW) has posed significant challenges due to an increase in population and rural-urban development. This research therefore reviews various methodologies of investigating the effect of N-bundled conductors on right-of-way and power system parameters such as voltage, current, power and load. The review of technical literature reveals that the effects of transmission lines bundling of conductors on the right-of-way were not considered in most cases of the literature reviewed. The changes in the electrical power system parameters with changes in conductor area and bundling of conductors were not examined in most cases of the papers reviewed. Even the relationship between transmission line expansion and bundling of conductors was not studied in most cases. To that effect, this study therefore proposed solutions to the above-mentioned shortcomings observed in the area of transmission network expansion

Predicting customer purchase patterns in online retail using a cnn-based deep learning model

Accurately predicting customer purchase patterns in online retail enables personalized recommendations, targeted marketing, and improved business decision-making. However, challenges such as high-dimensional transactional data, class imbalance, and the limitations of traditional Machine Learning (ML) models often hinder predictive performance. In this study, a Convolutional Neural Network (CNN) based model was designed to predict customer purchase behavior from online retail transaction data. CNNs are particularly effective at learning complex patterns and feature relationships, making them well-suited for structured data representation. The experiment was conducted on an online retail dataset comprising customer purchase patterns obtained from the University of California, Irvine repository, one of the most widely used benchmark datasets for evaluating ML algorithms. The performance of the CNN model was evaluated using accuracy, precision, recall, F1-score, and the Area Under the Curve of the Receiver Operating Characteristic (AUC-ROC), achieving 93.6% accuracy, 100.0% precision, 91.1% recall, 95.4% F1-score, and an AUC-ROC of 0.98. These results demonstrate that deep learning can effectively model customer purchasing behavior, offering valuable insights for online retail platforms aiming to anticipate customer actions and optimize engagement strategies

Characterization and energy analyses of municipal solid waste in Obafemi Awolowo University, Southwestern, Nigeria

This study investigated the potential of generating electricity from municipal solid waste (MSW) generated in OAU. It also determined the quantity of municipal solid waste generated within OAU community and characterized the energy content of the combustible portion of the collected MSW. This was with a view to predicting theoretical quantity of electricity that can be generated from the MSW collected within OAU community.  Load-count-analysis was used for the MSW quantification while sampling method was used for the characterization of the collected samples. A total of ten samples of 10 kg each were collected from OAU dumpsite (Asunle) in September 2015 (wet season). The collected samples were sorted, weighed and separated into combustible and non-combustible fraction. The combustible portion was thoroughly mixed and shredded with milling machine to size of less than 3 mm for laboratory analysis. The calorific value of the samples was determined using bomb calorimeter following the standard method. The energy content of the MSW was analysed based on result of the quantification and the composition. Results of MSW quantification indicated that the total amount of waste generated on a daily basis was approximately 4.4 ton. Characterization showed that the waste was made up of approximately 34.8% paper, 18.1% textile, 9.4% electronics, 4.4% metal, 6.2% bio, 6.3% wood and 20.8% miscellaneous. Combustible fraction was 65.4% while the average moisture content was 19.04% on wet basis (w.b). The average calorific value obtained was 10.77 MJ/kg. The energy analysis indicated that, with minimum conversion efficiency (25%), 0.4MW of electricity could be generated. The study concluded that the MSW in OAU has capacity to generate electricity to the tune of 0.4MW on the basis of 4.5 tonne of waste collected per day with minimum conversion efficiency of 25%